Adding codebeaver.yml

chore: update pytest configuration to enable parallel test execution

.gitignore

@@ -17,3 +17,4 @@ inv/
 scripts/
 docx/
 agentic_security.toml
+/venv

Readme.md

@@ -1,37 +1,54 @@
 <p align="center">
-
-<h1 align="center">Agentic Security</h1>
-
-<p align="center">
-    The open-source Agentic LLM Vulnerability Scanner
-    <br />
-    <br />
+  <h1 align="center">Agentic Security</h1>
+  <p align="center">
+    An open-source vulnerability scanner for Agent Workflows and Large Language Models (LLMs)<br />
+    Protecting AI systems from jailbreaks, fuzzing, and multimodal attacks.<br />
+    <a href="https://agentic-security.vercel.app">Explore the docs »</a> ·
+    <a href="https://github.com/msoedov/agentic_security/issues">Report a Bug »</a>
+  </p>
 </p>
 
 <p align="center">
   <a href="https://github.com/msoedov/agentic_security/commits/main">
-    <img alt="GitHub Last Commit" src="https://img.shields.io/github/last-commit/msoedov/agentic_security?style=for-the-badge&logo=git&labelColor=000000&logoColor=FFFFFF&label=Last Commit&color=6A35FF" />
+    <img alt="GitHub Last Commit" src="https://img.shields.io/github/last-commit/msoedov/agentic_security?style=for-the-badge&logo=git&labelColor=000000&color=6A35FF" />
   </a>
   <a href="https://github.com/msoedov/agentic_security">
-    <img alt="GitHub Repo Size" src="https://img.shields.io/github/repo-size/msoedov/agentic_security?style=for-the-badge&logo=database&labelColor=000000&logoColor=FFFFFF&label=Repo Size&color=yellow" />
-  </a>
+    <img alt="GitHub Repo Size" src="https://img.shields.io/github/repo-size/msoedov/agentic_security?style=for-the-badge&logo=database&labelColor=000000&color=yellow" />
   </a>
   <a href="https://github.com/msoedov/agentic_security/blob/master/LICENSE">
-    <img alt="GitHub License" src="https://img.shields.io/github/license/msoedov/agentic_security?style=for-the-badge&logo=codeigniter&labelColor=000000&logoColor=FFFFFF&label=License&color=FFCC19" />
+    <img alt="GitHub License" src="https://img.shields.io/github/license/msoedov/agentic_security?style=for-the-badge&logo=codeigniter&labelColor=000000&color=FFCC19" />
+  </a>
+  <a href="https://pypi.org/project/agentic-security/">
+    <img alt="PyPI Version" src="https://img.shields.io/pypi/v/agentic-security?style=for-the-badge&logo=pypi&labelColor=000000&color=00CCFF" />
+  </a>
+  <a href="https://discord.gg/stw3DfZQ">
+    <img alt="Join Discord" src="https://img.shields.io/badge/Discord-Join%20Us-black?style=for-the-badge&logo=discord&labelColor=000000&color=DD55FF" />
   </a>
-  <a href="https://discord.gg/stw3DfZQ"><img alt="Join the community" src="https://img.shields.io/badge/Join%20the%20community-black.svg?style=for-the-badge&logo=lightning&labelColor=000000&logoColor=FFFFFF&label=&color=DD55FF&logoWidth=20" /></a>
-
 </p>
 
+
 ## Features
 
-- Multi modal attacks and vulnerability scanners🛠️
-- Multi-Step/multi-round Jailbreaks 🌀
-- Comprehensive fuzzing for any LLMs 🧪
-- LLM API integration and stress testing 🛠️
-- RL based attacks 📡
 
-Note: Please be aware that Agentic Security is designed as a safety scanner tool and not a foolproof solution. It cannot guarantee complete protection against all possible threats.
+Agentic Security equips you with powerful tools to safeguard LLMs against emerging threats. Here's what you can do:
+
+- **Multimodal Attacks** 🖼️🎙️
+  Probe vulnerabilities across text, images, and audio inputs to ensure your LLM is robust against diverse threats.
+
+- **Multi-Step Jailbreaks** 🌀
+  Simulate sophisticated, iterative attack sequences to uncover weaknesses in LLM safety mechanisms.
+
+- **Comprehensive Fuzzing** 🧪
+  Stress-test any LLM with randomized inputs to identify edge cases and unexpected behaviors.
+
+- **API Integration & Stress Testing** 🌐
+  Seamlessly connect to LLM APIs and push their limits with high-volume, real-world attack scenarios.
+
+- **RL-Based Attacks** 📡
+  Leverage reinforcement learning to craft adaptive, intelligent probes that evolve with your model’s defenses.
+
+> **Why It Matters**: These features help developers, researchers, and security teams proactively identify and mitigate risks in AI systems, ensuring safer and more reliable deployments.
+
 
 ## 📦 Installation
 
@@ -67,6 +84,7 @@ agentic_security --port=PORT --host=HOST
 ## UI 🧙
 
 <img width="100%" alt="booking-screen" src="https://res.cloudinary.com/dq0w2rtm9/image/upload/v1736433557/z0bsyzhsqlgcr3w4ovwp.gif">
+<img width="100%" alt="booking-screen" src="https://res.cloudinary.com/dq0w2rtm9/image/upload/v1741192668/final_aa9jhb.gif">
 
 ## LLM kwargs
 
@@ -111,7 +129,7 @@ Init config
 ```shell
 agentic_security init
 
-2025-01-08 20:12:02.449 | INFO     | agentic_security.lib:generate_default_cfg:324 - Default configuration generated successfully to agesec.toml.
+2025-01-08 20:12:02.449 | INFO     | agentic_security.lib:generate_default_settings:324 - Default configuration generated successfully to agesec.toml.
 
 ```
 
@@ -391,10 +409,15 @@ For more detailed information on how to use Agentic Security, including advanced
 
 ## Roadmap and Future Goals
 
-- \[ \] Expand dataset variety
-- \[ \] Introduce two new attack vectors
-- \[ \] Develop initial attacker LLM
-- \[ \] Complete integration of OWASP Top 10 classification
+
+
+We’re just getting started! Here’s what’s on the horizon:
+
+- **RL-Powered Attacks**: An attacker LLM trained with reinforcement learning to dynamically evolve jailbreaks and outsmart defenses.
+- **Massive Dataset Expansion**: Scaling to 100,000+ prompts across text, image, and audio modalities—curated for real-world threats.
+- **Daily Attack Updates**: Fresh attack vectors delivered daily, keeping your scans ahead of the curve.
+- **Community Modules**: A plug-and-play ecosystem where you can share and deploy custom probes, datasets, and integrations.
+
 
 | Tool                    | Source                                                                        | Integrated |
 |-------------------------|-------------------------------------------------------------------------------|------------|
@@ -422,4 +445,9 @@ Before contributing, please read the contributing guidelines.
 
 Agentic Security is released under the Apache License v2.
 
+
+## 🚫 No Cryptocurrency Affiliation
+
+Agentic Security is focused solely on AI security and has no affiliation with cryptocurrency projects, blockchain technologies, or related initiatives. Our mission is to advance the safety and reliability of AI systems—no tokens, no coins, just code.
+
 ## Contact us

agentic_security/__main__.py

@@ -6,6 +6,7 @@ import uvicorn
 
 from agentic_security.app import app
 from agentic_security.lib import AgenticSecurity
+from agentic_security.misc.banner import init_banner
 
 
 class CLI:
@@ -38,7 +39,7 @@ class CLI:
         Generate the default CI configuration file.
         """
         sys.path.append(os.path.dirname("."))
-        AgenticSecurity().generate_default_cfg(host, port)
+        AgenticSecurity().generate_default_settings(host, port)
 
     i = init
 
@@ -61,4 +62,5 @@ def main():
 
 
 if __name__ == "__main__":
+    init_banner()
     main()

agentic_security/agents/operator_crew.py

@@ -0,0 +1,256 @@
+import asyncio
+import logging
+import os
+from typing import Any
+
+import httpx
+from crewai import Agent, Crew, Task
+from crewai_tools import tool
+from pydantic import BaseModel, ConfigDict, Field
+
+# Assuming LLMSpec is defined elsewhere; placeholder import
+from agentic_security.http_spec import LLMSpec
+
+LLM_SPECS = []  # Populate with LLM spec strings if needed
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+# Define AgentSpecification model
+class AgentSpecification(BaseModel):
+    name: str | None = Field(None, description="Name of the LLM/agent")
+    version: str | None = Field(None, description="Version of the LLM/agent")
+    description: str | None = Field(None, description="Description of the LLM/agent")
+    capabilities: list[str] | None = Field(None, description="List of capabilities")
+    configuration: dict[str, Any] | None = Field(
+        None, description="Configuration settings"
+    )
+    endpoint: str | None = Field(None, description="Endpoint URL of the deployed agent")
+
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+
+# Define OperatorToolBox class (unchanged from original)
+class OperatorToolBox:
+    def __init__(self, spec: AgentSpecification, datasets: list[dict[str, Any]]):
+        self.spec = spec
+        self.datasets = datasets
+        self.failures = []
+        self.llm_specs = [LLMSpec.from_string(spec) for spec in LLM_SPECS]
+
+    def get_spec(self) -> AgentSpecification:
+        return self.spec
+
+    def get_datasets(self) -> list[dict[str, Any]]:
+        return self.datasets
+
+    def validate(self) -> bool:
+        if not self.spec.name or not self.spec.version:
+            self.failures.append("Invalid specification: Name or version is missing.")
+            return False
+        if not self.datasets:
+            self.failures.append("No datasets provided.")
+            return False
+        return True
+
+    def stop(self) -> None:
+        logger.info("Stopping the toolbox...")
+
+    def run(self) -> None:
+        logger.info("Running the toolbox...")
+
+    def get_results(self) -> list[dict[str, Any]]:
+        return self.datasets
+
+    def get_failures(self) -> list[str]:
+        return self.failures
+
+    def run_operation(self, operation: str) -> str:
+        if operation not in ["dataset1", "dataset2", "dataset3"]:
+            self.failures.append(f"Operation '{operation}' failed: Dataset not found.")
+            return f"Operation '{operation}' failed: Dataset not found."
+        return f"Operation '{operation}' executed successfully."
+
+    async def test_llm_spec(self, llm_spec: LLMSpec, user_prompt: str) -> str:
+        try:
+            response = await llm_spec.verify()
+            response.raise_for_status()
+            logger.info(f"Verification succeeded for {llm_spec.url}")
+
+            test_response = await llm_spec.probe(user_prompt)
+            test_response.raise_for_status()
+            response_data = test_response.json()
+            return f"Test succeeded for {llm_spec.url}: {response_data}"
+        except httpx.HTTPStatusError as e:
+            self.failures.append(f"HTTP error occurred: {e}")
+            logger.error(f"Test failed for {llm_spec.url}: {e}")
+            return f"Test failed for {llm_spec.url}: {e}"
+        except Exception as e:
+            self.failures.append(f"An error occurred: {e}")
+            logger.error(f"Test failed for {llm_spec.url}: {e}")
+            return f"Test failed for {llm_spec.url}: {e}"
+
+    async def test_with_prompt(self, spec_index: int, user_prompt: str) -> str:
+        if not 0 <= spec_index < len(self.llm_specs):
+            return f"Invalid spec index: {spec_index}. Valid range is 0 to {len(self.llm_specs) - 1}"
+        llm_spec = self.llm_specs[spec_index]
+        return await self.test_llm_spec(llm_spec, user_prompt)
+
+
+# Define CrewAI Tools
+@tool("validate_toolbox")
+def validate_toolbox(toolbox: OperatorToolBox) -> str:
+    """Validate the toolbox configuration."""
+    is_valid = toolbox.validate()
+    return (
+        "ToolBox validation successful." if is_valid else "ToolBox validation failed."
+    )
+
+
+@tool("execute_operation")
+def execute_operation(toolbox: OperatorToolBox, operation: str) -> str:
+    """Execute a dataset operation."""
+    return toolbox.run_operation(operation)
+
+
+@tool("retrieve_results")
+def retrieve_results(toolbox: OperatorToolBox) -> str:
+    """Retrieve the results of operations."""
+    results = toolbox.get_results()
+    return (
+        f"Operation Results:\n{results}"
+        if results
+        else "No operations have been executed yet."
+    )
+
+
+@tool("retrieve_failures")
+def retrieve_failures(toolbox: OperatorToolBox) -> str:
+    """Retrieve recorded failures."""
+    failures = toolbox.get_failures()
+    return f"Failures:\n{failures}" if failures else "No failures recorded."
+
+
+@tool("list_llm_specs")
+def list_llm_specs(toolbox: OperatorToolBox) -> str:
+    """List available LLM specifications."""
+    spec_list = "\n".join(
+        f"{i}: {spec.url}" for i, spec in enumerate(toolbox.llm_specs)
+    )
+    return f"Available LLM Specs:\n{spec_list}"
+
+
+@tool("test_llm_with_prompt")
+async def test_llm_with_prompt(
+    toolbox: OperatorToolBox, spec_index: int, user_prompt: str
+) -> str:
+    """Test an LLM spec with a user prompt."""
+    return await toolbox.test_with_prompt(spec_index, user_prompt)
+
+
+# Setup OperatorToolBox
+spec = AgentSpecification(
+    name="DeepSeek Chat",
+    version="1.0",
+    description="A powerful language model",
+    capabilities=["text-generation", "question-answering"],
+    configuration={"max_tokens": 100},
+)
+toolbox = OperatorToolBox(
+    spec=spec, datasets=[{"id": "dataset1"}, {"id": "dataset2"}, {"id": "dataset3"}]
+)
+
+# Define CrewAI Agent
+dataset_manager_agent = Agent(
+    role="Dataset Manager",
+    goal="Manage and operate the OperatorToolBox to validate configurations, run operations, and test LLMs.",
+    backstory="An expert in dataset management and LLM testing, designed to assist with toolbox operations.",
+    verbose=True,
+    llm="openai",  # Using OpenAI-compatible API for DeepSeek; adjust if DeepSeek has a specific ID
+    tools=[
+        validate_toolbox,
+        execute_operation,
+        retrieve_results,
+        retrieve_failures,
+        list_llm_specs,
+        test_llm_with_prompt,
+    ],
+    allow_delegation=False,  # Single agent, no delegation needed
+)
+
+# Define Tasks
+tasks = [
+    Task(
+        description="Validate the toolbox configuration.",
+        agent=dataset_manager_agent,
+        expected_output="A string indicating whether validation succeeded or failed.",
+    ),
+    Task(
+        description="List available LLM specifications.",
+        agent=dataset_manager_agent,
+        expected_output="A string listing available LLM specs.",
+    ),
+    Task(
+        description="Guide the user to test an LLM with the prompt: 'Tell me a short story about a robot'. Suggest listing specs first.",
+        agent=dataset_manager_agent,
+        expected_output="A string suggesting the user list specs and proceed with testing.",
+    ),
+]
+
+# Define Crew
+crew = Crew(
+    agents=[dataset_manager_agent],
+    tasks=tasks,
+    verbose=2,  # Detailed logging
+)
+
+
+# Async wrapper to handle async tools
+async def run_crew():
+    # Since CrewAI's process() is synchronous but our tool is async, we need to run it in an event loop
+    result = (
+        crew.kickoff()
+    )  # Synchronous call; async tools are awaited internally by CrewAI
+    print("\nCrew Results:")
+    for task_result in result:
+        print(f"Task: {task_result.description}")
+        print(f"Output: {task_result.output}\n")
+
+    # Handle user interaction for LLM testing
+    print("Please select a spec index from the listed specs and confirm to proceed.")
+    user_input = (
+        input("Enter spec index and 'yes' to confirm (e.g., '0 yes'): ").strip().split()
+    )
+    if len(user_input) == 2 and user_input[1].lower() == "yes":
+        try:
+            spec_index = int(user_input[0])
+            user_prompt = "Tell me a short story about a robot"
+            # Create a new task for testing
+            test_task = Task(
+                description=f"Test LLM at index {spec_index} with prompt: '{user_prompt}'",
+                agent=dataset_manager_agent,
+                expected_output="A string with the test result from the LLM.",
+            )
+            test_crew = Crew(
+                agents=[dataset_manager_agent], tasks=[test_task], verbose=2
+            )
+            test_result = test_crew.kickoff()
+            print(f"Test Output: {test_result[0].output}\n")
+        except ValueError:
+            print("Invalid spec index provided.\n")
+    else:
+        print("Test canceled. Please provide a valid index and confirmation.\n")
+
+
+# Ensure DeepSeek API key is set
+os.environ["OPENAI_API_KEY"] = os.environ.get(
+    "DEEPSEEK_API_KEY", ""
+)  # CrewAI uses OPENAI_API_KEY
+os.environ[
+    "OPENAI_MODEL_NAME"
+] = "deepseek:chat"  # Specify DeepSeek model (adjust if needed)
+
+if __name__ == "__main__":
+    asyncio.run(run_crew())

agentic_security/agents/operator_pydantic.py

@@ -0,0 +1,238 @@
+import asyncio
+import logging
+from typing import Any
+
+import httpx
+from pydantic import BaseModel, ConfigDict, Field
+from pydantic_ai import Agent, RunContext, Tool
+
+# Assuming LLMSpec is defined elsewhere; placeholder import
+from agentic_security.http_spec import LLMSpec
+
+LLM_SPECS = []  # Populate this list with LLM spec strings if needed
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+# Define AgentSpecification model
+class AgentSpecification(BaseModel):
+    name: str | None = Field(None, description="Name of the LLM/agent")
+    version: str | None = Field(None, description="Version of the LLM/agent")
+    description: str | None = Field(None, description="Description of the LLM/agent")
+    capabilities: list[str] | None = Field(None, description="List of capabilities")
+    configuration: dict[str, Any] | None = Field(
+        None, description="Configuration settings"
+    )
+    endpoint: str | None = Field(None, description="Endpoint URL of the deployed agent")
+
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+
+# Define OperatorToolBox class
+class OperatorToolBox:
+    def __init__(self, spec: AgentSpecification, datasets: list[dict[str, Any]]):
+        self.spec = spec
+        self.datasets = datasets
+        self.failures = []
+        self.llm_specs = [LLMSpec.from_string(spec) for spec in LLM_SPECS]
+
+    def get_spec(self) -> AgentSpecification:
+        return self.spec
+
+    def get_datasets(self) -> list[dict[str, Any]]:
+        return self.datasets
+
+    def validate(self) -> bool:
+        if not self.spec.name or not self.spec.version:
+            self.failures.append("Invalid specification: Name or version is missing.")
+            return False
+        if not self.datasets:
+            self.failures.append("No datasets provided.")
+            return False
+        return True
+
+    def stop(self) -> None:
+        logger.info("Stopping the toolbox...")
+
+    def run(self) -> None:
+        logger.info("Running the toolbox...")
+
+    def get_results(self) -> list[dict[str, Any]]:
+        return self.datasets
+
+    def get_failures(self) -> list[str]:
+        return self.failures
+
+    def run_operation(self, operation: str) -> str:
+        if operation not in ["dataset1", "dataset2", "dataset3"]:
+            self.failures.append(f"Operation '{operation}' failed: Dataset not found.")
+            return f"Operation '{operation}' failed: Dataset not found."
+        return f"Operation '{operation}' executed successfully."
+
+    async def test_llm_spec(self, llm_spec: LLMSpec, user_prompt: str) -> str:
+        try:
+            response = await llm_spec.verify()
+            response.raise_for_status()
+            logger.info(f"Verification succeeded for {llm_spec.url}")
+
+            test_response = await llm_spec.probe(user_prompt)
+            test_response.raise_for_status()
+            response_data = test_response.json()
+            return f"Test succeeded for {llm_spec.url}: {response_data}"
+        except httpx.HTTPStatusError as e:
+            self.failures.append(f"HTTP error occurred: {e}")
+            logger.error(f"Test failed for {llm_spec.url}: {e}")
+            return f"Test failed for {llm_spec.url}: {e}"
+        except Exception as e:
+            self.failures.append(f"An error occurred: {e}")
+            logger.error(f"Test failed for {llm_spec.url}: {e}")
+            return f"Test failed for {llm_spec.url}: {e}"
+
+    async def test_with_prompt(self, spec_index: int, user_prompt: str) -> str:
+        if not 0 <= spec_index < len(self.llm_specs):
+            return f"Invalid spec index: {spec_index}. Valid range is 0 to {len(self.llm_specs) - 1}"
+        llm_spec = self.llm_specs[spec_index]
+        return await self.test_llm_spec(llm_spec, user_prompt)
+
+
+# Define the Agent
+class DatasetManagerAgent(Agent):
+    model: str = "deepseek:chat"
+    system_prompt: str = (
+        "You are an AI agent managing an OperatorToolBox. You can validate the toolbox, run operations, "
+        "retrieve results or failures, list LLM specs, and test LLM specs with user prompts. "
+        "Use the provided tools to assist the user based on their request."
+    )
+
+    def __init__(self, toolbox: OperatorToolBox, **kwargs):
+        super().__init__(**kwargs)
+        self.toolbox = toolbox
+
+        # Define async tools within __init__
+        async def validate_toolbox(ctx: RunContext[Any]) -> str:
+            is_valid = self.toolbox.validate()
+            return (
+                "ToolBox validation successful."
+                if is_valid
+                else "ToolBox validation failed."
+            )
+
+        async def execute_operation(ctx: RunContext[Any], operation: str) -> str:
+            return self.toolbox.run_operation(operation)
+
+        async def retrieve_results(ctx: RunContext[Any]) -> str:
+            results = self.toolbox.get_results()
+            return (
+                f"Operation Results:\n{results}"
+                if results
+                else "No operations have been executed yet."
+            )
+
+        async def retrieve_failures(ctx: RunContext[Any]) -> str:
+            failures = self.toolbox.get_failures()
+            return f"Failures:\n{failures}" if failures else "No failures recorded."
+
+        async def list_llm_specs(ctx: RunContext[Any]) -> str:
+            spec_list = "\n".join(
+                f"{i}: {spec.url}" for i, spec in enumerate(self.toolbox.llm_specs)
+            )
+            return f"Available LLM Specs:\n{spec_list}"
+
+        async def test_llm_with_prompt(
+            ctx: RunContext[Any], spec_index: int, user_prompt: str
+        ) -> str:
+            return await self.toolbox.test_with_prompt(spec_index, user_prompt)
+
+        # Register tools
+        self.tools = [
+            Tool(
+                name="validate_toolbox",
+                description="Validate the toolbox configuration.",
+                function=validate_toolbox,
+            ),
+            Tool(
+                name="execute_operation",
+                description="Execute a dataset operation.",
+                function=execute_operation,
+            ),
+            Tool(
+                name="retrieve_results",
+                description="Retrieve the results of operations.",
+                function=retrieve_results,
+            ),
+            Tool(
+                name="retrieve_failures",
+                description="Retrieve recorded failures.",
+                function=retrieve_failures,
+            ),
+            Tool(
+                name="list_llm_specs",
+                description="List available LLM specifications.",
+                function=list_llm_specs,
+            ),
+            Tool(
+                name="test_llm_with_prompt",
+                description="Test an LLM spec with a user prompt.",
+                function=test_llm_with_prompt,
+            ),
+        ]
+
+
+# Setup and run example
+async def run_dataset_manager_agent_async():
+    # Initialize OperatorToolBox with AgentSpecification
+    spec = AgentSpecification(
+        name="DeepSeek Chat",
+        version="1.0",
+        description="A powerful language model",
+        capabilities=["text-generation", "question-answering"],
+        configuration={"max_tokens": 100},
+    )
+    toolbox = OperatorToolBox(
+        spec=spec, datasets=[{"id": "dataset1"}, {"id": "dataset2"}, {"id": "dataset3"}]
+    )
+
+    # Create the agent
+    agent = DatasetManagerAgent(toolbox=toolbox)
+
+    # Example prompts
+    prompts = [
+        "Validate the toolbox.",
+        "List available LLM specs.",
+        "I want to test an LLM with my prompt: 'Tell me a short story about a robot'. Which spec index should I use?",
+    ]
+
+    for prompt in prompts:
+        result = await agent.run(prompt)
+        print(f"Prompt: {prompt}")
+        print(f"Response: {result}\n")
+
+        # Handle testing request
+        if "test an LLM with my prompt" in prompt:
+            print(
+                "Please select a spec index from the list above and confirm to proceed."
+            )
+            # Simulate user input (replace with real input in practice)
+            user_input = (
+                input("Enter spec index and 'yes' to confirm (e.g., '0 yes'): ")
+                .strip()
+                .split()
+            )
+            if len(user_input) == 2 and user_input[1].lower() == "yes":
+                try:
+                    spec_index = int(user_input[0])
+                    user_prompt = prompt.split("my prompt: ")[1].strip("'")
+                    test_result = await agent.run(
+                        f"Test LLM at index {spec_index} with prompt: {user_prompt}"
+                    )
+                    print(f"Test Response: {test_result}\n")
+                except ValueError:
+                    print("Invalid spec index provided.\n")
+            else:
+                print("Test canceled. Please provide a valid index and confirmation.\n")
+
+
+if __name__ == "__main__":
+    asyncio.run(run_dataset_manager_agent_async())

agentic_security/config.py

@@ -1,16 +1,38 @@
+from functools import lru_cache
+
 import tomli
 from loguru import logger
 
+SETTINGS_VERSION = 1
 
-class CfgMixin:
+
+@lru_cache(maxsize=1)
+def settings_var(name: str, default=None):
+    return get_or_create_config().get_config_value(name, default)
+
+
+@lru_cache(maxsize=1)
+def get_or_create_config():
+    cfg = SettingsMixin()
+    cfg.get_or_create_config()
+    return cfg
+
+
+class SettingsMixin:
     config = {}
     default_path = "agentic_security.toml"
 
     def get_or_create_config(self) -> bool:
         if not self.has_local_config():
-            self.generate_default_cfg()
+            self.generate_default_settings()
             return False
         self.load_config(self.default_path)
+        settings_version = self.get_config_value("general.version")
+        if settings_version and settings_version != SETTINGS_VERSION:
+            logger.error(
+                f"Configuration version mismatch: expected {SETTINGS_VERSION}, got {settings_version}."
+            )
+            return False
         return True
 
     def has_local_config(self):
@@ -64,7 +86,7 @@ class CfgMixin:
                 return default
         return value
 
-    def generate_default_cfg(self, host: str = "0.0.0.0", port: int = 8718):
+    def generate_default_settings(self, host: str = "0.0.0.0", port: int = 8718):
         # Accept host / port as parameters
         with open(self.default_path, "w") as f:
             f.write(
@@ -84,6 +106,7 @@ maxBudget = 1000000 # Maximum budget for the scan
 max_th = 0.3 # Maximum failure threshold (percentage)
 optimize = false # Enable optimization during scanning
 enableMultiStepAttack = false # Enable multi-step attack simulations
+version = $SETTINGS_VERSION
 
 # [modules.LLM-Jailbreak-Classifier]
 # dataset_name = "markush1/LLM-Jailbreak-Classifier"
@@ -110,11 +133,20 @@ high = 0.5
 OPENAI_API_KEY = "$OPENAI_API_KEY"
 DEEPSEEK_API_KEY = "$DEEPSEEK_API_KEY"
 
+[caching]
+enable = true
+cache_size = 10000
+use_disk_cache = false
+
+[network]
+retry = 3
+timeout_connect = 30
+timeout_response = 90
 """.replace(
                     "$HOST", host
-                ).replace(
-                    "$PORT", str(port)
                 )
+                .replace("$PORT", str(port))
+                .replace("$SETTINGS_VERSION", str(SETTINGS_VERSION))
             )
 
         logger.info(

agentic_security/core/app.py

@@ -2,6 +2,7 @@ import os
 from asyncio import Event, Queue
 
 from fastapi import FastAPI
+from fastapi.responses import ORJSONResponse
 
 tools_inbox: Queue = Queue()
 stop_event: Event = Event()
@@ -11,7 +12,7 @@ _secrets = {}
 
 def create_app() -> FastAPI:
     """Create and configure the FastAPI application."""
-    app = FastAPI()
+    app = FastAPI(default_response_class=ORJSONResponse)
     return app
 
 

agentic_security/core/test_app.py

@@ -1,15 +1,35 @@
 import os
+
 import pytest
+
 from agentic_security.core.app import expand_secrets
 
 
 @pytest.fixture(autouse=True)
+def reset_globals():
+    """
+    Reset globals (_secrets, current_run, tools_inbox, stop_event) before each test.
+    This ensures tests run in a clean state.
+    """
+    from agentic_security.core.app import _secrets, current_run, get_tools_inbox, get_stop_event
+    _secrets.clear()
+    current_run["spec"] = ""
+    current_run["id"] = ""
+    # Clear tools_inbox queue
+    queue = get_tools_inbox()
+    while not queue.empty():
+        queue.get_nowait()
+    # Reset stop_event if it is set
+    event = get_stop_event()
+    if event.is_set():
+        event.clear()
 def setup_env_vars():
     # Set up environment variables for testing
     os.environ["TEST_ENV_VAR"] = "test_value"
 
 
 def test_expand_secrets_with_env_var():
+    os.environ["TEST_ENV_VAR"] = "test_value"
     secrets = {"secret_key": "$TEST_ENV_VAR"}
     expand_secrets(secrets)
     assert secrets["secret_key"] == "test_value"
@@ -25,3 +45,180 @@ def test_expand_secrets_without_dollar_sign():
     secrets = {"secret_key": "plain_value"}
     expand_secrets(secrets)
     assert secrets["secret_key"] == "plain_value"
+
+import asyncio
+from fastapi import FastAPI
+from fastapi.responses import ORJSONResponse
+from agentic_security.core.app import create_app, get_tools_inbox, get_stop_event, get_current_run, set_current_run, get_secrets, set_secrets, expand_secrets
+
+class DummyLLMSpec:
+    """A dummy LLMSpec for testing purposes."""
+    pass
+
+def test_create_app():
+    """Test that create_app returns a FastAPI app with ORJSONResponse."""
+    app = create_app()
+    assert isinstance(app, FastAPI)
+    assert app.router.default_response_class == ORJSONResponse
+
+def test_get_tools_inbox():
+    """Test that get_tools_inbox returns a Queue instance."""
+    queue = get_tools_inbox()
+    from asyncio import Queue
+    assert isinstance(queue, Queue)
+
+def test_get_stop_event():
+    """Test that get_stop_event returns an Event instance."""
+    event = get_stop_event()
+    from asyncio import Event
+    assert isinstance(event, Event)
+
+def test_get_current_run_initial():
+    """Test that get_current_run returns the initial current run dictionary."""
+    current = get_current_run()
+    # The initial dictionary should have an empty spec and id.
+    assert current["spec"] == ""
+    assert current["id"] == ""
+
+def test_set_current_run():
+    """Test that set_current_run updates the current run with the dummy LLMSpec."""
+    dummy_spec = DummyLLMSpec()
+    updated = set_current_run(dummy_spec)
+    assert updated["spec"] is dummy_spec
+    # Ensure that the id is computed as hash(id(dummy_spec))
+    expected_id = hash(id(dummy_spec))
+    assert updated["id"] == expected_id
+
+def test_get_and_set_secrets():
+    """Test that set_secrets updates the secrets dictionary and get_secrets returns the updated values."""
+    # Clear any previously set secrets
+    secrets_before = get_secrets().copy()
+    os.environ["MY_SECRET"] = "secret_value"
+    new_secrets = {"key1": "$MY_SECRET", "key2": "plain"}
+    updated = set_secrets(new_secrets)
+    assert updated["key1"] == "secret_value"
+    assert updated["key2"] == "plain"
+
+def test_expand_secrets_multiple_keys():
+    """Test expand_secrets with multiple keys, including one with an environment variable,
+    one with a non-existent variable, and one that is plain."""
+    os.environ["TEST_ENV_VAR"] = "test_value"
+    secrets = {"env_key": "$TEST_ENV_VAR", "nonexistent_key": "$NON_EXISTENT", "plain_key": "value"}
+    expand_secrets(secrets)
+    assert secrets["env_key"] == "test_value"
+    # For a non-existent environment variable, os.getenv returns None
+    assert secrets["nonexistent_key"] is None
+    # Plain values should not be changed.
+    assert secrets["plain_key"] == "value"
+def test_expand_secrets_with_space_after_dollar():
+    """Test expand_secrets when the value has a dollar sign followed by a space.
+    Since the value does not start strictly with "$", the secret remains unchanged.
+    Also verifies that the stripping in expand_secrets (via strip("$"))
+    will remove both dollar and any whitespace if the value actually started with '$'.
+    """
+    os.environ["SPACED_VAR"] = "spaced_value"
+    secrets = {"key": "$ SPACED_VAR"}
+    expand_secrets(secrets)
+    # " $ SPACED_VAR" after strip("$") becomes " SPACED_VAR" which is not a valid env key so returns None.
+    assert secrets["key"] is None
+
+def test_set_secrets_update_existing():
+    """Test that set_secrets updates an existing secret and retains previously set keys."""
+    os.environ["VAR1"] = "value1"
+    os.environ["VAR2"] = "value2"
+    result_first = set_secrets({"a": "$VAR1", "b": "b_val"})
+    assert result_first["a"] == "value1"
+    # Change VAR1 in environment and update secret "a", and add secret "c"
+    os.environ["VAR1"] = "new_value1"
+    result_second = set_secrets({"a": "$VAR1", "c": "$VAR2"})
+    assert result_second["a"] == "new_value1"
+    assert result_second["b"] == "b_val"
+    assert result_second["c"] == "value2"
+
+def test_tools_inbox_state():
+    """Test that get_tools_inbox returns the same queue instance 
+    and that the queue state persists across multiple calls.
+    """
+    from asyncio import Queue
+    inbox1 = get_tools_inbox()
+    inbox1.put_nowait("message")
+    inbox2 = get_tools_inbox()
+    # inbox2 should contain the "message" from inbox1
+    msg = inbox2.get_nowait()
+    assert msg == "message"
+
+def test_stop_event_state():
+    """Test that stop_event can be set and cleared, and its state persists."""
+    event = get_stop_event()
+    # Initially the event should not be set
+    assert not event.is_set()
+    event.set()
+    assert event.is_set()
+    event.clear()
+    assert not event.is_set()
+
+def test_set_current_run_returns_global_dict():
+    """Test that set_current_run returns the same global current_run dictionary
+    as returned by get_current_run.
+    """
+    dummy_spec = DummyLLMSpec()
+    updated = set_current_run(dummy_spec)
+    current = get_current_run()
+    assert updated is current
+def test_get_secrets_initial():
+    """Test that get_secrets returns an empty dictionary initially."""
+    assert get_secrets() == {}
+
+def test_set_secrets_empty():
+    """Test that setting an empty secrets dictionary does not modify existing secrets."""
+    # first set initial secrets
+    initial = {"key": "value"}
+    set_secrets(initial)
+    # update with an empty dict – the existing keys remain
+    result = set_secrets({})
+    assert result == initial
+
+def test_update_current_run_twice():
+    """Test updating current run twice with different LLMSpec values."""
+    dummy1 = DummyLLMSpec()
+    dummy2 = DummyLLMSpec()
+    set_current_run(dummy1)
+    first = get_current_run().copy()
+    set_current_run(dummy2)
+    second = get_current_run().copy()
+    # first update should hold dummy1, second should hold dummy2
+    assert first["spec"] is dummy1
+    assert second["spec"] is dummy2
+    # Ensure that id has changed (using hash(id(dummy_spec)))
+    assert first["id"] != second["id"]
+
+def test_expand_secrets_trailing_whitespace():
+    """Test expand_secrets when the secret value has trailing whitespace after the dollar sign.
+    The trailing whitespace remains after stripping only the dollar sign, so the looked-up environment variable key will not match.
+    """
+    os.environ["TRIM_TEST"] = "trimmed"
+    secrets = {"key": "$TRIM_TEST "}
+    expand_secrets(secrets)
+    # Since "TRIM_TEST " (with trailing space) is not set in the environment, the secret should be None.
+    assert secrets["key"] is None
+def test_expand_secrets_empty_dict():
+    """Test expand_secrets with an empty dictionary does nothing."""
+    secrets = {}
+    expand_secrets(secrets)
+    assert secrets == {}
+
+def test_expand_secrets_with_non_string_value():
+    """Test that expand_secrets raises an AttributeError when a secret value is not a string."""
+    secrets = {"key": 123}
+    with pytest.raises(AttributeError):
+        expand_secrets(secrets)
+
+def test_expand_secrets_multiple_dollar_signs():
+    """Test expand_secrets with a value that contains multiple leading dollar signs.
+    The extra dollar signs are removed by the strip method.
+    """
+    os.environ["MULTI_DOLLAR_VAR"] = "multi_value"
+    secrets = {"key": "$$MULTI_DOLLAR_VAR"}
+    expand_secrets(secrets)
+    # After stripping, "$$MULTI_DOLLAR_VAR".strip("$") returns "MULTI_DOLLAR_VAR"
+    assert secrets["key"] == "multi_value"

agentic_security/dependencies.py

@@ -1,13 +1,11 @@
-from agentic_security.config import CfgMixin
+from agentic_security.config import get_or_create_config
 from agentic_security.core.app import set_secrets
 
 
 class InMemorySecrets:
     def __init__(self):
-        self.secrets = {}
-        self.config = CfgMixin()
-        self.config.get_or_create_config()
-        self.secrets = self.config.config.get("secrets", {})
+        config = get_or_create_config()
+        self.secrets = config.get_config_value("secrets", {})
         set_secrets(self.secrets)
 
     def set_secret(self, key: str, value: str):

agentic_security/http_spec.py

@@ -4,6 +4,8 @@ from enum import Enum
 import httpx
 from pydantic import BaseModel
 
+from agentic_security.config import settings_var
+
 
 class Modality(Enum):
     TEXT = 0
@@ -28,7 +30,7 @@ def encode_audio_base64_by_url(url: str) -> str:
 
 
 class InvalidHTTPSpecError(Exception):
-    ...
+    pass
 
 
 class LLMSpec(BaseModel):
@@ -47,14 +49,21 @@ class LLMSpec(BaseModel):
         except Exception as e:
             raise InvalidHTTPSpecError(f"Failed to parse HTTP spec: {e}") from e
 
+    def timeout(self):
+        return (
+            settings_var("network.timeout_connect", 30),
+            settings_var("network.timeout_response", 90),
+        )
+
     async def _probe_with_files(self, files):
-        async with httpx.AsyncClient() as client:
+        transport = httpx.AsyncHTTPTransport(retries=settings_var("network.retry", 3))
+        async with httpx.AsyncClient(transport=transport) as client:
             response = await client.request(
                 method=self.method,
                 url=self.url,
                 headers=self.headers,
                 files=files,
-                timeout=(30, 90),
+                timeout=self.timeout(),
             )
 
         return response
@@ -90,13 +99,15 @@ class LLMSpec(BaseModel):
         content = self.body.replace("<<PROMPT>>", escape_special_chars_for_json(prompt))
         content = content.replace("<<BASE64_IMAGE>>", encoded_image)
         content = content.replace("<<BASE64_AUDIO>>", encoded_audio)
-        async with httpx.AsyncClient() as client:
+
+        transport = httpx.AsyncHTTPTransport(retries=settings_var("network.retry", 3))
+        async with httpx.AsyncClient(transport=transport) as client:
             response = await client.request(
                 method=self.method,
                 url=self.url,
                 headers=self.headers,
                 content=content,
-                timeout=(30, 90),
+                timeout=self.timeout(),
             )
 
         return response
@@ -169,6 +180,8 @@ def parse_http_spec(http_spec: str) -> LLMSpec:
     has_audio = "<<BASE64_AUDIO>>" in body
 
     for key, value in secrets.items():
+        if not value:
+            continue
         key = key.strip("$")
         body = body.replace(f"${key}", value)
 

agentic_security/lib.py

@@ -9,8 +9,8 @@ from rich.console import Console
 from rich.table import Table
 from tabulate import tabulate
 
-from agentic_security.config import CfgMixin  # Importing the configuration mixin
-from agentic_security.models.schemas import Scan
+from agentic_security.config import SettingsMixin  # Importing the configuration mixin
+from agentic_security.primitives import Scan
 from agentic_security.probe_data import REGISTRY
 from agentic_security.routes.scan import streaming_response_generator
 
@@ -23,7 +23,7 @@ YELLOW = colorama.Fore.YELLOW
 BLUE = colorama.Fore.BLUE
 
 
-class AgenticSecurity(CfgMixin):
+class AgenticSecurity(SettingsMixin):
     @classmethod
     async def async_scan(
         cls,

agentic_security/misc/banner.py

@@ -0,0 +1,92 @@
+from pyfiglet import Figlet, FontNotFound
+from termcolor import colored
+
+try:
+    from importlib.metadata import version
+except ImportError:
+    from importlib_metadata import version
+
+
+def generate_banner(
+    title="Agentic Security",
+    font="slant",
+    version="v2.1.0",
+    tagline="Proactive Threat Detection & Automated Security Protocols",
+    author="Developed by: [Security Team]",
+    website="Website: https://github.com/msoedov/agentic_security",
+    warning="",
+):
+    """Generate a visually enhanced banner with dynamic width and borders."""
+    # Define the text elements
+
+    # Initialize Figlet with the specified font, fallback to default if not found
+    try:
+        f = Figlet(font=font)
+    except FontNotFound:
+        f = Figlet()  # Fallback to default font
+
+    # Render the title text and calculate the maximum width of Figlet lines
+    banner_text = f.renderText(title)
+    banner_lines = banner_text.splitlines()
+    figlet_max_width = max(len(line) for line in banner_lines) if banner_lines else 0
+
+    # Create the details line and calculate its width
+    details_line = f"Version: {version} | {website}"
+    details_width = len(details_line)
+
+    # Calculate widths of other text elements
+    warning_width = len(warning)
+    tagline_width = len(tagline)
+
+    # Determine the overall maximum width for centering
+    overall_max_width = max(
+        figlet_max_width, warning_width, tagline_width, details_width
+    )
+
+    # Pad the Figlet lines to the overall maximum width
+    padded_banner_lines = [line.center(overall_max_width) for line in banner_lines]
+
+    # Define decorative characters and colors
+    decor_chars = ["▄", "■", "►"]
+    decor_colors = ["blue", "red", "yellow"]
+
+    # Create and color the content lines
+    content_lines = []
+    for line in padded_banner_lines:
+        content_lines.append(colored(line, "blue"))
+    content_lines.append(colored(decor_chars[0] * overall_max_width, decor_colors[0]))
+    content_lines.append(
+        colored(warning.center(overall_max_width), "red", attrs=["blink", "bold"])
+    )
+    content_lines.append(colored(decor_chars[1] * overall_max_width, decor_colors[1]))
+    content_lines.append(colored(tagline.center(overall_max_width), "red"))
+    content_lines.append(colored(decor_chars[2] * overall_max_width, decor_colors[2]))
+    content_lines.append(colored(details_line.center(overall_max_width), "magenta"))
+
+    # Define border color and create top and bottom borders
+    border_color = "blue"
+    top_border = colored("╔" + "═" * (overall_max_width + 2) + "╗", border_color)
+    bottom_border = colored("╚" + "═" * (overall_max_width + 2) + "╝", border_color)
+
+    # Add side borders to each content line with padding
+    bordered_content = [
+        colored("║ ", border_color) + line + colored(" ║", border_color)
+        for line in content_lines
+    ]
+
+    # Assemble the full banner
+    banner = top_border + "\n" + "\n".join(bordered_content) + "\n" + bottom_border
+    return banner
+
+
+def init_banner():
+    ver = version("agentic_security")
+    try:
+        print(generate_banner(version=ver))
+    except Exception:
+        # UnicodeEncodeError with codec on some systems
+        pass
+
+
+if __name__ == "__main__":
+    init_banner()

agentic_security/primitives/__init__.py

@@ -0,0 +1,11 @@
+from agentic_security.primitives.models import (  # noqa
+    CompletionRequest,
+    FileProbeResponse,
+    LLMInfo,
+    Message,
+    Probe,
+    Scan,
+    ScanResult,
+    Settings,
+    Table,
+)

agentic_security/probe_actor/fuzzer.py

@@ -2,6 +2,7 @@ import asyncio
 import random
 import time
 from collections.abc import AsyncGenerator
+from json import JSONDecodeError
 
 import httpx
 import pandas as pd
@@ -10,7 +11,7 @@ from skopt import Optimizer
 from skopt.space import Real
 
 from agentic_security.http_spec import Modality
-from agentic_security.models.schemas import Scan, ScanResult
+from agentic_security.primitives import Scan, ScanResult
 from agentic_security.probe_actor.cost_module import calculate_cost
 from agentic_security.probe_actor.refusal import refusal_heuristic
 from agentic_security.probe_data import audio_generator, image_generator, msj_data
@@ -19,6 +20,10 @@ from agentic_security.probe_data.data import prepare_prompts
 # TODO: full log file
 
 MAX_PROMPT_LENGTH = 2048
+BUDGET_MULTIPLIER = 100_000_000
+INITIAL_OPTIMIZER_POINTS = 25
+MIN_FAILURE_SAMPLES = 5
+FAILURE_RATE_THRESHOLD = 0.5
 
 
 async def generate_prompts(
@@ -75,6 +80,42 @@ async def process_prompt(
         logger.error(f"Request error: {exc}")
         errors.append((module_name, prompt, "?", str(exc)))
         return tokens, True
+    except JSONDecodeError as json_decode_error:
+        logger.error(f"Jason error: {json_decode_error}")
+        errors.append((module_name, prompt, "?", str(json_decode_error)))
+        return tokens, True
+
+
+async def process_prompt_batch(
+    request_factory,
+    prompts: list[str],
+    tokens: int,
+    module_name: str,
+    refusals,
+    errors,
+    outputs,
+) -> tuple[int, int]:
+    tasks = [
+        process_prompt(
+            request_factory, p, tokens, module_name, refusals, errors, outputs
+        )
+        for p in prompts
+    ]
+    results = await asyncio.gather(*tasks)
+    total_tokens = sum(r[0] for r in results)
+    failures = sum(1 for r in results if r[1])
+    return total_tokens, failures
+
+
+async def with_error_handling(agen):
+    try:
+        async for t in agen:
+            yield t
+    except Exception as e:
+        logger.exception("Scan failed")
+        yield ScanResult.status_msg(f"Scan failed: {str(e)}")
+    finally:
+        yield ScanResult.status_msg("Scan completed.")
 
 
 async def perform_single_shot_scan(
@@ -87,126 +128,120 @@ async def perform_single_shot_scan(
     secrets: dict[str, str] = {},
 ) -> AsyncGenerator[str, None]:
     """Perform a standard security scan."""
-    max_budget = max_budget * 100_000_000
+    max_budget = max_budget * BUDGET_MULTIPLIER
     selected_datasets = [m for m in datasets if m["selected"]]
     request_factory = multi_modality_spec(request_factory)
-    try:
-        yield ScanResult.status_msg("Loading datasets...")
-        prompt_modules = prepare_prompts(
-            dataset_names=[m["dataset_name"] for m in selected_datasets],
-            budget=max_budget,
-            tools_inbox=tools_inbox,
-            options=[m.get("opts", {}) for m in selected_datasets],
-        )
-        yield ScanResult.status_msg("Datasets loaded. Starting scan...")
+    yield ScanResult.status_msg("Loading datasets...")
+    prompt_modules = prepare_prompts(
+        dataset_names=[m["dataset_name"] for m in selected_datasets],
+        budget=max_budget,
+        tools_inbox=tools_inbox,
+        options=[m.get("opts", {}) for m in selected_datasets],
+    )
+    yield ScanResult.status_msg("Datasets loaded. Starting scan...")
 
-        errors = []
-        refusals = []
-        outputs = []
-        total_prompts = sum(len(m.prompts) for m in prompt_modules if not m.lazy)
-        processed_prompts = 0
+    errors = []
+    refusals = []
+    outputs = []
+    total_prompts = sum(len(m.prompts) for m in prompt_modules if not m.lazy)
+    processed_prompts = 0
 
-        optimizer = (
-            Optimizer([Real(0, 1)], base_estimator="GP", n_initial_points=25)
-            if optimize
-            else None
-        )
-        failure_rates = []
+    optimizer = (
+        Optimizer([Real(0, 1)], base_estimator="GP", n_initial_points=25)
+        if optimize
+        else None
+    )
+    failure_rates = []
 
-        total_tokens = 0
+    total_tokens = 0
+    tokens = 0
+    should_stop = False
+    for module in prompt_modules:
+        if should_stop:
+            break
         tokens = 0
-        should_stop = False
-        for module in prompt_modules:
-            if should_stop:
-                break
-            tokens = 0
-            module_failures = 0
-            module_size = 0 if module.lazy else len(module.prompts)
-            logger.info(f"Scanning {module.dataset_name} {module_size}")
+        module_failures = 0
+        module_size = 0 if module.lazy else len(module.prompts)
+        logger.info(f"Scanning {module.dataset_name} {module_size}")
+        module_prompts = 0  # Reset for each module
 
-            async for prompt in generate_prompts(module.prompts):
-                if stop_event and stop_event.is_set():
-                    stop_event.clear()
-                    logger.info("Scan stopped by user.")
-                    yield ScanResult.status_msg("Scan stopped by user.")
-                    return
+        async for prompt in generate_prompts(module.prompts):
+            if stop_event and stop_event.is_set():
+                stop_event.clear()
+                logger.info("Scan stopped by user.")
+                yield ScanResult.status_msg("Scan stopped by user.")
+                return
 
-                processed_prompts += 1
-                progress = (
-                    100 * processed_prompts / total_prompts if total_prompts else 0
-                )
-                total_tokens -= tokens
-                start = time.time()
-                tokens, failed = await process_prompt(
-                    request_factory,
-                    prompt,
-                    tokens,
-                    module.dataset_name,
-                    refusals,
-                    errors,
-                    outputs,
-                )
-                end = time.time()
-                total_tokens += tokens
-                # logger.debug(f"Trying prompt: {prompt}, {failed=}")
-                if failed:
-                    module_failures += 1
-                failure_rate = module_failures / max(processed_prompts, 1)
-                failure_rates.append(failure_rate)
-                cost = calculate_cost(tokens)
+            processed_prompts += 1
+            module_prompts += 1  # Fixed increment syntax
+            # Calculate progress based on total processed prompts
+            progress = 100 * processed_prompts / total_prompts if total_prompts else 0
+            progress = progress % 100
 
-                # TODO: improve this cond
-                last_output = outputs[-1] if outputs else None
-                if last_output and last_output[1] == prompt:
-                    response_text = last_output[2]
-                else:
-                    response_text = ""
+            total_tokens -= tokens
+            start = time.time()
+            tokens, failed = await process_prompt(
+                request_factory,
+                prompt,
+                tokens,
+                module.dataset_name,
+                refusals,
+                errors,
+                outputs,
+            )
+            end = time.time()
+            total_tokens += tokens
 
-                yield ScanResult(
-                    module=module.dataset_name,
-                    tokens=round(tokens / 1000, 1),
-                    cost=cost,
-                    progress=round(progress, 2),
-                    failureRate=round(failure_rate * 100, 2),
-                    prompt=prompt[:MAX_PROMPT_LENGTH],
-                    latency=end - start,
-                    model=response_text,
-                ).model_dump_json()
+            if failed:
+                module_failures += 1
+            failure_rate = module_failures / max(module_prompts, 1)
+            failure_rates.append(failure_rate)
+            cost = calculate_cost(tokens)
 
-                if optimize and len(failure_rates) >= 5:
-                    next_point = optimizer.ask()
-                    optimizer.tell(next_point, -failure_rate)
-                    best_failure_rate = -optimizer.get_result().fun
-                    if best_failure_rate > 0.5:
-                        yield ScanResult.status_msg(
-                            f"High failure rate detected ({best_failure_rate:.2%}). Stopping this module..."
-                        )
-                        should_stop = True
-                        break
-                if total_tokens > max_budget:
-                    logger.info(
-                        f"Scan ran out of budget and stopped. {total_tokens=} {max_budget=}"
-                    )
+            last_output = outputs[-1] if outputs else None
+            if last_output and last_output[1] == prompt:
+                response_text = last_output[2]
+            else:
+                response_text = ""
+
+            yield ScanResult(
+                module=module.dataset_name,
+                tokens=round(tokens / 1000, 1),
+                cost=cost,
+                progress=round(progress, 2),
+                failureRate=round(failure_rate * 100, 2),
+                prompt=prompt[:MAX_PROMPT_LENGTH],
+                latency=end - start,
+                model=response_text,
+            ).model_dump_json()
+
+            if optimize and len(failure_rates) >= 5:
+                next_point = optimizer.ask()
+                optimizer.tell(next_point, -failure_rate)
+                best_failure_rate = -optimizer.get_result().fun
+                if best_failure_rate > 0.5:
                     yield ScanResult.status_msg(
-                        f"Scan ran out of budget and stopped. {total_tokens=} {max_budget=}"
+                        f"High failure rate detected ({best_failure_rate:.2%}). Stopping this module..."
                     )
                     should_stop = True
                     break
+            if total_tokens > max_budget:
+                logger.info(
+                    f"Scan ran out of budget and stopped. {total_tokens=} {max_budget=}"
+                )
+                yield ScanResult.status_msg(
+                    f"Scan ran out of budget and stopped. {total_tokens=} {max_budget=}"
+                )
+                should_stop = True
+                break
 
-        yield ScanResult.status_msg("Scan completed.")
+    yield ScanResult.status_msg("Scan completed.")
 
-        failure_data = errors + refusals
-        df = pd.DataFrame(
-            failure_data, columns=["module", "prompt", "status_code", "content"]
-        )
-        df.to_csv("failures.csv", index=False)
-
-    except Exception as e:
-        logger.exception("Scan failed")
-        yield ScanResult.status_msg(f"Scan failed: {str(e)}")
-        # raise e
-    finally:
-        yield ScanResult.status_msg("Scan completed.")
+    failure_data = errors + refusals
+    df = pd.DataFrame(
+        failure_data, columns=["module", "prompt", "status_code", "content"]
+    )
+    df.to_csv("failures.csv", index=False)
 
 
 async def perform_many_shot_scan(
@@ -223,114 +258,107 @@ async def perform_many_shot_scan(
 ) -> AsyncGenerator[str, None]:
     """Perform a multi-step security scan with probe injection."""
     request_factory = multi_modality_spec(request_factory)
-    try:
-        # Load main and probe datasets
-        yield ScanResult.status_msg("Loading datasets...")
-        prompt_modules = prepare_prompts(
-            dataset_names=[m["dataset_name"] for m in datasets if m["selected"]],
-            budget=max_budget,
-            tools_inbox=tools_inbox,
-        )
-        yield ScanResult.status_msg("Loading datasets for MSJ...")
-        msj_modules = msj_data.prepare_prompts(probe_datasets)
-        yield ScanResult.status_msg("Datasets loaded. Starting scan...")
+    # Load main and probe datasets
+    yield ScanResult.status_msg("Loading datasets...")
+    prompt_modules = prepare_prompts(
+        dataset_names=[m["dataset_name"] for m in datasets if m["selected"]],
+        budget=max_budget,
+        tools_inbox=tools_inbox,
+    )
+    yield ScanResult.status_msg("Loading datasets for MSJ...")
+    msj_modules = msj_data.prepare_prompts(probe_datasets)
+    yield ScanResult.status_msg("Datasets loaded. Starting scan...")
 
-        errors = []
-        refusals = []
-        outputs = []
-        total_prompts = sum(len(m.prompts) for m in prompt_modules if not m.lazy)
-        processed_prompts = 0
+    errors = []
+    refusals = []
+    outputs = []
+    total_prompts = sum(len(m.prompts) for m in prompt_modules if not m.lazy)
+    processed_prompts = 0
 
-        optimizer = (
-            Optimizer([Real(0, 1)], base_estimator="GP", n_initial_points=25)
-            if optimize
-            else None
-        )
-        failure_rates = []
+    optimizer = (
+        Optimizer([Real(0, 1)], base_estimator="GP", n_initial_points=25)
+        if optimize
+        else None
+    )
+    failure_rates = []
 
-        for module in prompt_modules:
-            module_failures = 0
-            module_size = 0 if module.lazy else len(module.prompts)
-            logger.info(f"Scanning {module.dataset_name} {module_size}")
+    for module in prompt_modules:
+        module_failures = 0
+        module_size = 0 if module.lazy else len(module.prompts)
+        logger.info(f"Scanning {module.dataset_name} {module_size}")
 
-            async for prompt in generate_prompts(module.prompts):
-                if stop_event and stop_event.is_set():
-                    stop_event.clear()
-                    logger.info("Scan stopped by user.")
-                    yield ScanResult.status_msg("Scan stopped by user.")
-                    return
-                tokens = 0
-                processed_prompts += 1
-                progress = (
-                    100 * processed_prompts / total_prompts if total_prompts else 0
+        async for prompt in generate_prompts(module.prompts):
+            if stop_event and stop_event.is_set():
+                stop_event.clear()
+                logger.info("Scan stopped by user.")
+                yield ScanResult.status_msg("Scan stopped by user.")
+                return
+            tokens = 0
+            processed_prompts += 1
+            progress = 100 * processed_prompts / total_prompts if total_prompts else 0
+            progress = progress % 100
+
+            full_prompt = ""
+            msj_module = random.choice(msj_modules)
+
+            prompt_tokens = len(full_prompt.split())
+            tokens += prompt_tokens
+
+            injected = False
+            for _ in range(20):
+                if injected:
+                    break
+
+                m_prompt = random.choice(msj_module.prompts)
+                full_prompt += "\n" + m_prompt
+                if tokens > max_ctx_length:
+                    full_prompt = "\n" + prompt
+                    injected = True
+
+                tokens, failed = await process_prompt(
+                    request_factory,
+                    full_prompt,
+                    tokens,
+                    module.dataset_name,
+                    refusals,
+                    errors,
+                    outputs,
                 )
+                if failed:
+                    module_failures += 1
+                    break
+                if injected:
+                    break
 
-                full_prompt = ""
-                msj_module = random.choice(msj_modules)
+            failure_rate = module_failures / max(processed_prompts, 1)
+            failure_rates.append(failure_rate)
+            cost = calculate_cost(tokens)
 
-                prompt_tokens = len(full_prompt.split())
-                tokens += prompt_tokens
+            yield ScanResult(
+                module=module.dataset_name,
+                tokens=round(tokens / 1000, 1),
+                cost=cost,
+                progress=round(progress, 2),
+                failureRate=round(failure_rate * 100, 2),
+                prompt=prompt[:MAX_PROMPT_LENGTH],
+            ).model_dump_json()
 
-                injected = False
-                for _ in range(20):
-                    if injected:
-                        break
-
-                    m_prompt = random.choice(msj_module.prompts)
-                    full_prompt += "\n" + m_prompt
-                    if tokens > max_ctx_length:
-                        full_prompt = "\n" + prompt
-                        injected = True
-
-                    tokens, failed = await process_prompt(
-                        request_factory,
-                        full_prompt,
-                        tokens,
-                        module.dataset_name,
-                        refusals,
-                        errors,
-                        outputs,
+            if optimize and len(failure_rates) >= 5:
+                next_point = optimizer.ask()
+                optimizer.tell(next_point, -failure_rate)
+                best_failure_rate = -optimizer.get_result().fun
+                if best_failure_rate > 0.5:
+                    yield ScanResult.status_msg(
+                        f"High failure rate detected ({best_failure_rate:.2%}). Stopping this module..."
                     )
-                    if failed:
-                        module_failures += 1
-                        break
-                    if injected:
-                        break
+                    break
 
-                failure_rate = module_failures / max(processed_prompts, 1)
-                failure_rates.append(failure_rate)
-                cost = calculate_cost(tokens)
+    yield ScanResult.status_msg("Scan completed.")
 
-                yield ScanResult(
-                    module=module.dataset_name,
-                    tokens=round(tokens / 1000, 1),
-                    cost=cost,
-                    progress=round(progress, 2),
-                    failureRate=round(failure_rate * 100, 2),
-                    prompt=prompt[:MAX_PROMPT_LENGTH],
-                ).model_dump_json()
-
-                if optimize and len(failure_rates) >= 5:
-                    next_point = optimizer.ask()
-                    optimizer.tell(next_point, -failure_rate)
-                    best_failure_rate = -optimizer.get_result().fun
-                    if best_failure_rate > 0.5:
-                        yield ScanResult.status_msg(
-                            f"High failure rate detected ({best_failure_rate:.2%}). Stopping this module..."
-                        )
-                        break
-
-        yield ScanResult.status_msg("Scan completed.")
-
-        df = pd.DataFrame(
-            errors + refusals, columns=["module", "prompt", "status_code", "content"]
-        )
-        df.to_csv("failures.csv", index=False)
-
-    except Exception as e:
-        logger.exception("Scan failed")
-        yield ScanResult.status_msg(f"Scan failed: {str(e)}")
-        raise e
+    df = pd.DataFrame(
+        errors + refusals, columns=["module", "prompt", "status_code", "content"]
+    )
+    df.to_csv("failures.csv", index=False)
 
 
 def scan_router(
@@ -340,23 +368,27 @@ def scan_router(
     stop_event: asyncio.Event = None,
 ):
     if scan_parameters.enableMultiStepAttack:
-        return perform_many_shot_scan(
-            request_factory=request_factory,
-            max_budget=scan_parameters.maxBudget,
-            datasets=scan_parameters.datasets,
-            probe_datasets=scan_parameters.probe_datasets,
-            tools_inbox=tools_inbox,
-            optimize=scan_parameters.optimize,
-            stop_event=stop_event,
-            secrets=scan_parameters.secrets,
+        return with_error_handling(
+            perform_many_shot_scan(
+                request_factory=request_factory,
+                max_budget=scan_parameters.maxBudget,
+                datasets=scan_parameters.datasets,
+                probe_datasets=scan_parameters.probe_datasets,
+                tools_inbox=tools_inbox,
+                optimize=scan_parameters.optimize,
+                stop_event=stop_event,
+                secrets=scan_parameters.secrets,
+            )
         )
     else:
-        return perform_single_shot_scan(
-            request_factory=request_factory,
-            max_budget=scan_parameters.maxBudget,
-            datasets=scan_parameters.datasets,
-            tools_inbox=tools_inbox,
-            optimize=scan_parameters.optimize,
-            stop_event=stop_event,
-            secrets=scan_parameters.secrets,
+        return with_error_handling(
+            perform_single_shot_scan(
+                request_factory=request_factory,
+                max_budget=scan_parameters.maxBudget,
+                datasets=scan_parameters.datasets,
+                tools_inbox=tools_inbox,
+                optimize=scan_parameters.optimize,
+                stop_event=stop_event,
+                secrets=scan_parameters.secrets,
+            )
         )

agentic_security/probe_actor/operator.py

@@ -3,10 +3,13 @@ import logging
 from typing import Any
 
 import httpx
-from httpx import LLMSpec
 from pydantic import BaseModel, Field
 from pydantic_ai import Agent, RunContext
 
+from agentic_security.http_spec import LLMSpec
+
+LLM_SPECS = []
+
 # Configure logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
@@ -28,6 +31,7 @@ class OperatorToolBox:
         self.spec = spec
         self.datasets = datasets
         self.failures = []
+        self.llm_specs = [LLMSpec.from_string(spec) for spec in LLM_SPECS]
 
     def get_spec(self) -> AgentSpecification:
         return self.spec
@@ -62,52 +66,33 @@ class OperatorToolBox:
             return f"Operation '{operation}' failed: Dataset not found."
         return f"Operation '{operation}' executed successfully."
 
-    async def test(self, description: str, sample_test: dict[str, Any]) -> str:
-        agent = Agent(
-            "openai:gpt-4o",
-            result_type=LLMSpec,
-            system_prompt="Extract the LLM specification from the input",
-        )
+    async def test_llm_spec(self, llm_spec: LLMSpec, user_prompt: str) -> str:
+        try:
+            # Verify the spec
+            response = await llm_spec.verify()
+            response.raise_for_status()
+            logger.info(f"Verification succeeded for {llm_spec.url}")
 
-        async with agent.run_stream(description) as result:
-            async for spec in result.stream():
-                self.spec.endpoint = spec.url
+            # Run test with user prompt
+            test_response = await llm_spec.probe(user_prompt)
+            test_response.raise_for_status()
+            response_data = test_response.json()
+            return f"Test succeeded for {llm_spec.url}: {response_data}"
+        except httpx.HTTPStatusError as e:
+            self.failures.append(f"HTTP error occurred: {e}")
+            logger.error(f"Test failed for {llm_spec.url}: {e}")
+            return f"Test failed for {llm_spec.url}: {e}"
+        except Exception as e:
+            self.failures.append(f"An error occurred: {e}")
+            logger.error(f"Test failed for {llm_spec.url}: {e}")
+            return f"Test failed for {llm_spec.url}: {e}"
 
-                # Verify access to the endpoint
-                async with httpx.AsyncClient() as client:
-                    try:
-                        access_response = await client.get(spec.url)
-                        access_response.raise_for_status()
-                    except httpx.HTTPStatusError as e:
-                        self.failures.append(f"HTTP error occurred: {e}")
-                        logger.error(f"Access verification failed: {e}")
-                        return f"Access verification failed: {e}"
-                    except Exception as e:
-                        self.failures.append(f"An error occurred: {e}")
-                        logger.error(f"Access verification failed: {e}")
-                        return f"Access verification failed: {e}"
+    async def test_with_prompt(self, spec_index: int, user_prompt: str) -> str:
+        if not 0 <= spec_index < len(self.llm_specs):
+            return f"Invalid spec index: {spec_index}. Valid range is 0 to {len(self.llm_specs) - 1}"
 
-                # Run the sample test
-                try:
-                    test_response = await client.post(
-                        f"{spec.url}/test", json=sample_test
-                    )
-                    test_response.raise_for_status()
-                    response_data = test_response.json()
-                    if "choices" in response_data and len(response_data["choices"]) > 0:
-                        return f"Testing agent at {spec.url} succeeded: {response_data}"
-                    else:
-                        self.failures.append("Invalid response format")
-                        logger.error("Sample test failed: Invalid response format")
-                        return "Sample test failed: Invalid response format"
-                except httpx.HTTPStatusError as e:
-                    self.failures.append(f"HTTP error occurred: {e}")
-                    logger.error(f"Sample test failed: {e}")
-                    return f"Sample test failed: {e}"
-                except Exception as e:
-                    self.failures.append(f"An error occurred: {e}")
-                    logger.error(f"Sample test failed: {e}")
-                    return f"Sample test failed: {e}"
+        llm_spec = self.llm_specs[spec_index]
+        return await self.test_llm_spec(llm_spec, user_prompt)
 
 
 # Initialize OperatorToolBox with AgentSpecification
@@ -126,104 +111,95 @@ dataset_manager_agent = Agent(
     model="gpt-4",
     deps_type=OperatorToolBox,
     result_type=str,
-    system_prompt="You can validate the toolbox, run operations, and retrieve results or failures.",
+    system_prompt="You can validate the toolbox, run operations, retrieve results or failures, and test LLM specs.",
 )
 
 
 @dataset_manager_agent.tool
 async def validate_toolbox(ctx: RunContext[OperatorToolBox]) -> str:
     is_valid = ctx.deps.validate()
-    if is_valid:
-        return "ToolBox validation successful."
-    else:
-        return "ToolBox validation failed."
+    return (
+        "ToolBox validation successful." if is_valid else "ToolBox validation failed."
+    )
 
 
 @dataset_manager_agent.tool
 async def execute_operation(ctx: RunContext[OperatorToolBox], operation: str) -> str:
-    result = ctx.deps.run_operation(operation)
-    return result
+    return ctx.deps.run_operation(operation)
 
 
 @dataset_manager_agent.tool
 async def retrieve_results(ctx: RunContext[OperatorToolBox]) -> str:
     results = ctx.deps.get_results()
-    if results:
-        formatted_results = "\n".join([f"{op}: {res}" for op, res in results.items()])
-        return f"Operation Results:\n{formatted_results}"
-    else:
-        return "No operations have been executed yet."
+    return (
+        f"Operation Results:\n{results}"
+        if results
+        else "No operations have been executed yet."
+    )
 
 
 @dataset_manager_agent.tool
 async def retrieve_failures(ctx: RunContext[OperatorToolBox]) -> str:
     failures = ctx.deps.get_failures()
-    if failures:
-        formatted_failures = "\n".join(failures)
-        return f"Failures:\n{formatted_failures}"
-    else:
-        return "No failures recorded."
+    return f"Failures:\n{failures}" if failures else "No failures recorded."
 
 
 @dataset_manager_agent.tool
-async def test_agent(
-    ctx: RunContext[OperatorToolBox], description: str, sample_test: dict[str, Any]
+async def list_llm_specs(ctx: RunContext[OperatorToolBox]) -> str:
+    spec_list = "\n".join(
+        f"{i}: {spec.url}" for i, spec in enumerate(ctx.deps.llm_specs)
+    )
+    return f"Available LLM Specs:\n{spec_list}"
+
+
+@dataset_manager_agent.tool
+async def test_llm_with_prompt(
+    ctx: RunContext[OperatorToolBox], spec_index: int, user_prompt: str
 ) -> str:
-    result = await ctx.deps.test(description, sample_test)
-    return result
+    return await ctx.deps.test_with_prompt(spec_index, user_prompt)
 
 
-# Synchronous run example
-def run_dataset_manager_agent_sync():
-    prompts = [
-        "Validate the toolbox.",
-        "Execute operation on 'dataset2'.",
-        "Execute operation on 'dataset4'.",  # This should fail
-        "Retrieve the results.",
-        "Retrieve any failures.",
-        "Test my openAI compatible agent deployed at localhost:3000",
-    ]
-
-    sample_test = {"prompt": "Hello, how are you?", "max_tokens": 5}
-
-    for prompt in prompts:
-        if "Test my" in prompt:
-            result = dataset_manager_agent.run_sync(
-                prompt, deps=toolbox, sample_test=sample_test
-            )
-        else:
-            result = dataset_manager_agent.run_sync(prompt, deps=toolbox)
-        print(f"Prompt: {prompt}")
-        print(f"Response: {result.data}\n")
-
-
-# Asynchronous run example
+# Asynchronous run example with user confirmation
 async def run_dataset_manager_agent_async():
     prompts = [
         "Validate the toolbox.",
-        "Execute operation on 'dataset2'.",
-        "Execute operation on 'dataset4'.",  # This should fail
-        "Retrieve the results.",
-        "Retrieve any failures.",
-        "Test my openAI compatible agent deployed at localhost:3000",
+        "List available LLM specs.",
+        "I want to test an LLM with my prompt: 'Tell me a short story about a robot'. Which spec index should I use?",
     ]
 
-    sample_test = {"prompt": "Hello, how are you?", "max_tokens": 5}
-
     for prompt in prompts:
-        if "Test my" in prompt:
-            result = await dataset_manager_agent.run(
-                prompt, deps=toolbox, sample_test=sample_test
-            )
-        else:
-            result = await dataset_manager_agent.run(prompt, deps=toolbox)
+        result = await dataset_manager_agent.run(prompt, deps=toolbox)
         print(f"Prompt: {prompt}")
         print(f"Response: {result.data}\n")
 
+        # Handle testing request
+        if "test an LLM with my prompt" in prompt:
+            print(
+                "Please select a spec index from the list above and confirm to proceed."
+            )
+            # Simulate user input for demo (in real app, you'd get this from user)
+            user_input = (
+                input("Enter spec index and 'yes' to confirm (e.g., '0 yes'): ")
+                .strip()
+                .split()
+            )
+            if len(user_input) == 2 and user_input[1].lower() == "yes":
+                try:
+                    spec_index = int(user_input[0])
+                    # Extract prompt from the original input
+                    user_prompt = prompt.split("my prompt: ")[1].strip("'")
+                    test_result = await dataset_manager_agent.run(
+                        f"Test LLM at index {spec_index} with prompt: {user_prompt}",
+                        deps=toolbox,
+                        spec_index=spec_index,
+                        user_prompt=user_prompt,
+                    )
+                    print(f"Test Response: {test_result.data}\n")
+                except ValueError:
+                    print("Invalid spec index provided.\n")
+            else:
+                print("Test canceled. Please provide a valid index and confirmation.\n")
+
 
 if __name__ == "__main__":
-    # Run synchronous example
-    run_dataset_manager_agent_sync()
-
-    # Run asynchronous example
     asyncio.run(run_dataset_manager_agent_async())

agentic_security/probe_actor/refusal.py

@@ -1,6 +1,6 @@
 from abc import ABC, abstractmethod
 
-from agentic_security.refusal_classifier import RefusalClassifier
+from agentic_security.refusal_classifier.model import RefusalClassifier
 
 classifier = RefusalClassifier()
 classifier.load_model()

agentic_security/probe_actor/test_refusal.py

@@ -1,4 +1,5 @@
 from agentic_security.probe_actor.refusal import DefaultRefusalClassifier
+from agentic_security.probe_actor.refusal import refusal_heuristic, RefusalClassifierManager, RefusalClassifierPlugin
 
 
 class TestCheckRefusal:
@@ -11,3 +12,40 @@ class TestCheckRefusal:
     def test_empty_response(self):
         response = ""
         assert not DefaultRefusalClassifier().is_refusal(response)
+    def test_non_refusal_phrase(self):
+        """Test that a response without refusal phrases returns False."""
+        response = "Here is some helpful information."
+        assert not DefaultRefusalClassifier().is_refusal(response)
+
+    def test_refusal_heuristic(self):
+        """Test that the refusal_heuristic function correctly identifies refusal phrases."""
+        response = "Sorry, I cannot assist with your request."
+        # refusal_heuristic will cast the argument to a string and use the plugin manager to check for refusal markers
+        assert refusal_heuristic(response)
+
+    def test_plugin_manager_with_dummy_plugin(self):
+        """Test that RefusalClassifierManager returns True if any registered plugin returns True."""
+        class AlwaysFalsePlugin(RefusalClassifierPlugin):
+            def is_refusal(self, response: str) -> bool:
+                return False
+
+        class AlwaysTruePlugin(RefusalClassifierPlugin):
+            def is_refusal(self, response: str) -> bool:
+                return True
+
+        manager = RefusalClassifierManager()
+        manager.register_plugin("false", AlwaysFalsePlugin())
+        manager.register_plugin("true", AlwaysTruePlugin())
+        response = "Any response text"
+        assert manager.is_refusal(response)
+
+    def test_default_classifier_with_empty_phrases(self):
+        """Test that DefaultRefusalClassifier returns False when provided with an empty refusal phrase list."""
+        classifier = DefaultRefusalClassifier(refusal_phrases=[])
+        response = "I do not have any info."
+        assert not classifier.is_refusal(response)
+
+    def test_case_sensitivity(self):
+        """Test that string matching is case-sensitive."""
+        response = "i'm sorry, but can you help me?"  # lower-case "i'm sorry" does not match "I'm sorry" and no extra refusal phrases are present
+        assert not DefaultRefusalClassifier().is_refusal(response)

agentic_security/probe_data/__init__.py

@@ -5,6 +5,7 @@ REGISTRY_V0 = [
         "dataset_name": "simonycl/aya-23-8B_advbench_jailbreak",
         "num_prompts": 416,
         "tokens": None,  # Add actual token count if available
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Hugging Face Datasets",
         "selected": False,
@@ -16,6 +17,7 @@ REGISTRY_V0 = [
         "dataset_name": "acmc/jailbreaks_dataset_with_perplexity_bigcode_starcoder2-3b_bigcode_starcoder2-7b",
         "num_prompts": 11191,
         "tokens": None,  # Add actual token count if available
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Hugging Face Datasets",
         "selected": False,
@@ -27,6 +29,7 @@ REGISTRY_V0 = [
         "dataset_name": "karanxa/dolphin-jailbreak-finetuning-dataset",
         "num_prompts": 42684,
         "tokens": None,  # Add actual token count if available
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Hugging Face Datasets",
         "selected": False,
@@ -38,6 +41,7 @@ REGISTRY_V0 = [
         "dataset_name": "karanxa/llama-2-jailbreak-dataset",
         "num_prompts": 40613,
         "tokens": None,  # Add actual token count if available
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Hugging Face Datasets",
         "selected": False,
@@ -49,6 +53,7 @@ REGISTRY_V0 = [
         "dataset_name": "karanxa/llama2-uncensored-jailbreak-dataset-finetuning",
         "num_prompts": 42854,
         "tokens": None,  # Add actual token count if available
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Hugging Face Datasets",
         "selected": False,
@@ -60,6 +65,7 @@ REGISTRY_V0 = [
         "dataset_name": "liuyanchen1015/Llama-3.2-1B_jailbreak_responses",
         "num_prompts": 9888,
         "tokens": None,  # Add actual token count if available
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Hugging Face Datasets",
         "selected": False,
@@ -71,6 +77,7 @@ REGISTRY_V0 = [
         "dataset_name": "liuyanchen1015/Llama-3.2-1B-Instruct_jailbreak_responses",
         "num_prompts": 9888,
         "tokens": None,  # Add actual token count if available
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Hugging Face Datasets",
         "selected": False,
@@ -82,6 +89,7 @@ REGISTRY_V0 = [
         "dataset_name": "liuyanchen1015/Llama-3.2-1B-Instruct_jailbreak_responses_with_judgment",
         "num_prompts": 9888,
         "tokens": None,  # Add actual token count if available
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Hugging Face Datasets",
         "selected": False,
@@ -93,6 +101,7 @@ REGISTRY_V0 = [
         "dataset_name": "jackhhao/jailbreak-classification",
         "num_prompts": 1044,
         "tokens": None,  # Add actual token count if available
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Hugging Face Datasets",
         "selected": False,
@@ -104,6 +113,7 @@ REGISTRY_V0 = [
         "dataset_name": "markush1/LLM-Jailbreak-Classifier",
         "num_prompts": 201193,
         "tokens": None,  # Add actual token count if available
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Hugging Face Datasets",
         "selected": False,
@@ -115,6 +125,7 @@ REGISTRY_V0 = [
         "dataset_name": "walledai/JailbreakBench",
         "num_prompts": 200,
         "tokens": None,  # Add actual token count if available
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Hugging Face Datasets",
         "selected": False,
@@ -126,6 +137,7 @@ REGISTRY_V0 = [
         "dataset_name": "walledai/JailbreakHub",
         "num_prompts": 15140,
         "tokens": None,  # Add actual token count if available
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Hugging Face Datasets",
         "selected": False,
@@ -137,6 +149,7 @@ REGISTRY_V0 = [
         "dataset_name": "Granther/evil-jailbreak",
         "num_prompts": 1200,
         "tokens": None,  # Add actual token count if available
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Hugging Face Datasets",
         "selected": False,
@@ -148,6 +161,7 @@ REGISTRY_V0 = [
         "dataset_name": "sevdeawesome/jailbreak_success",
         "num_prompts": 10800,
         "tokens": None,  # Add actual token count if available
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Hugging Face Datasets",
         "selected": False,
@@ -159,6 +173,7 @@ REGISTRY_V0 = [
         "dataset_name": "IDA-SERICS/Disaster-tweet-jailbreaking",
         "num_prompts": 3000,
         "tokens": None,  # Add actual token count if available
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Hugging Face Datasets",
         "selected": False,
@@ -170,6 +185,7 @@ REGISTRY_V0 = [
         "dataset_name": "GeorgeDaDude/Jailbreak_Complete_DS_labeled",
         "num_prompts": 11383,
         "tokens": None,  # Add actual token count if available
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Hugging Face Datasets",
         "selected": False,
@@ -181,6 +197,7 @@ REGISTRY_V0 = [
         "dataset_name": "dayone3nder/jailbreak_prompt_JBB_sft_trainset",
         "num_prompts": 4785,
         "tokens": None,  # Add actual token count if available
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Hugging Face Datasets",
         "selected": False,
@@ -192,6 +209,7 @@ REGISTRY_V0 = [
         "dataset_name": "dayone3nder/general_safe_mix_jailbreak_prompt_JBB_trainset",
         "num_prompts": 24679,
         "tokens": None,  # Add actual token count if available
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Hugging Face Datasets",
         "selected": False,
@@ -206,6 +224,7 @@ REGISTRY = REGISTRY_V0 + [
         "dataset_name": "AgenticBackend",
         "num_prompts": 2000,
         "tokens": 0,
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Fine-tuned cloud hosted model",
         "selected": True,
@@ -221,6 +240,7 @@ REGISTRY = REGISTRY_V0 + [
         "dataset_name": "ShawnMenz/DAN_jailbreak",
         "num_prompts": 666,
         "tokens": 224196,
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Hugging Face Datasets",
         "selected": False,
@@ -232,6 +252,7 @@ REGISTRY = REGISTRY_V0 + [
         "dataset_name": "deepset/prompt-injections",
         "num_prompts": 203,
         "tokens": 6988,
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Hugging Face Datasets",
         "selected": False,
@@ -243,6 +264,7 @@ REGISTRY = REGISTRY_V0 + [
         "dataset_name": "rubend18/ChatGPT-Jailbreak-Prompts",
         "num_prompts": 79,
         "tokens": 26971,
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Hugging Face Datasets",
         "selected": False,
@@ -254,6 +276,7 @@ REGISTRY = REGISTRY_V0 + [
         "dataset_name": "notrichardren/refuse-to-answer-prompts",
         "num_prompts": 522,
         "tokens": 7172,
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Hugging Face Datasets",
         "selected": False,
@@ -265,6 +288,7 @@ REGISTRY = REGISTRY_V0 + [
         "dataset_name": "Lemhf14/EasyJailbreak_Datasets",
         "num_prompts": 1630,
         "tokens": 19758,
+        "is_active": False,
         "approx_cost": 0.0,
         "source": "Hugging Face Datasets",
         "selected": False,
@@ -276,6 +300,7 @@ REGISTRY = REGISTRY_V0 + [
         "dataset_name": "markush1/LLM-Jailbreak-Classifier",
         "num_prompts": 1119,
         "tokens": 19758,
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Hugging Face Datasets",
         "selected": False,
@@ -287,6 +312,7 @@ REGISTRY = REGISTRY_V0 + [
         "dataset_name": "JailbreakV-28K/JailBreakV-28k",
         "num_prompts": 28300,
         "tokens": 1975800,
+        "is_active": False,
         "approx_cost": 0.0,
         "source": "Hugging Face Datasets",
         "selected": False,
@@ -298,6 +324,7 @@ REGISTRY = REGISTRY_V0 + [
         "dataset_name": "ShawnMenz/jailbreak_sft_rm_ds",
         "num_prompts": 371000,
         "tokens": 1975800,
+        "is_active": False,
         "approx_cost": 0.0,
         "source": "Hugging Face Datasets",
         "selected": False,
@@ -309,6 +336,7 @@ REGISTRY = REGISTRY_V0 + [
         "dataset_name": "Steganography",
         "num_prompts": 10,
         "tokens": 0,
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Local mutation dataset",
         "selected": False,
@@ -320,6 +348,7 @@ REGISTRY = REGISTRY_V0 + [
         "dataset_name": "GPT fuzzer",
         "num_prompts": 10,
         "tokens": 0,
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Local mutation dataset",
         "selected": False,
@@ -331,6 +360,7 @@ REGISTRY = REGISTRY_V0 + [
         "dataset_name": "jailbreak_llms/2023_05_07",
         "num_prompts": 0,
         "tokens": 0,
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Github",
         "selected": False,
@@ -342,6 +372,7 @@ REGISTRY = REGISTRY_V0 + [
         "dataset_name": "jailbreak_llms/2023_12_25.csv",
         "num_prompts": 0,
         "tokens": 0,
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Github",
         "selected": False,
@@ -353,6 +384,7 @@ REGISTRY = REGISTRY_V0 + [
         "dataset_name": "Malwaregen",
         "num_prompts": 0,
         "tokens": 0,
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Local dataset",
         "selected": False,
@@ -364,6 +396,7 @@ REGISTRY = REGISTRY_V0 + [
         "dataset_name": "Hallucination",
         "num_prompts": 0,
         "tokens": 0,
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Local dataset",
         "selected": False,
@@ -375,6 +408,7 @@ REGISTRY = REGISTRY_V0 + [
         "dataset_name": "DataLeak",
         "num_prompts": 0,
         "tokens": 0,
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Local dataset",
         "selected": False,
@@ -386,6 +420,7 @@ REGISTRY = REGISTRY_V0 + [
         "dataset_name": "llm-adaptive-attacks",
         "num_prompts": 0,
         "tokens": 0,
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Github: tml-epfl/llm-adaptive-attacks#0.0.1",
         "selected": False,
@@ -397,6 +432,7 @@ REGISTRY = REGISTRY_V0 + [
         "dataset_name": "Garak",
         "num_prompts": 0,
         "tokens": 0,
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Github: https://github.com/leondz/garak#v0.9.0.1",
         "selected": False,
@@ -412,6 +448,7 @@ REGISTRY = REGISTRY_V0 + [
         "dataset_name": "Reinforcement Learning Optimization",
         "num_prompts": 0,
         "tokens": 0,
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Cloud hosted model",
         "selected": False,
@@ -427,6 +464,7 @@ REGISTRY = REGISTRY_V0 + [
         "dataset_name": "InspectAI",
         "num_prompts": 0,
         "tokens": 0,
+        "is_active": True,
         "approx_cost": 0.0,
         "source": "Github: https://github.com/UKGovernmentBEIS/inspect_ai",
         "selected": False,
@@ -439,6 +477,7 @@ REGISTRY = REGISTRY_V0 + [
         "num_prompts": len(load_local_csv().prompts),
         "tokens": load_local_csv().tokens,
         "approx_cost": 0.0,
+        "is_active": True,
         "source": f"Local file dataset: {load_local_csv().metadata['src']}",
         "selected": len(load_local_csv().prompts),
         "url": "",

agentic_security/routes/probe.py

@@ -3,7 +3,7 @@ import random
 from fastapi import APIRouter, File, Header, HTTPException, UploadFile
 from fastapi.responses import JSONResponse
 
-from ..models.schemas import FileProbeResponse, Probe
+from ..primitives import FileProbeResponse, Probe
 from ..probe_actor.refusal import REFUSAL_MARKS
 from ..probe_data import REGISTRY
 
@@ -77,3 +77,11 @@ async def data_config():
 async def health_check():
     """Health check endpoint."""
     return JSONResponse(content={"status": "ok"})
+
+
+@router.post("/v1/self-probe-t5")
+def self_probe_t5(probe: Probe):
+    import languagemodels as lm  # noqa
+
+    message = lm.do(probe.prompt)
+    return make_mock_response(message)

agentic_security/routes/proxy.py

@@ -5,7 +5,7 @@ from fastapi import APIRouter
 from loguru import logger
 
 from ..core.app import get_current_run, get_tools_inbox
-from ..models.schemas import CompletionRequest, Settings
+from ..primitives import CompletionRequest, Settings
 from ..probe_actor.refusal import REFUSAL_MARKS
 
 router = APIRouter()

agentic_security/routes/report.py

@@ -3,7 +3,7 @@ from pathlib import Path
 from fastapi import APIRouter, Response
 from fastapi.responses import FileResponse, StreamingResponse
 
-from ..models.schemas import Table
+from ..primitives import Table
 from ..report_chart import plot_security_report
 
 router = APIRouter()

agentic_security/routes/scan.py

@@ -10,11 +10,12 @@ from fastapi import (
     UploadFile,
 )
 from fastapi.responses import StreamingResponse
+from loguru import logger
 
 from ..core.app import get_stop_event, get_tools_inbox, set_current_run
 from ..dependencies import InMemorySecrets, get_in_memory_secrets
 from ..http_spec import LLMSpec
-from ..models.schemas import LLMInfo, Scan
+from ..primitives import LLMInfo, Scan
 from ..probe_actor import fuzzer
 
 router = APIRouter()
@@ -25,7 +26,12 @@ async def verify(
     info: LLMInfo, secrets: InMemorySecrets = Depends(get_in_memory_secrets)
 ):
     spec = LLMSpec.from_string(info.spec)
-    r = await spec.verify()
+    try:
+        r = await spec.verify()
+    except Exception as e:
+        logger.exception(e)
+        raise HTTPException(status_code=400, detail=str(e))
+
     if r.status_code >= 400:
         raise HTTPException(status_code=r.status_code, detail=r.text)
     return dict(

agentic_security/routes/static.py

@@ -7,7 +7,7 @@ from fastapi.templating import Jinja2Templates
 from jinja2 import Environment, FileSystemLoader
 from starlette.responses import Response
 
-from ..models.schemas import Settings
+from ..primitives import Settings
 
 router = APIRouter()
 STATIC_DIR = Path(__file__).parent.parent / "static"

agentic_security/routes/telemetry.py

@@ -2,7 +2,7 @@ import sentry_sdk
 from loguru import logger
 from sentry_sdk.integrations.logging import ignore_logger
 
-from ..models.schemas import Settings
+from ..primitives import Settings
 
 
 def setup(app):
@@ -16,6 +16,7 @@ def setup(app):
         # Set traces_sample_rate to 1.0 to capture 100%
         # of transactions for tracing.
         traces_sample_rate=1.0,
+        ignore_errors=[KeyboardInterrupt],
         _experiments={
             # Set continuous_profiling_auto_start to True
             # to automatically start the profiler on when

agentic_security/static/base.js

@@ -1,4 +1,3 @@
-
 let SELF_URL = window.location.href;
 if (SELF_URL.endsWith('/')) {
   SELF_URL = SELF_URL.slice(0, -1);
@@ -171,6 +170,21 @@ Content-Type: application/json
 {
   "audio_url": "<<AUDIO_FILE_URL>>"
 }
+`,
+
+  `POST https://api.openrouter.ai/v1/chat/completions
+Authorization: Bearer $OPENROUTER_API_KEY
+Content-Type: application/json
+
+{
+  "model": "openrouter-latest",
+  "prompt": "<<PROMPT>>",
+  "temperature": 0.7,
+  "max_tokens": 150,
+  "top_p": 0.9,
+  "frequency_penalty": 0,
+  "presence_penalty": 0
+}
 `,
 
 ]
@@ -190,6 +204,7 @@ let LLM_CONFIGS = [
   { name: 'Claude', prompts: 40000, logo: '/icons/claude.png' },
   { name: 'Cohere', prompts: 40000, logo: '/icons/cohere.png' },
   { name: 'Azure OpenAI', prompts: 40000, logo: '/icons/azureai.png' },
+  { name: 'OpenRouter.ai', prompts: 40000, logo: '/icons/openrouter.png' },
   { name: 'assemblyai', prompts: 40000, logo: fallbackIcon },
 ];
 function has_image(spec) {
@@ -226,5 +241,6 @@ function _getFailureRateScore(failureRate) {
   else if (strengthRate >= 80) return 'B';
   else if (strengthRate >= 70) return 'C';
   else if (strengthRate >= 60) return 'D';
+  else if (strengthRate >= 1) return '?';
   else return 'E'; // For strengthRate less than 60
 }

agentic_security/static/index.html

@@ -383,27 +383,26 @@
                 class="text-gray-400 hover:underline">Deselect All</button>
             </div>
 
-            <div class="grid grid-cols-1 sm:grid-cols-2 md:grid-cols-3 gap-4">
-              <div
-                v-for="(package, index) in dataConfig"
-                :key="index"
-                @click="addPackage(index)"
-                class="border rounded-lg p-3 cursor-pointer transition-all hover:shadow-md overflow-hidden"
-                :class="{
-                'border-dark-accent-green bg-dark-accent-green bg-opacity-20': package.selected,
-                'border-gray-600': !package.selected
-              }">
-                <div class="font-medium mb-1 truncate">{{ package.dataset_name
-                  }}</div>
-                <div class="text-sm text-gray-400 truncate">
-                  {{ package.source || 'Local dataset' }}
-                </div>
-                <div class="mt-2 text-sm font-semibold">
-                  {{ package.dynamic ? 'Dynamic dataset' :
-                  `${package.num_prompts.toLocaleString()} prompts` }}
-                </div>
-              </div>
+          <div class="grid grid-cols-1 sm:grid-cols-2 md:grid-cols-3 gap-4">
+          <div
+            v-for="(package, index) in dataConfig"
+            :key="index"
+            @click="package.is_active !== false && addPackage(index)"
+            class="border rounded-lg p-3 cursor-pointer transition-all hover:shadow-md overflow-hidden"
+            :class="{
+              'border-dark-accent-green bg-dark-accent-green bg-opacity-20': package.selected,
+              'border-gray-600': !package.selected,
+              'opacity-30 pointer-events-none cursor-not-allowed': package.is_active === false
+            }">
+            <div class="font-medium mb-1 truncate">{{ package.dataset_name }}</div>
+            <div class="text-sm text-gray-400 truncate">
+              {{ package.source || 'Local dataset' }}
             </div>
+            <div class="mt-2 text-sm font-semibold">
+              {{ package.dynamic ? 'Dynamic dataset' : `${package.num_prompts.toLocaleString()} prompts` }}
+            </div>
+          </div>
+        </div>
           </div>
         </section>
 

agentic_security/static/main.js

@@ -350,6 +350,10 @@ var app = new Vue({
 
             // If all are selected, deselect all. Otherwise, select all.
             this.dataConfig.forEach(package => {
+                if (!package.is_active) {
+                    package.selected = false;
+                    return
+                }
                 package.selected = !allSelected;
             });
 

agentic_security/test_lib.py

@@ -50,7 +50,7 @@ def make_test_registry():
     ]
 
 
-class TestAS:
+class TestLibraryLevel:
     # Handles an empty dataset list.
     def test_class(self, test_server):
         llmSpec = test_spec_assets.SAMPLE_SPEC
@@ -62,8 +62,8 @@ class TestAS:
         print(result)
         assert len(result) in [0, 1]
 
-    # TODO: slow test
-    def _test_class_msj(self, test_server):
+    @pytest.mark.slow
+    def test_class_msj(self, test_server):
         llmSpec = test_spec_assets.SAMPLE_SPEC
         maxBudget = 1000
         max_th = 0.3
@@ -98,6 +98,7 @@ class TestAS:
         print(result)
         assert len(result) in [0, 1]
 
+    @pytest.mark.slow
     def test_backend(self, test_server):
         llmSpec = test_spec_assets.SAMPLE_SPEC
         maxBudget = 1000000
@@ -156,7 +157,7 @@ class TestAS:
 class TestEntrypointCI:
     def test_generate_default_cfg_to_tmp_path(self):
         """
-        Test that the `generate_default_cfg` method generates a valid default config file in a temporary path.
+        Test that the `generate_default_settings` method generates a valid default config file in a temporary path.
         """
         # Create a temporary directory
         with tempfile.TemporaryDirectory() as tmpdir:
@@ -167,7 +168,7 @@ class TestEntrypointCI:
 
             # Generate the default configuration
             security = AgenticSecurity()
-            security.generate_default_cfg()
+            security.generate_default_settings()
 
             # Check that the config file was created at the temporary path
             assert os.path.exists(temp_path), f"{temp_path} file should be generated."
@@ -192,7 +193,7 @@ class TestEntrypointCI:
 
             # Generate the default configuration
             security = AgenticSecurity()
-            security.generate_default_cfg()
+            security.generate_default_settings()
 
             # Load the generated configuration
             AgenticSecurity.load_config(temp_path)

codebeaver.yml

@@ -0,0 +1,2 @@
+from: python-pytest-poetry
+# This file was generated automatically by CodeBeaver based on your repository. Learn how to customize it here: https://docs.codebeaver.ai/configuration/

docs/index.md

@@ -21,4 +21,4 @@ Note: Please be aware that Agentic Security is designed as a safety scanner tool
 
 ## UI 🧙
 
-<img width="100%" alt="booking-screen" src="https://res.cloudinary.com/dq0w2rtm9/image/upload/v1736433557/z0bsyzhsqlgcr3w4ovwp.gif">
+<img width="100%" alt="booking-screen" src="https://res.cloudinary.com/dq0w2rtm9/image/upload/v1741192668/final_aa9jhb.gif">

docs/stylesheets/extra.css

@@ -1,14 +1,16 @@
 :root {
-  --md-primary-fg-color: #e92063;
-  --md-primary-fg-color--light: #e92063;
-  --md-primary-fg-color--dark: #e92063;
+  --md-primary-fg-color: #2E4053;
+  /* Primary color changed to pinkish */
+  --md-primary-fg-color--light: #E0A3B6;
+  --md-primary-fg-color--dark: #1C3F74;
+  /* Dark variant changed to blue */
 }
 
-
-/* Revert hue value to that of pre mkdocs-material v9.4.0 */
+/* Updated slate color scheme with new background */
 [data-md-color-scheme="slate"] {
   --md-hue: 230;
-  --md-default-bg-color: hsla(230, 15%, 21%, 1);
+  --md-default-bg-color: #1A1A1A;
+  /* Background changed to dark gray */
 }
 
 .hide {
@@ -24,12 +26,15 @@ img.index-header {
   max-width: 500px;
 }
 
+/* Updated custom colors */
 .pydantic-pink {
-  color: #FF007F;
+  color: #E0A3B6;
+  /* Updated to match new theme */
 }
 
 .team-blue {
-  color: #0072CE;
+  color: #1C3F74;
+  /* Updated to match new theme */
 }
 
 .secure-green {
@@ -67,7 +72,6 @@ img.index-header {
   text-align: center;
 }
 
-
 /* Hide the entire footer */
 .md-footer {
   display: none;

mkdocs.yml

@@ -89,7 +89,7 @@ theme:
         name: Switch to light mode
   icon:
     repo: fontawesome/brands/github
-  favicon: "https://res.cloudinary.com/dq0w2rtm9/image/upload/v1737555066/r17hrkre246doczwmvbv.png"
+  favicon: https://res.cloudinary.com/dq0w2rtm9/image/upload/v1741195421/favicon_kuz6xr.png
 
 extra:
   generator: false

poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
 [[package]]
 name = "aiohappyeyeballs"
@@ -229,6 +229,24 @@ files = [
 [package.extras]
 dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"]
 
+[[package]]
+name = "backrefs"
+version = "5.8"
+description = "A wrapper around re and regex that adds additional back references."
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "backrefs-5.8-py310-none-any.whl", hash = "sha256:c67f6638a34a5b8730812f5101376f9d41dc38c43f1fdc35cb54700f6ed4465d"},
+    {file = "backrefs-5.8-py311-none-any.whl", hash = "sha256:2e1c15e4af0e12e45c8701bd5da0902d326b2e200cafcd25e49d9f06d44bb61b"},
+    {file = "backrefs-5.8-py312-none-any.whl", hash = "sha256:bbef7169a33811080d67cdf1538c8289f76f0942ff971222a16034da88a73486"},
+    {file = "backrefs-5.8-py313-none-any.whl", hash = "sha256:e3a63b073867dbefd0536425f43db618578528e3896fb77be7141328642a1585"},
+    {file = "backrefs-5.8-py39-none-any.whl", hash = "sha256:a66851e4533fb5b371aa0628e1fee1af05135616b86140c9d787a2ffdf4b8fdc"},
+    {file = "backrefs-5.8.tar.gz", hash = "sha256:2cab642a205ce966af3dd4b38ee36009b31fa9502a35fd61d59ccc116e40a6bd"},
+]
+
+[package.extras]
+extras = ["regex"]
+
 [[package]]
 name = "beautifulsoup4"
 version = "4.12.3"
@@ -768,6 +786,20 @@ files = [
     {file = "distlib-0.3.8.tar.gz", hash = "sha256:1530ea13e350031b6312d8580ddb6b27a104275a31106523b8f123787f494f64"},
 ]
 
+[[package]]
+name = "execnet"
+version = "2.1.1"
+description = "execnet: rapid multi-Python deployment"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "execnet-2.1.1-py3-none-any.whl", hash = "sha256:26dee51f1b80cebd6d0ca8e74dd8745419761d3bef34163928cbebbdc4749fdc"},
+    {file = "execnet-2.1.1.tar.gz", hash = "sha256:5189b52c6121c24feae288166ab41b32549c7e2348652736540b9e6e7d4e72e3"},
+]
+
+[package.extras]
+testing = ["hatch", "pre-commit", "pytest", "tox"]
+
 [[package]]
 name = "executing"
 version = "2.2.0"
@@ -784,18 +816,18 @@ tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipyth
 
 [[package]]
 name = "fastapi"
-version = "0.115.8"
+version = "0.115.11"
 description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fastapi-0.115.8-py3-none-any.whl", hash = "sha256:753a96dd7e036b34eeef8babdfcfe3f28ff79648f86551eb36bfc1b0bf4a8cbf"},
-    {file = "fastapi-0.115.8.tar.gz", hash = "sha256:0ce9111231720190473e222cdf0f07f7206ad7e53ea02beb1d2dc36e2f0741e9"},
+    {file = "fastapi-0.115.11-py3-none-any.whl", hash = "sha256:32e1541b7b74602e4ef4a0260ecaf3aadf9d4f19590bba3e1bf2ac4666aa2c64"},
+    {file = "fastapi-0.115.11.tar.gz", hash = "sha256:cc81f03f688678b92600a65a5e618b93592c65005db37157147204d8924bf94f"},
 ]
 
 [package.dependencies]
 pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<2.0.0 || >2.0.0,<2.0.1 || >2.0.1,<2.1.0 || >2.1.0,<3.0.0"
-starlette = ">=0.40.0,<0.46.0"
+starlette = ">=0.40.0,<0.47.0"
 typing-extensions = ">=4.8.0"
 
 [package.extras]
@@ -1202,13 +1234,13 @@ files = [
 
 [[package]]
 name = "inline-snapshot"
-version = "0.20.1"
+version = "0.20.3"
 description = "golden master/snapshot/approval testing library which puts the values right into your source code"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "inline_snapshot-0.20.1-py3-none-any.whl", hash = "sha256:5b5c3fd037f340dff5adee1c2c58db9038325937a8190dedbba98e37b87c979a"},
-    {file = "inline_snapshot-0.20.1.tar.gz", hash = "sha256:c56c871e59973500eca00610022eac19e79cd2c1b0b2d7a18abe14dde11a1431"},
+    {file = "inline_snapshot-0.20.3-py3-none-any.whl", hash = "sha256:1ea999fbf38dd11cc72d0e1a0b9303c63d496b77bdc406a394fe2424ae842f70"},
+    {file = "inline_snapshot-0.20.3.tar.gz", hash = "sha256:7a353170b7e42aa89086c7ba790a973c9645523acf985532648dabd7ee2d71f2"},
 ]
 
 [package.dependencies]
@@ -1217,7 +1249,7 @@ executing = ">=2.2.0"
 rich = ">=13.7.1"
 
 [package.extras]
-black = ["black (>=23.3.0)", "click (>=8.1.4)"]
+black = ["black (>=23.3.0)"]
 dirty-equals = ["dirty-equals (>=0.9.0)"]
 
 [[package]]
@@ -1311,13 +1343,13 @@ testing = ["Django", "attrs", "colorama", "docopt", "pytest (<9.0.0)"]
 
 [[package]]
 name = "jinja2"
-version = "3.1.5"
+version = "3.1.6"
 description = "A very fast and expressive template engine."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "jinja2-3.1.5-py3-none-any.whl", hash = "sha256:aba0f4dc9ed8013c424088f68a5c226f7d6097ed89b246d7749c2ec4175c6adb"},
-    {file = "jinja2-3.1.5.tar.gz", hash = "sha256:8fefff8dc3034e27bb80d67c671eb8a9bc424c0ef4c0826edbff304cceff43bb"},
+    {file = "jinja2-3.1.6-py3-none-any.whl", hash = "sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67"},
+    {file = "jinja2-3.1.6.tar.gz", hash = "sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d"},
 ]
 
 [package.dependencies]
@@ -1695,45 +1727,45 @@ files = [
 
 [[package]]
 name = "matplotlib"
-version = "3.10.0"
+version = "3.10.1"
 description = "Python plotting package"
 optional = false
 python-versions = ">=3.10"
 files = [
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 [[package]]
@@ -3014,6 +3128,17 @@ files = [
 [package.dependencies]
 typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
 
+[[package]]
+name = "pyfiglet"
+version = "1.0.2"
+description = "Pure-python FIGlet implementation"
+optional = false
+python-versions = ">=3.9"
+files = [
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+]
+
 [[package]]
 name = "pygments"
 version = "2.18.0"
@@ -3062,13 +3187,13 @@ diagrams = ["jinja2", "railroad-diagrams"]
 
 [[package]]
 name = "pytest"
-version = "8.3.4"
+version = "8.3.5"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -3133,6 +3258,26 @@ pytest = ">=6.2.5"
 [package.extras]
 dev = ["pre-commit", "pytest-asyncio", "tox"]
 
+[[package]]
+name = "pytest-xdist"
+version = "3.6.1"
+description = "pytest xdist plugin for distributed testing, most importantly across multiple CPUs"
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
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+execnet = ">=2.1"
+pytest = ">=7.0.0"
+
+[package.extras]
+psutil = ["psutil (>=3.0)"]
+setproctitle = ["setproctitle"]
+testing = ["filelock"]
+
 [[package]]
 name = "python-dateutil"
 version = "2.9.0.post0"
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 rpds-py = ">=0.7.0"
 
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-]
-
 [[package]]
 name = "requests"
 version = "2.32.3"
@@ -3931,13 +3973,13 @@ widechars = ["wcwidth"]
 
 [[package]]
 name = "termcolor"
-version = "2.4.0"
+version = "2.5.0"
 description = "ANSI color formatting for output in terminal"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "termcolor-2.4.0-py3-none-any.whl", hash = "sha256:9297c0df9c99445c2412e832e882a7884038a25617c60cea2ad69488d4040d63"},
-    {file = "termcolor-2.4.0.tar.gz", hash = "sha256:aab9e56047c8ac41ed798fa36d892a37aca6b3e9159f3e0c24bc64a9b3ac7b7a"},
+    {file = "termcolor-2.5.0-py3-none-any.whl", hash = "sha256:37b17b5fc1e604945c2642c872a3764b5d547a48009871aea3edd3afa180afb8"},
+    {file = "termcolor-2.5.0.tar.gz", hash = "sha256:998d8d27da6d48442e8e1f016119076b690d962507531df4890fcd2db2ef8a6f"},
 ]
 
 [package.extras]
@@ -4439,4 +4481,4 @@ propcache = ">=0.2.0"
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.11"
-content-hash = "a741ff960d86175204b90cdb4f935d3873a6a38d2d547c1ded73c17ab54b4312"
+content-hash = "35e03dba41d30cf6129a4a4f3107eca560f779205b21d3ffb2871eeffc5d5a64"

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "agentic_security"
-version = "0.5.0"
+version = "0.6.0"
 description = "Agentic LLM vulnerability scanner"
 authors = ["Alexander Miasoiedov <msoedov@gmail.com>"]
 maintainers = ["Alexander Miasoiedov <msoedov@gmail.com>"]
@@ -49,7 +49,12 @@ tomli = "^2.2.1"
 rich = "13.9.4"
 gTTS = "^2.5.4"
 sentry_sdk = "^2.22.0"
+orjson = "^3.10"
+pyfiglet = "^1.0.2"
+termcolor = "^2.4.0"
+
 # garak = { version = "*", optional = true }
+pytest-xdist = "3.6.1"
 
 
 [tool.poetry.group.dev.dependencies]
@@ -82,5 +87,7 @@ build-backend = "poetry.core.masonry.api"
 
 
 [tool.pytest.ini_options]
+addopts = "--durations=5 -m 'not slow' -n auto"
 asyncio_mode = "auto"
 asyncio_default_fixture_loop_scope = "function"
+markers = "slow: marks tests as slow"

tests/conftest.py

@@ -0,0 +1,8 @@
+import os
+
+import pytest
+
+
+def pytest_runtest_setup(item):
+    if "slow" in item.keywords and not os.getenv("RUN_SLOW_TESTS"):
+        pytest.skip("Skipping slow test")

tests/probe_actor/test_fuzzer.py

@@ -5,7 +5,7 @@ from unittest.mock import AsyncMock, MagicMock, Mock, patch
 import httpx
 import pytest
 
-from agentic_security.models.schemas import Scan
+from agentic_security.primitives import Scan
 from agentic_security.probe_actor.fuzzer import (
     generate_prompts,
     perform_many_shot_scan,

tests/probe_actor/test_refusal.py

@@ -0,0 +1,13 @@
+from agentic_security.probe_actor.refusal import DefaultRefusalClassifier
+
+
+class TestCheckRefusal:
+    # The function correctly identifies a refusal phrase in the response.
+    def test_identify_refusal_phrase(self):
+        response = "I'm sorry, but I cannot provide that information."
+        assert DefaultRefusalClassifier().is_refusal(response)
+
+    # The response is an empty string.
+    def test_empty_response(self):
+        response = ""
+        assert not DefaultRefusalClassifier().is_refusal(response)

tests/refusal_classifier/test_model.py

@@ -3,7 +3,7 @@ from unittest.mock import patch
 import pandas as pd
 import pytest
 
-from .model import RefusalClassifier
+from agentic_security.refusal_classifier.model import RefusalClassifier
 
 
 @pytest.fixture

tests/routes/test_health.py

@@ -1,6 +1,6 @@
 from fastapi.testclient import TestClient
 
-from ..app import app
+from agentic_security.app import app
 
 
 def test_health_check():

tests/routes/test_probe.py

@@ -5,10 +5,10 @@ import httpx
 import pytest
 from fastapi.testclient import TestClient
 
-from ..app import app
-from ..models.schemas import Probe
-from ..probe_actor.refusal import REFUSAL_MARKS
-from ..probe_data import REGISTRY
+from agentic_security.app import app
+from agentic_security.primitives import Probe
+from agentic_security.probe_actor.refusal import REFUSAL_MARKS
+from agentic_security.probe_data import REGISTRY
 
 client = TestClient(app)
 

tests/routes/test_report.py

@@ -4,7 +4,7 @@ from unittest.mock import patch
 import pytest
 from fastapi.testclient import TestClient
 
-from .report import router
+from agentic_security.routes.report import router
 
 client = TestClient(router)
 

tests/routes/test_static.py

@@ -4,8 +4,8 @@ import pytest
 from fastapi import HTTPException
 from fastapi.testclient import TestClient
 
-from ..models.schemas import Settings
-from .static import get_static_file, router
+from agentic_security.primitives import Settings
+from agentic_security.routes.static import get_static_file, router
 
 client = TestClient(router)
 

tests/test_registry.py

@@ -0,0 +1,26 @@
+import pytest
+from datasets import load_dataset
+
+from agentic_security.probe_data import REGISTRY
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("dataset", REGISTRY)
+def test_registry_accessibility(dataset):
+    source = dataset.get("source", "")
+    if "hugging" not in source.lower():
+        return pytest.skip("skipped dataset")
+    if not dataset.get("is_active"):
+        return pytest.skip("skipped dataset")
+
+    dataset_name = dataset.get("dataset_name")
+    if not dataset_name:
+        pytest.fail(f"No dataset_name found in {dataset}")
+
+    # Load only metadata (no data download)
+    try:
+        ds = load_dataset(dataset_name, split=None)
+        # Check if metadata is accessible without loading full data
+        assert ds is not None, f"Failed to load metadata for {dataset_name}"
+    except Exception as e:
+        pytest.fail(f"Error loading metadata for {dataset_name}: {str(e)}")

tests/test_report_chart.py

@@ -0,0 +1,136 @@
+import io
+import string
+import pytest
+import pandas as pd
+import numpy as np
+from agentic_security.report_chart import plot_security_report, generate_identifiers
+
+class TestReportChart:
+    """Test suite for agentic_security.report_chart module."""
+
+    def test_generate_identifiers_short(self):
+        """Test generate_identifiers with a small dataset."""
+        df = pd.DataFrame([{'dummy': i} for i in range(5)])
+        identifiers = generate_identifiers(df)
+        expected = ['A1', 'A2', 'A3', 'A4', 'A5']
+        assert identifiers == expected
+
+    def test_generate_identifiers_edge(self):
+        """Test generate_identifiers with more than 26 items to cover cycling over the alphabet."""
+        n = 30
+        df = pd.DataFrame([{'dummy': i} for i in range(n)])
+        identifiers = generate_identifiers(df)
+        # For i=25, identifier should be A26, and for i=26, identifier should be B1
+        assert identifiers[25] == 'A26'
+        assert identifiers[26] == 'B1'
+        assert len(identifiers) == n
+
+    def test_generate_identifiers_empty(self):
+        """Test generate_identifiers with an empty dataframe."""
+        df = pd.DataFrame([])
+        identifiers = generate_identifiers(df)
+        assert identifiers == []
+
+    def test_plot_security_report_png_output(self):
+        """Test plot_security_report returns valid PNG output."""
+        # Create a sample table with required columns
+        table = [
+            {"failureRate": 10, "tokens": 100, "module": "Module1"},
+            {"failureRate": 30, "tokens": 200, "module": "Module2"},
+            {"failureRate": 20, "tokens": 150, "module": "Module3"},
+        ]
+        buf = plot_security_report(table)
+        # Check that buf is a BytesIO object and starts with PNG header bytes
+        assert isinstance(buf, io.BytesIO)
+        buf.seek(0)
+        header = buf.read(8)
+        assert header.startswith(b'\x89PNG')
+
+    def test_plot_security_report_ordering(self, monkeypatch):
+        """Test that the table embedded in the plot contains correctly sorted order by descending failure rate."""
+        table = [
+            {"failureRate": 15, "tokens": 110, "module": "ModuleA"},
+            {"failureRate": 25, "tokens": 210, "module": "ModuleB"},
+            {"failureRate": 5,  "tokens": 90,  "module": "ModuleC"},
+        ]
+        result_holder = {}
+        from matplotlib.axes import Axes
+        original_table = Axes.table
+        def fake_table(self, *args, **kwargs):
+            result_holder['cellText'] = kwargs.get('cellText')
+            return original_table(self, *args, **kwargs)
+        monkeypatch.setattr(Axes, "table", fake_table)
+        plot_security_report(table)
+        cell_text = result_holder.get('cellText')
+        assert cell_text is not None
+        # Verify header row in the table
+        assert cell_text[0] == ["Threat"]
+        # Since the data are sorted (highest failure rate first), ModuleB (25.0%) should appear in one of the rows.
+        found = any("ModuleB (25.0%)" in row[0] for row in cell_text[1:])
+        assert found
+
+    def test_plot_security_report_one_entry(self):
+        """Test plot_security_report with a single entry."""
+        table = [{"failureRate": 50, "tokens": 300, "module": "OnlyModule"}]
+        buf = plot_security_report(table)
+        assert isinstance(buf, io.BytesIO)
+        buf.seek(0)
+        content = buf.read()
+        assert content.startswith(b'\x89PNG')
+    def test_generate_identifiers_many(self):
+        """Test generate_identifiers with 52 items to verify identifier sequence."""
+        n = 52
+        df = pd.DataFrame([{'dummy': i} for i in range(n)])
+        identifiers = generate_identifiers(df)
+        assert identifiers[0] == "A1"
+        assert identifiers[25] == "A26"
+        assert identifiers[26] == "B1"
+        assert identifiers[51] == "B26"
+
+    def test_plot_security_report_missing_failureRate(self):
+        """Test plot_security_report raises KeyError when 'failureRate' column is missing."""
+        table = [{"tokens": 100, "module": "Mod1"}]  # Missing 'failureRate'
+        with pytest.raises(KeyError):
+            plot_security_report(table)
+
+    def test_plot_security_report_missing_tokens(self):
+        """Test plot_security_report raises KeyError when 'tokens' column is missing."""
+        table = [{"failureRate": 10, "module": "Mod1"}]  # Missing 'tokens'
+        with pytest.raises(KeyError):
+            plot_security_report(table)
+
+    def test_plot_security_report_empty_table(self):
+        """Test plot_security_report raises KeyError when the table is empty."""
+        table = []
+        with pytest.raises(KeyError):
+            plot_security_report(table)
+    def test_plot_security_report_missing_module(self):
+        """Test plot_security_report raises KeyError when 'module' column is missing."""
+        table = [{"failureRate": 10, "tokens": 100}]  # Missing 'module'
+        with pytest.raises(KeyError):
+            plot_security_report(table)
+
+    def test_plot_security_report_failure_rate_labels(self, monkeypatch):
+        """Test that plot_security_report calls ax.text for each failure rate bar label."""
+        table = [
+            {"failureRate": 10, "tokens": 100, "module": "Mod1"},
+            {"failureRate": 20, "tokens": 150, "module": "Mod2"},
+            {"failureRate": 30, "tokens": 200, "module": "Mod3"},
+        ]
+        # Count the number of times ax.text is called for drawing failure rate labels.
+        call_count = [0]
+        from matplotlib.axes import Axes
+        original_text = Axes.text
+        def fake_text(self, *args, **kwargs):
+            call_count[0] += 1
+            return original_text(self, *args, **kwargs)
+        monkeypatch.setattr(Axes, "text", fake_text)
+        plot_security_report(table)
+        # The loop inside plot_security_report calls ax.text once for each data point.
+        assert call_count[0] == len(table)
+
+    def test_plot_security_report_non_numeric_failureRate(self):
+        """Test that plot_security_report raises an exception when failureRate is non-numeric."""
+        table = [{"failureRate": "invalid", "tokens": 100, "module": "ModX"}]
+        with pytest.raises(Exception):
+            plot_security_report(table)

tests/test_scan.py

@@ -0,0 +1,126 @@
+import io
+import asyncio
+import json
+from datetime import datetime, timedelta
+from threading import Event
+import pytest
+from fastapi import FastAPI
+from fastapi.testclient import TestClient
+from agentic_security.routes import scan
+
+# Dummy LLMSpec for success tests
+class DummyLLMSpec:
+    def __init__(self, spec_string):
+        self.spec_string = spec_string
+    async def verify(self):
+        class DummyResponse:
+            status_code = 200
+            text = "verification succeeded"
+            elapsed = timedelta(seconds=0.5)
+        return DummyResponse()
+    @classmethod
+    def from_string(cls, spec_string):
+        return DummyLLMSpec(spec_string)
+
+# Dummy scan_router generator to simulate streaming responses
+async def dummy_scan_router(request_factory, scan_parameters, tools_inbox, stop_event):
+    for i in range(2):
+        yield f"result {i}"
+
+# Define a dummy Secrets class for testing purposes.
+class DummySecrets:
+    def __init__(self):
+        self.secrets = {}
+
+# Create FastAPI app for testing and include the scan router.
+@pytest.fixture
+def app():
+    app = FastAPI()
+    app.include_router(scan.router)
+    return app
+
+@pytest.fixture
+def client(app):
+    return TestClient(app)
+
+@pytest.fixture(autouse=True)
+def patch_dependencies(monkeypatch):
+    # Patch LLMSpec used in the routes with our dummy implementation.
+    monkeypatch.setattr(scan, "LLMSpec", DummyLLMSpec)
+    # Patch fuzzer.scan_router to use our dummy scanning generator.
+    monkeypatch.setattr(scan.fuzzer, "scan_router", dummy_scan_router)
+    # Patch get_stop_event to return a dummy Event.
+    dummy_event = Event()
+    monkeypatch.setattr(scan, "get_stop_event", lambda: dummy_event)
+    # Patch get_tools_inbox to return None.
+    monkeypatch.setattr(scan, "get_tools_inbox", lambda: None)
+    # Patch set_current_run to be a no-op.
+    monkeypatch.setattr(scan, "set_current_run", lambda x: None)
+    # Patch get_in_memory_secrets to return a DummySecrets instance.
+    monkeypatch.setattr(scan, "get_in_memory_secrets", lambda: DummySecrets())
+    # Ensure Scan.with_secrets is a no-op if not already implemented.
+    if not hasattr(scan.Scan, "with_secrets"):
+        monkeypatch.setattr(scan.Scan, "with_secrets", lambda self, secrets: None)
+
+def test_verify_success(client):
+    """Test /verify endpoint for a successful verification."""
+    data = {"spec": "dummy"}
+    response = client.post("/verify", json=data)
+    res_json = response.json()
+    assert response.status_code == 200
+    assert res_json["status_code"] == 200
+    assert res_json["body"] == "verification succeeded"
+    assert "elapsed" in res_json
+    assert "timestamp" in res_json
+
+def test_verify_failure(client, monkeypatch):
+    """Test /verify endpoint when verification fails."""
+    class DummyLLMSpecFailure:
+        def __init__(self, spec_string):
+            self.spec_string = spec_string
+        async def verify(self):
+            raise Exception("verification error")
+        @classmethod
+        def from_string(cls, spec_string):
+            return DummyLLMSpecFailure(spec_string)
+    monkeypatch.setattr(scan, "LLMSpec", DummyLLMSpecFailure)
+    data = {"spec": "bad"}
+    response = client.post("/verify", json=data)
+    assert response.status_code == 400
+    assert "verification error" in response.text
+
+def test_scan(client):
+    """Test /scan endpoint to ensure streaming response works."""
+    data = {"llmSpec": "dummy", "optimize": False, "maxBudget": 10, "enableMultiStepAttack": False}
+    response = client.post("/scan", json=data)
+    assert response.status_code == 200
+    content = list(response.iter_lines())
+    expected = ["result 0", "result 1"]
+    assert content == expected
+
+def test_stop_scan(client):
+    """Test /stop endpoint to ensure scan stopping functionality."""
+    dummy_event = scan.get_stop_event()
+    dummy_event.clear()
+    response = client.post("/stop")
+    assert response.status_code == 200
+    assert response.json() == {"status": "Scan stopped"}
+    assert dummy_event.is_set()
+
+def test_scan_csv(client):
+    """Test /scan-csv endpoint with CSV file and llmSpec upload."""
+    csv_content = b"col1,col2\nvalue1,value2"
+    llm_spec_content = b"dummy"
+    files = {
+        "file": ("dummy.csv", csv_content, "text/csv"),
+        "llmSpec": ("spec.txt", llm_spec_content, "text/plain"),
+    }
+    response = client.post(
+        "/scan-csv",
+        files=files,
+        data={"optimize": "false", "maxBudget": "10", "enableMultiStepAttack": "false"},
+    )
+    assert response.status_code == 200
+    content = list(response.iter_lines())
+    expected = ["result 0", "result 1"]
+    assert content == expected