Refine getting started guide

build(deps): bump requests from 2.32.3 to 2.32.4

Readme.md

@@ -21,9 +21,7 @@
   <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>
+
 </p>
 
 

agentic_security/probe_data/data.py

@@ -245,61 +245,47 @@ def load_jailbreak_v28k() -> ProbeDataset:
         return create_probe_dataset("JailbreakV-28K/JailBreakV-28k", [])
 
 
-@cache_to_disk()
-def load_local_csv() -> ProbeDataset:
-    """Load prompts from local CSV files."""
-    csv_files = [f for f in os.listdir(".") if f.endswith(".csv")]
-    logger.info(f"Found {len(csv_files)} CSV files: {csv_files}")
-
-    prompts = []
-    for file in csv_files:
-        try:
-            df = pd.read_csv(file)
-            if "prompt" in df.columns:
-                prompts.extend(df["prompt"].tolist())
-            else:
-                logger.warning(f"File {file} lacks a suitable prompt column")
-        except Exception as e:
-            logger.error(f"Error reading {file}: {e}")
-
-    return create_probe_dataset("Local CSV", prompts, {"src": str(csv_files)})
-
-
 @cache_to_disk(1)
-def load_csv(file: str) -> ProbeDataset:
-    """Load prompts from local CSV files."""
+def file_dataset(file) -> list[str]:
     prompts = []
     try:
-        df = pd.read_csv(file)
-        prompts = df["prompt"].tolist()
+        df = pd.read_csv(os.path.join("./datasets", file), encoding_errors="ignore")
         if "prompt" in df.columns:
-            prompts.extend(df["prompt"].tolist())
+            prompts = df["prompt"].tolist()
         else:
             logger.warning(f"File {file} lacks a suitable prompt column")
     except Exception as e:
         logger.error(f"Error reading {file}: {e}")
+    return prompts
+
+
+def load_local_csv() -> ProbeDataset:
+    """Load prompts from local CSV files."""
+    os.makedirs("./datasets", exist_ok=True)
+    csv_files = [f for f in os.listdir("./datasets") if f.endswith(".csv")]
+    logger.info(f"Found {len(csv_files)} CSV files: {csv_files}")
+
+    prompts = []
+    for file in csv_files:
+        prompts.extend(file_dataset(file))
+    return create_probe_dataset("Local CSV", prompts, {"src": str(csv_files)})
+
+
+def load_csv(file: str) -> ProbeDataset:
+    """Load prompts from local CSV files."""
+    prompts = file_dataset(file)
     return create_probe_dataset(f"fs://{file}", prompts, {"src": str(file)})
 
 
-@cache_to_disk(1)
 def load_local_csv_files() -> list[ProbeDataset]:
     """Load prompts from local CSV files and return a list of ProbeDataset objects."""
-    csv_files = [f for f in os.listdir(".") if f.endswith(".csv")]
+    csv_files = [f for f in os.listdir("./datasets") if f.endswith(".csv")]
     logger.info(f"Found {len(csv_files)} CSV files: {csv_files}")
 
     datasets = []
 
     for file in csv_files:
-        try:
-            df = pd.read_csv(file)
-            if "prompt" in df.columns:
-                prompts = df["prompt"].tolist()
-                datasets.append(create_probe_dataset(file, prompts, {"src": file}))
-            else:
-                logger.warning(f"File {file} lacks a suitable prompt column")
-        except Exception as e:
-            logger.error(f"Error reading {file}: {e}")
-
+        datasets.append(create_probe_dataset(file, file_dataset(file), {"src": file}))
     return datasets
 
 
@@ -336,12 +322,20 @@ class StenographyTransformer:
                 )
                 continue
 
-            size = len(dataset.prompts)
+            # Convert async generator (if any) to a list
+            prompts = (
+                list(dataset.prompts) if hasattr(dataset.prompts, "__iter__") else []
+            )
+            size = len(prompts)
+
+            if size == 0:
+                continue
+
             sample_size = max(1, int(size * 0.25))  # At least 1 sample
 
             for name, fn in self.TRANSFORMATIONS.items():
                 logger.info(f"Applying {name} to {dataset.dataset_name}")
-                sampled = random.sample(dataset.prompts, min(sample_size, size))
+                sampled = random.sample(prompts, min(sample_size, size))
                 try:
                     transformed = [
                         f"Follow the instructions in {name}: {fn(str(p))}"

docs/getting_started.md

@@ -1,23 +1,54 @@
 # Getting Started
 
-Welcome to Agentic Security! This guide will help you get started with using the tool.
+Welcome to Agentic Security! This guide will help you orient yourself within the project and start using the tool quickly.
+
+## Project Overview
+
+Agentic Security is an open-source vulnerability scanner for Large Language Models (LLMs). It provides both a command line interface and a FastAPI server so you can probe models for weaknesses such as jailbreaks or refusal patterns. The tool supports multimodal attacks, multi-step scans and reinforcement-learning based probes.
+
+## Repository Layout
+
+```
+agentic_security/
+├── __main__.py    - CLI entry point
+├── app.py         - FastAPI app assembly
+├── lib.py         - SecurityScanner and utilities
+├── config.py      - Configuration handling
+├── core/          - app state and logging helpers
+├── probe_actor/   - scanning logic and RL modules
+├── probe_data/    - dataset registry and loaders
+├── routes/        - API endpoints
+└── ui/            - Web UI assets (Vue)
+```
+
+`tests/` contains unit tests, and `docs/` houses the project documentation.
 
 ## Quick Start
 
 1. Ensure you have completed the [installation](installation.md) steps.
-1. Run the following command to start the application:
+2. Run the following command to start the application:
    ```bash
    agentic_security
    ```
-1. Access the application at `http://localhost:8718`.
+   The server will start on `http://localhost:8718`.
+3. Explore available commands with:
+   ```bash
+   agentic_security --help
+   ```
 
 ## Basic Usage
 
-- To view available commands, use:
+- To view available commands, run:
   ```bash
   agentic_security --help
   ```
 
 ## Next Steps
 
-Explore the [Configuration](configuration.md) section to customize your setup.
+- Review the [Quickstart Guide](quickstart.md) for a fast setup walkthrough.
+- Check [http_spec.md](http_spec.md) to learn how LLM endpoints are described.
+- Browse the `probe_actor` and `probe_data` modules to understand how scanning works and how datasets are loaded.
+- Explore the [Configuration](configuration.md) section to customize your setup.
+- Run the tests in `tests/` to verify your environment once dependencies are installed.
+
+This guide should give you a solid foundation for exploring and extending Agentic Security. For more details, see the rest of the documentation.

poetry.lock

@@ -1151,37 +1151,37 @@ tests = ["pytest (>=7.1.3,<8.4.0)", "pytest-cov", "testfixtures"]
 
 [[package]]
 name = "h11"
-version = "0.16.0"
+version = "0.14.0"
 description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.7"
 groups = ["main", "dev"]
 files = [
-    {file = "h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86"},
-    {file = "h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1"},
+    {file = "h11-0.14.0-py3-none-any.whl", hash = "sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761"},
+    {file = "h11-0.14.0.tar.gz", hash = "sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d"},
 ]
 
 [[package]]
 name = "httpcore"
-version = "1.0.9"
+version = "1.0.5"
 description = "A minimal low-level HTTP client."
 optional = false
 python-versions = ">=3.8"
 groups = ["main", "dev"]
 files = [
-    {file = "httpcore-1.0.9-py3-none-any.whl", hash = "sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55"},
-    {file = "httpcore-1.0.9.tar.gz", hash = "sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8"},
+    {file = "httpcore-1.0.5-py3-none-any.whl", hash = "sha256:421f18bac248b25d310f3cacd198d55b8e6125c107797b609ff9b7a6ba7991b5"},
+    {file = "httpcore-1.0.5.tar.gz", hash = "sha256:34a38e2f9291467ee3b44e89dd52615370e152954ba21721378a87b2960f7a61"},
 ]
 
 [package.dependencies]
 certifi = "*"
-h11 = ">=0.16"
+h11 = ">=0.13,<0.15"
 
 [package.extras]
 asyncio = ["anyio (>=4.0,<5.0)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
-trio = ["trio (>=0.22.0,<1.0)"]
+trio = ["trio (>=0.22.0,<0.26.0)"]
 
 [[package]]
 name = "httpx"
@@ -3751,19 +3751,19 @@ rpds-py = ">=0.7.0"
 
 [[package]]
 name = "requests"
-version = "2.32.3"
+version = "2.32.4"
 description = "Python HTTP for Humans."
 optional = false
 python-versions = ">=3.8"
 groups = ["main", "dev"]
 files = [
-    {file = "requests-2.32.3-py3-none-any.whl", hash = "sha256:70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6"},
-    {file = "requests-2.32.3.tar.gz", hash = "sha256:55365417734eb18255590a9ff9eb97e9e1da868d4ccd6402399eaf68af20a760"},
+    {file = "requests-2.32.4-py3-none-any.whl", hash = "sha256:27babd3cda2a6d50b30443204ee89830707d396671944c998b5975b031ac2b2c"},
+    {file = "requests-2.32.4.tar.gz", hash = "sha256:27d0316682c8a29834d3264820024b62a36942083d52caf2f14c0591336d3422"},
 ]
 
 [package.dependencies]
 certifi = ">=2017.4.17"
-charset-normalizer = ">=2,<4"
+charset_normalizer = ">=2,<4"
 idna = ">=2.5,<4"
 urllib3 = ">=1.21.1,<3"
 

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "agentic_security"
-version = "0.7.2"
+version = "0.7.4"
 description = "Agentic LLM vulnerability scanner"
 authors = ["Alexander Miasoiedov <msoedov@gmail.com>"]
 maintainers = ["Alexander Miasoiedov <msoedov@gmail.com>"]