fix(numpy issue):

build(deps): bump datasets from 1.18.4 to 3.0.1

.flake8

@@ -2,4 +2,4 @@
     max-line-length = 160
     per-file-ignores =
         # Ignore docstring lints for tests
-        *: D100, D101, D102, D103, D104, D107, D105, D202, D205, D400, E501, D401
+        *: D100, D101, D102, D103, D104, D107, D105, D202, D205, D400, E501, D401, D200

.github/workflows/test.yml

@@ -16,9 +16,9 @@ jobs:
     strategy:
       matrix:
         python-version:
-          - "3.9"
           - "3.10"
           - "3.11"
+          - "3.12"
     steps:
       - uses: actions/checkout@v3
       - name: Install poetry

.pre-commit-config.yaml

@@ -55,12 +55,6 @@ repos:
         language_version: python3
 
 
-  - repo: https://github.com/myint/docformatter
-    rev: v1.4
-    hooks:
-      - id: docformatter
-        args: [--in-place]
-
   - repo: https://github.com/hadialqattan/pycln
     rev: v2.1.1 # Possible releases: https://github.com/hadialqattan/pycln/releases
     hooks:

agentic_security/app.py

@@ -33,6 +33,9 @@ app.add_middleware(
 )
 
 tools_inbox = Queue()
+# Global stop event for cancelling scans
+stop_event = Event()  # Added stop_event to cancel the scan
+
 FEATURE_PROXY = False
 
 
@@ -99,6 +102,7 @@ def streaming_response_generator(scan_parameters: Scan):
             datasets=scan_parameters.datasets,
             tools_inbox=tools_inbox,
             optimize=scan_parameters.optimize,
+            stop_event=stop_event,  # Pass the stop_event to the generator
         ):
             yield scan_result + "\n"  # Adding a newline for separation
 
@@ -238,6 +242,12 @@ config.dictConfig(
 )
 
 
+@app.post("/stop")
+async def stop_scan():
+    stop_event.set()  # Set the stop event to cancel the scan
+    return {"status": "Scan stopped"}
+
+
 class LogNon200ResponsesMiddleware(BaseHTTPMiddleware):
     async def dispatch(self, request: Request, call_next):
         try:

agentic_security/probe_actor/fuzzer.py

@@ -1,8 +1,8 @@
+import asyncio
 import os
 from typing import AsyncGenerator
 
 import httpx
-import numpy as np
 import pandas as pd
 from loguru import logger
 from pydantic import BaseModel
@@ -50,6 +50,7 @@ async def perform_scan(
     datasets: list[dict[str, str]] = [],
     tools_inbox=None,
     optimize=False,
+    stop_event: asyncio.Event = None,
 ) -> AsyncGenerator[str, None]:
     if IS_VERCEL:
         yield ScanResult.status_msg(
@@ -82,6 +83,12 @@ async def perform_scan(
         )
         should_stop_early = False
         async for prompt in prompt_iter(module.prompts):
+            if stop_event and stop_event.is_set():  # Check if stop_event is set
+                stop_event.clear()  # Clear the event for the next scan
+                logger.info("Scan stopped by user.")
+                yield ScanResult.status_msg("Scan stopped by user.")
+                return  # Exit the scan gracefully
+
             processed_prompts += 1
             progress = 100 * processed_prompts / total_prompts if total_prompts else 0
 

agentic_security/probe_actor/refusal.py

@@ -1,3 +1,8 @@
+from agentic_security.refusal_classifier import RefusalClassifier
+
+classifier = RefusalClassifier()
+classifier.load_model()
+
 REFUSAL_MARKS = [
     "I'm sorry",
     "Sorry",
@@ -45,4 +50,4 @@ def check_refusal(response: str, refusal_phrases: list = REFUSAL_MARKS) -> bool:
 
 def refusal_heuristic(request_json):
     # TODO: improve this heuristic
-    return check_refusal(str(request_json))
+    return check_refusal(str(request_json)) or classifier.is_refusal(str(request_json))

agentic_security/refusal_classifier/__init__.py

@@ -0,0 +1 @@
+from .model import RefusalClassifier  # noqa

agentic_security/refusal_classifier/model.py

@@ -0,0 +1,113 @@
+import importlib.resources as pkg_resources
+import os
+
+import joblib
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.preprocessing import StandardScaler
+from sklearn.svm import OneClassSVM
+
+
+class RefusalClassifier:
+    def __init__(self, model_path=None, vectorizer_path=None, scaler_path=None):
+        self.model = None
+        self.vectorizer = None
+        self.scaler = None
+        self.model_path = (
+            model_path
+            or "agentic_security/refusal_classifier/oneclass_svm_model.joblib"
+        )
+        self.vectorizer_path = (
+            vectorizer_path
+            or "agentic_security/refusal_classifier/tfidf_vectorizer.joblib"
+        )
+        self.scaler_path = (
+            scaler_path or "agentic_security/refusal_classifier/scaler.joblib"
+        )
+
+    def train(self, data_paths):
+        """
+        Train the refusal classifier.
+
+        Parameters:
+        - data_paths (list): List of file paths to CSV files containing the training data.
+        """
+        # Load and concatenate data from multiple CSV files
+        texts = []
+        for data_path in data_paths:
+            df = pd.read_csv(os.path.expanduser(data_path))
+            # Assuming the CSV has columns named 'GPT4_response', 'ChatGPT_response', 'Claude_response'
+            responses = pd.concat(
+                [df["GPT4_response"], df["ChatGPT_response"], df["Claude_response"]],
+                ignore_index=True,
+            )
+            texts.extend(responses.tolist())
+
+        # Remove any NaN values
+        texts = [text for text in texts if isinstance(text, str)]
+
+        # Vectorize the text data
+        self.vectorizer = TfidfVectorizer(max_features=1000)
+        X = self.vectorizer.fit_transform(texts)
+
+        # Scale the features
+        self.scaler = StandardScaler(with_mean=False)
+        X_scaled = self.scaler.fit_transform(X)
+
+        # Train the One-Class SVM model
+        self.model = OneClassSVM(kernel="rbf", gamma="auto", nu=0.05)
+        self.model.fit(X_scaled)
+
+    def save_model(self):
+        """
+        Save the trained model, vectorizer, and scaler to disk.
+        """
+        joblib.dump(self.model, self.model_path)
+        joblib.dump(self.vectorizer, self.vectorizer_path)
+        joblib.dump(self.scaler, self.scaler_path)
+
+    def load_model(self):
+        """
+        Load the trained model, vectorizer, and scaler from disk.
+        """
+        try:
+            self.model = joblib.load(self.model_path)
+            self.vectorizer = joblib.load(self.vectorizer_path)
+            self.scaler = joblib.load(self.scaler_path)
+        except FileNotFoundError:
+            # Load from package resources
+            package = (
+                __package__  # This should be 'agentic_security.refusal_classifier'
+            )
+
+            # Load model
+            with pkg_resources.open_binary(package, "oneclass_svm_model.joblib") as f:
+                self.model = joblib.load(f)
+
+            # Load vectorizer
+            with pkg_resources.open_binary(package, "tfidf_vectorizer.joblib") as f:
+                self.vectorizer = joblib.load(f)
+
+            # Load scaler
+            with pkg_resources.open_binary(package, "scaler.joblib") as f:
+                self.scaler = joblib.load(f)
+
+    def is_refusal(self, text):
+        """
+        Predict whether a given text is a refusal response.
+
+        Parameters:
+        - text (str): The input text to classify.
+
+        Returns:
+        - bool: True if the text is a refusal response, False otherwise.
+        """
+        if not self.model or not self.vectorizer or not self.scaler:
+            raise ValueError(
+                "Model, vectorizer, or scaler not loaded. Call load_model() first."
+            )
+
+        x = self.vectorizer.transform([text])
+        x_scaled = self.scaler.transform(x)
+        prediction = self.model.predict(x_scaled)
+        return prediction[0] == 1  # Returns True if it's a refusal response

agentic_security/static/index.html

@@ -335,6 +335,7 @@
           </button>
           <button
             @click="startScan"
+            v-if="!scanRunning"
             class="bg-dark-accent-green text-dark-bg rounded-lg px-6 py-3 font-medium hover:bg-opacity-80 transition-colors flex items-center">
             <svg xmlns="http://www.w3.org/2000/svg" width="24" height="24"
               viewBox="0 0 24 24" fill="none" stroke="currentColor"
@@ -342,6 +343,18 @@
               class="mr-2"><polygon points="5 3 19 12 5 21 5 3"></polygon></svg>
             Run Scan
           </button>
+          <button
+            @click="stopScan"
+            v-if="scanRunning"
+            class="bg-dark-accent-red text-dark-bg rounded-lg px-6 py-3 font-medium hover:bg-opacity-80 transition-colors flex items-center">
+            <!-- Stop Icon -->
+            <svg xmlns="http://www.w3.org/2000/svg" width="24" height="24"
+              viewBox="0 0 24 24" fill="none" stroke="currentColor"
+              stroke-width="2" stroke-linecap="round" stroke-linejoin="round"
+              class="mr-2"><rect x="6" y="6" width="12"
+                height="12"></rect></svg>
+            Stop Scan
+          </button>
         </section>
 
         <!-- Progress Bar -->

agentic_security/static/main.js

@@ -341,6 +341,15 @@ var app = new Vue({
             }
             this.budget = value;
         },
+        stopScan: async function () {
+            this.scanRunning = false;
+            const response = await fetch(`${URL}/stop`, {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json',
+                },
+            });
+        },
         startScan: async function () {
             this.showLLMSpec = false;
             let payload = {
@@ -358,6 +367,7 @@ var app = new Vue({
             });
             this.okMsg = 'Scan started';
             this.mainTable = [];
+            this.scanRunning = true;
             const reader = response.body.getReader();
             let receivedLength = 0; // received that many bytes at the moment
             let chunks = []; // array of received binary chunks (comprises the body)

poetry.lock

@@ -205,33 +205,33 @@ tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
 
 [[package]]
 name = "black"
-version = "24.8.0"
+version = "24.10.0"
 description = "The uncompromising code formatter."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
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+    {file = "black-24.10.0-cp39-cp39-win_amd64.whl", hash = "sha256:2cd9c95431d94adc56600710f8813ee27eea544dd118d45896bb734e9d7a0dc7"},
+    {file = "black-24.10.0-py3-none-any.whl", hash = "sha256:3bb2b7a1f7b685f85b11fed1ef10f8a9148bceb49853e47a294a3dd963c1dd7d"},
+    {file = "black-24.10.0.tar.gz", hash = "sha256:846ea64c97afe3bc677b761787993be4991810ecc7a4a937816dd6bddedc4875"},
 ]
 
 [package.dependencies]
@@ -245,7 +245,7 @@ typing-extensions = {version = ">=4.0.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
 colorama = ["colorama (>=0.4.3)"]
-d = ["aiohttp (>=3.7.4)", "aiohttp (>=3.7.4,!=3.9.0)"]
+d = ["aiohttp (>=3.10)"]
 jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
 uvloop = ["uvloop (>=0.15.2)"]
 
@@ -486,43 +486,45 @@ tests = ["pytest", "pytest-cov", "pytest-xdist"]
 
 [[package]]
 name = "datasets"
-version = "1.18.4"
+version = "3.0.1"
 description = "HuggingFace community-driven open-source library of datasets"
 optional = false
-python-versions = "*"
+python-versions = ">=3.8.0"
 files = [
-    {file = "datasets-1.18.4-py3-none-any.whl", hash = "sha256:e13695ad7aeda2af4430ac1a0b62def9c4b60bb4cc14dbaa240e6683cac50c49"},
-    {file = "datasets-1.18.4.tar.gz", hash = "sha256:8f28a7afc2f894c68cb017335a32812f443fe41bc59c089cbd15d7412d3f7f96"},
+    {file = "datasets-3.0.1-py3-none-any.whl", hash = "sha256:db080aab41c8cc68645117a0f172e5c6789cbc672f066de0aa5a08fc3eebc686"},
+    {file = "datasets-3.0.1.tar.gz", hash = "sha256:40d63b09e76a3066c32e746d6fdc36fd3f29ed2acd49bf5b1a2100da32936511"},
 ]
 
 [package.dependencies]
 aiohttp = "*"
-dill = "*"
-fsspec = {version = ">=2021.05.0", extras = ["http"]}
-huggingface-hub = ">=0.1.0,<1.0.0"
+dill = ">=0.3.0,<0.3.9"
+filelock = "*"
+fsspec = {version = ">=2023.1.0,<=2024.6.1", extras = ["http"]}
+huggingface-hub = ">=0.22.0"
 multiprocess = "*"
 numpy = ">=1.17"
 packaging = "*"
 pandas = "*"
-pyarrow = ">=3.0.0,<4.0.0 || >4.0.0"
-requests = ">=2.19.0"
-responses = "<0.19"
-tqdm = ">=4.62.1"
+pyarrow = ">=15.0.0"
+pyyaml = ">=5.1"
+requests = ">=2.32.2"
+tqdm = ">=4.66.3"
 xxhash = "*"
 
 [package.extras]
-apache-beam = ["apache-beam (>=2.26.0)"]
-audio = ["librosa"]
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 name = "dill"
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 ]
 
 [[package]]
@@ -1858,13 +1855,13 @@ testing = ["pytest", "pytest-benchmark"]
 
 [[package]]
 name = "pre-commit"
-version = "3.8.0"
+version = "4.0.1"
 description = "A framework for managing and maintaining multi-language pre-commit hooks."
 optional = false
 python-versions = ">=3.9"
 files = [
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 ]
 
 [package.dependencies]
@@ -2224,24 +2221,6 @@ urllib3 = ">=1.21.1,<3"
 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
-[[package]]
-name = "responses"
-version = "0.18.0"
-description = "A utility library for mocking out the `requests` Python library."
-optional = false
-python-versions = ">=3.7"
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-
-[package.dependencies]
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-
-[package.extras]
-tests = ["coverage (>=6.0.0)", "flake8", "mypy", "pytest (>=4.6)", "pytest-cov", "pytest-localserver", "types-mock", "types-requests"]
-
 [[package]]
 name = "rich"
 version = "13.7.1"
@@ -2566,13 +2545,13 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "uvicorn"
-version = "0.31.0"
+version = "0.32.0"
 description = "The lightning-fast ASGI server."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "uvicorn-0.31.0-py3-none-any.whl", hash = "sha256:cac7be4dd4d891c363cd942160a7b02e69150dcbc7a36be04d5f4af4b17c8ced"},
-    {file = "uvicorn-0.31.0.tar.gz", hash = "sha256:13bc21373d103859f68fe739608e2eb054a816dea79189bc3ca08ea89a275906"},
+    {file = "uvicorn-0.32.0-py3-none-any.whl", hash = "sha256:60b8f3a5ac027dcd31448f411ced12b5ef452c646f76f02f8cc3f25d8d26fd82"},
+    {file = "uvicorn-0.32.0.tar.gz", hash = "sha256:f78b36b143c16f54ccdb8190d0a26b5f1901fe5a3c777e1ab29f26391af8551e"},
 ]
 
 [package.dependencies]
@@ -2840,4 +2819,4 @@ multidict = ">=4.0"
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "b1012a1ae4674fb78a8243cff274350a34abb86c3f48be826cbe5f993542daed"
+content-hash = "2b3b2e1ee13dc10f8888222e2bfb5ce6505d0359e3e40987a9cf84e771e7dfff"

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "agentic_security"
-version = "0.2.1"
+version = "0.2.6"
 description = "Agentic LLM vulnerability scanner"
 authors = ["Alexander Miasoiedov <msoedov@gmail.com>"]
 maintainers = ["Alexander Miasoiedov <msoedov@gmail.com>"]
@@ -26,26 +26,27 @@ agentic_security = "agentic_security.__main__:entrypoint"
 
 [tool.poetry.dependencies]
 python = "^3.10"
-fastapi = "^0.115.0"
-uvicorn = "^0.31.0"
-fire = ">=0.5,<0.7"
+fastapi = "^0.115.2"
+uvicorn = "^0.32.0"
+fire = "0.7.0"
 loguru = "^0.7.2"
 httpx = ">=0.25.1,<0.28.0"
 cache-to-disk = "^2.0.0"
 pandas = ">=1.4,<3.0"
-datasets = "^1.14.0"
+datasets = ">=1.14,<4.0"
 tabulate = ">=0.8.9,<0.10.0"
 colorama = "^0.4.4"
 matplotlib = "^3.9.2"
 pydantic = "2.9.2"
 scikit-optimize = "^0.10.2"
+scikit-learn = "1.5.1"
+numpy = "^1.24.3"
 
 [tool.poetry.group.dev.dependencies]
-black = "^24.8.0"
-mypy = "^1.11.2"
-httpx = ">=0.25.1,<0.28.0"
+black = "^24.10.0"
+mypy = "^1.12.0"
 pytest = "^8.3.3"
-pre-commit = "^3.8.0"
+pre-commit = "^4.0.1"
 inline-snapshot = "^0.13.3"
 langchain-groq = "^0.2.0"
 huggingface-hub = "^0.25.1"