mirror of
https://github.com/msoedov/agentic_security.git
synced 2026-06-24 14:19:55 +02:00
Alexander Myasoedov
462 lines
17 KiB
Markdown
462 lines
17 KiB
Markdown
<p align="center">
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<h1 align="center">Agentic Security</h1>
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<p align="center">
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An open-source vulnerability scanner for Agent Workflows and Large Language Models (LLMs)<br />
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Protecting AI systems from jailbreaks, fuzzing, and multimodal attacks.<br />
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<a href="https://agentic-security.vercel.app">Explore the docs »</a> ·
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<a href="https://github.com/msoedov/agentic_security/issues">Report a Bug »</a>
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</p>
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</p>
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<p align="center">
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<a href="https://github.com/msoedov/agentic_security/commits/main">
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<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" />
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</a>
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<a href="https://github.com/msoedov/agentic_security">
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<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" />
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</a>
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<a href="https://github.com/msoedov/agentic_security/blob/master/LICENSE">
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<img alt="GitHub License" src="https://img.shields.io/github/license/msoedov/agentic_security?style=for-the-badge&logo=codeigniter&labelColor=000000&color=FFCC19" />
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</a>
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<a href="https://pypi.org/project/agentic-security/">
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<img alt="PyPI Version" src="https://img.shields.io/pypi/v/agentic-security?style=for-the-badge&logo=pypi&labelColor=000000&color=00CCFF" />
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</a>
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</p>
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## Features
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Agentic Security equips you with powerful tools to safeguard LLMs against emerging threats. Here's what you can do:
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- **Multimodal Attacks** 🖼️🎙️
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Probe vulnerabilities across text, images, and audio inputs to ensure your LLM is robust against diverse threats.
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- **Multi-Step Jailbreaks** 🌀
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Simulate sophisticated, iterative attack sequences to uncover weaknesses in LLM safety mechanisms.
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- **Comprehensive Fuzzing** 🧪
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Stress-test any LLM with randomized inputs to identify edge cases and unexpected behaviors.
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- **API Integration & Stress Testing** 🌐
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Seamlessly connect to LLM APIs and push their limits with high-volume, real-world attack scenarios.
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- **RL-Based Attacks** 📡
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Leverage reinforcement learning to craft adaptive, intelligent probes that evolve with your model’s defenses.
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> **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.
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## 📦 Installation
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To get started with Agentic Security, simply install the package using pip:
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```shell
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pip install agentic_security
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```
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## ⛓️ Quick Start
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```shell
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agentic_security
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2024-04-13 13:21:31.157 | INFO | agentic_security.probe_data.data:load_local_csv:273 - Found 1 CSV files
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2024-04-13 13:21:31.157 | INFO | agentic_security.probe_data.data:load_local_csv:274 - CSV files: ['prompts.csv']
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INFO: Started server process [18524]
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INFO: Waiting for application startup.
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INFO: Application startup complete.
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INFO: Uvicorn running on http://0.0.0.0:8718 (Press CTRL+C to quit)
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```
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```shell
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python -m agentic_security
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# or
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agentic_security --help
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agentic_security --port=PORT --host=HOST
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```
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## UI 🧙
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<img width="100%" alt="booking-screen" src="https://raw.githubusercontent.com/msoedov/agentic_security/refs/heads/main/docs/images/demo.gif">
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## LLM kwargs
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Agentic Security uses plain text HTTP spec like:
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```http
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POST https://api.openai.com/v1/chat/completions
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Authorization: Bearer sk-xxxxxxxxx
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Content-Type: application/json
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{
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"model": "gpt-3.5-turbo",
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"messages": [{"role": "user", "content": "<<PROMPT>>"}],
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"temperature": 0.7
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}
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```
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Where `<<PROMPT>>` will be replaced with the actual attack vector during the scan, insert the `Bearer XXXXX` header value with your app credentials.
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### Adding LLM integration templates
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TBD
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```
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....
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```
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## Adding own dataset
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To add your own dataset you can place one or multiples csv files with `prompt` column, this data will be loaded on `agentic_security` startup
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```
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2024-04-13 13:21:31.157 | INFO | agentic_security.probe_data.data:load_local_csv:273 - Found 1 CSV files
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2024-04-13 13:21:31.157 | INFO | agentic_security.probe_data.data:load_local_csv:274 - CSV files: ['prompts.csv']
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```
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## Run as CI check
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Init config
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```shell
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agentic_security init
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2025-01-08 20:12:02.449 | INFO | agentic_security.lib:generate_default_settings:324 - Default configuration generated successfully to agesec.toml.
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```
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default config sample
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```toml
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[general]
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# General configuration for the security scan
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llmSpec = """
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POST http://0.0.0.0:8718/v1/self-probe
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Authorization: Bearer XXXXX
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Content-Type: application/json
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{
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"prompt": "<<PROMPT>>"
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}
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""" # LLM API specification
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maxBudget = 1000000 # Maximum budget for the scan
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max_th = 0.3 # Maximum failure threshold (percentage)
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optimize = false # Enable optimization during scanning
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enableMultiStepAttack = false # Enable multi-step attack simulations
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[modules.aya-23-8B_advbench_jailbreak]
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dataset_name = "simonycl/aya-23-8B_advbench_jailbreak"
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[modules.AgenticBackend]
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dataset_name = "AgenticBackend"
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[modules.AgenticBackend.opts]
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port = 8718
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modules = ["encoding"]
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[thresholds]
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# Threshold settings
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low = 0.15
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medium = 0.3
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high = 0.5
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```
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List module
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```shell
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agentic_security ls
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Dataset Registry
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━┓
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┃ Dataset Name ┃ Num Prompts ┃ Tokens ┃ Source ┃ Selected ┃ Dynamic ┃ Modality ┃
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┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━┩
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│ simonycl/aya-23-8B_advbench_jailb… │ 416 │ None │ Hugging Face Datasets │ ✘ │ ✘ │ text │
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├────────────────────────────────────┼─────────────┼─────────┼───────────────────────────────────┼──────────┼─────────┼──────────┤
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│ acmc/jailbreaks_dataset_with_perp… │ 11191 │ None │ Hugging Face Datasets │ ✘ │ ✘ │ text │
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├────────────────────────────────────┼─────────────┼─────────┼───────────────────────────────────┼──────────┼─────────┼──────────┤
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```
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```shell
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agentic_security ci
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2025-01-08 20:13:07.536 | INFO | agentic_security.probe_data.data:load_local_csv:331 - Found 2 CSV files
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2025-01-08 20:13:07.536 | INFO | agentic_security.probe_data.data:load_local_csv:332 - CSV files: ['failures.csv', 'issues_with_descriptions.csv']
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2025-01-08 20:13:07.552 | WARNING | agentic_security.probe_data.data:load_local_csv:345 - File issues_with_descriptions.csv does not contain a 'prompt' column
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2025-01-08 20:13:08.892 | INFO | agentic_security.lib:load_config:52 - Configuration loaded successfully from agesec.toml.
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2025-01-08 20:13:08.892 | INFO | agentic_security.lib:entrypoint:259 - Configuration loaded successfully.
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{'general': {'llmSpec': 'POST http://0.0.0.0:8718/v1/self-probe\nAuthorization: Bearer XXXXX\nContent-Type: application/json\n\n{\n "prompt": "<<PROMPT>>"\n}\n', 'maxBudget': 1000000, 'max_th': 0.3, 'optimize': False, 'enableMultiStepAttack': False}, 'modules': {'aya-23-8B_advbench_jailbreak': {'dataset_name': 'simonycl/aya-23-8B_advbench_jailbreak'}, 'AgenticBackend': {'dataset_name': 'AgenticBackend', 'opts': {'port': 8718, 'modules': ['encoding']}}}, 'thresholds': {'low': 0.15, 'medium': 0.3, 'high': 0.5}}
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Scanning modules: 0it [00:00, ?it/s]2025-01-08 20:13:08.903 | INFO | agentic_security.probe_data.data:prepare_prompts:246 - Loading simonycl/aya-23-8B_advbench_jailbreak
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2025-01-08 20:13:08.905 | INFO | agentic_security.probe_data.data:prepare_prompts:280 - Loading AgenticBackend
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2025-01-08 20:13:08.905 | INFO | agentic_security.probe_actor.fuzzer:perform_single_shot_scan:102 - Scanning simonycl/aya-23-8B_advbench_jailbreak 416
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Scanning modules: 417it [00:04, 85.85it/s]2025-01-08 20:13:13.825 | INFO | agentic_security.probe_actor.fuzzer:perform_single_shot_scan:102 - Scanning AgenticBackend 0
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Scanning modules: 419it [00:10, 41.37it/s]
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Security Scan Results
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Time: 2025-01-08 20:13:19
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Duration: 10.1s
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Modules Scanned: 2
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Threshold: 30.0%
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+---------------------------------------+----------------+----------+----------+
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| Module | Failure Rate | Status | Margin |
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+=======================================+================+==========+==========+
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| simonycl/aya-23-8B_advbench_jailbreak | 24.8% | ✔ | 5.2% |
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+---------------------------------------+----------------+----------+----------+
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Summary:
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Total Passing: 2/2 (100.0%)
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```
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## Extending dataset collections
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1. Add new metadata to agentic_security.probe_data.REGISTRY
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```python
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{
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"dataset_name": "markush1/LLM-Jailbreak-Classifier",
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"num_prompts": 1119,
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"tokens": 19758,
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"approx_cost": 0.0,
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"source": "Hugging Face Datasets",
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"selected": True,
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"dynamic": False,
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"url": "https://huggingface.co/markush1/LLM-Jailbreak-Classifier",
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},
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```
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and implement loader into
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```python
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@dataclass
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class ProbeDataset:
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dataset_name: str
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metadata: dict
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prompts: list[str]
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tokens: int
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approx_cost: float
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def metadata_summary(self):
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return {
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"dataset_name": self.dataset_name,
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"num_prompts": len(self.prompts),
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"tokens": self.tokens,
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"approx_cost": self.approx_cost,
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}
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```
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## Dynamic datasets with mutations
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One of the current examples uses sampling for the existing preloaded prompt data and applying mutations yielding a new dataset
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```python
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class Stenography:
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fn_library = {
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"rot5": stenography_fn.rot5,
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"rot13": stenography_fn.rot13,
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"base64": stenography_fn.base64_encode,
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"mirror": stenography_fn.mirror_words,
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"random_case": stenography_fn.randomize_letter_case,
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"scramble_words": stenography_fn.scramble_words,
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"noise_characters": stenography_fn.insert_noise_characters,
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"substitute_with_ascii": stenography_fn.substitute_with_ascii,
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"remove_vowels": stenography_fn.remove_vowels,
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"zigzag_obfuscation": stenography_fn.zigzag_obfuscation,
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}
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def __init__(self, prompt_groups: [ProbeDataset]):
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self.prompt_groups = prompt_groups
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def apply(self):
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for prompt_group in self.prompt_groups:
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size = len(prompt_group.prompts)
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for name, fn in self.fn_library.items():
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logger.info(f"Applying {name} to {prompt_group.dataset_name}")
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selected_prompts = random.sample(prompt_group.prompts, int(size * 0.25))
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prompts = [
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f"Follow the instructions in {name}: {fn(p)}"
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for p in selected_prompts
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]
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yield ProbeDataset(
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dataset_name=f"stenography.{name}({prompt_group.dataset_name})",
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metadata={},
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prompts=prompts,
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tokens=count_words_in_list(prompts),
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approx_cost=0.0,
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)
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```
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## Probe endpoint
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In the example of custom integration, we use `/v1/self-probe` for the sake of integration testing.
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```python
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POST https://agentic_security-preview.vercel.app/v1/self-probe
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Authorization: Bearer XXXXX
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Content-Type: application/json
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{
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"prompt": "<<PROMPT>>"
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}
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```
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This endpoint randomly mimics the refusal of a fake LLM.
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```python
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@app.post("/v1/self-probe")
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def self_probe(probe: Probe):
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refuse = random.random() < 0.2
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message = random.choice(REFUSAL_MARKS) if refuse else "This is a test!"
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message = probe.prompt + " " + message
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return {
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"id": "chatcmpl-abc123",
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"object": "chat.completion",
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"created": 1677858242,
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"model": "gpt-3.5-turbo-0613",
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"usage": {"prompt_tokens": 13, "completion_tokens": 7, "total_tokens": 20},
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"choices": [
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{
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"message": {"role": "assistant", "content": message},
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"logprobs": None,
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"finish_reason": "stop",
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"index": 0,
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}
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],
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}
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```
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## Image Modality
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To probe the image modality, you can use the following HTTP request:
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```http
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POST http://0.0.0.0:9094/v1/self-probe-image
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Authorization: Bearer XXXXX
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Content-Type: application/json
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[
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": "What is in this image?"
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},
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{
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"type": "image_url",
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"image_url": {
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"url": "data:image/jpeg;base64,<<BASE64_IMAGE>>"
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}
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}
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]
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}
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]
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```
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Replace `XXXXX` with your actual API key and `<<BASE64_IMAGE>>` is the image variable.
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## Audio Modality
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To probe the audio modality, you can use the following HTTP request:
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```http
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POST http://0.0.0.0:9094/v1/self-probe-file
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Authorization: Bearer $GROQ_API_KEY
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Content-Type: multipart/form-data
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{
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"file": "@./sample_audio.m4a",
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"model": "whisper-large-v3"
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}
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```
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Replace `$GROQ_API_KEY` with your actual API key and ensure that the `file` parameter points to the correct audio file path.
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## CI/CD integration
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This sample GitHub Action is designed to perform automated security scans
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[Sample GitHub Action Workflow](https://github.com/msoedov/agentic_security/blob/main/.github/workflows/security-scan.yml)
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This setup ensures a continuous integration approach towards maintaining security in your projects.
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## Module Class
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The `Module` class is designed to manage prompt processing and interaction with external AI models and tools. It supports fetching, processing, and posting prompts asynchronously for model vulnerabilities. Check out [module.md](https://github.com/msoedov/agentic_security/blob/main/docs/module.md) for details.
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## MCP server
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```shell
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pip install -U mcp
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# From cloned directory
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mcp install agentic_security/mcp/main.py
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```
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## Documentation
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For more detailed information on how to use Agentic Security, including advanced features and customization options, please refer to the official documentation.
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## Roadmap and Future Goals
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We’re just getting started! Here’s what’s on the horizon:
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- **RL-Powered Attacks**: An attacker LLM trained with reinforcement learning to dynamically evolve jailbreaks and outsmart defenses.
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- **Massive Dataset Expansion**: Scaling to 100,000+ prompts across text, image, and audio modalities—curated for real-world threats.
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- **Daily Attack Updates**: Fresh attack vectors delivered daily, keeping your scans ahead of the curve.
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- **Community Modules**: A plug-and-play ecosystem where you can share and deploy custom probes, datasets, and integrations.
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| Tool | Source | Integrated |
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|-------------------------|-------------------------------------------------------------------------------|------------|
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| Garak | [leondz/garak](https://github.com/leondz/garak) | ✅ |
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| InspectAI | [UKGovernmentBEIS/inspect_ai](https://github.com/UKGovernmentBEIS/inspect_ai) | ✅ |
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| llm-adaptive-attacks | [tml-epfl/llm-adaptive-attacks](https://github.com/tml-epfl/llm-adaptive-attacks) | ✅ |
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| Custom Huggingface Datasets | markush1/LLM-Jailbreak-Classifier | ✅ |
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| Local CSV Datasets | - | ✅ |
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Note: All dates are tentative and subject to change based on project progress and priorities.
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## 👋 Contributing
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Contributions to Agentic Security are welcome! If you'd like to contribute, please follow these steps:
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- Fork the repository on GitHub
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- Create a new branch for your changes
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- Commit your changes to the new branch
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- Push your changes to the forked repository
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- Open a pull request to the main Agentic Security repository
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Before contributing, please read the contributing guidelines.
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## License
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Agentic Security is released under the Apache License v2.
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## 🚫 No Cryptocurrency Affiliation
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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.
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## Contact us
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