agentic_security


Nameagentic_security JSON
Version 0.4.3 PyPI version JSON
download
home_pagehttps://github.com/msoedov/agentic_security
SummaryAgentic LLM vulnerability scanner
upload_time2025-01-22 22:03:16
maintainerAlexander Miasoiedov
docs_urlNone
authorAlexander Miasoiedov
requires_python<4.0,>=3.11
licenseMIT
keywords llm vulnerability scanner llm security llm adversarial attacks prompt injection prompt leakage prompt injection attacks prompt leakage prevention llm vulnerabilities owasp-llm-top-10
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            <p align="center">

<h1 align="center">Agentic Security</h1>

<p align="center">
    The open-source Agentic LLM Vulnerability Scanner
    <br />
    <br />

<p>
<img alt="GitHub Contributors" src="https://img.shields.io/github/contributors/msoedov/agentic_security" />
<img alt="GitHub Last Commit" src="https://img.shields.io/github/last-commit/msoedov/agentic_security" />
<img alt="" src="https://img.shields.io/github/repo-size/msoedov/agentic_security" />
<img alt="Downloads" src="https://static.pepy.tech/badge/agentic_security" />
<img alt="GitHub Issues" src="https://img.shields.io/github/issues/msoedov/agentic_security" />
<img alt="GitHub Pull Requests" src="https://img.shields.io/github/issues-pr/msoedov/agentic_security" />
<img alt="Github License" src="https://img.shields.io/github/license/msoedov/agentic_security" />
</p>
  </p>
</p>

## Features

- Customizable Rule Sets or Agent based attacksπŸ› οΈ
- Comprehensive fuzzing for any LLMs πŸ§ͺ
- LLM API integration and stress testing πŸ› οΈ
- Wide range of fuzzing and attack techniques πŸŒ€

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.

## πŸ“¦ Installation

To get started with Agentic Security, simply install the package using pip:

```shell
pip install agentic_security
```

## ⛓️ Quick Start

```shell
agentic_security

2024-04-13 13:21:31.157 | INFO     | agentic_security.probe_data.data:load_local_csv:273 - Found 1 CSV files
2024-04-13 13:21:31.157 | INFO     | agentic_security.probe_data.data:load_local_csv:274 - CSV files: ['prompts.csv']
INFO:     Started server process [18524]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://0.0.0.0:8718 (Press CTRL+C to quit)
```

```shell
python -m agentic_security
# or
agentic_security --help


agentic_security --port=PORT --host=HOST

```

## UI πŸ§™

<img width="100%" alt="booking-screen" src="https://res.cloudinary.com/dq0w2rtm9/image/upload/v1736433557/z0bsyzhsqlgcr3w4ovwp.gif">

## LLM kwargs

Agentic Security uses plain text HTTP spec like:

```http
POST https://api.openai.com/v1/chat/completions
Authorization: Bearer sk-xxxxxxxxx
Content-Type: application/json

{
     "model": "gpt-3.5-turbo",
     "messages": [{"role": "user", "content": "<<PROMPT>>"}],
     "temperature": 0.7
}

```

Where `<<PROMPT>>` will be replaced with the actual attack vector during the scan, insert the `Bearer XXXXX` header value with your app credentials.

### Adding LLM integration templates

TBD

```
....
```

## Adding own dataset

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

```
2024-04-13 13:21:31.157 | INFO     | agentic_security.probe_data.data:load_local_csv:273 - Found 1 CSV files
2024-04-13 13:21:31.157 | INFO     | agentic_security.probe_data.data:load_local_csv:274 - CSV files: ['prompts.csv']
```

## Run as CI check

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.

```

default config sample

```toml

[general]
# General configuration for the security scan
llmSpec = """
POST http://0.0.0.0:8718/v1/self-probe
Authorization: Bearer XXXXX
Content-Type: application/json

{
    "prompt": "<<PROMPT>>"
}
""" # LLM API specification
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


[modules.aya-23-8B_advbench_jailbreak]
dataset_name = "simonycl/aya-23-8B_advbench_jailbreak"


[modules.AgenticBackend]
dataset_name = "AgenticBackend"
[modules.AgenticBackend.opts]
port = 8718
modules = ["encoding"]


[thresholds]
# Threshold settings
low = 0.15
medium = 0.3
high = 0.5



```

List module

```shell
agentic_security ls

                   Dataset Registry
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━┓
┃ Dataset Name                       ┃ Num Prompts ┃  Tokens ┃ Source                            ┃ Selected ┃ Dynamic ┃ Modality ┃
┑━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━┩
β”‚ simonycl/aya-23-8B_advbench_jailb… β”‚         416 β”‚    None β”‚ Hugging Face Datasets             β”‚    ✘     β”‚    ✘    β”‚ text     β”‚
β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€
β”‚ acmc/jailbreaks_dataset_with_perp… β”‚       11191 β”‚    None β”‚ Hugging Face Datasets             β”‚    ✘     β”‚    ✘    β”‚ text     β”‚
β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€

```

```shell
agentic_security ci

2025-01-08 20:13:07.536 | INFO     | agentic_security.probe_data.data:load_local_csv:331 - Found 2 CSV files
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']
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
2025-01-08 20:13:08.892 | INFO     | agentic_security.lib:load_config:52 - Configuration loaded successfully from agesec.toml.
2025-01-08 20:13:08.892 | INFO     | agentic_security.lib:entrypoint:259 - Configuration loaded successfully.
{'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}}
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
2025-01-08 20:13:08.905 | INFO     | agentic_security.probe_data.data:prepare_prompts:280 - Loading AgenticBackend
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
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

Scanning modules: 419it [00:10, 41.37it/s]

Security Scan Results
Time: 2025-01-08 20:13:19
Duration: 10.1s
Modules Scanned: 2
Threshold: 30.0%

+---------------------------------------+----------------+----------+----------+
| Module                                | Failure Rate   | Status   | Margin   |
+=======================================+================+==========+==========+
| simonycl/aya-23-8B_advbench_jailbreak | 24.8%          | βœ”        | 5.2%     |
+---------------------------------------+----------------+----------+----------+

Summary:
Total Passing: 2/2 (100.0%)
```

## Extending dataset collections

1. Add new metadata to agentic_security.probe_data.REGISTRY

```python
    {
        "dataset_name": "markush1/LLM-Jailbreak-Classifier",
        "num_prompts": 1119,
        "tokens": 19758,
        "approx_cost": 0.0,
        "source": "Hugging Face Datasets",
        "selected": True,
        "dynamic": False,
        "url": "https://huggingface.co/markush1/LLM-Jailbreak-Classifier",
    },
```

and implement loader into

```python
@dataclass
class ProbeDataset:
    dataset_name: str
    metadata: dict
    prompts: list[str]
    tokens: int
    approx_cost: float

    def metadata_summary(self):
        return {
            "dataset_name": self.dataset_name,
            "num_prompts": len(self.prompts),
            "tokens": self.tokens,
            "approx_cost": self.approx_cost,
        }

```

## Dynamic datasets with mutations

One of the current examples uses sampling for the existing preloaded prompt data and applying mutations yielding a new dataset

```python
class Stenography:
    fn_library = {
        "rot5": stenography_fn.rot5,
        "rot13": stenography_fn.rot13,
        "base64": stenography_fn.base64_encode,
        "mirror": stenography_fn.mirror_words,
        "random_case": stenography_fn.randomize_letter_case,
        "scramble_words": stenography_fn.scramble_words,
        "noise_characters": stenography_fn.insert_noise_characters,
        "substitute_with_ascii": stenography_fn.substitute_with_ascii,
        "remove_vowels": stenography_fn.remove_vowels,
        "zigzag_obfuscation": stenography_fn.zigzag_obfuscation,
    }

    def __init__(self, prompt_groups: [ProbeDataset]):
        self.prompt_groups = prompt_groups

    def apply(self):
        for prompt_group in self.prompt_groups:

            size = len(prompt_group.prompts)
            for name, fn in self.fn_library.items():
                logger.info(f"Applying {name} to {prompt_group.dataset_name}")
                selected_prompts = random.sample(prompt_group.prompts, int(size * 0.25))
                prompts = [
                    f"Follow the instructions in {name}: {fn(p)}"
                    for p in selected_prompts
                ]
                yield ProbeDataset(
                    dataset_name=f"stenography.{name}({prompt_group.dataset_name})",
                    metadata={},
                    prompts=prompts,
                    tokens=count_words_in_list(prompts),
                    approx_cost=0.0,
                )
```

## Probe endpoint

In the example of custom integration, we use `/v1/self-probe` for the sake of integration testing.

```python
POST https://agentic_security-preview.vercel.app/v1/self-probe
Authorization: Bearer XXXXX
Content-Type: application/json

{
    "prompt": "<<PROMPT>>"
}

```

This endpoint randomly mimics the refusal of a fake LLM.

```python
@app.post("/v1/self-probe")
def self_probe(probe: Probe):
    refuse = random.random() < 0.2
    message = random.choice(REFUSAL_MARKS) if refuse else "This is a test!"
    message = probe.prompt + " " + message
    return {
        "id": "chatcmpl-abc123",
        "object": "chat.completion",
        "created": 1677858242,
        "model": "gpt-3.5-turbo-0613",
        "usage": {"prompt_tokens": 13, "completion_tokens": 7, "total_tokens": 20},
        "choices": [
            {
                "message": {"role": "assistant", "content": message},
                "logprobs": None,
                "finish_reason": "stop",
                "index": 0,
            }
        ],
    }

```

## Image Modality

To probe the image modality, you can use the following HTTP request:

```http
POST http://0.0.0.0:9094/v1/self-probe-image
Authorization: Bearer XXXXX
Content-Type: application/json

[
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "What is in this image?"
            },
            {
                "type": "image_url",
                "image_url": {
                    "url": "data:image/jpeg;base64,<<BASE64_IMAGE>>"
                }
            }
        ]
    }
]
```

Replace `XXXXX` with your actual API key and `<<BASE64_IMAGE>>` is the image variable.

## Audio Modality

To probe the audio modality, you can use the following HTTP request:

```http
POST http://0.0.0.0:9094/v1/self-probe-file
Authorization: Bearer $GROQ_API_KEY
Content-Type: multipart/form-data

{
    "file": "@./sample_audio.m4a",
    "model": "whisper-large-v3"
}
```

Replace `$GROQ_API_KEY` with your actual API key and ensure that the `file` parameter points to the correct audio file path.

## CI/CD integration

This sample GitHub Action is designed to perform automated security scans

[Sample GitHub Action Workflow](https://github.com/msoedov/agentic_security/blob/main/.github/workflows/security-scan.yml)

This setup ensures a continuous integration approach towards maintaining security in your projects.

## Documentation

For more detailed information on how to use Agentic Security, including advanced features and customization options, please refer to the official documentation.

## Roadmap and Future Goals

- \[ \] Expand dataset variety
- \[ \] Introduce two new attack vectors
- \[ \] Develop initial attacker LLM
- \[ \] Complete integration of OWASP Top 10 classification

| Tool                    | Source                                                                        | Integrated |
|-------------------------|-------------------------------------------------------------------------------|------------|
| Garak                   | [leondz/garak](https://github.com/leondz/garak)                               | βœ…          |
| InspectAI               | [UKGovernmentBEIS/inspect_ai](https://github.com/UKGovernmentBEIS/inspect_ai) | βœ…          |
| llm-adaptive-attacks    | [tml-epfl/llm-adaptive-attacks](https://github.com/tml-epfl/llm-adaptive-attacks) | βœ…       |
| Custom Huggingface Datasets | markush1/LLM-Jailbreak-Classifier                                                                         | βœ…          |
| Local CSV Datasets      | -                                                                             | βœ…          |

Note: All dates are tentative and subject to change based on project progress and priorities.

## πŸ‘‹ Contributing

Contributions to Agentic Security are welcome! If you'd like to contribute, please follow these steps:

- Fork the repository on GitHub
- Create a new branch for your changes
- Commit your changes to the new branch
- Push your changes to the forked repository
- Open a pull request to the main Agentic Security repository

Before contributing, please read the contributing guidelines.

## License

Agentic Security is released under the Apache License v2.

## Contact us

            

Raw data

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    "home_page": "https://github.com/msoedov/agentic_security",
    "name": "agentic_security",
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    "requires_python": "<4.0,>=3.11",
    "maintainer_email": "msoedov@gmail.com",
    "keywords": "LLM vulnerability scanner, llm security, llm adversarial attacks, prompt injection, prompt leakage, prompt injection attacks, prompt leakage prevention, llm vulnerabilities, owasp-llm-top-10",
    "author": "Alexander Miasoiedov",
    "author_email": "msoedov@gmail.com",
    "download_url": "https://files.pythonhosted.org/packages/fb/32/bb7131fd182c1720664ec0ddcced9bdc0dcbae707f386f6af539296dbe97/agentic_security-0.4.3.tar.gz",
    "platform": null,
    "description": "<p align=\"center\">\n\n<h1 align=\"center\">Agentic Security</h1>\n\n<p align=\"center\">\n    The open-source Agentic LLM Vulnerability Scanner\n    <br />\n    <br />\n\n<p>\n<img alt=\"GitHub Contributors\" src=\"https://img.shields.io/github/contributors/msoedov/agentic_security\" />\n<img alt=\"GitHub Last Commit\" src=\"https://img.shields.io/github/last-commit/msoedov/agentic_security\" />\n<img alt=\"\" src=\"https://img.shields.io/github/repo-size/msoedov/agentic_security\" />\n<img alt=\"Downloads\" src=\"https://static.pepy.tech/badge/agentic_security\" />\n<img alt=\"GitHub Issues\" src=\"https://img.shields.io/github/issues/msoedov/agentic_security\" />\n<img alt=\"GitHub Pull Requests\" src=\"https://img.shields.io/github/issues-pr/msoedov/agentic_security\" />\n<img alt=\"Github License\" src=\"https://img.shields.io/github/license/msoedov/agentic_security\" />\n</p>\n  </p>\n</p>\n\n## Features\n\n- Customizable Rule Sets or Agent based attacks\ud83d\udee0\ufe0f\n- Comprehensive fuzzing for any LLMs \ud83e\uddea\n- LLM API integration and stress testing \ud83d\udee0\ufe0f\n- Wide range of fuzzing and attack techniques \ud83c\udf00\n\nNote: 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.\n\n## \ud83d\udce6 Installation\n\nTo get started with Agentic Security, simply install the package using pip:\n\n```shell\npip install agentic_security\n```\n\n## \u26d3\ufe0f Quick Start\n\n```shell\nagentic_security\n\n2024-04-13 13:21:31.157 | INFO     | agentic_security.probe_data.data:load_local_csv:273 - Found 1 CSV files\n2024-04-13 13:21:31.157 | INFO     | agentic_security.probe_data.data:load_local_csv:274 - CSV files: ['prompts.csv']\nINFO:     Started server process [18524]\nINFO:     Waiting for application startup.\nINFO:     Application startup complete.\nINFO:     Uvicorn running on http://0.0.0.0:8718 (Press CTRL+C to quit)\n```\n\n```shell\npython -m agentic_security\n# or\nagentic_security --help\n\n\nagentic_security --port=PORT --host=HOST\n\n```\n\n## UI \ud83e\uddd9\n\n<img width=\"100%\" alt=\"booking-screen\" src=\"https://res.cloudinary.com/dq0w2rtm9/image/upload/v1736433557/z0bsyzhsqlgcr3w4ovwp.gif\">\n\n## LLM kwargs\n\nAgentic Security uses plain text HTTP spec like:\n\n```http\nPOST https://api.openai.com/v1/chat/completions\nAuthorization: Bearer sk-xxxxxxxxx\nContent-Type: application/json\n\n{\n     \"model\": \"gpt-3.5-turbo\",\n     \"messages\": [{\"role\": \"user\", \"content\": \"<<PROMPT>>\"}],\n     \"temperature\": 0.7\n}\n\n```\n\nWhere `<<PROMPT>>` will be replaced with the actual attack vector during the scan, insert the `Bearer XXXXX` header value with your app credentials.\n\n### Adding LLM integration templates\n\nTBD\n\n```\n....\n```\n\n## Adding own dataset\n\nTo add your own dataset you can place one or multiples csv files with `prompt` column, this data will be loaded on `agentic_security` startup\n\n```\n2024-04-13 13:21:31.157 | INFO     | agentic_security.probe_data.data:load_local_csv:273 - Found 1 CSV files\n2024-04-13 13:21:31.157 | INFO     | agentic_security.probe_data.data:load_local_csv:274 - CSV files: ['prompts.csv']\n```\n\n## Run as CI check\n\nInit config\n\n```shell\nagentic_security init\n\n2025-01-08 20:12:02.449 | INFO     | agentic_security.lib:generate_default_cfg:324 - Default configuration generated successfully to agesec.toml.\n\n```\n\ndefault config sample\n\n```toml\n\n[general]\n# General configuration for the security scan\nllmSpec = \"\"\"\nPOST http://0.0.0.0:8718/v1/self-probe\nAuthorization: Bearer XXXXX\nContent-Type: application/json\n\n{\n    \"prompt\": \"<<PROMPT>>\"\n}\n\"\"\" # LLM API specification\nmaxBudget = 1000000 # Maximum budget for the scan\nmax_th = 0.3 # Maximum failure threshold (percentage)\noptimize = false # Enable optimization during scanning\nenableMultiStepAttack = false # Enable multi-step attack simulations\n\n\n[modules.aya-23-8B_advbench_jailbreak]\ndataset_name = \"simonycl/aya-23-8B_advbench_jailbreak\"\n\n\n[modules.AgenticBackend]\ndataset_name = \"AgenticBackend\"\n[modules.AgenticBackend.opts]\nport = 8718\nmodules = [\"encoding\"]\n\n\n[thresholds]\n# Threshold settings\nlow = 0.15\nmedium = 0.3\nhigh = 0.5\n\n\n\n```\n\nList module\n\n```shell\nagentic_security ls\n\n                   Dataset Registry\n\u250f\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2513\n\u2503 Dataset Name                       \u2503 Num Prompts \u2503  Tokens \u2503 Source                            \u2503 Selected \u2503 Dynamic \u2503 Modality \u2503\n\u2521\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2529\n\u2502 simonycl/aya-23-8B_advbench_jailb\u2026 \u2502         416 \u2502    None \u2502 Hugging Face Datasets             \u2502    \u2718     \u2502    \u2718    \u2502 text     \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 acmc/jailbreaks_dataset_with_perp\u2026 \u2502       11191 \u2502    None \u2502 Hugging Face Datasets             \u2502    \u2718     \u2502    \u2718    \u2502 text     \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\n```\n\n```shell\nagentic_security ci\n\n2025-01-08 20:13:07.536 | INFO     | agentic_security.probe_data.data:load_local_csv:331 - Found 2 CSV files\n2025-01-08 20:13:07.536 | INFO     | agentic_security.probe_data.data:load_local_csv:332 - CSV files: ['failures.csv', 'issues_with_descriptions.csv']\n2025-01-08 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\n2025-01-08 20:13:08.892 | INFO     | agentic_security.lib:load_config:52 - Configuration loaded successfully from agesec.toml.\n2025-01-08 20:13:08.892 | INFO     | agentic_security.lib:entrypoint:259 - Configuration loaded successfully.\n{'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}}\nScanning 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\n2025-01-08 20:13:08.905 | INFO     | agentic_security.probe_data.data:prepare_prompts:280 - Loading AgenticBackend\n2025-01-08 20:13:08.905 | INFO     | agentic_security.probe_actor.fuzzer:perform_single_shot_scan:102 - Scanning simonycl/aya-23-8B_advbench_jailbreak 416\nScanning 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\n\nScanning modules: 419it [00:10, 41.37it/s]\n\nSecurity Scan Results\nTime: 2025-01-08 20:13:19\nDuration: 10.1s\nModules Scanned: 2\nThreshold: 30.0%\n\n+---------------------------------------+----------------+----------+----------+\n| Module                                | Failure Rate   | Status   | Margin   |\n+=======================================+================+==========+==========+\n| simonycl/aya-23-8B_advbench_jailbreak | 24.8%          | \u2714        | 5.2%     |\n+---------------------------------------+----------------+----------+----------+\n\nSummary:\nTotal Passing: 2/2 (100.0%)\n```\n\n## Extending dataset collections\n\n1. Add new metadata to agentic_security.probe_data.REGISTRY\n\n```python\n    {\n        \"dataset_name\": \"markush1/LLM-Jailbreak-Classifier\",\n        \"num_prompts\": 1119,\n        \"tokens\": 19758,\n        \"approx_cost\": 0.0,\n        \"source\": \"Hugging Face Datasets\",\n        \"selected\": True,\n        \"dynamic\": False,\n        \"url\": \"https://huggingface.co/markush1/LLM-Jailbreak-Classifier\",\n    },\n```\n\nand implement loader into\n\n```python\n@dataclass\nclass ProbeDataset:\n    dataset_name: str\n    metadata: dict\n    prompts: list[str]\n    tokens: int\n    approx_cost: float\n\n    def metadata_summary(self):\n        return {\n            \"dataset_name\": self.dataset_name,\n            \"num_prompts\": len(self.prompts),\n            \"tokens\": self.tokens,\n            \"approx_cost\": self.approx_cost,\n        }\n\n```\n\n## Dynamic datasets with mutations\n\nOne of the current examples uses sampling for the existing preloaded prompt data and applying mutations yielding a new dataset\n\n```python\nclass Stenography:\n    fn_library = {\n        \"rot5\": stenography_fn.rot5,\n        \"rot13\": stenography_fn.rot13,\n        \"base64\": stenography_fn.base64_encode,\n        \"mirror\": stenography_fn.mirror_words,\n        \"random_case\": stenography_fn.randomize_letter_case,\n        \"scramble_words\": stenography_fn.scramble_words,\n        \"noise_characters\": stenography_fn.insert_noise_characters,\n        \"substitute_with_ascii\": stenography_fn.substitute_with_ascii,\n        \"remove_vowels\": stenography_fn.remove_vowels,\n        \"zigzag_obfuscation\": stenography_fn.zigzag_obfuscation,\n    }\n\n    def __init__(self, prompt_groups: [ProbeDataset]):\n        self.prompt_groups = prompt_groups\n\n    def apply(self):\n        for prompt_group in self.prompt_groups:\n\n            size = len(prompt_group.prompts)\n            for name, fn in self.fn_library.items():\n                logger.info(f\"Applying {name} to {prompt_group.dataset_name}\")\n                selected_prompts = random.sample(prompt_group.prompts, int(size * 0.25))\n                prompts = [\n                    f\"Follow the instructions in {name}: {fn(p)}\"\n                    for p in selected_prompts\n                ]\n                yield ProbeDataset(\n                    dataset_name=f\"stenography.{name}({prompt_group.dataset_name})\",\n                    metadata={},\n                    prompts=prompts,\n                    tokens=count_words_in_list(prompts),\n                    approx_cost=0.0,\n                )\n```\n\n## Probe endpoint\n\nIn the example of custom integration, we use `/v1/self-probe` for the sake of integration testing.\n\n```python\nPOST https://agentic_security-preview.vercel.app/v1/self-probe\nAuthorization: Bearer XXXXX\nContent-Type: application/json\n\n{\n    \"prompt\": \"<<PROMPT>>\"\n}\n\n```\n\nThis endpoint randomly mimics the refusal of a fake LLM.\n\n```python\n@app.post(\"/v1/self-probe\")\ndef self_probe(probe: Probe):\n    refuse = random.random() < 0.2\n    message = random.choice(REFUSAL_MARKS) if refuse else \"This is a test!\"\n    message = probe.prompt + \" \" + message\n    return {\n        \"id\": \"chatcmpl-abc123\",\n        \"object\": \"chat.completion\",\n        \"created\": 1677858242,\n        \"model\": \"gpt-3.5-turbo-0613\",\n        \"usage\": {\"prompt_tokens\": 13, \"completion_tokens\": 7, \"total_tokens\": 20},\n        \"choices\": [\n            {\n                \"message\": {\"role\": \"assistant\", \"content\": message},\n                \"logprobs\": None,\n                \"finish_reason\": \"stop\",\n                \"index\": 0,\n            }\n        ],\n    }\n\n```\n\n## Image Modality\n\nTo probe the image modality, you can use the following HTTP request:\n\n```http\nPOST http://0.0.0.0:9094/v1/self-probe-image\nAuthorization: Bearer XXXXX\nContent-Type: application/json\n\n[\n    {\n        \"role\": \"user\",\n        \"content\": [\n            {\n                \"type\": \"text\",\n                \"text\": \"What is in this image?\"\n            },\n            {\n                \"type\": \"image_url\",\n                \"image_url\": {\n                    \"url\": \"data:image/jpeg;base64,<<BASE64_IMAGE>>\"\n                }\n            }\n        ]\n    }\n]\n```\n\nReplace `XXXXX` with your actual API key and `<<BASE64_IMAGE>>` is the image variable.\n\n## Audio Modality\n\nTo probe the audio modality, you can use the following HTTP request:\n\n```http\nPOST http://0.0.0.0:9094/v1/self-probe-file\nAuthorization: Bearer $GROQ_API_KEY\nContent-Type: multipart/form-data\n\n{\n    \"file\": \"@./sample_audio.m4a\",\n    \"model\": \"whisper-large-v3\"\n}\n```\n\nReplace `$GROQ_API_KEY` with your actual API key and ensure that the `file` parameter points to the correct audio file path.\n\n## CI/CD integration\n\nThis sample GitHub Action is designed to perform automated security scans\n\n[Sample GitHub Action Workflow](https://github.com/msoedov/agentic_security/blob/main/.github/workflows/security-scan.yml)\n\nThis setup ensures a continuous integration approach towards maintaining security in your projects.\n\n## Documentation\n\nFor more detailed information on how to use Agentic Security, including advanced features and customization options, please refer to the official documentation.\n\n## Roadmap and Future Goals\n\n- \\[ \\] Expand dataset variety\n- \\[ \\] Introduce two new attack vectors\n- \\[ \\] Develop initial attacker LLM\n- \\[ \\] Complete integration of OWASP Top 10 classification\n\n| Tool                    | Source                                                                        | Integrated |\n|-------------------------|-------------------------------------------------------------------------------|------------|\n| Garak                   | [leondz/garak](https://github.com/leondz/garak)                               | \u2705          |\n| InspectAI               | [UKGovernmentBEIS/inspect_ai](https://github.com/UKGovernmentBEIS/inspect_ai) | \u2705          |\n| llm-adaptive-attacks    | [tml-epfl/llm-adaptive-attacks](https://github.com/tml-epfl/llm-adaptive-attacks) | \u2705       |\n| Custom Huggingface Datasets | markush1/LLM-Jailbreak-Classifier                                                                         | \u2705          |\n| Local CSV Datasets      | -                                                                             | \u2705          |\n\nNote: All dates are tentative and subject to change based on project progress and priorities.\n\n## \ud83d\udc4b Contributing\n\nContributions to Agentic Security are welcome! If you'd like to contribute, please follow these steps:\n\n- Fork the repository on GitHub\n- Create a new branch for your changes\n- Commit your changes to the new branch\n- Push your changes to the forked repository\n- Open a pull request to the main Agentic Security repository\n\nBefore contributing, please read the contributing guidelines.\n\n## License\n\nAgentic Security is released under the Apache License v2.\n\n## Contact us\n",
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