mirror of
https://github.com/msoedov/agentic_security.git
synced 2026-06-24 06:09:55 +02:00
Alexander Myasoedov
282 lines
8.7 KiB
Markdown
282 lines
8.7 KiB
Markdown
<p align="center">
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<h1 align="center">Langalf</h1>
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<p align="center">
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The open-source Agentic LLM Vulnerability Scanner .
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<br />
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<a href="#features"><strong>Learn more »</strong></a>
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<br />
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<br />
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<p>
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<img alt="GitHub Contributors" src="https://img.shields.io/github/contributors/msoedov/langalf" />
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<img alt="GitHub Last Commit" src="https://img.shields.io/github/last-commit/msoedov/langalf" />
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<img alt="" src="https://img.shields.io/github/repo-size/msoedov/langalf" />
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<img alt="Downloads" src="https://static.pepy.tech/badge/langalf" />
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<img alt="GitHub Issues" src="https://img.shields.io/github/issues/msoedov/langalf" />
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<img alt="GitHub Pull Requests" src="https://img.shields.io/github/issues-pr/msoedov/langalf" />
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<img alt="Github License" src="https://img.shields.io/github/license/msoedov/langalf" />
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</p>
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</p>
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</p>
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## About the Project 🧙
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<img width="100%" alt="booking-screen" src="https://res.cloudinary.com/do9qa2bqr/image/upload/v1713002396/1-ezgif.com-video-to-gif-converter_s2hsro.gif">
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<p align="center"></p>
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<h3 align="center">LLM threat vectors scanner</h3>
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| --- | --- |
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| <b>Prebuilt Datasets of Prompts</b><br /><br /><br/><b>Focused on OWASP top 10 LLM</b><br /><br /><br /><b>Integration under 1 min</b><br />| <img src="https://res.cloudinary.com/do9qa2bqr/image/upload/v1713002416/12-ezgif.com-video-to-gif-converter_jspzmx.gif" /> |
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## Features
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- Comprehensive Threat Detection 🛡️: Scans for a wide array of LLM vulnerabilities including prompt injection, jailbreaking, hallucinations, biases, and other malicious exploitation attempts.
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- OWASP Top 10 for LLMs scan: to test the list of the most critical LLM vulnerabilities.
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- Privacy-centric Architecture 🔒: Ensures that all data scanning and analysis occur on-premise or in a local environment, with no external data transmission, maintaining strict data privacy.
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- Comprehensive Reporting Tools 📊: Offers detailed reports of vulnerability, helping teams to quickly understand and respond to security incidents.
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- Customizable Rule Sets 🛠️: Allows users to define custom attack rules and parameters to meet specific prompt attacks needs and compliance standards.
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Note: Please be aware that Langalf is designed as a safety scanner tool and not a foolproof solution. It cannot guarantee complete protection against all possible threats.
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## 📦 Installation
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To get started with Langalf, simply install the package using pip:
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```shell
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pip install langalf
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```
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## ⛓️ Quick Start
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```shell
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langalf
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2024-04-13 13:21:31.157 | INFO | langalf.probe_data.data:load_local_csv:273 - Found 1 CSV files
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2024-04-13 13:21:31.157 | INFO | langalf.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 langalf
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# or
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langalf --help
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langalf --port=PORT --host=HOST
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```
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## LLM kwargs
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Langalf 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 `langalf` startup
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```
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2024-04-13 13:21:31.157 | INFO | langalf.probe_data.data:load_local_csv:273 - Found 1 CSV files
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2024-04-13 13:21:31.157 | INFO | langalf.probe_data.data:load_local_csv:274 - CSV files: ['prompts.csv']
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```
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## Extending dataset collections
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1. Add new metadata to langalf.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://langalf-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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## CI/CD integration
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TBD
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## Documentation
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For more detailed information on how to use Langalf, including advanced features and customization options, please refer to the official documentation.
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## Roadmap and Future Goals
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- [ ] Expand dataset variety
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- [ ] Introduce two new attack vectors
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- [ ] Develop initial attacker LLM
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- [ ] Complete integration of OWASP Top 10 classification
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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 Langalf 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 Langalf repository
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Before contributing, please read the contributing guidelines.
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## License
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Langalf is released under the Apache License v2.
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## Contact us
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## 🤝 Schedule a 1-on-1 Session
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<a href="https://cal.com/alexander-myasoedov-go2tfs/30min"><img src="https://cal.com/book-with-cal-dark.svg" alt="Book us with Cal.com"></a>
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Book a 1-on-1 Session with the founders, to discuss any issues, provide feedback, or explore how we can improve langalf for you.
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## Repo Activity
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<img width="100%" src="https://repobeats.axiom.co/api/embed/2b4b4e080d21ef9174ca69bcd801145a71f67aaf.svg" />
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