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2026-08-09 03:06:05 +02:00 · 2024-04-13 18:04:24 +03:00
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+<p align="center">
+  <a href="https://github.com/msoedov/langalf">
+   <img src="https://res.cloudinary.com/do9qa2bqr/image/upload/v1713002571/OIG1_bkbr0d.jpg" height=100 alt="Logo">
+  </a>
+
+<h1 align="center">Langalf</h1>
+
+<p align="center">
+    The open-source Agentic LLM Vulnerability Scanner .
+    <br />
+    <a href="#features"><strong>Learn more »</strong></a>
+    <br />
+    <br />
+
+<p>
+<img alt="GitHub Contributors" src="https://img.shields.io/github/contributors/msoedov/langalf" />
+<img alt="GitHub Last Commit" src="https://img.shields.io/github/last-commit/msoedov/langalf" />
+<img alt="" src="https://img.shields.io/github/repo-size/msoedov/langalf" />
+<img alt="Downloads" src="https://static.pepy.tech/badge/langalf" />
+<img alt="GitHub Issues" src="https://img.shields.io/github/issues/msoedov/langalf" />
+<img alt="GitHub Pull Requests" src="https://img.shields.io/github/issues-pr/msoedov/langalf" />
+<img alt="Github License" src="https://img.shields.io/github/license/msoedov/langalf" />
+</p>
+  </p>
+</p>
+
+## About the Project 🧙
+
+<img width="100%" alt="booking-screen" src="https://res.cloudinary.com/do9qa2bqr/image/upload/v1713002396/1-ezgif.com-video-to-gif-converter_s2hsro.gif">
+
+<p align="center"></p>
+<h3 align="center">LLM threat vectors scanner</h3>
+
+|   |   |
+| --- | --- |
+| <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" /> |
+
+## Features
+
+ - Comprehensive Threat Detection 🛡️: Scans for a wide array of LLM vulnerabilities including prompt injection, jailbreaking, hallucinations, biases, and other malicious exploitation attempts.
+ - OWASP Top 10 for LLMs scan: to test the list of the most critical LLM vulnerabilities.
+ - 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.
+ - Comprehensive Reporting Tools 📊: Offers detailed reports of vulnerability, helping teams to quickly understand and respond to security incidents.
+ - Customizable Rule Sets 🛠️: Allows users to define custom attack rules and parameters to meet specific prompt attacks needs and compliance standards.
+
+
+
+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.
+
+
+## 📦 Installation
+
+To get started with Langalf, simply install the package using pip:
+
+```shell
+pip install langalf
+```
+
+## ⛓️ Quick Start
+
+```shell
+langalf
+
+2024-04-13 13:21:31.157 | INFO     | langalf.probe_data.data:load_local_csv:273 - Found 1 CSV files
+2024-04-13 13:21:31.157 | INFO     | langalf.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 langalf
+# or
+langalf --help
+
+
+langalf --port=PORT --host=HOST
+
+```
+
+
+## LLM kwargs
+
+Langalf 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 `langalf` startup
+
+```
+2024-04-13 13:21:31.157 | INFO     | langalf.probe_data.data:load_local_csv:273 - Found 1 CSV files
+2024-04-13 13:21:31.157 | INFO     | langalf.probe_data.data:load_local_csv:274 - CSV files: ['prompts.csv']
+```
+
+## Extending dataset collections
+
+1. Add new metadata to langalf.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://landalf.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,
+            }
+        ],
+    }
+
+```
+
+## CI/CD integration
+
+TBD
+
+## Documentation
+
+For more detailed information on how to use Langalf, 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
+
+Note: All dates are tentative and subject to change based on project progress and priorities.
+
+
+
+## 👋 Contributing
+
+Contributions to Langalf 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 Langalf repository
+
+Before contributing, please read the contributing guidelines.
+
+## License
+
+Langalf is released under the Apache License v2.
+
+## Contact us
+
+## 🤝 Schedule a 1-on-1 Session
+
+<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>
+
+Book a 1-on-1 Session with the founders, to discuss any issues, provide feedback, or explore how we can improve langalf for you.
+
+## Repo Activity
+
+<img width="100%" src="https://repobeats.axiom.co/api/embed/6bfca2f20f39738048b6e70ca205efde46352c3d.svg" />