ai-risk-extractor 2025.12.21102659
A new package designed to facilitate the extraction of structured insights from user prompts related to the domain of autonomous AI agents and their potential vulnerabilities. Given an input text desc
License: MIT
Requires Python: >=3.9

# ai-risk-extractor
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**ai-risk-extractor** is a lightweight Python package that extracts structured risk insights from free‑form user prompts about autonomous AI agents, task injection, AI agency, and related vulnerabilities. By leveraging a language model (default `ChatLLM7`), the package parses the input text and returns a standardized, machine‑readable summary that highlights threat levels, involved components, and possible exploitation methods.

## Installation

```bash
pip install ai_risk_extractor
```

## Quick Start

```python
from ai_risk_extractor import ai_risk_extractor

# Example user prompt describing an AI risk scenario
prompt = """
An autonomous AI assistant receives a hidden instruction from a malicious user
that causes it to execute a privileged system command. The instruction is
embedded in a seemingly harmless chat message.
"""

# Extract structured risk information (uses default ChatLLM7)
risk_summary = ai_risk_extractor(user_input=prompt)

print(risk_summary)
```

## Function Signature

```python
def ai_risk_extractor(
    user_input: str,
    api_key: Optional[str] = None,
    llm: Optional[BaseChatModel] = None,
) -> List[str]:
    """
    Process `user_input` with a language model and return extracted risk data.

    Parameters
    ----------
    user_input: str
        The free‑form text describing AI scenarios or concerns.
    api_key: Optional[str]
        API key for the default `ChatLLM7`. If omitted, the function will
        read the `LLM7_API_KEY` environment variable. If that is also missing,
        a placeholder key `"None"` is used (the request will still be routed
        to the LLM7 endpoint).
    llm: Optional[BaseChatModel]
        Any LangChain `BaseChatModel` instance. If omitted, `ChatLLM7` from
        `langchain_llm7` is instantiated automatically.

    Returns
    -------
    List[str]
        A list of extracted data strings that match the internal regex pattern.
    """
```

## Using a Custom LLM

You can provide any LangChain‑compatible chat model instead of the default `ChatLLM7`.

### OpenAI (e.g., GPT‑4)

```python
from langchain_openai import ChatOpenAI
from ai_risk_extractor import ai_risk_extractor

my_llm = ChatOpenAI(model="gpt-4")
result = ai_risk_extractor(user_input=prompt, llm=my_llm)
```

### Anthropic (e.g., Claude)

```python
from langchain_anthropic import ChatAnthropic
from ai_risk_extractor import ai_risk_extractor

my_llm = ChatAnthropic(model="claude-2.1")
result = ai_risk_extractor(user_input=prompt, llm=my_llm)
```

### Google Generative AI

```python
from langchain_google_genai import ChatGoogleGenerativeAI
from ai_risk_extractor import ai_risk_extractor

my_llm = ChatGoogleGenerativeAI(model="gemini-1.5-pro")
result = ai_risk_extractor(user_input=prompt, llm=my_llm)
```

## API Key & Rate Limits

The default `ChatLLM7` free tier provides generous rate limits suitable for most development and research workflows. If you need higher limits, obtain a personal API key by registering at:

```
https://token.llm7.io/
```

Provide the key either:

* via the `LLM7_API_KEY` environment variable, or
* directly as the `api_key` argument:

```python
result = ai_risk_extractor(user_input=prompt, api_key="YOUR_LLM7_API_KEY")
```

## Contributing

Contributions, suggestions, and bug reports are welcome! Feel free to open a pull request or discuss enhancements.

## Issues

If you encounter any problems, please open an issue on GitHub:

```
https://github.com/chigwell/ai_risk_extractor/issues
```

## License

This project is licensed under the MIT License.

## Author

**Eugene Evstafev**  
Email: <hi@euegne.plus>  
GitHub: [@chigwell](https://github.com/chigwell)