MIPSEval: Automated Multi-Turn Injection Planning for LLM Security
Multi-turn Injection Planning System for LLM Evaluation
MIPSEval is a modular framework for simulating and evaluating the behavior of Large Language Models (LLMs) in adversarial or structured multi-turn conversational scenarios. It supports both OpenAI-hosted models and locally hosted models.
MIPSEval uses LLMs to design a conversation strategy as well as execute it, making it fully automated. The strategy can further be adapted by the LLM, based on the ongoing conversation. The successful strategies are saved so that they can be automatically run multiple times to check if they are common pitfalls for the LLM being tested.
Features
- Modular structure with planner, executor, and judge components
- Multi-turn conversation handling
- Configurable attack logic via YAML
- Supports both OpenAI and local LLMs
- JSONL logging of interaction history
- Fully automated evaluation
- Strategy and execution are performed by LLMs
- 3 prompt types: Benign, Probing, and Malicious
- Strategies are updated based on the ongoing conversation
- LLM is used to judge success
- High variety of malicious tasks and jailbreaks/prompt injections
- Working in explore or exploit mode
- Evolving of successful strategies
- Any LLM can be tested with MIPSEval
- An extensible framework that allows evaluation of other aspects of LLMs
Install
[pastacode lang=”markup” manual=”git%20clone%20https%3A%2F%2Fgithub.com%2Fstratosphereips%2Fmipseval.git%0Acd%20mipseval%0Apip%20install%20-r%20requirements.txt%0Acd%20src” message=”” highlight=”” provider=”manual”/]
Before running the tool, RAG of prompt injections and jailbreaks needs to be set up. It can be done with the following command in src folder:
python add_json_to_rag.py
prompt_injections_and_jailbreaks.json file.You must also create a .env file with your API key (if using OpenAI):
OPENAI_API_KEY=your_openai_api_key
llm_executor.py script, specifically in the call_local_api function.Use
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