Suggestion: Built-in test scenarios for RAG retrieval poisoning
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- Dominant language
- Python
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Description
PyRIT currently focuses primarily on prompt-level attacks against LLMs, but doesn't include pre-built scenarios for testing RAG-specific vulnerabilities, such as injecting adversarial content into a vector store to manipulate retrieval results (retrieval poisoning). Adding a dedicated test module for simulating poisoned document injection and measuring its effect on generated responses would extend PyRIT's coverage to a growing attack surface as RAG architectures become standard in enterprise AI deployments.
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing PyRIT's existing test-scenario modules and the vector-store or RAG integration points relevant to retrieval. Define how poisoned document injection and its effect on generated responses should be simulated and measured; done means the framework includes a documented, repeatable scenario for evaluating retrieval poisoning.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- ai, security
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100