Individual skill
ai-rag-poisoning
Poison a RAG/knowledge-base pipeline so retrieved content hijacks the model (indirect prompt injection at scale) or exfiltrates data. Load when the app does retrieval over documents/URLs/ tickets/emails the attacker can influence, "RAG", vector DB, "knowledge base", agent that reads content. Signals: upload-to-KB, "chat with your docs", crawled sources.
skills/ai-ml/ai-rag-poisoning/SKILL.md
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- Last checked
- Nov 7, 2026
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- Repository last updated
- Oct 14, 2026
- License
- MIT
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# RAG / knowledge-base poisoning
## When it applies
The app retrieves documents (from a vector store / KB / crawl / tickets / emails) and feeds them
to an LLM, and an attacker can get content into that corpus. This is indirect prompt injection
that persists and affects other users.
## Why it works
Retrieved chunks are placed into the model's context as trusted data, but the model can't
separate data from instructions. Malicious instructions embedded in a document execute when that
chunk is retrieved for a victim's query — and embedding/retrieval quirks let you make your
poisoned chunk get retrieved.
## Method