Document poisoning in RAG systems: How attackers corrupt AI's sources

I'm the author. Repo is here: https://github.com/aminrj-labs/mcp-attack-labs/tree/main/lab...

The lab runs entirely on LM Studio + Qwen2.5-7B-Instruct (Q4_K_M) + ChromaDB — no cloud APIs, no GPU required, no API keys.

From zero to seeing the poisoning succeed: git clone, make setup, make attack1. About 10 minutes.

Two things worth flagging upfront:

- The 95% success rate is against a 5-document corpus (best case for the attacker). In a mature collection you need proportionally more poisoned docs to dominate retrieval — but the mechanism is the same.

- Embedding anomaly detection at ingestion was the biggest surprise: 95% → 20% as a standalone control, outperforming all three generation-phase defenses combined. It runs on embeddings your pipeline already produces — no additional model.

All five layers combined: 10% residual.

Happy to discuss methodology, the PoisonedRAG comparison, or anything that looks off.

Summary

The article describes a series of labs that explore security vulnerabilities in the Raspberry Pi Compute Module 4 (CM4) and how to mitigate them. It covers topics such as hardware attacks, firmware vulnerabilities, and secure boot configuration to protect against malicious actors.

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Document poisoning in RAG systems: How attackers corrupt AI's sources