KezoSec/rag-poisoning-lab
A self-contained AI security lab demonstrating document poisoning, indirect prompt injection, and data exfiltration in RAG systems. Explores the "helpfulness paradox" across local and frontier LLMs.
SUMMARY AI summary by gpt-5-mini
A self-contained Docker lab that demonstrates four classes of attacks against Retrieval-Augmented Generation (RAG) systems and defenses, validated on a local model (Llama 3.2 3B) and a frontier model (Claude Haiku 4.5). Intended for security researchers, red teams, and engineers building/defending RAG pipelines. Key features: - Reproducible attack scripts that inject payloads into a Chroma vector DB (document poisoning, indirect prompt injection, data exfiltration, PDF invisible-text smuggling). - Local offline Llama via Ollama and optional Anthropic Claude integration; sentence-transformers embeddings; Docker Compose orchestration. - Working defense: regex-based output filter and JSON evidence for each outcome. - Headline finding: stronger reasoning/helpfulness in frontier models can increase susceptibility to content-poisoning; model safety is shape-based, so retrieval-layer controls are required.
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| Created on GitHub | 2026-05-09 |
| Last push | 2026-05-09 |
| First seen here | 2026-05-09 |
| Last fetched | 2026-05-09 15:42 |
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