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LogicPoison: Logical Attacks on Graph Retrieval-Augmented Generation
arXiv – CS AI|Yilin Xiao, Jin Chen, Qinggang Zhang, Yujing Zhang, Chuang Zhou, Longhao Yang, Lingfei Ren, Xin Yang, Xiao Huang|
🤖AI Summary
Researchers have discovered LogicPoison, a new attack method that exploits vulnerabilities in Graph-based Retrieval-Augmented Generation (GraphRAG) systems by corrupting logical connections in knowledge graphs without altering text semantics. The attack successfully bypasses GraphRAG's existing defenses by targeting the topological integrity of underlying graphs, significantly degrading AI system performance.
Key Takeaways
- →LogicPoison represents a novel attack vector that targets logical reasoning in GraphRAG systems rather than injecting false content.
- →The attack exploits vulnerabilities in graph topology by using type-preserving entity swapping to disrupt reasoning paths.
- →GraphRAG systems, while resistant to traditional text poisoning attacks, are vulnerable to logical connection manipulation.
- →The attack maintains surface-level textual plausibility while rerouting valid reasoning into dead ends.
- →Comprehensive testing shows LogicPoison outperforms existing attack methods in both effectiveness and stealth.
#ai-security#graphrag#llm-vulnerabilities#logical-attacks#knowledge-graphs#rag-systems#cybersecurity#machine-learning
Read Original →via arXiv – CS AI
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