TopoGuard: Graph Theory-Based Defense Against Split-Knowledge Attacks on RAG
A new arXiv preprint introduces TopoGuard, a set of graph theory-based methods designed to defend Retrieval Augmented Generation (RAG) systems against split-knowledge attacks. These attacks involve injecting individually benign documents that, when combined, create misleading associations undetectable by existing per-document filters. TopoGuard constructs a semantic similarity graph from retrieved documents to identify suspicious contexts, and in experiments on HotpotQA, it detected 21 times more attacks than LlamaGuard-2-8B at a 1% false positive rate.
Why it matters: The work highlights a previously under-addressed vulnerability in RAG systems and proposes a practical, efficient defense that could improve the security of AI applications relying on document retrieval.
Full story at: arXiv Computation and Language ↗