arXiv:2506.04390v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) systems are vulnerable to attacks that inject poisoned passages into the retrieved context, even at low corruption rates. We show that existing attacks are not designed to be stealthy, allowing reliable detection and mitigation. We formalize a distinguishability-based security game to quantify stealth for such attacks. If a few poisoned passages control the response, they must bias the inference process more than the benign ones, inherently compromising stealth. This motivates analyzing intermediate
The proliferation of RAG systems makes understanding and mitigating their exploitability critical, especially as advanced persistent threats become more sophisticated.
This research provides a framework for analyzing attack stealth in RAG systems, which is crucial for developing robust, secure AI applications and protecting against data manipulation.
The formalization of a distinguishability-based security game offers a new methodology for evaluating the stealth of poisoning attacks, potentially leading to more resilient RAG defenses.
- · AI security researchers
- · Developers of RAG systems
- · Enterprises deploying RAG
- · Cybersecurity firms
- · Malicious actors
- · Organizations with vulnerable RAG systems
- · Undetected poisoning attack methods
Improved defense mechanisms will emerge, making RAG systems more resistant to data poisoning and manipulation.
The cost and complexity of launching effective, stealthy poisoning attacks on RAG will increase significantly, deterring less sophisticated actors.
Increased trust in RAG system outputs will accelerate their adoption across sensitive domains like finance, intelligence, and healthcare, but also push attackers towards more novel, harder-to-detect vectors.
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Read at arXiv cs.AI