arXiv:2606.25721v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems are vulnerable to corpus poisoning attacks that manipulate model outputs through malicious retrieved documents. Existing detection methods typically rely on auxiliary classifiers or additional LLM-based verification, introducing substantial computational overhead. We present TRACE, a lightweight detection framework that identifies poisoning attacks by tracing answer-related tokens through token influence attribution. TRACE first discovers recurrent high-influence keywords across retrieved documents a
The proliferation of RAG systems and an increasing awareness of their vulnerabilities necessitate the development of robust, lightweight detection mechanisms for protecting AI integrity.
This development addresses a critical security flaw in AI systems, enabling more reliable and trustworthy AI deployments, especially in sensitive applications.
The ability to efficiently trace and mitigate poisoning attacks in RAG systems improves the robustness and trustworthiness of AI, potentially accelerating their adoption in critical infrastructure and decision-making processes.
- · AI developers
- · Cybersecurity firms
- · Organizations deploying RAG systems
- · AI ethics and safety researchers
- · Malicious actors
- · Organizations relying on insecure RAG systems
Increased trust and accelerated adoption of Retrieval-Augmented Generation systems across various industries.
Reduced incidence of AI-driven misinformation or manipulated outcomes, strengthening societal reliance on AI for informational tasks.
The development of more sophisticated, adaptive poisoning techniques as attackers respond to enhanced detection capabilities, leading to an ongoing AI security arms race.
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Read at arXiv cs.CL