arXiv:2603.22934v3 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) improves large language model applications by grounding generation in retrieved evidence, but also introduces corpus poisoning as a new attack surface. In this setting, an adversary injects or edits passages so that they enter the Top-$K$ results for target queries and influence downstream generation. Existing defences often rely on content filtering, auxiliary models, or generator-side reasoning, which complicates deployment. We propose ProGRank, a post hoc, training-free retriever-side defence for dense-
The proliferation of RAG systems makes them an increasingly attractive target for adversarial attacks, necessitating immediate defensive innovations.
This research provides a practical, efficient defense against corpus poisoning in RAG, a critical vulnerability for AI applications relying on external data retrieval.
A new category of post hoc, training-free RAG defense makes robust, secure AI systems more achievable without significant deployment overhead.
- · AI application developers
- · Organizations deploying RAG systems
- · Cybersecurity firms
- · Trustworthy AI research
- · Adversarial attackers
- · Low-security RAG deployments
- · Complex RAG defense solutions
Enterprises can confidently deploy RAG-based AI, reducing risks of misinformation or malicious manipulation.
Increased trust in AI outputs could accelerate adoption of language models in sensitive applications like finance and healthcare.
This could lead to a 'security arms race' where new poisoning methods emerge, driving continuous innovation in AI defense mechanisms.
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Read at arXiv cs.AI