arXiv:2605.22041v1 Announce Type: cross Abstract: While RAG systems are increasingly deployed in dynamic web search, temporal volatility amplifies their vulnerability to adversarial attacks. Existing static-oriented defenses struggle to handle evolving threats and incur prohibitive storage costs in dynamic settings. We propose RADAR, a framework that models reliable context selection as a graph-based energy minimization problem, solved exactly via Max-Flow Min-Cut. By incorporating a Bayesian memory node, RADAR recursively updates a belief state instead of archiving raw historical documents, e
This research addresses the growing vulnerability of RAG systems to adversarial attacks amidst their increasing deployment in dynamic environments like web search. The development of more robust AI defenses is critical as these systems become more integrated into real-world applications.
Reliable context selection in RAG systems is crucial for maintaining AI integrity and operational efficiency, especially as AI agents interact with dynamic and potentially compromised information. This affects the trust and utility of AI applications across various sectors.
The proposed RADAR framework introduces a dynamic defense mechanism against retrieval corruption, moving beyond static defenses with a graph-based energy minimization approach and a Bayesian memory node. This potentially enhances the resilience and efficiency of RAG systems by adaptively handling evolving threats.
- · AI developers
- · Cybersecurity sector
- · AI-driven search engines
- · Enterprises deploying RAG
- · Adversarial attackers
- · Static-oriented AI defense providers
RAG systems become more resilient to adversarial attacks and dynamic data changes, leading to improved reliability.
Increased trust in AI-powered information retrieval could accelerate the adoption of advanced AI agents in sensitive applications.
The development of adaptive, graph-based AI defenses might influence broader cybersecurity strategies, moving towards more dynamic and predictive threat response mechanisms.
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Read at arXiv cs.LG