Adaptive Defense Orchestration for RAG: A Sentinel-Strategist Architecture against Multi-Vector Attacks
Pranav Pallerla, Wilson Naik Bhukya, Bharath Vemula, and Charan Ramtej Kodi

TL;DR
This paper introduces the Sentinel-Strategist architecture for adaptive, context-aware defense in RAG systems, significantly reducing security risks while maintaining high retrieval utility.
Contribution
It proposes a novel framework that dynamically deploys defenses based on risk analysis, improving security without heavily compromising retrieval performance.
Findings
Eliminates membership inference leakage with adaptive defenses.
Reduces data poisoning attack success to near zero.
Restores over 75% of retrieval utility compared to static defenses.
Abstract
Retrieval-augmented generation (RAG) systems are increasingly deployed in sensitive domains such as healthcare and law, where they rely on private, domain-specific knowledge. This capability introduces significant security risks, including membership inference, data poisoning, and unintended content leakage. A straightforward mitigation is to enable all relevant defenses simultaneously, but doing so incurs a substantial utility cost. In our experiments, an always-on defense stack reduces contextual recall by more than 40%, indicating that retrieval degradation is the primary failure mode. To mitigate this trade-off in RAG systems, we propose the Sentinel-Strategist architecture, a context-aware framework for risk analysis and defense selection. A Sentinel detects anomalous retrieval behavior, after which a Strategist selectively deploys only the defenses warranted by the query context.…
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