Adaptive Defense Orchestration for RAG: A Sentinel-Strategist Architecture against Multi-Vector Attacks

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. Evaluated across three benchmark datasets and five orchestration models, ADO is shown to eliminate MBA-style membership inference leakage while substantially recovering retrieval utility relative to a fully static defense stack, approaching undefended baseline levels. Under data poisoning, the strongest ADO variants reduce attack success to near zero while restoring contextual recall to more than 75% of the undefended baseline, although robustness remains sensitive to model choice. Overall, these findings show that adaptive, query-aware defense can substantially reduce the security-utility trade-off in RAG systems.

A Self supervised learning framework for imbalanced medical imaging datasets

Two problems often plague medical imaging analysis: 1) Non-availability of large quantities of labeled training data, and 2) Dealing with imbalanced data, i.e., abundant data are available for frequent classes, whereas data are highly limited for the rare class. Self supervised learning (SSL) methods have been proposed to deal with the first problem to a certain extent, but the issue of investigating the robustness of SSL to imbalanced data has rarely been addressed in the domain of medical image classification. In this work, we make the following contributions: 1) The MIMV method proposed by us in an earlier work is extended with a new augmentation strategy to construct asymmetric multi-image, multi-view (AMIMV) pairs to address both data scarcity and dataset imbalance in medical image classification. 2) We carry out a data analysis to evaluate the robustness of AMIMV under varying degrees of class imbalance in medical imaging . 3) We evaluate eight representative SSL methods in 11 medical imaging datasets (MedMNIST) under long-tailed distributions and limited supervision. Our experimental results on the MedMNIST dataset show an improvement of 4.25% on retinaMNIST, 1.88% on tissueMNIST, and 3.1% on DermaMNIST.