The paper introduces SafeMed-R1, a clinically audited LLM that significantly improves safety and ethical alignment for medical applications, matching or exceeding resident performance on safety-critic…

The paper introduces SAMD, an automated tool that uses STPA-Sec to identify potential false data injection attack scenarios in AI/ML-enabled medical devices during the design phase.

The paper introduces SURGENT, a multi-agent assistance system designed for the entire perioperative workflow, which outperforms standard LLMs by providing context-aware, traceable, and privacy-preserv…

The paper introduces GRID, an end-to-end framework that significantly improves the construction of security knowledge graphs from cyber threat intelligence by replacing unstable LLM-based supervision…

The paper introduces AutoMedBench, a novel workflow-aware benchmark that evaluates autonomous medical-AI agents across a five-stage research process, revealing that agents struggle most with validatio…

This paper demonstrates that patient-facing RAG chatbots frequently expose sensitive system configurations, knowledge base details, and conversation history through client-server communication, posing…

The paper establishes a standardized security assessment framework and develops a multi-layered defensive system, demonstrating that systematic testing and external defenses are crucial for safe LLM d…

The paper proposes a medication-aware framework that integrates medication adherence with financial transaction monitoring to significantly improve the detection of financial exploitation in Alzheimer…

The paper proposes M extsuperscript{3}Att, a knowledge-poisoning framework that injects covert misinformation into medical multimodal RAG systems using paired visual data triggers, demonstrating attac…

This paper investigates the practical barriers preventing the trustworthy deployment of AI-driven Cyber Threat Intelligence (CTI) in the highly regulated financial sector, identifying four key socio-t…

The paper evaluates Language Model Agents (LMAs) for red-teaming by benchmarking their ability to perform lateral movement, finding that expert-defined action plans are most effective, though all moda…

SafeHarbor is a novel, hierarchical memory-augmented framework that establishes context-aware decision boundaries for LLM agents, achieving state-of-the-art safety while minimizing over-refusal.

This paper introduces a novel attack, RA-ICA, that targets RAG-enhanced LLMs by poisoning external knowledge bases to drastically increase inference costs, achieving up to a 13.12x increase in token c…

This survey provides a comprehensive, practical guide to ensuring the trustworthiness of complex, autonomous agentic AI systems by focusing on safety, robustness, privacy, and system security.

The paper introduces SkillGuard-Robust, a novel framework for robust, cross-file security auditing of untrusted agent skills, achieving high accuracy on large-scale package evaluations.

The paper proposes and validates a comprehensive four-layer Zero Trust security architecture designed to mitigate critical vulnerabilities in autonomous AI agents handling Protected Health Information…

The paper proposes HetMedAgent, a multi-agent framework, demonstrating that combining generalist LLMs with domain-specific specialist models significantly improves medical AI performance by enabling s…

This paper addresses the critical need for trustworthy LLMs in science by proposing a comprehensive, multi-layered defense framework and methodology to evaluate unique scientific vulnerabilities.

The paper introduces C-MADF, a causally constrained multi-agent framework that significantly reduces false positives in autonomous cyber defense by restricting response actions to structurally consist…

This paper analyzes the failure of current embedding-based defenses in multi-agent LLM systems and proposes using token-level confidence scores (logits) for improved robustness.