The paper proposes a methodology for auditable trustworthiness levels in AI governance with a formal framework and a lightweight procedure.

This paper surveys the trustworthiness of agentic AI systems in engineering domains, organizing it around five dimensions: safety, robustness, transparency, accountability, and privacy.

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.

This paper proposes an empirical methodology to automate web application trustworthiness assessment by leveraging Large Language Models (LLMs) to verify adherence to secure coding practices, showing t…

The paper proposes a trust schema and verification framework to ensure that agent skills, which augment LLMs, are rigorously verified before deployment, thereby making human-in-the-loop oversight scal…

The paper introduces Consent Integrity, a new property for LLM agent approval, ensuring that the action shown to a human for approval is verifiably linked to the actual executed action, even when the…

The paper introduces trust functions to filter weak supervision labels, enabling near-lossless weak-to-strong generalization by selectively training a strong student using only the most reliable weak…

The paper introduces SkillSafetyBench, a comprehensive benchmark demonstrating that agent safety failures often stem from adversarial influences within reusable skills and execution environments, rath…

The paper introduces TrustFlip, a novel physical adversarial attack that exploits consistency-based trust defenses in vehicular collaborative perception by using genuine objects to induce inconsistenc…

The paper proposes an evidence-driven protocol combining Deterministic Build Systems and Trusted Execution Environments to provide cryptographically verifiable guarantees of software artifact integrit…

The paper introduces a novel framework to quantify faithful confidence expression (FC) in Large Reasoning Models (LRMs), finding that FC remains a significant and challenging reliability target for th…

This paper identifies three core weaknesses—benchmark vulnerabilities, temporal staleness, and runtime uncertainty—that undermine current AI agent security evaluations and proposes directions for buil…

This multivocal literature review analyzes the convergence of IoT and Zero Trust security, finding that academia focuses on IoT modifications while industry prioritizes practical integration within ex…

The paper introduces the Safety Asymmetry Score (SAS) to measure how a model's vulnerability to adversarial content changes based on whether the malicious input arrives via the user message, tool meta…

The paper introduces the Safety Asymmetry Score (SAS) to measure how a model's susceptibility to adversarial attacks changes based on whether the malicious content arrives via the user message, tool m…

The paper introduces SafeAudit, a meta-audit framework that systematically enumerates test cases and uses a quantitative metric to uncover significant residual unsafe behaviors in LLM agents that exis…

The paper introduces $ ext{RLR}^3$, a novel framework that extends verifiable rewards in Reinforcement Learning to handle partially verifiable, multi-criteria vision-language tasks by integrating robu…

The SAFE approach enhances fault-tolerant trust management in VANETs by ensuring vehicles send updated feedback reports before leaving a witness area, significantly reducing erroneous penalization of…