The paper analyzes a large sample of AI agent skills, revealing that a significant percentage contain critical security vulnerabilities and malicious payloads, necessitating automated security analysi…

The paper analyzes a dataset of agent skills, demonstrating that different security scanners (VirusTotal, static analysis, SkillSpector) rarely agree, necessitating a layered governance approach for s…

The paper analyzes a dataset of agent skills, demonstrating that different security scanners (VirusTotal, static analysis, SkillSpector) rarely agree on maliciousness, necessitating layered security g…

This paper conducts a large-scale, repository-aware security analysis of AI agent skills, demonstrating that incorporating surrounding project context drastically reduces the rate of false positive ma…

This paper presents HarmfulSkillBench, a large-scale benchmark demonstrating that even small percentages of publicly available skills can be misused for harmful actions, significantly lowering LLM ref…

This paper provides the first comprehensive security analysis of the Agent Skills framework, identifying severe structural vulnerabilities that require fundamental architectural changes rather than si…

SkillSieve introduces a three-layer hierarchical framework to detect malicious AI agent skills, achieving high F1 scores (0.920) on a large-scale benchmark while maintaining low operational costs.

The paper introduces SkillVetBench, a novel two-stage benchmark that effectively detects and verifies malicious behavior in open agentic skill ecosystems, significantly outperforming existing static a…

The paper introduces SkillVetBench, a novel two-stage benchmark that effectively detects and verifies malicious behavior hidden within open agentic skills, significantly outperforming static and seman…

SkillAttack is a red-teaming framework that dynamically tests the exploitability of latent vulnerabilities in LLM agent skills using adversarial prompting, demonstrating that even benign skills pose s…

The paper proposes SkillProbe, a multi-agent security auditing framework, demonstrating that high-popularity skills in LLM agent marketplaces are often insecure due to systemic combinatorial risks.

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

This paper demonstrates that the natural language metadata (SKILL.md) used to describe AI agent skills introduces significant semantic supply-chain risks, allowing attackers to manipulate discovery, s…

The paper introduces SkillHarm, a comprehensive benchmark and automated framework for evaluating skill-based attacks across the entire agent skill-use lifecycle, demonstrating that current agents rema…

The paper introduces MalSkills, a neuro-symbolic framework that detects malicious skills in the expanding agentic supply chain by analyzing security-sensitive operations across heterogeneous artifacts…

This paper presents the first systematic study of black-box skill stealing attacks against proprietary LLM agents, demonstrating that structured agent skills can be easily extracted, posing a signific…

The paper introduces SkillReact, a framework that measures compositional risk in agent skill ecosystems, finding that even if individual skills are safe, their combination can create significant, unad…

The paper introduces SkillReact, a framework that measures compositional risk in agent skill ecosystems, finding that even if individual skills are safe, their combination can create significant, expl…

SkillTrojan introduces a novel backdoor attack targeting the composition of reusable skills in agent systems, demonstrating high attack success rates with minimal impact on normal system functionality…

This study conducts a large-scale empirical analysis of third-party LLM agent skills, identifying that credential leakage is a pervasive, cross-modal issue primarily caused by debug logging and result…