SkillSentry: Adaptive Honey Worlds for Dynamic Safety Testing of Agent Skills
Nizhang Li, Zonghao Ying, Xiangfan Wu, Zonglei Jing, Xixun Lin, Hao Zhang, Wenxin Zhang, Jiaye Lin, Quanchen Zou, Xiangzheng Zhang
cs.CR
Submitted: 2026-08-04
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Detecting Malicious Agent Skills in the Wild using Attention
- Safety Testing LLM Agents at Scale: From Risk Discovery to Evidence-Grounded Verification
- MalSkillBench: A Runtime-Verified Benchmark of Malicious Agent Skills
- One Skill, Two Masters: Serving Users and Attackers through Skill Injection
- Cloak and Detonate: Scanner Evasion and Dynamic Detection of Agent Skill Malware
- SkillJect: Effectively Automating Skill-Based Prompt Injection for Skill-Enabled Agents
- HarmfulSkillBench: How Do Harmful Skills Weaponize Your Agents?
- SkillMutator: Benchmarking and Defending Language-and-Code Cross-modal Attacks on LLM Agent Skills
- Towards Secure Agent Skills: Architecture, Threat Taxonomy, and Security Analysis
- PhantomSkill: Malicious Code Injection in Agent Skill Ecosystems
- "Do Not Mention This to the User": Detecting and Understanding Malicious Agent Skills in the Wild
- Under the Hood of SKILL.md: Semantic Supply-chain Attacks on AI Agent Skill Registry
- Skill-Inject: Measuring Agent Vulnerability to Skill File Attacks
Related papers
- SoK: AI-Augmented Binary Reversing
- Relaxed Sender Anonymity for CBDC Interbank Settlement: A Zero-Knowledge Approach on Permissioned EVM
- Calibration-Family Overfit: Why Trusted Sabotage Monitors Don't Transfer Across Lineages
- Estimating the Social Cost of Corporate Data Breaches
- Efficient Fuzzy PSI under One-Sided Assumptions
- Sealing the Audit-Runtime Gap for LLM Skills