arXiv AI
SkillScreen: High-Recall Screening of Malicious Agent Skills with Decision Models
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arXiv AI
Oct 1
Pretext: Defeating Malicious Skill Detection Frameworks for AI Agents
The paper titled "Pretext: Defeating Malicious Skill Detection Frameworks for AI Agents" demonstrates how an attacker can bypass current skill‑scanning defenses by crafting malicious skills that evade both static analysis and LLM‑based semantic checks. By moving malicious payloads into natural language and distributing instructions across files, the white‑box attacker named Pretext achieves high evasion rates—up to 97% against a frozen detector and 77% against a co‑adaptive one—across three open‑source models.
arXiv AI
Aug 21
MaliciousSkillBench: A Comprehensive Benchmark for Malicious Agent Skill Detection
arXiv:2608. 19901v1 Announce Type: cross Abstract: Agent Skills extend LLM agents with reusable instruction packages that may also include scripts, resources, and service configuration.
arXiv AI
Jul 28
SkillSieve: A Hierarchical Triage Framework for Detecting Malicious AI Agent Skills
arXiv:2604. 06550v3 Announce Type: replace-cross Abstract: Agent skills combine natural-language instructions with executable code while inheriting an agent's filesystem, credential, and network access.
arXiv AI
Sep 30
SKILLLITE: Evidence-Guided Malicious Skill Auditing with Compact LLMs
arXiv:2609.36879v1 Announce Type: cross Abstract: As LLM-based agents perform increasingly complex tasks, Agent Skills have emerged as a flexible mechanism for extending their capabilities. An Agent...
Hugging Face Trending Papers
Sep 30
Pretext: Defeating Malicious Skill Detection Frameworks for AI Agents
Skills extend an agent's capabilities by injecting instructions and information into the context, and are widely used by agents such as OpenClaw and Claude Code. Prior work shows third-party marketpla...
arXiv AI
Aug 11
SkillsMetric: Mapping the Detection Boundary of Static Analysis for Malicious Agent Skills
arXiv:2608. 08468v1 Announce Type: cross Abstract: Agent Skills---structured packages of instructions and scripts that augment LLM-based agents---are rapidly proliferating, yet their security properties remain under-explored.