arXiv cs.CL· Jerrold Huang, Junwei Quan, Saiyue Lyu, Zihan Guo, Yingqiang Ge·· 3 天前AI 评分26
SkillScreen：用决策模型高召回筛查恶意 Agent 技能
SkillScreen: High-Recall Screening of Malicious Agent Skills with Decision Models
AI 导读
SkillScreen 是一个紧凑决策模型，单次前向传播即可从 SKILL.md 直接为技能恶意性打分。在 MaliciousSkillBench 上，它对未见攻击家族实现 99% 召回，仅误报 20-25% 良性技能，明显少于词级分类器和提示词 LLM 评判。但面对陌生的良性来源，误报会急剧上升，说明识别新攻击并不等于能可靠筛查新的技能集合。
正文 · 原文
Abstract:Agent skills turn natural-language instructions into actions, creating a security challenge: legitimate automation and malicious behaviour can look deceptively similar. A practical screen must catch nearly every attack while keeping the review load manageable. We present SkillScreen, a compact decision model trained to score skill maliciousness directly from SKILL$.$md in a single forward pass. On MaliciousSkillBench, SkillScreen flags 20-25% of benign skills at 99% recall on unseen attack families, substantially fewer than the word-level classifier and prompted LLM judges tested. Under the same training protocol, it also generalizes better than a fully fine-tuned encoder to unseen sources. Yet unfamiliar benign sources sharply increase false positives, showing that recognizing new attacks does not ensure reliable screening of new skill collections. Our results demonstrate the value of task-trained decision models for high-recall skill screening. Project page: this https URL
| Comments: | 21 pages, including references and appendices |
| Subjects: | Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.17727 [cs.CR] |
| (or arXiv:2610.17727v2 [cs.CR] for this version) | |
| https://doi.org/10.48550/arXiv.2610.17727 arXiv-issued DOI via DataCite |
Submission history
From: Jerrold Huang [view email]
[v1]
Tue, 13 Oct 2026 05:03:43 UTC (639 KB)
[v2]
Wed, 14 Oct 2026 04:56:03 UTC (639 KB)
来源：arXiv cs.CL · arxiv.org