arXiv 论文提出 CORSA：面向技能路由的检索与执行双阶段注入攻击优化

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Abstract:AI agents increasingly rely on modular third-party "skills" that are dynamically selected by skill routers to execute complex tasks. While recent studies highlight the threat of prompt injections embedded in these skills, existing evaluations often assume settings where the malicious skill is already selected for execution. We show that this assumption can substantially overestimate attack success. In realistic multi-skill environments, injected skills must first compete for retrieval, reducing the effective attack success rate (ASR) of existing injections by 87-97%. To address this limitation, we introduce CORSA (Cluster Optimization for Router-Aware Skill Attacks), a router-aware attack that optimizes skill injections for both retrieval and execution across clusters of related tasks. We evaluate skill injection attacks under router-managed multi-skill settings by extending the benchmark introduced by SkillRouter with eight malicious payload categories. CORSA uses successive optimization stages to first improve retrieval and then optimize end-to-end attack success, while we evaluate user utility and injection naturalism separately. Our experiments show that CORSA substantially improves both retrieval and end-to-end attack success over existing skill injections while preserving user utility, and that the resulting attacks transfer across different router architectures and LLM backbones.

Comments:	16 pages, 5 figures
Subjects:	Cryptography and Security (cs.CR); Machine Learning (cs.LG)
Cite as:	arXiv:2610.08098 [cs.CR]
	(or arXiv:2610.08098v1 [cs.CR] for this version)
	https://doi.org/10.48550/arXiv.2610.08098 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Haneen Najjar [view email]
[v1] Tue, 6 Oct 2026 10:28:27 UTC (316 KB)