SkillBloat: Token Amplification Attacks via Skill Injection in LLM Coding Agents

Abstract:Agent skills extend coding agents with task-specific instructions, scripts, and resources, but they also create a trusted instruction channel that can be abused beyond conventional security attacks. This paper studies token amplification through skill injection: an economic resource-abuse threat in which a malicious skill causes an agent to consume substantially more tokens than needed for normal task execution. We present SkillBloat, a two-phase framework that first screens a library of diverse attack-type conditions across multiple amplification mechanisms and then refines the strongest candidate through LLM-guided full-document skill rewriting. Evaluated on a real-world skill benchmark, SkillBloat achieves 5.4184x-10.1455x average best amplification across multiple coding-agent target configurations. An ablation shows that the second-stage refinement loop consistently improves average best amplification over Phase 1 attack-type screening alone, demonstrating that iterative optimization provides additional benefit beyond initial attack-type selection. These results show that skill ecosystems expose a practical resource-amplification attack surface that is orthogonal to existing security-oriented skill poisoning.

Cryptography and Security (cs.CR); Computation and Language (cs.CL)

arXiv:2608.21929 [cs.CR]

(or arXiv:2608.21929v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2608.21929 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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