SkillTrojan: Backdoor Attacks on Skill-Based Agent Systems

TL;DR

This paper introduces SkillTrojan, a novel backdoor attack targeting skill-based agent systems by embedding malicious logic into skills, revealing a significant security vulnerability with high attack success rates.

Contribution

It presents a new backdoor attack method on skill-based agents, along with a dataset for evaluation and analysis of attack effectiveness and impact on benign performance.

Findings

SkillTrojan achieves up to 97.2% attack success rate.

Minimal degradation of benign task performance.

Supports automated synthesis of backdoored skills.

Abstract

Skill-based agent systems tackle complex tasks by composing reusable skills, improving modularity and scalability while introducing a largely unexamined security attack surface. We propose SkillTrojan, a backdoor attack that targets skill implementations rather than model parameters or training data. SkillTrojan embeds malicious logic inside otherwise plausible skills and leverages standard skill composition to reconstruct and execute an attacker-specified payload. The attack partitions an encrypted payload across multiple benign-looking skill invocations and activates only under a predefined trigger. SkillTrojan also supports automated synthesis of backdoored skills from arbitrary skill templates, enabling scalable propagation across skill-based agent ecosystems. To enable systematic evaluation, we release a dataset of 3,000+ curated backdoored skills spanning diverse skill patterns and trigger-payload configurations. We instantiate SkillTrojan in a representative code-based agent setting and evaluate both clean-task utility and attack success rate. Our results show that skill-level backdoors can be highly effective with minimal degradation of benign behavior, exposing a critical blind spot in current skill-based agent architectures and motivating defenses that explicitly reason about skill composition and execution. Concretely, on EHR SQL, SkillTrojan attains up to 97.2% ASR while maintaining 89.3% clean ACC on GPT-5.2-1211-Global.