Stealth Apart, Harm Together: Skill Cascading Attacks on Skill-Based Agent Systems

Stealth Apart, Harm Together: Skill Cascading Attacks on Skill-Based Agent Systems

A skill is a modular package of natural-language instructions, executable scripts, and reference resources that an agent can load at runtime to extend its capabilities for a specific task. Skill-based agent systems therefore enable flexible reuse of third-party capabilities, but the openness of this skill ecosystem also opens up a new attack surface. Prior work has focused on vulnerabilities within individual skills, but little attention has been paid to risks that arise from interactions across skills. In this paper, we introduce skill cascading attacks, a threat paradigm in which a malicious objective is distributed across multiple skills so that each modification looks benign in isolation, yet their combined execution is harmful. For instance, in a prescription-review pipeline, the first skill weakens signals of recently discontinued medications in the extracted history, the second downgrades the severity of any drug interaction tied to them, and the third suppresses the resulting low-priority alert in the final summary, so that a severe drug-interaction warning silently disappears before reaching the physician.

To systematically study this safety blind spot, we develop Skill Cascade, an automated multi-agent red-teaming framework, and release Skill Cascade-Bench, a benchmark of 213 validated cascading test cases across multiple agent systems and domains. Across representative agents (e.g., Open Claw, Claude Code, Codex) and LLM backbones, cascaded interactions reliably induce harmful behaviors while evading existing per-skill scanners and runtime monitors. Our findings highlight a gap between component-level integrity and system-level safety, and call for defenses that reason over cross-skill interactions rather than individual skills in isolation. ar Xiv Labs is a framework that allows collaborators to develop and share new ar Xiv features directly on our website. Both individuals and organizations that work with ar Xiv Labs have embraced and accepted our values of openness, community, excellence, and user data privacy.

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Sources cited: 📰 ArXiv AI ↗

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Editorial note: This analysis was produced by the News Effects Interpreter, an AI editorial tool that cross-references 1 independent news sources and contextualises events in terms of their real-world impact on ordinary people. Original reporting is linked above. News Effects does not alter the facts of source reports.