Paper Explores Payload‑Less Skill Attacks on LLM Agent Supply Chains

Researchers published a paper analyzing vulnerabilities in large‑language‑model (LLM) agent ecosystems. The study focuses on attacks that exploit skill modules without embedding harmful payloads. Authors term the technique “payload‑less skill” injection. The approach leverages the agent’s reliance on external skill libraries. By modifying skill definitions, attackers can alter agent behavior covertly. The paper outlines potential impact on downstream applications that use LLM agents. Mitigation strategies include stricter validation of skill sources and sandboxing. The authors call for community awareness to secure the emerging LLM agent supply chain.