Chaining Skills to Hijack LLM Agents
Quick summary
arXiv:2610.01564v1 Announce Type: cross Abstract: LLM agents use skills to improve performance on specialized tasks. To complete a user request, an agent may invoke several skills in sequence, allowing information produced under one skill to guide the next. Because skills may come from open-source repositories, this handoff can also carry attacker-controlled claims into later decisions. In this paper, we introduce APEX, which constructs and refines adversarial skill chains tailored to a user task and an attacker-selected action. The key insight is that an agent-written record of genuine task p
Key takeaways
- arXiv:2610.01564v1 Announce Type: cross Abstract: LLM agents use skills to improve performance on specialized tasks.
- To complete a user request, an agent may invoke several skills in sequence, allowing information produced under one skill to guide the next.
- Because skills may come from open-source repositories, this handoff can also carry attacker-controlled claims into later decisions.
Why it matters
“Chaining Skills to Hijack LLM Agents” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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