Summary
Some powerful AI helpers solve complicated tasks by using special skills and hidden ways of thinking that they learn when working. The authors study if a weaker AI controlled by someone else can copy these abilities just by watching the stronger AI do its work, without seeing its secret parts. They found that copying obvious skills isn’t enough because the weaker AI misses important step-by-step behaviors that make the stronger AI successful. Their new method, AgentLeak, looks at where the weaker AI fails compared to the stronger one and learns what behaviors to add, greatly improving the weaker AI’s success. This shows that just protecting secret skills isn’t enough to keep AI abilities private, since watching how the AI acts can reveal important hidden knowledge.
Large Language ModelAI agentskill stealingprocedural knowledgeblack-box attacktask-solving capabilitycapability cloningexecution behaviormodel securitycapability gap
Authors
Xiaoting Lyu, Yuhong Wu, Yufei Han, Shichang Liu, Liang Zhang, Bin Wang, Bin Wang, Xiaobo Ma, Wei Wang
Abstract
Large language model (LLM) agents increasingly achieve long-horizon tasks by combining foundation models with explicit skills and implicit procedural knowledge acquired through execution. The resulting task-solving capabilities have become valuable proprietary assets, raising a new security question: can a substantially weaker attacker-controlled agent acquire the capabilities of a stronger proprietary agent through limited black-box interaction? Existing skill-stealing attacks recover explicit skill artifacts, yet we show that artifact leakage does not necessarily transfer capability: a weaker agent may possess the same skills but still fail because it lacks procedural behaviors implicitly realized by the stronger agent. Our key insight is that the skill execution gap itself forms a leakage surface, where missing behaviors are exposed through observable differences between successful victim executions and failed attacker executions. Based on this, we present AgentLeak, a black-box capability-cloning attack that identifies capability-critical behaviors from these execution differences and incorporates them into attacker-side skills, while keeping the attacker's model, harness, and tools unchanged. Across 20 task scenarios comprising 600 instances, diverse agent systems, and multiple backbone models, AgentLeak improves task pass rates by over 40% compared with direct skill reuse and recovers more than 80% of the victim--attacker capability gap. Our findings reveal a confidentiality risk in LLM agents: protecting explicit artifacts alone is insufficient, as observable execution behavior can leak the procedural knowledge required to reconstruct proprietary task-solving capabilities in low-capability and attacker-controlled agents.