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ADLER is presented, a decoding protocol for streaming LLM APIs in which every message to the cloud depends only on the public view and the answer the user sees, regardless of how the client combines the two models or how the cloud behaves.
Results show that skill-bundled images can induce unauthorized actions even as agents complete legitimate tasks, so task success alone does not establish safe skill use.
PoisonedEvolution is introduced, a trajectory-poisoning attack on self-evolving skill systems that exposes evidence promotion as a security boundary for self-evolving agents.
SKILLTRACE is presented, a multi-trace provenance auditing framework for LLM-agent skill reuse that represents the Operational Trace as a Skill Operational Graph (SOG) that captures activation, procedure, and resource-flow structure.
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