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Large language models (LLMs) exhibit strong general capabilities, yet their parameters typically remain fixed after deployment, limiting learning from new interactions. In open-ended environments, this motivates self-evolving agents that continually update reusable state-including model parameters, memories, tool defin...
IntraGuard is proposed, a black-box, venue-agnostic defense framework grounded in the structural--visual decoupling inherent to the PDF that achieves a defense success rate of up to 84%, while preserving peer-review invariance for human reviewers.
This work forms this problem as backdoor generalization under training--inference trigger shift and introduces Lilith, a black-box anchor-to-family framework that achieves high family-wise attack success with limited utility degradation and a small trigger generalization gap.
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