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This work addresses this second threat model by excluding attacker-controllable features and their deterministic descendants from the scored representation, and proves that this yields an exact, pathwise coverage guarantee rather than a probabilistic bound.
This work distinguishes whether stages are dependent from whether an audit sample is large enough to certify that dependence, and gives matching upper and information-theoretic lower sample-complexity bounds, and shows that coarse-to-fine label selection can create near-perfect measured correlation without learned depe...
A decision-contract theory showing error is only reassigned among harmful automation, human deferral, and semantic masking, and an error-conservation law showing error is only reassigned among harmful automation, human deferral, and semantic masking is developed.
Large language models (LLMs) are increasingly explored as network intrusion detection classifiers, but their adversarial robustness under realistic attacker constraints remains unclear. We present a controllability-aware black-box transfer framework for LLM-based network traffic classifiers. The framework partitions fl...
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