Autonomous security agents operate as staged pipelines, such as classifying network traffic and then attributing attacks to a specific technique. Split conformal prediction gives each stage finite-sample coverage, but deployment requires a trajectory-level guarantee across the full chain. These guarantees do not compose automatically when stages are independently trained and calibrated. Bonferroni allocation is distribution-free but conservative under correlated errors. We show that a natural pairwise-correlation extension to three or more stages is invalid because it gives a lower rather than an upper bound, and derive a valid spanning-tree alternative. We distinguish whether stages are dependent from whether an audit sample is large enough to certify that dependence, and give matching upper and information-theoretic lower sample-complexity bounds. We also show that coarse-to-fine label selection can create near-perfect measured correlation without learned dependence. On a two-stage intrusion-detection pipeline across 6 open LLMs and 2 datasets, removing this artifact reduces measured correlation from near 1 to 0-0.78. A direct audit of trajectory failure becomes 13.7% tighter than Bonferroni once the audit reaches the required sample size, but is worse when undersized. A modular certificate using per-stage certificates and a pairwise overlap bound yields a positive average gain of 0.6%, quantifying the cost of lacking joint access. Same-model, cross-model, and permuted-pairing tests show that residual dependence reflects shared sample difficulty, not shared model representations. Average trajectory coverage across 12 configurations is 92.7% +/- 2.4% at alpha = 0.10. Under cross-dataset deployment, single-step miscoverage reaches 100% even when accuracy remains 78%, showing that distribution shift destroys calibrated confidence before raw accuracy.
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 flow features into directly controllable (DC), indirectly controllable (IC), and uncontrollable (UC) groups according to network communication semantics, then restricts perturbations to DC features while freezing IC/UC features. Using a shared XGBoost surrogate, we generate finite-difference PGD, greedy coordinate-wise, and NES adversarial examples and transfer them to seven LLM targets and two conventional ML targets across five IDS benchmarks from 1999 to 2022. Across 27 valid LLM configurations and over 500,000 adversarial examples, we find that LLM transfer vulnerability is substantial but dataset- and comparator-dependent. Compared with LightGBM, LLMs are more vulnerable on RT-IoT2022 and CIC-IDS-2018, comparable on NSL-KDD and UNSW-NB15, and less vulnerable on HIKARI-2021; compared with the averaged ML baseline, LLMs show higher ASR on all five datasets. We further observe a consistent cross-architecture transfer hierarchy: gradient- and score-based perturbations transfer more effectively than greedy perturbations across all 27 LLM cells and 9/10 ML cells. Cross-surrogate validation with tree, neural, and linear surrogates yields similar LLM ASR, reducing evidence that the findings are XGBoost-specific. Constraint violation rate is 0\% by construction.