When Do Execution Records Support a Covert-Channel Attribution? An Evidence-Sufficiency Test for LLM Auditors
Drishti Sharma · Team DriWorks
Submitted to AI Collusion Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.
Recovering a hidden message does not establish that an agent’s action depends on the decoded input. We evaluate whether LLM auditors distinguish these propositions using a synthetic benchmark with five programmed mechanisms and four evidence views. Each model evaluates 400 cases grouped into 20 matched blocks, producing separate judgments of target recovery and functional dependence. DeepSeek V4.1 Flash, Qwen3.8 Flash and GPT-OSS-120B produced 798, 765 and 758 correct judgments out of 800, respectively, compared with 566 for an evidence-level-only baseline. Complete execution records yielded 96–100% correctness on dependence. With one policy trial hidden, Qwen made unsupported positive dependence judgments in 8 of 36 ambiguous cases, whereas GPT-OSS made unsupported negative judgments in 6 of 36. Block-based analysis preserves dependencies among matched cases. The benchmark measures evidence sufficiency under explicit rules and trusted records; it does not test discovery of unknown covert channels or establish real-world collusion-detection capability.
Reviews
Good: The use of matched cases with identical observable evidence but different underlying mechanisms is particularly strong. It directly tests whether auditors recognize when evidence is insufficient to establish causal dependence, rather than simply inferring it from matching actions. Separately measuring unsupported positive and negative conclusions also makes the evaluation more informative than aggregate accuracy alone.
Weakness: Because the auditors are given trusted execution records, a limited set of possible mechanisms, and explicit criteria for what constitutes sufficient evidence, much of the task reduces to applying predefined decision rules rather than independently establishing causality. In real investigations, the harder challenge is determining whether evidence is reliable and complete, identifying alternative explanations, and establishing whether an observed action genuinely depended on recovered information when the underlying mechanism is unknown.
Recommendation: Extend the benchmark with controlled evidence failures: remove critical records, introduce contradictory observations, manipulate individual execution logs, and vary whether counterfactual trials actually isolate the claimed cause. Measure whether auditors correctly identify the specific evidentiary gap, request the information needed to resolve it, and revise their conclusions when new evidence becomes available. This would test investigative reasoning and robustness beyond following predefined evidentiary rules.
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Cite this project
@misc{sharma2026execution,
title = {{When Do Execution Records Support a Covert-Channel Attribution? An Evidence-Sufficiency Test for LLM Auditors}},
author = {Drishti Sharma},
year = {2026},
month = oct,
note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/when-do-execution-records-support-a-covertchannel-attribution-an-evidencesufficiency-test-for-llm-auditors-056x}},
url = {https://apartresearch.com/sprints/projects/when-do-execution-records-support-a-covertchannel-attribution-an-evidencesufficiency-test-for-llm-auditors-056x}
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