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.
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@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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