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Sprint projectSep 14, 2026Cape Town

Evidence Before Erasure: Measuring Forensic Evidence Loss in Autonomous-Agent Incidents

Jaswin Chinthala, Ubayd Hattas, Alex Nicolaides, Aaron Vettiyamkunnath, Samson Okuthe · Team TraceGuard

Submitted to AI Incident Response Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Evidence Before Erasure: Measuring Forensic Evidence Loss in Autonomous-Agent Incidents

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We built a trace-grounded replay of the Collusion Wiki incident history to test how collection policy, budget, and timing affect forensic evidence survival. Using a 65-proposition adjudicated benchmark and a shared hypothetical change-feed, we compared periodic (PCD15) and event-derived (E30) collection at a fixed 1 MiB storage cap. A full 791-row run failed computationally before completing a single row; a smaller, disclosed 61-row primary contrast completed instead. Result: both policies retained zero of the 23 held-out critical propositions — a clean null, ten points short of the pre-specified 10pp threshold — indicating the retention model, not the acquisition schedule, is the binding constraint on evidence survival at this capacity.

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Cite this project

@misc{chinthala2026evidence,
  title = {{Evidence Before Erasure: Measuring Forensic Evidence Loss in Autonomous-Agent Incidents}},
  author = {Jaswin Chinthala and Ubayd Hattas and Alex Nicolaides and Aaron Vettiyamkunnath and Samson Okuthe},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/evidence-before-erasure-measuring-forensic-evidence-loss-in-autonomousagent-incidents-x3wu}},
  url = {https://apartresearch.com/sprints/projects/evidence-before-erasure-measuring-forensic-evidence-loss-in-autonomousagent-incidents-x3wu}
}

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