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Sprint projectSep 13, 2026Dhaka

False Containment: Measuring the Evidence Required to Verify Autonomous AI Incident Resolution

Daud Ibrahim, Govardhan Reddy, Soumya Parthasarathy, Disha Singha , Taemin Park · Team 906

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

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Report: False Containment: Measuring the Evidence Required to Verify Autonomous AI Incident Resolution

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We study false containment: cases where an autonomous incident responder appears to have resolved an incident while the underlying compromise remains operational. We introduce a controlled matched-pair benchmark across four persistence families, using deterministic simulator state and predefined security postconditions as ground truth. Two response agents produced 16 trajectories, from which we generated 64 cumulative evidence packets and collected 192 blinded verifier judgments. We find that claims, remediation logs, and neutral telemetry can still produce confident errors, while stronger mechanism-specific verification substantially improves closure judgments. Our results suggest that incident resolution is not only an evidence-volume problem, but an evidence-scope problem: verification must test the security-relevant capability that could sustain or recreate the compromise.

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How much would this matter for AI safety if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical AI safety problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.

  1. Great question: if an AI says "I fixed it," how do you know it's true? They show a claim alone isn't enough proof - you need a real check. Well done, honest paper. Only note: no mention of what AI tools they used to write it.

  2. The proposed benchmark provides a useful proof-of-concept for evaluating false containment.

    However, the methodology explaining how it could be systematically expanded beyond the current synthetic cases is missing.

    In addition, its positioning relative to existing incident-response benchmarks is unclear.

  3. This paper introduces a controlled benchmark for verifying incident closure when apparent remediation may leave residual access intact. Across four evidence levels, direct operational checks substantially improve verifier coverage and correctness in the tested cases. Its strongest contribution is a structured way to investigate what evidence justifies declaring an incident resolved.

    Strength: The paper makes an important safety problem measurable: apparent remediation can leave the capability for continued compromise intact. Matched cases, deterministic ground truth, and explicit abstention provide a useful foundation for studying trustworthy incident closure.

    Recommendation:

    - Clarify whether the main contribution is better evidence or better verification reasoning. E4 gives the model a direct operational check, which may explain much of its improvement. Comparing the model with a simple rule using the same check would clarify what the verifier adds and where further research should focus—obtaining the right evidence, interpreting it, or selecting additional checks.

    - Develop the scope finding into a closure safeguard. The alternate-path cases show that passing one check can leave another route active. Test whether an explicit set of required security postconditions helps the verifier withhold closure when evidence is incomplete. This would connect the benchmark's central insight to a concrete way of preventing premature incident resolution.

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Cite this project

@misc{ibrahim2026false,
  title = {{False Containment: Measuring the Evidence Required to Verify Autonomous AI Incident Resolution}},
  author = {Daud Ibrahim and Govardhan Reddy and Soumya Parthasarathy and Disha Singha and Taemin Park},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/false-containment-measuring-the-evidence-required-to-verify-autonomous-ai-incident-resolution-n5i5}},
  url = {https://apartresearch.com/sprints/projects/false-containment-measuring-the-evidence-required-to-verify-autonomous-ai-incident-resolution-n5i5}
}

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