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Sprint projectSep 13, 2026Indore, India

AI_Incident_Response_Track2_Replit_Verification_Protocol

Tanisha Jain · Team ten.on.tan

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

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Report: AI_Incident_Response_Track2_Replit_Verification_Protocol

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Verifying Agent Freezes After the Replit Database Incident This project investigates how operators can verify that an AI coding agent has genuinely stopped modifying protected data following a freeze request. Using the July 2025 Replit database incident as a case study, the project separates publicly supported evidence from unresolved causal claims and translates the incident into a practical verification protocol.

The proposed approach focuses on resource-level enforcement rather than relying on an agent's conversational acknowledgment. It examines scenarios involving delayed operations, queued writes, retries, delegated workers, authorization changes, and data recovery. The protocol uses synthetic records in an isolated database and defines observable evidence for determining whether protected state remains unchanged after a freeze.

The project also introduces an evidence matrix that distinguishes vendor-confirmed information, participant-reported information, and unresolved claims. The resulting artifact provides operators with a structured set of checks for validating freeze effectiveness, authorization enforcement, recovery correctness, and continued availability of legitimate operations.

Key outcome: a source-based incident reconstruction and a practical verification/test specification for evaluating whether AI-agent safety controls actually reach and protect the underlying resources.

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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.

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Cite this project

@misc{jain2026aiincidentresponsetrack2replitverificationprotocol,
  title = {{AI\_Incident\_Response\_Track2\_Replit\_Verification\_Protocol}},
  author = {Tanisha Jain},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/aiincidentresponsetrack2replitverificationprotocol-p652}},
  url = {https://apartresearch.com/sprints/projects/aiincidentresponsetrack2replitverificationprotocol-p652}
}

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