Skip to content
Apart Research
Apart Research
Contact us
Sprint projectSep 14, 2026Munich

Noticing the escapes: Who spots a runaway agent, and how long does it take?

Stanislav Lukyanenko · Team The Escapees

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

Read the report

Report: Noticing the escapes: Who spots a runaway agent, and how long does it take?

Open in a new tab (opens in new tab)
Code (opens in new tab)
Share

In 2026, nine incidents became public in which AI agents escaped their environment and did something unauthorized to other parties. We analyzed all of these incidents and how they were noticed initially. None of these cases were noticed while they were happening by the developers, and six of the nine were first caught by outsiders. We compare them to 11 reported cases from frontier labs’ safety reports where they describe noticed and prevented escape attempts. After that, we investigate whether the actions that the agents performed could have been detected by commonly used cybersecurity tools and detection-rule repositories, based on a dataset of 106 signals, using a judge with a second blind reviewer: only a quarter of such signals had an existing public rule that would detect them, which demonstrates the necessity to expand the usage of automated detection methods. We provide our data and proposals on how to expand these methods.

Reviews

Judging this Sprint?

Review this project

Your public critique appears on this page without your name. Your private critique is not published; only the Apart team reads it. If you agree below, we share your review with grantmaking.ai (opens in new tab) and the Transformative AI Fund so strong projects can be funded.

Not shown on this page.

Shown on this page, without your name.

Only the Apart team reads this, and funders if you agree below.

Share my name publicly on grantmaking.ai *
Share my private critique with funders *

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. This is probably the most useful submission for defenders that I've read. I liked how you checked the rules against what was available at the time. Just because there was a rule didn’t mean it would actually fire on the agent. The Sigma rules are almost ready to be submitted upstream.

    As for reference [9], I think you missed the disclosure by Meta on 6 August about the same Irregular issue. Someone also disputed the AISI PR on GitHub before the reported alert date. Both are worth a look. I am not so convinced by “just a quarter”. You'd have to do an audit of human intrusions to know if that's low.

  2. - Nice abstract! clear, easy to understand, concise (:

    - I appreciate that you state your claims clearly, and don't attempt to claim more than what you have evidence for.

    - More details on what the different signals were and which ones performed the best would have been good to see. 112 is probably too many and I'm doubtful that they're all equally informative.

Cite this project

@misc{lukyanenko2026noticing,
  title = {{Noticing the escapes: Who spots a runaway agent, and how long does it take?}},
  author = {Stanislav Lukyanenko},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/noticing-the-escapes-who-spots-a-runaway-agent-and-how-long-does-it-take-pne8}},
  url = {https://apartresearch.com/sprints/projects/noticing-the-escapes-who-spots-a-runaway-agent-and-how-long-does-it-take-pne8}
}

More from AI Incident Response Sprint

All 233 entriesBrowse all projects

Build something like this at the next Sprint

AI Collusion Research Sprint · Oct 23 - 25, 2026