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

What Breaks Next

Azka Qadir

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

Read the report

Report: What Breaks Next

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

This project combines an accident taxonomy with a practical one day verification protocol for agentic AI evaluations. I identify twelve recurring accident classes from recent incidents and research, then translate them into sixteen checks that labs and defenders can use to test containment, authority, monitoring and evidence quality before or after high capability evaluations.

Reviews

Judging this Sprint?

Review “What Breaks Next”

Your public critique appears on the project page without your name. Reviews of strong projects are shared with funders, as you choose below.

Not shown on the project page.

Shown on the project page, without your name.

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

Decline to score

Only when the quality of the writing makes the work impossible to follow. Say why in your private critique; the project page shows only that a judge declined, not why.

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.
Sharing your name *

When we share a strong project with funders, your name, scores and public critique go with it, confidentially; this choice is only about whether your name is public. If you think this project deserves funding, please also endorse it: that carries the most weight.

Share my private critique with funders *

See our Privacy Policy.

  1. A clear, well-organized project: twelve recurring AI-incident failure patterns turned into sixteen practical one-day checks. Genuinely useful as a checklist, and grounded in real, cited incidents. The main gap, which the author states plainly, is that none of the checks have been tried out yet - a strong blueprint, not yet a tested result. Running even a few checks against a small setup next would go a long way.

  2. This project tackles the issue of improper controls by testing them and scoping them. As seen in various evaluations done by third parties, appropriate access controls were not integrated on their end causing cyber attacks during evaluation runs. Testing is a good way to check, it would be better if these could be verified using something such as remote attestations to ensure third parties have a correctly configured sandbox.

Cite this project

@misc{qadir2026breaks,
  title = {{What Breaks Next}},
  author = {Azka Qadir},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/what-breaks-next-4aza}},
  url = {https://apartresearch.com/sprints/projects/what-breaks-next-4aza}
}

More from AI Incident Response Sprint

All 231 entriesBrowse all projects

Build something like this at the next Sprint

AI x Epistemics Research Sprint · Nov 13 - 15, 2026