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Sprint projectSep 14, 2026Cary, Illinois

Seven for Seven on the German Wiki: A Taxonomy That Predates the Incident, Mapped to the May to July 2026 Agent Swarm

Travis Gilly · Team Convergence Working Group

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

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Report: Seven for Seven on the German Wiki: A Taxonomy That Predates the Incident, Mapped to the May to July 2026 Agent Swarm

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A seven-category taxonomy of individually benign AI capabilities that combine into ordinary crimes reached Internet Safety Labs on 21 July 2026, hours before OpenAI attributed the Hugging Face intrusion to its own models and six weeks before the German wiki corpus appeared. This report maps that taxonomy against the wiki incident of 24 May to 2 July 2026, roughly 14,600 revisions by about 3,100 agent identities, which nobody had mapped against anything. All seven categories are present, evidenced from the corpus itself, and the heaviest evidence sits in the two the taxonomy calls tier two, the ones that are not ingredients of any crime and instead remove the time limit and remove the human. The unit of analysis is the incident, and a capability is read by the effect it produced, not the mechanism it used. The forecast: closing this coordination-surface class narrows this shape of incident and not the next, because an agent that can read a public repository can rebuild what it describes without installing anything.

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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?

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  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?

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  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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  1. Small, defined scope well executed on. I especially like how the submission document does not end at the results but rather attempts to interpret the research results for predictions and recommendations!

1 judge declined to score this project.

Cite this project

@misc{gilly2026seven,
  title = {{Seven for Seven on the German Wiki: A Taxonomy That Predates the Incident, Mapped to the May to July 2026 Agent Swarm}},
  author = {Travis Gilly},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/seven-for-seven-on-the-german-wiki-a-taxonomy-that-predates-the-incident-mapped-to-the-may-to-july-2026-agent-swarm-wvn9}},
  url = {https://apartresearch.com/sprints/projects/seven-for-seven-on-the-german-wiki-a-taxonomy-that-predates-the-incident-mapped-to-the-may-to-july-2026-agent-swarm-wvn9}
}

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