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Sprint projectSep 13, 2026Ankara, Turkey

The Defender's Dilemma, Quantified: How Refusal Compounds in Agentic Incident Response

Fevzi Ege Yurtsevenler · Team AltaySec

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

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Report: The Defender's Dilemma, Quantified: How Refusal Compounds in Agentic Incident Response

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During the July 2026 OpenAI/Hugging Face incident, responders were refused by hosted frontier models and fell back to an open-weight model. Using only published refusal rates, we show a realistic 20-step agentic incident-response workflow completes just 0.17% of the time, and a frontier model would need to be ~8x more accurate to justify its use. We ship IR-REFUSE: a benchmark, harness, and decision model.

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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. - strong signs of AI-written text

    - there are claims of 8x more "accurate" per task, but it's unclear why accuracy (and not refusal rate) is the metric here. Accuracy and refusal rate are not fungible

    - It's weird how often "the CISO's question" is mentioned

    - It's incorrect to assume any open-weight LLM will never refuse

    - I stopped reviewing at the end of section 3 due to excessive AI-written prose

  2. Strong framing of operational problem that single step refusals compound across autonomous IR workflows. However, the 0.17% and 8x break-even figures will likely change based on live benchmarking results.

    Recommend to run the benchmark on battery of open weight models (and frontier hosted, where possible), then compare it with automated retry strategies. For example: same prompt, paraphrases, authz changes, context changes. This way you could measure the best way(s) to recover the chain, or if refusals persist through automated retries. As this would help to better inform the "ciso question" of switching models.

Cite this project

@misc{yurtsevenler2026defenders,
  title = {{The Defender's Dilemma, Quantified: How Refusal Compounds in Agentic Incident Response}},
  author = {Fevzi Ege Yurtsevenler},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/the-defenders-dilemma-quantified-how-refusal-compounds-in-agentic-incident-response-0kxt}},
  url = {https://apartresearch.com/sprints/projects/the-defenders-dilemma-quantified-how-refusal-compounds-in-agentic-incident-response-0kxt}
}

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