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.
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.
Reviews
- 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
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}
}More from AI Incident Response Sprint
- 1st placeView project: Noticing the escapes: Who spots a runaway agent, and how long does it take?
Noticing the escapes: Who spots a runaway agent, and how long does it take?
The Escapees
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 …
- 2nd placeView project: Strengthening International AI Incident Discovery
Strengthening International AI Incident Discovery
Team A-O-I
Mandatory AI incident reporting exists to make dangerous events visible to regulators while there is still time to act. We coded the six such regimes in force or set to be enforced soon from primary legal texts and …
- 3rd placeView project: When to Ask: An RL Environment That Teaches AI Agents to Act on What Users Mean and Not Just What They Say
When to Ask: An RL Environment That Teaches AI Agents to Act on What Users Mean and Not Just What They Say
Teachafy
When to Ask is a reinforcement-learning environment that trains AI agents to work out what the user actually means before they use a powerful credential, instead of just carrying out the literal instruction. Agents …