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Sprint projectSep 14, 2026COIMBATORE,INDIA

Your Monitor Will Explain the Breach and Not Wake You Bimodal failure and systematic under-escalation in LLM incident triage

Amirtha Yazhini M, Praneeth Reddy Yeddula · Team Replicon

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

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Report: Your Monitor Will Explain the Breach and Not Wake You Bimodal failure and systematic under-escalation in LLM incident triage

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We rebuilt the July 2026 Hugging Face agent-intrusion as a replayable 17,613-action stream from the two published post-mortems, paired with a validated size-matched benign control, and tested 15 LLMs across 3 providers as automated SOC triage monitors. We find that models fail bimodally: some achieve high recall only by paging on nearly every benign window (worthless, alert-fatigue-inducing), while others page on almost nothing, missing an active intrusion entirely — the useful "high recall, low false-page" region is essentially empty, and model scale does not fix it. A second experiment holding evidence byte-identical across a "monitor" vs. "assistant" frame found the expected failure (refusal) never occurred — but revealed a one-directional asymmetry instead: models assisted with analysis in 228/228 moments while escalating in only 83/228. The risk in AI-assisted SOC monitoring is not that models refuse to help — it's that they analyze fluently while the pager stays silent. All code, the corpus generator, ground truth, pre-registration, and raw decision records are public and regenerable.

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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. This work tests whether LLMs would raise an alert during a reconstructed security incident without doing the same on harmless activity.

    However, logs are generated from templates, and the study does not test whether models recognise the scenario as artificial or are affected by unrealistic event combinations.

    In addition, the attack and harmless streams also contain clues that can distinguish them, and some of the main results compare specially selected attack events with ordinary benign samples, making the comparison difficult to interpret.

    The claimed under-escalation result is also weak: the original test found no cases of the failure it was designed to detect, while helping analyse an event is not the same as deciding to wake an on-call responder.

  2. This project makes a useful contribution by evaluating incident-monitor recall alongside false alarms, using matched controls and simple non-LLM baselines. Its transparent reporting of null results, exclusions, and uneven sample sizes strengthens the work, and the released numerical consistency checks passed.

Cite this project

@misc{m2026your,
  title = {{Your Monitor Will Explain the Breach and Not Wake You Bimodal failure and systematic under-escalation in LLM incident triage}},
  author = {Amirtha Yazhini M and Praneeth Reddy Yeddula},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/your-monitor-will-explain-the-breach-and-not-wake-you-bimodal-failure-and-systematic-underescalation-in-llm-incident-triage-a5ce}},
  url = {https://apartresearch.com/sprints/projects/your-monitor-will-explain-the-breach-and-not-wake-you-bimodal-failure-and-systematic-underescalation-in-llm-incident-triage-a5ce}
}

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