Topic starter
18/08/2026 2:32 pm
TL;DR: NYU CTF Bench tested 11 models across 81 real CSAW challenges and found that Claude Opus 4.6 led at 79% accuracy, while GPT-5.2 balanced 63% accuracy with better cost efficiency and several faster models lagged badly, according to Cotool. For practitioners, the lesson is that multi-step security analysis still depends more on reasoning depth and reliability than raw speed or low per-task cost.
Editorial analysis by NHI Mgmt Group, based on content published by Cotool: “NYU CTF Bench”.
Key questions
Q: How should security teams evaluate LLMs for multi-step security analysis?
A: Use real tasks that require chained reasoning, such as reverse engineering or forensics, and judge the model on task completion as well as correctness.
Q: Why can a faster model be the wrong choice for security workflows?
A: Speed only helps if the model can still solve the task.
Q: What should teams measure before trusting an LLM in investigative work?
A: Measure accuracy, completion rate, and the amount of human correction the workflow still needs.
Practitioner guidance
- Evaluate models on real security task classes Test candidate models on reverse engineering, forensics, and problem-solving tasks that resemble your actual workload, not on generic chat prompts.
- Prioritise completion reliability over raw speed Measure whether a model can finish the full workflow cleanly, because incomplete runs create hidden analyst overhead even when the model appears fast.
- Separate cost efficiency from task effectiveness Compare cost per task only after confirming that the model solves enough of the benchmark to justify its operating role.
Bottom line: CTF-style benchmarks show that security work rewards sustained reasoning and task completion more than raw speed.
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18/08/2026 3:19 pm
Reasoning depth is the decisive variable in security-adjacent LLM use. Benchmarks built on real challenges expose a different failure mode from ordinary chat tests: the model must keep state, follow evidence, and finish the task. That makes raw speed a weak proxy for usefulness in security operations. Practitioners should treat depth of reasoning as the primary selection criterion for investigative workloads.
A question worth separating out:
Q: What is the difference between model speed and operational reliability in AI security use cases?
A: Speed is how quickly the model produces an answer, while operational reliability is whether it can complete the full task repeatedly without errors or human rescue. Security teams should care more about the second metric when the model is being used for investigation, triage, or other decision-support work.
👉 Read our full editorial: LLM benchmark results show accuracy beats speed in CTF tasks