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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.
NHIMG editorial — based on content published by Cotool: NYU CTF Bench Feb 2026 Real CTF challenges from CSAW competitions
By the numbers:
- Claude Opus 4.6 led with 79% accuracy, solving 64 of 81 challenges.
- GPT-5.2 came in next at 63%, with GPT-5.2 Codex close behind at 62%.
Questions worth separating out
A: Look for unmonitored access to decompilers, sandbox images, firmware copies, recovery material, and MCP-connected tooling.
Q: Why do faster AI models often underperform on security investigation tasks?
A: Security investigations reward sustained reasoning, context retention, and careful tool use more than raw response speed.
Q: What breaks when AI governance is built only around approved tools?
A: Tool-only governance fails when employees shift to new or personal AI services faster than policy can update.
Practitioner guidance
- Define the task class before deploying AI Separate summarisation, hypothesis generation, and evidence-driven investigation into different approval tiers.
- Benchmark on full workflows, not prompt snippets Use challenge sets that force the model to read files, reason over artifacts, and produce a final answer under realistic conditions.
- Gate tool-enabled models with least privilege Restrict sandbox commands, file access, and outbound interaction to the minimum required for the task.
What's in the full report
Cotool's full benchmark write-up covers the operational detail this post intentionally leaves for the source:
- Per-model score breakdowns across all 81 tasks, useful if you need to compare performance by workload class
- Methodology notes on sandbox execution, prompt constraints, and error handling that matter when reproducing the benchmark
- Cost and latency tables for each model, which help teams assess trade-offs for security operations use cases
- Category-level challenge composition across reverse engineering, forensics, and miscellaneous tasks
👉 Read Cotool's benchmark analysis of LLM performance on CSAW CTF tasks →
LLM CTF benchmark results: what they mean for security teams?
Explore further
18/08/2026 3:19 pm
Security benchmarks for AI tools need to reflect operational reality, not conversational polish. The benchmark’s design, with isolated files, standard tools, and no hints, mirrors the actual difficulty of investigative work far better than simple question-answer tests. That matters because many enterprise AI evaluations overestimate usefulness when they reward surface-level responsiveness instead of reliable execution. For security teams, the lesson is to treat benchmark design as part of governance, not just methodology.
A question worth separating out:
Q: When should organisations trust AI recommendations in security operations?
A: Organisations should trust AI recommendations only when they can trace the output to a decision, monitor the model in production, and explain the reasoning well enough for audit and incident review. If human reviewers cannot understand the basis for the recommendation, the system is not ready for a trusted control path.
👉 Read our full editorial: LLM benchmark results show accuracy beats speed in CTF tasks
