LLM benchmark results show accuracy beats speed in CTF tasks

At a glance

What this is: Cotool’s benchmark of 11 models on 81 CSAW CTF challenges found that the strongest models solved real multi-step security tasks far more reliably than faster, cheaper alternatives.

Why it matters: This matters because security teams evaluating AI for forensics, reverse engineering, and incident support need to understand where model speed stops helping and reasoning quality starts determining operational value.

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%.
• Gemini 3.0 Flash offered the best value at about $0.25 per task for 54% accuracy.

👉 Read Cotool's benchmark analysis of LLM performance on CSAW CTF tasks

Context

Benchmarks for security-oriented AI only matter when they measure end-to-end problem solving rather than isolated prompts. In this case, the primary question is not whether a model can answer a security question, but whether it can work through a realistic challenge, preserve context, and finish with a correct result. That is directly relevant to how teams should think about AI support for incident analysis, forensics, and reverse engineering.

The identity angle is indirect but real: as security workflows absorb AI assistance, the practical control question becomes whether the model can be trusted to interpret evidence, not just generate text. For NHI and agentic AI programmes, this is a reminder that delegated analysis still needs bounded access, reviewable outputs, and clear accountability. The starting position is typical of early AI evaluation work, where performance claims often collapse once tasks become more operationally demanding.

Key questions

Q: How do security teams know if AI-assisted reverse engineering is becoming a risk in their environment?

A: Look for unmonitored access to decompilers, sandbox images, firmware copies, recovery material, and MCP-connected tooling. If those resources are broadly available, an AI agent can assemble a decryption path or exposure map even when no single source looks dangerous on its own. The signal is tool breadth plus sensitive artefact proximity.

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. A faster model may terminate early, miss a transformation, or fail to connect evidence across steps. In practice, the useful model is the one that produces a correct outcome with enough traceability to support review.

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. It also misses the bigger issue that the same sensitive data can travel through multiple interfaces. Without data-aware enforcement, organisations end up policing names of tools instead of controlling exposure.

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.

Technical breakdown

Why end-to-end CTF evaluation is harder than prompt answering

A capture-the-flag benchmark measures whether a model can chain analysis steps across a full task, not just produce a plausible explanation. Here that means reading challenge files, using tools in a sandbox, testing hypotheses, and refining output until the flag is correct. That differs from ordinary Q&A because the model must maintain state, resist dead ends, and handle sparse hints. For security work, this is closer to real investigation than to chat-style summarisation, which is why accuracy becomes a stronger signal than fluency.

Practical implication: teams should test AI against multi-step workflows, not isolated prompts, before relying on it for analysis support.

Why accuracy, not latency, is the governing metric in security tasks

In investigative work, the value of speed depends on whether the answer is right. A fast model that exits early or misses a critical transformation may be cheaper, but it does not reduce analyst burden if the result cannot be trusted. The benchmark shows that slower models often spent more time reasoning through the problem and earned materially better solve rates. That pattern matters in security operations because many tasks have high consequence and low tolerance for partial correctness, especially in forensics and reverse engineering.

Practical implication: judge model value by task completion and correctness first, then optimise for cost and speed.

What tool-enabled sandboxing changes for AI security workflows

The benchmark gives each model a Debian Linux sandbox with standard tools, which is closer to a controlled agentic workflow than a plain language test. That setup matters because once a model can invoke tools, the security question shifts from text generation to action selection, file handling, and evidence interpretation. In governance terms, the model behaves more like a constrained AI agent than a simple chatbot. That raises familiar controls around privilege, auditability, and output validation, particularly when the output feeds incident response or vulnerability analysis.

Practical implication: wrap tool-using AI in least-privilege, logging, and review controls before it touches sensitive security evidence.

NHI Mgmt Group analysis

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.

Reasoning depth is the named concept this benchmark exposes. The gap between the strongest frontier models and lower-performing systems shows that multi-step security analysis depends on sustained reasoning, not just model size or response speed. In practice, this means organisations should not assume any AI system that can summarise a log can also work through an intrusion chain or forensic artifact. The practitioner conclusion is to qualify AI by task class, not by general capability claims.

Tool access turns the model into a governed security actor, even when no autonomy is intended. Once a model can read files, issue commands, and iterate on results, the control problem shifts toward identity, authorisation, and traceability. That intersects with NHI governance because the model’s runtime permissions, not its natural-language interface, determine risk. Security leaders should evaluate these systems the way they evaluate other privileged non-human workloads.

Cost efficiency without solve rate is a false economy in security operations. The benchmark shows that a cheap model can look attractive while failing most tasks, which is especially dangerous in incident support where missed context costs more than token spend. The field should stop treating cost per task as a standalone success metric and pair it with completion and correctness. Practitioners should align AI adoption with measurable task outcomes, not budget optics.

This benchmark supports a split between assistive AI and decision-bearing AI. Models that can help an analyst explore a problem are not automatically fit to make or execute security decisions. That distinction matters for governance, because the more a model’s output influences containment, prioritisation, or triage, the more it needs evidence handling, human review, and explicit accountability. Teams should classify AI by decision impact before expanding use.

What this signals

Reasoning quality is now a governance issue, not just a benchmark topic. If organisations are going to let AI assist with forensics or reverse engineering, they need to classify it as a controlled capability with clear ownership, logging, and review. The control lesson is simple: capability without traceability becomes operational debt very quickly.

The next stage of AI adoption in security will be less about using a model and more about governing a workflow that includes evidence, tools, and privileged runtime actions. That is where NHI-style controls become relevant, because the system needs identity, scope, and auditability even when the underlying model is not autonomous. Teams should design for reviewable output chains before they expand use.

For practitioners

• Define the task class before deploying AI Separate summarisation, hypothesis generation, and evidence-driven investigation into different approval tiers. Do not let a model that performs well on one class of task inherit trust for all security workflows.
• 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. Track completion rate, accuracy, and recoverable failures together.
• Gate tool-enabled models with least privilege Restrict sandbox commands, file access, and outbound interaction to the minimum required for the task. Log every action so reviewers can reconstruct how the model reached an output.
• Require human review for decision-bearing outputs Treat any AI conclusion that could affect containment, escalation, or prioritisation as a reviewed recommendation rather than an automated decision. That boundary is essential when the model is acting on sensitive evidence.

Key takeaways

• Benchmarking AI for security work only matters when the test reproduces real multi-step analysis, not surface-level prompt answering.
• The strongest models in this benchmark won because they solved tasks more reliably, not because they were fastest or cheapest.
• As soon as AI can use tools in a security workflow, it needs identity, privilege, and logging controls that look a lot like other governed non-human systems.

Key terms

• End-to-End Benchmarking: A testing approach that measures whether a system can complete a full real-world workflow from start to finish. In security AI, it matters because isolated prompt performance often overstates practical usefulness when the task requires tool use, context retention, and correct final output.
• Tool-Enabled AI Workflow: An AI setup where the model can interact with files, commands, or external systems during a task. This changes the risk profile because the system is no longer only generating text. Governance must cover scope, logging, and review of every action the model can take.
• Decision-Bearing Output: A result that could affect containment, triage, escalation, or another operational security decision. These outputs deserve higher scrutiny than advisory text because the cost of error is larger. Organisations should require verification, accountability, and traceability before such outputs are acted on.

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

👉 Cotool's full write-up includes the model tables, cost comparisons, and task methodology behind these results.

Deepen your knowledge

NHI Foundation Level course, the industry's only accredited NHI security programme, covers NHI governance, workload identity, secrets management, and agentic AI identity. It helps security practitioners build the governance foundations needed for controlled AI and identity programmes.