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What NIST’s DeepSeek evaluations measure
NIST’s Center for AI Standards and Innovation (CAISI) evaluated AI models on cybersecurity challenge suites. These results describe how often models completed specified tasks in evaluation settings. They are not measurements of how reliably a product protects a company’s systems, detects live attacks, or responds to incidents.
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The results also come from different model generations and benchmark releases. Keep the 2026 CTF-Archive-Diamond result separate from the 2025 suite averages rather than treating them as one continuous ranking.
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In its April 2026 evaluation, CAISI reported the following task-completion results on CTF-Archive-Diamond, a CAISI-developed set based on 285 difficult capture-the-flag challenges:
#1 Best Overall
| Model | CTF-Archive-Diamond tasks solved |
|---|---|
| DeepSeek V4 Pro | 32% (imputed from a subset of samples) |
| GPT-5.5 (xhigh reasoning) | 71% |
| Opus 4.6 (max) | 46% |
| GPT-5.4 mini (xhigh reasoning) | 32% |
These are CAISI’s reported results for those models on that benchmark, not a comparison with conventional security products. CAISI also characterized DeepSeek V4’s aggregate capability as about eight months behind the frontier in its evaluation; that estimate is not a measure of every task or model.
CAISI’s 2025 DeepSeek V3.1 results
In a separate 2025 study spanning 19 benchmarks, CAISI evaluated three DeepSeek models and four U.S. reference models. Its summary reported these averages for DeepSeek V3.1 and the best U.S. reference model in each listed benchmark:
| Benchmark suite | DeepSeek V3.1 | Best U.S. reference model |
|---|---|---|
| CVE-Bench | 37% | 67% |
| Cybench | 40% | 74% |
| Sampled CTF-Archive problems | 28% | 51% |
These are task-completion averages from CAISI’s 2025 evaluation, not current rankings for later model releases. CVE-Bench, Cybench, and CTF-Archive are different suites, so their percentages should not be compared as though they measure one identical capability.
Why this is not a head-to-head with traditional tools
“Traditional cybersecurity tools” covers products with distinct jobs. Static analysis examines code for potential defects; software-composition and dependency scanners identify components and known vulnerabilities; vulnerability scanners probe systems for weaknesses; endpoint protection monitors devices; and SIEM platforms collect and correlate security events. A model’s score on a challenge set does not establish how it performs any of those jobs in production.
Rank #3
- Different task: challenge completion is not the same as continuous monitoring, vulnerability coverage, or incident response.
- Different evidence: a benchmark result does not show detection rates, false alarms, coverage of a particular environment, or the outcome of a real deployment.
- Different operating conditions: security products may rely on signatures, rules, telemetry, vulnerability databases, or integrations. A model may generate explanations or code, but its usefulness depends on the task, available context, and controls around its actions.
To claim one is better than another would require a defined task, comparable test conditions, and product-specific evidence. CAISI’s model evaluations do not provide that comparison.
Where an AI model can fit in a defensive workflow
For authorized defensive work, an AI assistant can be used to help explain findings, review code, or support a security professional’s investigation. Treat its output as a proposal to verify, not as a confirmed vulnerability, a clean bill of health, or permission to test a system. Pair it with controls designed for the specific job—for example, dependency scanning for known vulnerable packages or endpoint monitoring for activity on managed devices.
OWASP warns that agentic coding tools may run commands, install packages, edit files, run tests, and access networks. The tool’s permissions can therefore matter as much as the model’s benchmark score. OWASP also notes that a model may suggest a dependency version that has since acquired known CVEs. A code review by a model is not a substitute for a current dependency audit.
How to use AI assistance without handing it the keys
- Define the authorized scope. Limit testing to systems and tasks you are permitted to assess. Do not use a model to probe third-party systems without authorization.
- Give the agent the minimum access it needs. Constrain filesystem and network access independently, and limit command execution and package installation where possible.
- Review consequential actions. OpenAI’s API guidance for relevant high-cyber-capability models recommends checking sensitive tool calls against approved scope and obtaining human approval for ambiguous or high-risk changes. This is provider-specific guidance, not a guarantee that risk is eliminated.
- Verify the output with independent checks. Review generated changes, run appropriate tests, and use a current dependency audit to check packages against known vulnerabilities.
- Keep an audit trail and a safe stop. Record tool actions and retain a human route to investigate uncertain results or halt activity. OpenAI’s guidance also recommends fail-closed review behavior for the applicable workflow.
These controls address different failure modes: scope review helps prevent unauthorized actions, isolation limits what a tool can reach, and independent checks catch errors in generated code or vulnerability advice. None turns a model into a security product or makes its output inherently trustworthy.
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