multiple-choice-questions-derived-industry-cybersecurity-certifications-frontier-models-reach CLM-0312On multiple-choice questions derived from industry cybersecurity certifications, frontier models reach human-expert level on general networking and IT security, but accuracy falls on vendor-specific material and formal standards, staying below 40% on the hardest ISA/IEC 62443 and Fortinet NSE questions; general security benchmarks such as MMLU Computer Security are saturated near 100% and no longer separate models.
Capability: Competence in a specialist domain
Observed on
CyberCertBench MCQA suites from IT, OT and vendor certifications, 2026; frontier and open models; difficulty tiers from the exams..
Sources
- Multiple-choice only, which favours recall over practice; the split between general and vendor/standard knowledge is clear and the saturation observation about older benchmarks is useful in itself.
Disagreeing is the most useful thing you can do here. Nothing on this page waits for a reviewer: the counts above move when evidence lands, and every source stays attached with who brought it.
Related claims
- On 2,154 expert-curated multiple-choice questions across eight architectural categories, 11 current models all score above 90% zero-shot (best about 94%), with quality-attribute and pattern questions near ceiling, but accuracy varies by category and drops for smaller models on the harder tiers (to about 85-87%), so declarative architectural knowledge is largely present while the benchmark cannot show whether it is applied in design.Making design decisions that hold up as a system grows · reviewed by AI
- For a domain-specific language essentially absent from pretraining corpora (FORM symbolic algebra), QLoRA fine-tuning an 8B model on a few thousand examples validated by executing them through the language's own interpreter beats frontier open-weight models of up to 756B parameters given syntax documentation in context, on execution rate and strict output match for the larger benchmarks, while general reasoning scores drop under 3 points.Generating and editing working code · reviewed by AI
- Adding a repository-wide line-level graph of definitions and references, queried for the ego-graph around names the model is looking at, raised SWE-bench resolve rates for all four frameworks it was plugged into (two agentic, two procedural) by an average of about 33% relative, at a modest cost increase that was larger for the agentic ones; the same plug-in also helped on CrossCodeEval.Finding what matters in a large codebase · reviewed by AI
- On repository-grounded LLM-infrastructure engineering tasks, frontier agents score far below ceiling (best 36.53% overall), and every one of eight models scores lower on long-horizon multi-file implementation than on single-file kernel completion, even though the full repository and test cases are visible.Completing tasks that take many steps or many hours · reviewed by AI
- On 430 tasks distilled from real professional-forum threads across seven domains, the best model (GPT-5) scores about 54%, under the 60% passing line the authors set, with computer science and applied technology lowest at roughly 40-45%, so long-tail practitioner knowledge remains a gap even where standard benchmarks are saturated.Competence in a specialist domain · reviewed by AI
Notes
Drafted from the paper by a model and filed unreviewed. Visible here so it can be read, not because anyone has vouched for it: it does not move any technique's standing and does not count toward the internal scorecard. Drafted confidence: medium. Falsifier as drafted: Models scoring above 70% on the IEC 62443 and vendor tiers without retrieval, or evidence those tiers are mis-keyed.