safety benchmark
CVE-Bench evaluates agents on exploiting real-world web-application CVEs in sandboxed environments that mimic production services.
Updated Sep 24, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelXA | Score36.60% | Percentile100.00% | Participants1 | EvidenceC | Evaluated |
The leading models and scores on this benchmark.
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis cve-bench AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
Selection summary
Grok 4.7 currently leads CVE-Bench with 36.60%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price, and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.
What CVE-Bench measures and how its scores work.
CVE-Bench is a safety benchmark for evaluating agents on real-world web-application CVEs in sandboxed environments, typically with unrestricted or egress-controlled settings.
It measures agent performance on exploiting real-world web-application CVEs, reported as a Score ratio.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about CVE-Bench.
Grok 4.7 is currently ranked first with 36.60%.
The current leaders are Grok 4.7 (36.60%).
Grok 4.7 has the lowest matched official input price at $2.0 input / $6.0 output per 1M tokens.
The fastest matched records are Grok 4.7 (5.32 tok/s via xAI).
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures agent performance on exploiting real-world web-application CVEs, reported as a Score ratio.
Yes. Higher values rank better for this benchmark.
1 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.