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safety benchmark

ExploitBench

ExploitBench is a cybersecurity benchmark that evaluates a model's ability to discover and exploit software vulnerabilities, reported as the fraction of challenges where the model captures the target (Cap%).

Updated Aug 7, 2026

Published models4
Registry coverage4
MetricScore
EvidenceB

On this page

  • Ranking
  • Distribution
  • Highlights
  • About
  • FAQ

ExploitBench leaderboard

Sorted by the source-provided rank. Higher score is better according to the registry.

4 rows
Columns

Show columns

1ANClaude Fable 5Anthropic78.0%100.0%4CAug 7, 2026
2OPGPT-5.6 SolOpenAI73.5%66.7%4CAug 7, 2026
3OPGPT-5.6 TerraOpenAI52.9%33.3%4CAug 7, 2026
4OPGPT-5.6 LunaOpenAI33.2%0.0%4CAug 7, 2026

Score distribution

Top published rows on the benchmark's original scale.

ExploitBench

ExploitBench highlights

The top published results on this benchmark's own scale.

Rank #1Claude Fable 578.0%Rank #2GPT-5.6 Sol73.5%Rank #3GPT-5.6 Terra52.9%Rank #4GPT-5.6 Luna33.2%

What is ExploitBench?

Definition and scoring fields from the benchmark registry.

ExploitBench is a cybersecurity benchmark that evaluates a model's ability to discover and exploit software vulnerabilities, reported as the fraction of challenges where the model captures the target (Cap%).

Scores are shown in ratio. The current registry marks this benchmark as not independently verified with evidence level B.

Family
ExploitBench
Modality
text
Primary category
safety
Score direction
higher
LLMBoard eligible
No
Evaluation key
exploitbench|llm-stats-current

Source-native results are preserved. Eligibility for the overall LLMBoard score is a separate policy decision.

FAQ

Common questions about ExploitBench.

Which model scores highest on ExploitBench?

Claude Fable 5 is currently ranked first with 78.0%.

What does ExploitBench measure?

ExploitBench is a cybersecurity benchmark that evaluates a model's ability to discover and exploit software vulnerabilities, reported as the fraction of challenges where the model captures the target (Cap%).

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

4 unique published model results are currently shown.

Does this benchmark affect the overall score?

This benchmark is preserved as source-native evidence but is not eligible for the current overall score.

Rankings

OverallCodingText ArenaPricing

Modalities

Image GenerationVideo GenerationSpeech-to-TextEmbeddings

Benchmarks

All BenchmarksReasoningMathCoding

Vendors

All VendorsOpenAIAnthropicGoogle
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