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CyberGym

Canonical page on the main site: sinoaihub.com/benchmarks/cybergym

Type

benchmark

Key facts

  • Task type: Cybersecurity vulnerability analysis (real-world vulnerability discovery)
  • Dataset size: Large-scale task suite sourced from ARVO and OSS-Fuzz (~240GB data)
  • Evaluation method: Docker-isolated environments; agents analyze vulnerabilities and generate proofs of concept; pre-/post-patch versions
  • Scoring: Success rate on vulnerability analysis tasks (PoC generation)
  • Recorded evaluations: 4

Description

Cybersecurity agent benchmark focused on vulnerability discovery tasks.

Evaluations

benchmark model score metric date source_type source_url model_version
CyberGym deepseek-v4-1-flash 88.1 accuracy 2026-09-10 vendor_reported https://api-docs.deepseek.com/updates —
CyberGym deepseek-v4-pro 83.3 accuracy 2026-08-13 vendor_reported https://api-docs.deepseek.com/updates —
CyberGym glm-5.3 84.5 accuracy 2026-08-18 vendor_reported https://docs.z.ai/guides/llm/glm-5.3 vuln discovery (GLM-5.2: 77.2)

Methodology

Task type: Cybersecurity vulnerability analysis (real-world vulnerability discovery)

Dataset size: Large-scale task suite sourced from ARVO and OSS-Fuzz (~240GB data)

Evaluation method: Docker-isolated environments; agents analyze vulnerabilities and generate proofs of concept; pre-/post-patch versions

Scoring: Success rate on vulnerability analysis tasks (PoC generation)

Relevant Models

Limitations

All scores are vendor-reported and not independently verified.

Verification Status

verified

Benchmark Changes

No documented benchmark changes on record as of 2026-09-29.

Last Verified

2026-09-20

Source history

No documented source-change events located as of 2026-09-20.

Sources

evidence_id source_name source_url source_type published verified confidence conflict
src-benchmarks-cybergym-1 DeepSeek API Change Log https://api-docs.deepseek.com/updates Official documentation — 2026-09-20 high —
src-benchmarks-cybergym-2 Z.ai docs — GLM-5.3 model page https://docs.z.ai/guides/llm/glm-5.3 Official documentation — 2026-09-20 high —