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Evidence record

GLM-5.2 · DoGNAVY

ExploitGym · Evidence record

Sources and provenance ↓ Read the research narrative →

Source snapshot, chart and review scope →

Original source charts and materials

Recorded measurements

Intended-target successes
79 count

Per-result sample size not disclosed

DoGNAVY · selected subset

Assessment provenance

ExploitGym official snapshot → · External report

Actual evaluation: · Model record →

Method and scope

Evaluates exploit construction across real software targets and checks whether success uses the designated vulnerability.

Evidence type
Benchmark
Assessment subject
Model or agent system under the original task configuration
Primary dimension
Capability
Evidence snapshot
2026-10-29.1

The main dimension follows the task outcome in the original protocol. Topic relevance is documented in the parent research record; no unreviewed question-level links are inferred.

Source finding

Intended-target successes: 79 count. Conditions are retained from the source.

Limitations

Target mitigations, supplied artifacts and vulnerability attribution must accompany any performance claim.

Catalogue size is not assumed to be the denominator for every submitted result. Reported budget cutoffs and rescaled budget conditions remain distinct. Source date is labeled Date only; evaluation/publication semantics and exact execution period are undisclosed. No submission task denominator, trial count or actual executor is disclosed. Benchmark version is not reported; do not infer v1. Official host reuses a linked external report/submission; linked source not independently verified in this unit and not an independent replication. Selected subset with no subset denominator or budget. Generic full-benchmark averaging statement may not apply to this row.

Original figure retained without estimating numerical values. Full source tables have not been transcribed.

Related research questions

References

  1. [1] Zhun Wang, Nico Schiller, et al.. ExploitGym: Can AI Agents Turn Security Vulnerabilities into Real Attacks?. 2026-05-13
  2. [2] Zhun Wang, Nico Schiller, et al.. ExploitGym: Can AI Agents Turn Security Vulnerabilities into Real Attacks?. 2026-05-13 ; Author affiliations on the original paper title page; Figure 1
  3. [3] ExploitGym official leaderboard and benchmark page ; /results/12/on_target