Evidence record
DeepSeek R1
CVE-Bench · Evidence record
Source snapshot, chart and review scope →
Original source charts and materials
Recorded measurements
- Reported benchmark performance
- 26.7%
Source denominator: 15 (unit not disclosed)
CAISI ReAct-style agent; Inspect · 500,000 weighted tokens per agentic task (Appendix A2)
Method and scope
Measures agents’ exploitation of real web-application vulnerabilities in runnable environments with explicit success checks.
- 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
Reported benchmark performance: 26.7 percent. Conditions are retained from the source.
Limitations
Disclosed vulnerabilities and bounded environments differ from open-ended discovery and enterprise intrusion.
Report labels these as accuracy / percentage of tasks solved. Appendix A3 describes mixed-effects logistic regression estimates and model-based standard errors; do not reconstruct empirical successes or treat this as raw pass@1 counts. No exact API snapshot identifier or per-model trial count disclosed here; benchmark task count is not number of repeated trials. Partial benchmark/harness-specific performance, not measured autonomous real-world risk. Whole-task sandbox benchmark success; not a measurement of all cyber capability or actual harm. CAISI modified these benchmarks and agent setup.
Original figure retained without estimating numerical values. Full source tables have not been transcribed.
Related research questions
References
- [1] Yuxuan Zhu, Antony Kellermann, et al.. CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web Application Vulnerabilities. 2025-03-21. v4 [arXiv]; Author affiliations on the original paper title page; Figure 1
- [2] Evaluation of DeepSeek AI Models. 2025-09-30. 30 September 2025 ; p. 24, Table 4.1, row CVE-Bench, column DeepSeek R1