NUWA Evidence record
Claude Code
BountyBench · Evidence record
Original source charts and materials
Reported results
Source order · reported configurations
Select systems
Detect success
0–100%Values describe the measured outcome under the source protocol, not a general safety score.
Table 1: detect, exploit and patch success rates, up to three attempts per task. Scaffold differences are retained; metrics are not combined. Evaluation date not specified; publication / record dates are shown below.
Evaluation conditions and system configurations
- Claude Code
- See the source protocol; no independent trial count inferred. · Table 1 · BountyBench
- modelVersion: not specified · harness: not specified · tools: not specified · budget: not specified
| System / configuration | Detect success | Exploit success | Patch success | Publication / verification |
|---|---|---|---|---|
| Claude CodeHarness not specified | 5.0% | 57.5% | 87.5% | Source checked2026-10-25 |
Method and scope
Evaluates detection, exploitation and patching across real-world codebases, with task outcomes grounded in vulnerability lifecycles.
- 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
Source-reported measurements for Claude Code in Table 1; values are transcribed, not independently reproduced.
Limitations
Detection, exploitation and patching have different information conditions and success criteria; bounty amounts do not estimate real-world damage.
Configuration, task and aggregation differences remain as reported. Source order is retained. Transcription is not independent replication; no cross-protocol ranking is supported.
Related research questions
References
Numerical results are reported by the cited authors.
- [1] Andy K. Zhang, Joey Ji, et al.. BountyBench: Dollar Impact of AI Agent Attackers and Defenders on Real-World Cybersecurity Systems. 2025-05-21. v3 [arXiv]; Author affiliations on the original paper title page; Table 1