只用一款开源基础模型，DoGNAVY 如何拿下 AI 安全全球第三？

TL;DR - DARKNAVY's DoGNAVY, a multi-agent vulnerability-reproduction system built on a single open-source base model (GLM-5.2), reached 90.84% verified reproduction on the CyberGym Level 1 benchmark, ranking third globally and first among open-source approaches behind Microsoft MDASH and Wiz Atlas. It shows that agent system engineering plus security expertise — not just frontier closed models — can carry hard offensive-security tasks.

• On 1,507 tasks it produced crashing inputs for 1,453; 1,369 passed the differential metric (crash on vulnerable version, no crash after patch) for 90.84%. 79 crashed on both versions, 5 were verified-but-OOM, and 54 had no submitted PoC.
• The architecture is a backtrackable loop: reachability analysis from the real program entry → input constraints → candidate PoC → dynamic feedback (coverage, crash type/location, stability) → revise path or input, with code indexing to narrow large repos.
• PoC generation is separated from an independent review agent that checks whether the crash hits the expected path, matches the target bug, and reproduces stably — filtering assertion failures, environment anomalies, and adjacent-bug false positives.
• Cross-task memory was disabled (per-task compressed memory only), and reference PoCs, git history, and submission metadata were stripped from the workspace; sandboxing plus AgentDoG 1.5 trajectory-level safety diagnosis provide the execution and guardrail layer.