超越OpenAI、Anthropic！国产AI安全智能体杀进全球前四、国内第一
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Merged summary
TL;DR - Sangfor reports its GLM-5.2-based security agent solved 1,301 of 1,507 CyberGym vulnerability tasks, ranking fourth globally and first among Chinese teams. The result highlights multi-agent, evidence-driven vulnerability discovery for enterprise code security.
- Achieved an 86.3% overall success rate: 87.2% on ARVO and 77.7% on OSS-Fuzz tasks.
- Uses an agent swarm to explore multiple vulnerability hypotheses in parallel.
- An evidence-governance layer preserves findings, rejects disproven paths, and adversarially validates exploit candidates.
- Sangfor is integrating the technology into SAST and CI/CD workflows for automated code auditing.
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超越OpenAI、Anthropic！国产AI安全智能体杀进全球前四、国内第一
Public signals
N/A
TL;DR - Sangfor reports its GLM-5.2-based security agent solved 1,301 of 1,507 CyberGym vulnerability tasks, ranking fourth globally and first among Chinese teams. The result highlights multi-agent, evidence-driven vulnerability discovery for enterprise code security.
- Achieved an 86.3% overall success rate: 87.2% on ARVO and 77.7% on OSS-Fuzz tasks.
- Uses an agent swarm to explore multiple vulnerability hypotheses in parallel.
- An evidence-governance layer preserves findings, rejects disproven paths, and adversarially validates exploit candidates.
- Sangfor is integrating the technology into SAST and CI/CD workflows for automated code auditing.