CWE-Bench-AA Benchmark Leaderboard
CWE-Bench-AA pass@1
得分
CWE-Bench-AA
CWE-Bench-AA:得分 vs. 每项任务成本
Token 使用量
CWE-Bench-AA:每项任务的输出 Token
成本
CWE-Bench-AA:每项任务成本
速度
CWE-Bench-AA:每项任务耗时
得分 vs. 发布日期
CWE-Bench-AA:得分 vs. 发布日期
常见问题
CWE-Bench-AA 是 Artificial Analysis 对 Collinear AI 防御性网络安全基准 CWE-bench 的实现。每项任务向编程智能体提供真实开源仓库的代码副本,并只要求审计代码和修复发现的问题;任务会描述需要关注的区域,但不会给出确切位置,也不会要求智能体创建 exploit。评估集包含 120 项私有保留任务,覆盖 OWASP Top 10(2025)的全部十个类别,涉及 C/C++、Go、Java、JavaScript/TypeScript、Python 和 Rust。
每项任务都有一个确定性的程序化验证器,在智能体完成后运行。只有当 exploit 不再有效且正常功能仍然有效时,任务才算解决。验证器返回 1 或 0。主要分数为 pass@1,即单次尝试解决 120 项任务的比例。
两者使用相同的私有保留任务和相同的确定性验证器,但运行方式有三点不同。Collinear AI 使用各模型供应商自己的智能体产品,例如 Claude Code、Codex,或用于开放权重模型的 Terminus-2;每项任务运行四次并报告 pass@1 和 pass@4;此外还记录由 LLM 评分的部分得分赛道。CWE-Bench-AA 使用我们自己的开源 Stirrup 智能体框架和统一的智能体 prompt 来运行所有模型,每项任务只运行一次,并且只使用确定性验证器评分。因此,本页分数不能与 Collinear AI 发布的结果直接比较。
不可以。这 120 项评估任务由 Collinear AI 和 Artificial Analysis 私有保留,不向任何外部组织提供,有助于降低训练数据污染的风险。Collinear AI 另行提供一个包含 1,000 多项任务、且与评估集完全不重叠的训练语料库。
是的。CWE-Bench-AA 是 Artificial Analysis Cyber Index 的三项评测之一,与 DeepsecBench-AA 和 CyberGym-E2E-AA 权重相同。它不计入 Artificial Analysis Intelligence Index。
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