CWE-Bench-AA Benchmark Leaderboard
CWE-Bench-AA pass@1
점수
CWE-Bench-AA
CWE-Bench-AA: 점수 vs. 작업당 비용
토큰 사용량
CWE-Bench-AA: 작업당 출력 토큰
비용
CWE-Bench-AA: 작업당 비용
속도
CWE-Bench-AA: 작업당 시간
점수 vs. 출시일
CWE-Bench-AA: 점수 vs. 출시일
자주 묻는 질문
CWE-Bench-AA는 Collinear AI의 방어적 사이버 보안 벤치마크 CWE-bench를 Artificial Analysis가 구현한 것입니다. 각 작업에서 코딩 에이전트는 실제 오픈 소스 저장소의 체크아웃과 코드를 감사해 발견한 문제를 수정하라는 단 하나의 지시를 받습니다. 작업은 우려 영역을 설명하지만 정확한 위치는 알려 주지 않으며 exploit 생성도 요구하지 않습니다. 평가 세트는 OWASP Top 10(2025)의 10개 범주를 모두 포함하는 120개 비공개 작업으로, C/C++, Go, Java, JavaScript/TypeScript, Python, Rust를 다룹니다.
각 작업에는 에이전트가 끝난 뒤 실행되는 결정적인 프로그램 검증기가 있습니다. exploit이 더 이상 작동하지 않고 정상 동작이 계속 작동해야 해결로 인정됩니다. 검증기는 1 또는 0을 반환합니다. 주요 점수 pass@1은 120개 작업 중 한 번의 시도로 해결한 비율입니다.
둘 다 동일한 비공개 작업과 같은 결정적 검증기를 사용하지만 실행 방식에는 세 가지 차이가 있습니다. Collinear AI는 Claude Code, Codex 또는 오픈 웨이트 모델용 Terminus-2처럼 각 공급자의 에이전트 제품으로 모델을 실행하고, 작업당 4회 시도해 pass@1과 pass@4를 보고하며, LLM이 평가하는 부분 점수 트랙도 기록합니다. CWE-Bench-AA는 모든 모델을 동일한 에이전트 prompt와 당사의 오픈 소스 Stirrup 에이전트 하네스로 실행하고, 작업당 한 번만 시도하며, 결정적 검증기만 채점합니다. 따라서 이 페이지의 점수는 Collinear AI가 공개한 결과와 직접 비교할 수 없습니다.
아니요. 120개 평가 작업은 Collinear AI와 Artificial Analysis가 비공개로 보유하며 외부 조직에는 제공하지 않습니다. 이는 학습 데이터 오염 위험을 줄이는 데 도움이 됩니다. Collinear AI는 평가 세트와 겹치지 않는 1,000개 이상의 작업으로 구성된 학습 코퍼스를 별도로 제공합니다.
예. CWE-Bench-AA는 Artificial Analysis Cyber Index를 구성하는 세 가지 평가 중 하나이며, DeepsecBench-AA 및 CyberGym-E2E-AA와 동일한 가중치를 가집니다. Artificial Analysis Intelligence Index에는 포함되지 않습니다.
평가 살펴보기
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