✨ 인류 중심 대 OpenAI 레드 팀 구성 방식은 엔터프라이즈 AI의 다양한 보안 우선순위를 드러냅니다.
★ 8 전문 정보 ★
Model providers want to prove the security and robustness of their models, releasing system cards and conducting red-team exercises with each new release. But it can be difficult for enterprises to parse through the results, which vary widely and can be misleading. Anthropic's 153-page system c
🎯 핵심 특징
✅ 고품질
검증된 정보만 제공
⚡ 빠른 업데이트
실시간 최신 정보
💎 상세 분석
전문가 수준 리뷰
📖 상세 정보
Model providers want to prove the security and robustness of their models, releasing system cards and conducting red-team exercises with each new release. But it can be difficult for enterprises to parse through the results, which vary widely and can be misleading. Anthropic's 153-page system card for Claude Opus 4.5 versus OpenAI's 60-page GPT-5 system card reveals a fundamental split in how these labs approach security validation. Anthropic discloses in their system card how they rely on multi-attempt attack success rates from 200-attempt reinforcement learning (RL) campaigns. OpenAI also reports attempted jailbreak resistance. Both metrics are valid. Neither tells the whole story.Security leaders deploying AI agents for browsing, code execution and autonomous action need to know what each red team evaluation actually measures, and where the blind spots are.What the attack data showsGray Swan's Shade platform ran adaptive adversarial campaigns against Claude models. Th