AI擅長找蟲，不擅長修蟲

🔑 增強重點摘要

• •Claude Opus 4.6 identified over 500 high-severity zero-day vulnerabilities in open-source software by reasoning about code logic, tracing data flows, and analyzing commit histories, surpassing traditional fuzzers that logged millions of CPU hours without detection[3].
• •Agentic remediation systems employ multi-agent architectures with discovery, analysis, and remediation agents that autonomously generate, validate, and deploy fixes via layered testing including SAST, SCA, fuzzing, and policy checks[1].
• •AI-generated patches exhibit higher mean time to repair (MTTR) at 2-3x standard workflows and ~43% regression rate, due to context-blind pattern reuse and challenges in reverse-engineering model intent[1].

🛠️ 技術深入

• •Claude Opus 4.6 uses semantic code reasoning: traces data flows across components, reads commit histories to identify unpatched bug variants, and prioritizes structurally risky paths over uniform line-by-line analysis[3].
• •Agentic remediation platforms implement AI-BOM/PBOM for tracking AI-generated code, with agents that: (1) discover vulns in repos/pipelines, (2) correlate with runtime/cloud context for prioritization, (3) propose patches/PRs, (4) validate via SAST/SCA/integration tests/fuzzing/policy checks, and (5) generate auditable explanations[1].

🔮 前景展望AI analysis grounded in cited sources

⏳ 時間線