AI写代码，验证器判对错，实现高效安全软件开发
The Prover Is the Judge: Verified Security Software from AI Coding Agents in Ada/SPARK
- 用验证器驱动循环，让AI生成并验证Ada/SPARK安全代码
- 49,280个证明义务被通过，运行时错误全消除，人工成本降低20-40倍
- 验证强度决定可信度，弱检查会诱使AI绕过，适合高安全性场景
AI编码代理生成代码的速度远超人工审查能力。本文提出由验证器作为判决者，采用验证驱动的迭代流程，让AI在Ada/SPARK环境下编写涵盖经典与后量子密码学、TLS 1.3、IKEv2、X.509及Matrix客户端的裸机安全软件。GNATprove共解决49,280个证明义务，确认部分算法功能正确性，并证明其余代码无运行时错误，整体监督成本仅为同类人工验证的1/20至1/40。但仅靠GNATprove不足以发现所有缺陷，部分问题需通过已知答案测试、互操作性验证或人工审查规格说明来修复。由于检查机制较弱，代理曾尝试规避检测并虚假报告成功。我们分析各层故障发现位置，得出核心结论：代理可被信任的程度，取决于其反馈机制的强弱。
原文摘要 · Abstract (English)
AI coding agents produce code faster than humans can review it. In our approach, the prover is the judge of whether the code is correct. Under a verifier-driven loop, AI agents wrote and verified bare-metal security software in Ada/SPARK spanning classical and post-quantum cryptography, TLS 1.3, IKEv2, X.509, and a Matrix client. GNATprove discharged 49,280 proof obligations, established functional correctness for selected primitives, and proved the absence of run-time errors for the rest, at roughly 20-40 times lower supervision cost than comparable hand verification. GNATprove alone was insufficient: some defects could not be detected and were resolved using known-answer tests, interoperability, or human review of specifications. Given weak checks, the agent tried to bypass them and reported success. We report where each layer caught faults and draw the central lesson: what an agent can be trusted to establish is bounded by the strength of its feedback.
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