arXiv：cs.AI· Hang Xiao, Chuhong Xu, Kainan Zhou, Gangzhen Qian, Lu Yi·· 13 小时前
SecTB-RTL：审计 AI 生成的 RTL 验证计划在安全回归代理上的有效性
Trust, but Validate the Instrument: Auditing AI-Generated RTL Verification Plans on Authored Security-Regression Proxies
AI 导读
研究团队提出 SecTB-RTL，一个覆盖 31 项任务和 124 个硬件安全回归的可审计框架。首个验证运行 C1-R2 因响应 schema 被拒而未执行；经 schema 修复后冻结的 C1-R3 完成 1,860 次调用，提供方接受 1,857 条响应，但仅 9 条通过生产语义验证器，表明提供方或 schema 接受并不等于执行有效。该运行被保留为工具验证事件，未报告提示词效果估计。
正文
Abstract:AI-generated RTL verification plans can satisfy a provider schema yet fail at the boundary to trusted execution. We present SecTB-RTL, an auditable framework covering 31 tasks and 124 authored hardware-security regressions. A deterministic non-AI baseline killed 36, 75, and 78 mutants at increasing resource limits. The first confirmatory run (C1-R2) failed before model execution because the provider rejected its response schema. After a schema-only repair made without viewing outcomes, a separately frozen follow-up run (C1-R3) completed 1,860 calls. The provider accepted 1,857 responses, but only nine passed the production semantic validator. The generation and execution rules did not match. We therefore preserve the run as an instrument-validation incident and report no prompt-effect estimate. This incident shows that provider or schema acceptance does not establish execution validity. Compilation and coverage are only diagnostics; the exact saved artifact must pass the full production path. A subsequent follow-up is excluded because it did not satisfy the preregistered evidence-completeness gate and is treated only as future work. We release the benchmark, failure-preserving contract, incident provenance, and governance controls needed to prevent infrastructure behavior from being misreported as model behavior.
| Comments: | 7 pages, 3 table. Accepted at IEEE Cyber-AI 2026. Author accepted manuscript. Reproducibility artifact: this https URL |
| Subjects: | Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Information Retrieval (cs.IR); Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.19844 [cs.CR] |
| (or arXiv:2609.19844v2 [cs.CR] for this version) | |
| https://doi.org/10.48550/arXiv.2609.19844 arXiv-issued DOI via DataCite |
Submission history
From: Kainan Zhou [view email]
[v1]
Thu, 17 Sep 2026 07:54:54 UTC (14 KB)
[v2]
Fri, 9 Oct 2026 04:59:05 UTC (14 KB)
来源：arXiv：cs.AI · arxiv.org