Trust, but Validate the Instrument: Auditing AI-Generated RTL Verification Plans on Authored Security-Regression Proxies
Researchers release SecTB-RTL, an auditable framework showing that AI-generated RTL verification plans passing provider schema checks can still fail execution validity.
The paper presents SecTB-RTL, an auditing framework covering 31 tasks and 124 authored hardware-security regressions for AI-generated RTL verification plans. A confirmatory run of 1,860 calls was accepted by the provider (1,857 responses) but only nine passed the production semantic validator, leading the authors to record it as an instrument-validation incident with no prompt-effect estimate. They release the benchmark, failure-preserving contract, incident provenance, and governance controls separating infrastructure behavior from model behavior.
- Framework spans 31 tasks and 124 authored hardware-security regressions
- Provider schema acceptance did not match production execution rules
- 1,860 calls made; only 9 responses passed the semantic validator
- Benchmark, incident provenance, and governance controls are released
From the original article
1 min read at arxiv.orgAI-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.
By Hang Xiao · arxiv.org
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