SecDrift: Measuring Sector-Conditioned Security Drift in AI-Generated Code
LLMs are increasingly used for code generation in critical infrastructure, yet the security effect of domain-specific prompting is understudied. We present SecDrift, a benchmark measuring sector-conditioned security drift: the change in static-analysis vulnerability rates when prompts are conditioned on industry contexts versus neutral baselines. We evaluate 7 LLMs (6 producing analyzable code) across 8 CISA critical infrastructure sectors and 9 CWE categories with 5 replicates (5,355 evaluations), using a 5-dimension transformation with a matched-baseline condition that holds the task fixed while substituting only domain terminology. Industry prompts naively appear more secure (14.0% vs....
| Authors | Narayanaswami Natraj Bharadwaj, Dhivya Chandramouleeswaran |
|---|---|
| Primary category | cs.CR |
| Published | 28 July 2026 |
| Last updated at source | 28 July 2026 |
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