Secure Agentic Coding through Counterexample-Grounded Feedback
As coding models and agent harnesses become more capable, reliable security verification is becoming a central bottleneck. Improving secure code generation requires a signal whose reported progress remains aligned with actual program behavior under optimization; otherwise, an optimizer can improve the signal's verdict without removing the underlying vulnerability. Existing model-based approaches require models to either infer program behavior from source code or specify correct outcomes for adversarial tests, placing substantial reasoning demands on the security signal. We introduce \falcon{}, a counterexample-grounded verification framework that separates attack exploration from behavior...
| Authors | Tue Le, Anh Nguyen Hoang, Gabriel Sasseville et al. |
|---|---|
| Primary category | cs.CR |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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