aiqbench analysis
The paper addresses a real problem for AI coding agents: a security signal can look better during optimization without removing the underlying vulnerability. Its proposed verifier, FALCON, grounds feedback in execution traces from counterexamples, and it reports better held-out security gains than static-analysis or learned rewards, both in test-time repair and as an RL reward. Readers should treat these as benchmark results on secure code generation, not evidence of deployment-ready robustness, but the reward-overoptimization finding is relevant to anyone training code models with verifiable rewards.
Secure Agentic Coding through Counterexample-Grounded Feedback
arXiv:2610.25916v1 Announce Type: cross Abstract: 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 behavioral verification and connects them through execution traces of the program under test. In test-time repair, \falcon{} yields substantially stronger held-out functional-security gains than alternative security signals across secure code generation benchmarks, while preserving or improving functionality. As an RL reward, \falcon{} also outperforms static-analysis and learned rewards on held-out functional-security, even when all policies successfully optimize their respective training rewards. Combining \falcon{} with either proxy reward further improves held-out performance, suggesting that execution-grounded feedback can mitigate reward overoptimization while preserving useful guidance from existing security signals.
Abstract: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 behavioral verification and connects them through execution traces of the program under test. In test-time repair, \falcon{} yields substantially stronger held-out functional-security gains than alternative security signals across secure code generation benchmarks, while preserving or improving functionality. As an RL reward, \falcon{} also outperforms static-analysis and learned rewards on held-out functional-security, even when all policies successfully optimize their respective training rewards. Combining \falcon{} with either proxy reward further improves held-out performance, suggesting that execution-grounded feedback can mitigate reward overoptimization while preserving useful guidance from existing security signals.
Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
arXiv:2610.25916 [cs.CR]
(or arXiv:2610.25916v2 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2610.25916 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 271 words