Secure Agentic Coding through Counterexample-Grounded Feedback

Key points

• As coding models and agent harnesses become more capable, reliable security verification is becoming a central bottleneck.1
• 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.1
• 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.1
• 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.1

Sources (1)

• [1]Secure Agentic Coding through Counterexample-Grounded Feedback
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 07:18 AM

  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.

  As coding models and agent harnesses become more capable, reliable security verification is becoming a central bottleneck.

Extractive summary: sentences quoted from the sources.