The paper introduces a three-level framework for AI secure code generation, finds that principle understanding statistically predicts code outcomes, but identifies persistent knowledge-actuation gaps across models and agents.
Hexacoder: Secure code generation via oracle-guided synthetic training data,
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
TSP reframes secure code generation as a tree-structured self-play process that supplies dense on-policy signals at vulnerability-prone nodes, yielding higher security pass rates and cross-language generalization than SFT or unstructured self-play.
citing papers explorer
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SoK: AI Secure Code Generation: Progress, Pitfalls, and Paths Forward
The paper introduces a three-level framework for AI secure code generation, finds that principle understanding statistically predicts code outcomes, but identifies persistent knowledge-actuation gaps across models and agents.
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Learn from Your Mistakes: Tree-like Self-Play for Secure Code LLMs
TSP reframes secure code generation as a tree-structured self-play process that supplies dense on-policy signals at vulnerability-prone nodes, yielding higher security pass rates and cross-language generalization than SFT or unstructured self-play.