Reinforcement Learning for Software Vulnerability Analysis: A Systematic Review with Emphasis on C/C++ Source Code and Static Analysis

2026-06-01 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Software Development & Engineering · Depth: Expert, extended

Summary

A systematic review, following PRISMA 2020 guidelines, analyzed 21 primary studies published between 2015 and 2026 on Reinforcement Learning (RL) for software vulnerability analysis, with an emphasis on C/C++ source code and static analysis. The review found that most studies (15) focus on fuzzing and guided program exploration, while only 3 address direct vulnerability detection and just 1 targets statement-level localization. Key findings indicate that statically extracted structural representations like Control Flow Graphs (CFGs) and Abstract Syntax Trees (ASTs) are rarely used as RL agent states. Furthermore, existing benchmarks lack comparability across different approaches. The review identifies a significant research gap: the absence of RL agents that leverage source-code CFGs as states to detect and localize vulnerable nodes, suggesting an underexplored intersection between structural static analysis and reward-driven decision-making.

Key takeaway

For AI Scientists and AI Security Engineers developing vulnerability detection tools, you should focus on integrating Reinforcement Learning with static source-code structural representations like Control Flow Graphs. This approach addresses a significant research gap, moving beyond fuzzing to enable more precise, statement-level vulnerability localization. Prioritize developing RL agents that navigate these structures, leveraging policy-gradient methods for sequential decision-making and fine-grained reward signals. This could significantly improve detection accuracy and reduce false positives in C/C++ codebases.

Key insights

RL for C/C++ vulnerability analysis is nascent, with a critical gap in using static source-code structural representations for detection.

Principles

• RL's value emerges in sequential decisions with delayed rewards.
• Fuzzing favors value-based RL (DQN) for discrete actions.
• Detection tasks benefit from policy-gradient methods.

Method

The review followed PRISMA 2020 guidelines, using the PICOC framework and three search strings across major scientific databases to identify and analyze studies.

In practice

• Consider policy-gradient RL for fine-grained vulnerability localization.
• Integrate CFGs/ASTs as RL agent states for static analysis.
• Prioritize benchmarks with fine-grained ground truth for evaluation.

Topics

• Reinforcement Learning
• Software Vulnerability Analysis
• C/C++ Static Analysis
• Control Flow Graphs
• Abstract Syntax Trees
• Vulnerability Localization

Best for: AI Scientist, AI Security Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.