Reinforcement Learning for Software Vulnerability Analysis: A Systematic Review with Emphasis on C/C++ Source Code and Static Analysis
arXiv:2606.28403v1 Announce Type: cross Abstract: Vulnerability detection in C/C++ software remains a major security challenge due to code complexity, manual memory management, and the limitations of traditional static analysis. Reinforcement Learning (RL) has emerged as a promising approach, particularly for fuzzing, test generation, program exploration, and, more recently, vulnerability detection and localization. Following PRISMA 2020 guidelines, this work reviews RL techniques for software vulnerability analysis, focusing on C/C++ source code and static analysis. We identified 21 primary s
The increasing complexity of software and the escalating threat landscape necessitate more advanced and automated methods for vulnerability detection, making AI/ML approaches like RL highly relevant.
This development indicates a significant push towards automated, AI-driven security analysis, which can dramatically improve the robustness of critical software infrastructure and reduce human error.
The adoption of Reinforcement Learning for static analysis shifts the paradigm from traditional rule-based or heuristic methods towards adaptive and autonomous bug-finding systems, particularly for complex languages like C/C++.
- · Cybersecurity Sector
- · Software Development Companies (C/C++)
- · AI/ML in Security Research
- · Cloud Security Providers
- · Traditional Manual Code Auditors
- · Cyberattackers targeting C/C++
- · Organizations with Poor DevSecOps
- · Software reliant on outdated security tools
Security tooling will integrate and rely heavily on RL-powered static analysis to automate vulnerability detection.
The cost and time required for security auditing of complex software will decrease, leading to faster and more secure product releases.
The enhanced security of critical infrastructure software, especially in defense and industrial control systems, will become a national security differentiator.
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