RAVEN: Retrieval-Augmented Vulnerability Exploration Network for Memory Corruption Analysis in User Code and Binary Programs
arXiv:2604.17948v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across various cybersecurity tasks, including vulnerability classification, detection, and patching. However, their potential in automated vulnerability report documentation and analysis remains underexplored. We present RAVEN (Retrieval Augmented Vulnerability Exploration Network), a framework leveraging LLM agents and Retrieval Augmented Generation (RAG) to synthesize comprehensive vulnerability analysis reports. Given vulnerable source code, RAVEN generates report
LLMs have matured significantly, enabling their application to complex tasks like vulnerability analysis, and the demand for automated cybersecurity solutions is high.
Automating vulnerability analysis speeds up identification and patching of software flaws, critical for digital infrastructure security and trust.
The process of generating comprehensive vulnerability reports can now be significantly augmented, potentially reducing manual effort and human error.
- · Cybersecurity firms
- · Software developers
- · AI/ML researchers in security
- · Organizations with large codebases
- · Manual security auditors
- · Cyber adversaries reliant on slow patching cycles
Increased efficiency in identifying and remediating memory corruption vulnerabilities in software.
Reduced incidence of successful cyberattacks exploiting known memory corruption flaws, leading to more resilient digital systems.
A potential shift in cybersecurity hiring towards roles focused on AI model training and oversight rather than manual analysis.
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Read at arXiv cs.AI