arXiv:2605.10240v3 Announce Type: replace-cross Abstract: Software vulnerability detection is critical for ensuring software security and reliability. Despite recent advances in deep learning, real-world vulnerability datasets suffer from two severe challenges: frequency imbalance and difficulty imbalance. We reinterpret these challenges from an embedding geometry perspective, observing that such imbalances induce geometric distortions in hyperspherical representation space. To address this issue, we propose MARGIN, a metric-based framework that learns discriminative vulnerability representati
The continuous evolution of software and increasing sophistication of cyber threats necessitate advanced detection methods for vulnerabilities, pushing AI research to address current limitations.
Improved vulnerability detection directly enhances software security and reliability, a critical foundation for nearly all modern technological infrastructure, impacting cybersecurity and operational stability.
The proposed MARGIN framework offers a new metric-based approach to overcome data imbalance issues in deep learning for vulnerability detection, potentially leading to more accurate and robust security tools.
- · Cybersecurity firms
- · Software developers
- · Organizations with large software footprints
- · AI/ML security research
- · Cyber attackers
- · Hackers (exploiting known vulnerabilities)
More sophisticated AI-driven tools will emerge to proactively identify and mitigate software vulnerabilities.
This could reduce the attack surface for cyber threats, raising the bar for successful exploits and potentially shifting geopolitical cyber dynamics.
Enhanced software integrity could accelerate the adoption of complex AI systems, as trust in their underlying code increases.
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Read at arXiv cs.LG