Software vulnerabilities leading to various detriments such as crashes, data
loss, and security breaches, significantly hinder the quality, affecting the
market adoption of software applications and systems. Although traditional
methods such as automated software testing, fault localization, and repair have
been intensively studied, static analysis tools are most commonly used and have
an inherent false positives rate, posing a solid challenge to developer
productivity. Large Language Models (LLMs) offer a promising solution to these
persistent issues. Among these, FalconLLM has shown substantial potential in
identifying intricate patterns and complex vulnerabilities, hence crucial in
software vulnerability detection. In this paper, for the first time, FalconLLM
is being fine-tuned for cybersecurity applications, thus introducing
SecureFalcon, an innovative model architecture built upon FalconLLM.
SecureFalcon is trained to differentiate between vulnerable and non-vulnerable
C code samples. We build a new training dataset, FormAI, constructed thanks to
Generative Artificial Intelligence (AI) and formal verification to evaluate its
performance. SecureFalcon achieved an impressive 94% accuracy rate in detecting
software vulnerabilities, emphasizing its significant potential to redefine
software vulnerability detection methods in cybersecurity.

本文介绍了 FalconLLM 在网络安全方面的应用，提出了 SecureFalcon 模型架构以便更好地检测软件漏洞。通过构造新的 FormAI 训练数据集，使用 SecureFalcon 模型，检测软件漏洞的准确率达到了 94%。