SecureFalcon: Are We There Yet in Automated Software Vulnerability Detection with LLMs? (2307.06616v2)

Published 13 Jul 2023 in cs.CR and cs.AI

Abstract: Software vulnerabilities can cause numerous problems, including crashes, data loss, and security breaches. These issues greatly compromise quality and can negatively impact the market adoption of software applications and systems. Traditional bug-fixing methods, such as static analysis, often produce false positives. While bounded model checking, a form of Formal Verification (FV), can provide more accurate outcomes compared to static analyzers, it demands substantial resources and significantly hinders developer productivity. Can Machine Learning (ML) achieve accuracy comparable to FV methods and be used in popular instant code completion frameworks in near real-time? In this paper, we introduce SecureFalcon, an innovative model architecture with only 121 million parameters derived from the Falcon-40B model and explicitly tailored for classifying software vulnerabilities. To achieve the best performance, we trained our model using two datasets, namely the FormAI dataset and the FalconVulnDB. The FalconVulnDB is a combination of recent public datasets, namely the SySeVR framework, Draper VDISC, Bigvul, Diversevul, SARD Juliet, and ReVeal datasets. These datasets contain the top 25 most dangerous software weaknesses, such as CWE-119, CWE-120, CWE-476, CWE-122, CWE-190, CWE-121, CWE-78, CWE-787, CWE-20, and CWE-762. SecureFalcon achieves 94% accuracy in binary classification and up to 92% in multiclassification, with instant CPU inference times. It outperforms existing models such as BERT, RoBERTa, CodeBERT, and traditional ML algorithms, promising to push the boundaries of software vulnerability detection and instant code completion frameworks.

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Authors (11)

Mohamed Amine Ferrag (34 papers)
Ammar Battah (2 papers)
Norbert Tihanyi (18 papers)
Thierry Lestable (4 papers)
Lucas C. Cordeiro (50 papers)
Ridhi Jain (11 papers)
Diana Maimut (2 papers)
Fatima Alwahedi (2 papers)
Narinderjit Singh Thandi (2 papers)
Abdechakour Mechri (2 papers)
Merouane Debbah (269 papers)

Citations (4)

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Summary

Enhancing Cybersecurity Through Generative AI: The Introduction of SecureFalcon

Introduction to SecureFalcon

In the contemporary era of digital technologies, the surge in cyber threats and vulnerabilities poses a significant challenge to the integrity and security of software systems. Traditional methods, while widely utilized, come with limitations such as a high false positives rate, which in turn can lead to a reduction in developer productivity due to the resources required to investigate these inaccuracies. Enter SecureFalcon, an innovative model architecture borne of the FalconLLM lineage, fine-tuned specifically for cybersecurity applications. This advancement heralds a significant shift in how vulnerabilities in C code are detected, leveraging the prowess of Generative AI and formal verification processes to evaluate performance.

SecureFalcon Performance Overview

At its core, SecureFalcon sets a new benchmark in software vulnerability detection with a remarkable 94% accuracy rate. This achievement underscores the model's ability to differentiate between vulnerable and non-vulnerable C code efficiently, thereby demonstrating its potential to redefine vulnerability detection methodologies within the cybersecurity domain. The model architecture is built upon FalconLLM, which ensures robust processing and understanding of extensive texts, facilitating a comprehensive identification of threats.

The Technology Behind SecureFalcon

SecureFalcon is trained and fine-tuned using a dataset of raw C code samples encompassing 42 unique Common Weakness Enumerations (CWEs). This approach addresses common challenges encountered in deep learning methodologies, such as model inadequacy and learning irrelevant features, through meticulous architecture choice and data preprocessing. By implementing a configured portion of the Falcon-7B model, the design achieves a lightweight and compact model that is capable of efficiently performing the task at hand, demonstrating the scalability and flexibility of the FalconLLM models in addressing specific use cases such as cybersecurity.

Implications and Future Directions

The introduction of SecureFalcon not only provides a practical solution to the pressing issue of software vulnerabilities but also opens up new avenues for research and development within AI and cybersecurity. The model's versatility and the high degree of accuracy in detecting software vulnerabilities offer promising insights into the future of language processing applications and their role in enhancing cybersecurity measures. Additionally, the use of AI in cybersecurity paves the way for further exploration into multi-classification methods and expanding the scope to include other programming languages, thus broadening the model's applicability and impact.

Furthermore, the success of SecureFalcon encourages the consideration of integrating such AI-driven models into continuous integration and deployment (CI/CD) pipelines, offering an automated and streamlined process for vulnerability detection and repair. This integration has the potential to significantly enhance the security, reliability, and efficiency of software development practices.

Conclusion

SecureFalcon represents a pivotal step forward in the application of LLMs for cybersecurity, offering an effective and efficient solution for detecting software vulnerabilities. The model leverages the strengths of Generative AI and formal verification, setting a new standard for accuracy and performance in vulnerability detection. As we look towards the future, the continued exploration and integration of AI technologies in cybersecurity hold the promise of more secure and resilient digital environments. SecureFalcon is not just a testament to the progress in AI and cybersecurity but also a beacon for future innovations aimed at safeguarding our digital world.

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