Enhancing Large Language Models for Identifying C/C++ Vulnerability-Contributing Commits

Belangrijkste concepten

CLNX, a lightweight middleware, effectively enhances the ability of Large Language Models to identify C/C++ vulnerability-contributing commits.

Samenvatting

The paper introduces CodeLinguaNexus (CLNX), a middleware framework that aims to improve the performance of Large Language Models (LLMs) in identifying C/C++ Vulnerability-Contributing Commits (VCCs).

The key highlights are:

1. C/C++ comprises half of the Open-Source Software (OSS) vulnerabilities over the past decade, and updates in OSS mainly occur through commits. Enhancing LLMs' ability to identify C/C++ VCCs is essential.

2. Current approaches primarily focus on further pre-training LLMs on massive code datasets, which is resource-intensive and poses efficiency challenges.

3. CLNX is designed to bridge the gap between C/C++ programs and LLMs in a lightweight manner. It performs two-stage naturalization:

   • Structure-level naturalization: Linearizes the complex structure of C/C++ source code and shortens the length.
   • Token-level naturalization: Transforms special C/C++ symbols into their natural language representations.

4. Extensive experiments show that CLNX significantly enhances the performance of LLMs on identifying C/C++ VCCs, achieving a new state-of-the-art. CLNX-equipped CodeBERT identifies 38 real-world OSS vulnerabilities.

5. CLNX is a cost-effective solution that improves LLMs' ability to identify VCCs in C/C++ projects without requiring additional pre-training.

Statistieken

52.13% of reported vulnerabilities in OSS are written in C/C++ over the past decade.
The dataset used for evaluation contains 25,872 C/C++ functions with their commits, including 10,894 VCCs.
CLNX-equipped CodeBERT identifies 38 real-world OSS vulnerabilities.

Citaten

"Large Language Models (LLMs) have shown great promise in vulnerability identification."
"As C/C++ comprises half of the Open-Source Software (OSS) vulnerabilities over the past decade and updates in OSS mainly occur through commits, enhancing LLMs' ability to identify C/C++ Vulnerability-Contributing Commits (VCCs) is essential."
"CLNX significantly enhances the performance of LLMs on identifying C/C++ VCCs, achieving new state-of-the-art and identifies 38 OSS vulnerabilities in the real world."

Belangrijkste Inzichten Gedestilleerd Uit

CLNX: Bridging Code and Natural Language for C/C++ Vulnerability-Contributing Commits Identification

by Zeqing Qin, ... om arxiv.org 09-12-2024

https://arxiv.org/pdf/2409.07407.pdf

Diepere vragen

How can CLNX's approach be extended to improve the performance of LLMs on identifying vulnerabilities in other programming languages?

The CodeLinguaNexus (CLNX) framework can be adapted to enhance the performance of Large Language Models (LLMs) in identifying vulnerabilities across various programming languages by implementing a few strategic modifications. Firstly, the naturalization techniques employed in CLNX, such as structure-level and token-level naturalization, can be tailored to accommodate the syntactic and semantic characteristics of different programming languages. For instance, languages like Java or Python have distinct paradigms and constructs that may require specific transformation rules for operators, API calls, and control flow symbols. By developing a comprehensive dictionary of language-specific symbols and their natural language equivalents, CLNX can effectively bridge the gap between code and natural language for these languages.
Secondly, the critical path identification process can be refined to consider the unique execution models and control structures prevalent in other languages. For example, languages with extensive use of asynchronous programming or functional paradigms may necessitate a different approach to identifying execution paths. Incorporating language-specific analysis tools, such as Abstract Syntax Trees (ASTs) and Control Flow Graphs (CFGs) tailored to each language, can enhance the accuracy of the critical path selection.
Lastly, leveraging community-driven datasets and vulnerability databases specific to each programming language can provide valuable insights into common vulnerabilities and their patterns. By integrating these datasets into the training and fine-tuning processes of LLMs, CLNX can improve its effectiveness in identifying vulnerabilities in a broader range of programming languages.

What are the potential limitations of CLNX's structure-level naturalization, and how could they be addressed to further improve the recall of identified vulnerabilities?

One of the primary limitations of CLNX's structure-level naturalization is its tendency to prioritize program length reduction, which can inadvertently lead to the omission of critical code information. This reduction strategy may result in a decrease in recall, as important vulnerability-related details might be lost when selecting the shortest execution path. To address this limitation, a more nuanced approach to critical path selection could be implemented.
For instance, incorporating data flow analysis alongside control flow analysis could provide a more comprehensive understanding of how data moves through the program. By considering both the execution paths and the data dependencies, CLNX could identify paths that not only cover the maximum number of vulnerability-related basic blocks but also retain essential context that may be relevant for vulnerability detection.
Additionally, enhancing the algorithm to evaluate multiple paths with similar coverage could help in selecting paths that maintain a balance between length and the richness of information. Implementing a scoring system that weighs the importance of different basic blocks based on their historical association with vulnerabilities could further refine the critical path selection process. This would ensure that while the model remains efficient, it does not sacrifice the depth of analysis required for effective vulnerability identification.

What other applications or domains could benefit from the lightweight and efficient code naturalization approach introduced by CLNX?

The lightweight and efficient code naturalization approach introduced by CLNX has the potential to benefit several applications and domains beyond vulnerability identification in C/C++. One significant area is automated code review and quality assurance. By applying CLNX's naturalization techniques, code review tools can better understand the semantics of the code, allowing for more accurate identification of code smells, anti-patterns, and potential bugs, thereby enhancing overall code quality.
Another promising application is in educational tools for programming. CLNX's ability to translate complex code into more understandable natural language representations can aid learners in grasping programming concepts and debugging techniques. This could be particularly beneficial in environments where students are learning multiple programming languages, as it would provide a consistent framework for understanding code across different syntaxes.
Furthermore, software maintenance and refactoring processes could leverage CLNX's naturalization capabilities. By simplifying the understanding of legacy codebases, developers can more easily identify areas for improvement, refactoring opportunities, and potential integration points for new features. This would streamline the maintenance process and reduce the cognitive load on developers working with complex or poorly documented code.
Lastly, the approach could be extended to security auditing and compliance in software systems. By naturalizing code, security auditors can more effectively analyze codebases for compliance with security standards and best practices, ensuring that software systems adhere to necessary regulations and guidelines. This could lead to more robust security postures in software development practices across various industries.