LogicEval: A Systematic Framework for Evaluating Automated Repair Techniques for Logical Vulnerabilities in Real-World Software
Summary: arXiv:2604.12994v1 Announce Type: cross
Abstract
Logical vulnerabilities in software stem from flaws in program logic rather than memory safety, which can lead to critical security failures. While existing automated program repair techniques primarily focus on repairing memory corruption vulnerabilities, they struggle with logical vulnerabilities due to their limited semantic understanding of the vulnerable code and its expected behavior. Recent advancements in large language models (LLMs) have shown promise in understanding and repairing code. However, no framework is currently available to analyze the capabilities and limitations of such techniques in relation to logical vulnerabilities.
Introduction
This paper introduces LogicEval, a systematic framework designed to evaluate both traditional and LLM-based repair approaches for addressing real-world logical vulnerabilities. The need for such a framework arises from the critical role software security plays in today’s digital landscape, where logical vulnerabilities can lead to severe consequences.
Methodology
To facilitate our assessment of automated repair techniques, we developed LogicDS, the first-ever dataset comprising 86 logical vulnerabilities, each assigned with Common Vulnerabilities and Exposures (CVE) identifiers that reflect their tangible security impact. This dataset serves as a benchmark for evaluating the effectiveness of various repair methods.
Key Findings
The evaluations conducted using LogicEval reveal several important insights:
- Compilation and testing failures in automated repairs are primarily driven by:
- Prompt Sensitivity: The specific phrasing and context provided in prompts significantly affect repair outcomes.
- Loss of Code Context: Understanding the broader context of the code is crucial for effective repairs; without it, the success rate diminishes.
- Difficulty in Patch Localization: Identifying the exact location of the logical flaw remains a challenging aspect for both traditional and LLM-based approaches.
Conclusion
LogicEval serves as a foundational framework for systematically evaluating and comparing various automated repair techniques addressing logical vulnerabilities in software. By leveraging the LogicDS dataset, researchers and practitioners can better understand the strengths and weaknesses of different approaches, paving the way for improved security measures in software development.
Future Work
There is significant potential for further research in this area, particularly in enhancing the capabilities of LLMs to understand and repair logical vulnerabilities. Future studies could focus on refining the dataset, expanding it to include more complex vulnerabilities, and exploring novel approaches that combine traditional methods with insights gained from LLMs.
Keywords
Logical vulnerabilities, automated program repair, large language models, software security, LogicEval, LogicDS, CVE.