arXiv subjects

Baijun Cheng

Publications and source records attributed to Baijun Cheng.

5 recordsLinked to original sources

arXiv2025

Boosting Pointer Analysis With LLM-Enhanced Allocation Function Detection

Pointer analysis is foundational for many static analysis tasks, yet its effectiveness is often hindered by imprecise modeling of heap allocations, particularly in C/C++ programs where custom allocation functions (CAFs) are pervasive. Existing approaches largely overlook these custom allocators, leading to coarse aliasing and low analysis precision. In this paper, we present CAFD, a novel and lightweight technique that enhances pointer analysis by automatically detecting side-effect-free custom allocation functions. CAFD employs a hybrid approach: it uses value-flow analysis to detect straightforward wrappers and leverages Large Language Models (LLMs) to reason about more complex allocation patterns with side effects, ensuring that only side-effect-free functions are modeled as allocators. This targeted enhancement enables precise modeling of heap objects at each call site, achieving context-sensitivity-like benefits without significant overhead. We evaluated CAFD on 17 real-world C projects, identifying over 700 CAFs. Integrating CAFD into a baseline pointer analysis yields a 38x increase in modeled heap objects and a 41.5% reduction in alias set sizes, with only 1.4x runtime overhead. Furthermore, the LLM-enhanced pointer analysis improves indirect call resolution and discovers 29 previously undetected memory bugs, including 6 from real-world industrial applications. These results demonstrate that precise modeling of CAFs has the capability to offer a scalable and practical path to improve pointer analysis in large software systems.

cs.SE↗

arXiv2025

Enhancing Semantic Understanding in Pointer Analysis using Large Language Models

Pointer analysis has been studied for over four decades. However, existing frameworks continue to suffer from the propagation of incorrect facts. A major limitation stems from their insufficient semantic understanding of code, resulting in overly conservative treatment of user-defined functions. Recent advances in large language models (LLMs) present new opportunities to bridge this gap. In this paper, we propose LMPA (LLM-enhanced Pointer Analysis), a vision that integrates LLMs into pointer analysis to enhance both precision and scalability. LMPA identifies user-defined functions that resemble system APIs and models them accordingly, thereby mitigating erroneous cross-calling-context propagation. Furthermore, it enhances summary-based analysis by inferring initial points-to sets and introducing a novel summary strategy augmented with natural language. Finally, we discuss the key challenges involved in realizing this vision.

cs.SE↗

arXiv2024

Semantic-Enhanced Indirect Call Analysis with Large Language Models

In contemporary software development, the widespread use of indirect calls to achieve dynamic features poses challenges in constructing precise control flow graphs (CFGs), which further impacts the performance of downstream static analysis tasks. To tackle this issue, various types of indirect call analyzers have been proposed. However, they do not fully leverage the semantic information of the program, limiting their effectiveness in real-world scenarios. To address these issues, this paper proposes Semantic-Enhanced Analysis (SEA), a new approach to enhance the effectiveness of indirect call analysis. Our fundamental insight is that for common programming practices, indirect calls often exhibit semantic similarity with their invoked targets. This semantic alignment serves as a supportive mechanism for static analysis techniques in filtering out false targets. Notably, contemporary large language models (LLMs) are trained on extensive code corpora, encompassing tasks such as code summarization, making them well-suited for semantic analysis. Specifically, SEA leverages LLMs to generate natural language summaries of both indirect calls and target functions from multiple perspectives. Through further analysis of these summaries, SEA can determine their suitability as caller-callee pairs. Experimental results demonstrate that SEA can significantly enhance existing static analysis methods by producing more precise target sets for indirect calls.

cs.SE↗

arXiv2024

SliceLocator: Locating Vulnerable Statements with Graph-based Detectors

Vulnerability detection is a crucial component in the software development lifecycle. Existing vulnerability detectors, especially those based on deep learning (DL) models, have achieved high effectiveness. Despite their capability of detecting vulnerable code snippets from given code fragments, the detectors are typically unable to further locate the fine-grained information pertaining to the vulnerability, such as the precise vulnerability triggering locations. Although explanation methods can filter important statements based on the predictions of code fragments, their effectiveness is limited by the fact that the model primarily learns the difference between vulnerable and non-vulnerable samples. In this paper, we propose SliceLocator, which, unlike previous approaches, leverages the detector's understanding of the differences between vulnerable and non-vulnerable samples, essentially, vulnerability-fixing statements. SliceLocator identifies the most relevant taint flow by selecting the highest-weighted flow path from all potential vulnerability-triggering statements in the program, in conjunction with the detector. We demonstrate that SliceLocator consistently performs well on four state-of-the-art GNN-based vulnerability detectors, achieving an accuracy of around 87% in flagging vulnerability-triggering statements across six common C/C++ vulnerabilities. It outperforms five widely used GNN-based explanation methods and two statement-level detectors.

cs.SE↗

arXiv2024

Beyond Fidelity: Explaining Vulnerability Localization of Learning-based Detectors

Vulnerability detectors based on deep learning (DL) models have proven their effectiveness in recent years. However, the shroud of opacity surrounding the decision-making process of these detectors makes it difficult for security analysts to comprehend. To address this, various explanation approaches have been proposed to explain the predictions by highlighting important features, which have been demonstrated effective in other domains such as computer vision and natural language processing. Unfortunately, an in-depth evaluation of vulnerability-critical features, such as fine-grained vulnerability-related code lines, learned and understood by these explanation approaches remains lacking. In this study, we first evaluate the performance of ten explanation approaches for vulnerability detectors based on graph and sequence representations, measured by two quantitative metrics including fidelity and vulnerability line coverage rate. Our results show that fidelity alone is not sufficient for evaluating these approaches, as fidelity incurs significant fluctuations across different datasets and detectors. We subsequently check the precision of the vulnerability-related code lines reported by the explanation approaches, and find poor accuracy in this task among all of them. This can be attributed to the inefficiency of explainers in selecting important features and the presence of irrelevant artifacts learned by DL-based detectors.

cs.CR↗