Improving the Context Length and Efficiency of Code Retrieval for Tracing Security Vulnerability Fixes

• URL: http://arxiv.org/abs/2503.22935v2
• Date: Sat, 31 May 2025 19:45:52 GMT
• Title: Improving the Context Length and Efficiency of Code Retrieval for Tracing Security Vulnerability Fixes
• Authors: Xueqing Liu, Jiangrui Zheng, Guanqun Yang, Siyan Wen, Qiushi Liu, Xiaoyin Wang,
• Abstract summary: Existing approaches to trace/retrieve the patching commit for fixing a CVE suffer from two major challenges.<n>We propose SITPatchTracer, a scalable and effective retrieval system for tracing known vulnerability patches.<n>Using SITPatchTracer, we have successfully traced and merged the patch links for 35 new CVEs in the GitHub Advisory database.
• Score: 7.512949497610182
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: An upstream task for software bill-of-materials (SBOMs) is the accurate localization of the patch that fixes a vulnerability. Nevertheless, existing work reveals a significant gap in the CVEs whose patches exist but are not traceable. Existing works have proposed several approaches to trace/retrieve the patching commit for fixing a CVE. However, they suffer from two major challenges: (1) They cannot effectively handle long diff code of a commit; (2) We are not aware of existing work that scales to the full repository with satisfactory accuracy. Upon identifying this gap, we propose SITPatchTracer, a scalable and effective retrieval system for tracing known vulnerability patches. To handle the context length challenge, SITPatchTracer proposes a novel hierarchical embedding technique which efficiently extends the context coverage to 6x that of existing work while covering all files in the commit. To handle the scalability challenge, SITPatchTracer utilizes a three-phase framework, balancing the effectiveness/efficiency in each phase. The evaluation of SITPatchTracer demonstrates it outperforms existing patch tracing methods (PatchFinder, PatchScout, VFCFinder) by a large margin. Furthermore, SITPatchTracer outperforms VoyageAI, the SOTA commercial code embedding LLM (\$1.8 per 10K commits) on the MRR and Recall@10 by 18\% and 28\% on our two datasets. Using SITPatchTracer, we have successfully traced and merged the patch links for 35 new CVEs in the GitHub Advisory database Our ablation study reveals that hierarchical embedding is a practically effective way of handling long context for patch retrieval.

Related papers

• Towards Storage-Efficient Visual Document Retrieval: An Empirical Study on Reducing Patch-Level Embeddings [70.26204343623215]
  ColPali/ColQwen2 encodes each page into multiple patch-level embeddings and leads to excessive memory usage.<n>This empirical study investigates methods to reduce patch embeddings per page at minimum performance degradation.
  arXiv  Detail & Related papers  (2025-06-05T13:06:01Z)
• SwingArena: Competitive Programming Arena for Long-context GitHub Issue Solving [90.32201622392137]
  We present SwingArena, a competitive evaluation framework for Large Language Models (LLMs)<n>Unlike traditional static benchmarks, SwingArena models the collaborative process of software by pairing LLMs as iterations, who generate patches, and reviewers, who create test cases and verify the patches through continuous integration (CI) pipelines.
  arXiv  Detail & Related papers  (2025-05-29T18:28:02Z)
• Enhancing Repository-Level Software Repair via Repository-Aware Knowledge Graphs [8.467850621024672]
  Repository-level software repair faces challenges in bridging semantic gaps between issue descriptions and code patches.<n>Existing approaches, which mostly depend on large language models (LLMs), suffer from semantic ambiguities, limited structural context understanding, and insufficient reasoning capability.<n>We propose a novel repository-aware knowledge graph (KG) that accurately links repository artifacts (issues and pull requests) and entities.
  arXiv  Detail & Related papers  (2025-03-27T17:21:47Z)
• How Robust Are Router-LLMs? Analysis of the Fragility of LLM Routing Capabilities [62.474732677086855]
  Large language model (LLM) routing has emerged as a crucial strategy for balancing computational costs with performance. We propose the DSC benchmark: Diverse, Simple, and Categorized, an evaluation framework that categorizes router performance across a broad spectrum of query types.
  arXiv  Detail & Related papers  (2025-03-20T19:52:30Z)
• ReF Decompile: Relabeling and Function Call Enhanced Decompile [50.86228893636785]
  The goal of decompilation is to convert compiled low-level code (e.g., assembly code) back into high-level programming languages.<n>This task supports various reverse engineering applications, such as vulnerability identification, malware analysis, and legacy software migration.
  arXiv  Detail & Related papers  (2025-02-17T12:38:57Z)
• Fine-Grained 1-Day Vulnerability Detection in Binaries via Patch Code Localization [12.73365645156957]
  1-day vulnerabilities in binaries have become a major threat to software security.<n>patch presence test is one of the effective ways to detect the vulnerability.<n>We propose a novel approach named PLocator, which leverages stable values from both the patch code and its context.
  arXiv  Detail & Related papers  (2025-01-29T04:35:37Z)
• SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub Issue Resolution [56.9361004704428]
  Large Language Models (LLMs) have demonstrated remarkable proficiency across a variety of complex tasks.<n>SWE-Fixer is a novel open-source framework designed to effectively and efficiently resolve GitHub issues.<n>We assess our approach on the SWE-Bench Lite and Verified benchmarks, achieving competitive performance among open-source models.
  arXiv  Detail & Related papers  (2025-01-09T07:54:24Z)
• CommitShield: Tracking Vulnerability Introduction and Fix in Version Control Systems [15.037460085046806]
  CommitShield is a tool for detecting vulnerabilities in code commits. It combines the code analysis capabilities of static analysis tools with the natural language and code understanding capabilities of large language models. We show that CommitShield improves recall by 76%-87% over state-of-the-art methods in the vulnerability fix detection task.
  arXiv  Detail & Related papers  (2025-01-07T08:52:55Z)
• Improving Discovery of Known Software Vulnerability For Enhanced Cybersecurity [0.0]
  Vulnerability detection relies on standardized identifiers such as Common Platformion (CPE) strings. Non-standardized CPE strings issued by software vendors create a significant challenge. Inconsistent naming conventions, and versioning practices lead to mismatches when querying databases.
  arXiv  Detail & Related papers  (2024-12-21T12:43:52Z)
• Repository-Level Graph Representation Learning for Enhanced Security Patch Detection [22.039868029497942]
  This paper proposes a Repository-level Security Patch Detection framework named RepoSPD.<n>RepoSPD comprises three key components: 1) a repository-level graph construction, RepoCPG, which represents software patches by merging pre-patch and post-patch source code at the repository level; 2) a structure-aware patch representation, which fuses the graph and sequence branch and aims at comprehending the relationship among multiple code changes; and 3) progressive learning, which facilitates the model in balancing semantic and structural information.
  arXiv  Detail & Related papers  (2024-12-11T03:29:56Z)
• Learning Graph-based Patch Representations for Identifying and Assessing Silent Vulnerability Fixes [5.983725940750908]
  Software projects are dependent on many third-party libraries, therefore high-risk vulnerabilities can propagate through the dependency chain to downstream projects. Silent vulnerability fixes cause downstream software to be unaware of urgent security issues in a timely manner, posing a security risk to the software. We propose GRAPE, a GRAph-based Patch rEpresentation that aims to provide a unified framework for getting vulnerability fix patches representation.
  arXiv  Detail & Related papers  (2024-09-13T03:23:11Z)
• The Impact of SBOM Generators on Vulnerability Assessment in Python: A Comparison and a Novel Approach [56.4040698609393]
  Software Bill of Materials (SBOM) has been promoted as a tool to increase transparency and verifiability in software composition. Current SBOM generation tools often suffer from inaccuracies in identifying components and dependencies. We propose PIP-sbom, a novel pip-inspired solution that addresses their shortcomings.
  arXiv  Detail & Related papers  (2024-09-10T10:12:37Z)
• PatchFinder: A Two-Phase Approach to Security Patch Tracing for Disclosed Vulnerabilities in Open-Source Software [15.867607171943698]
  We propose a two-phase framework with end-to-end correlation learning for better-tracing security patches. PatchFinder achieves a Recall@10 of 80.63% and a Mean Reciprocal Rank (MRR) of 0.7951. When applying PatchFinder in practice, we initially identified 533 patch commits and submitted them to the official, 482 of which have been confirmed by CVE Numbering Authorities.
  arXiv  Detail & Related papers  (2024-07-24T07:46:24Z)
• Vulnerability Detection with Code Language Models: How Far Are We? [40.455600722638906]
  PrimeVul is a new dataset for training and evaluating code LMs for vulnerability detection. It incorporates a novel set of data labeling techniques that achieve comparable label accuracy to human-verified benchmarks. It also implements a rigorous data de-duplication and chronological data splitting strategy to mitigate data leakage issues.
  arXiv  Detail & Related papers  (2024-03-27T14:34:29Z)
• Profile of Vulnerability Remediations in Dependencies Using Graph Analysis [40.35284812745255]
  This research introduces graph analysis methods and a modified Graph Attention Convolutional Neural Network (GAT) model. We analyze control flow graphs to profile breaking changes in applications occurring from dependency upgrades intended to remediate vulnerabilities. Results demonstrate the effectiveness of the enhanced GAT model in offering nuanced insights into the relational dynamics of code vulnerabilities.
  arXiv  Detail & Related papers  (2024-03-08T02:01:47Z)
• ReposVul: A Repository-Level High-Quality Vulnerability Dataset [13.90550557801464]
  We propose an automated data collection framework and construct the first repository-level high-quality vulnerability dataset named ReposVul. The proposed framework mainly contains three modules: (1) A vulnerability untangling module, aiming at distinguishing vulnerability-fixing related code changes from tangled patches, in which the Large Language Models (LLMs) and static analysis tools are jointly employed, (2) A multi-granularity dependency extraction module, aiming at capturing the inter-procedural call relationships of vulnerabilities, in which we construct multiple-granularity information for each vulnerability patch, including repository-level, file-level, function-level
  arXiv  Detail & Related papers  (2024-01-24T01:27:48Z)
• SliceLocator: Locating Vulnerable Statements with Graph-based Detectors [33.395068754566935]
  SliceLocator identifies the most relevant taint flow by selecting the highest-weighted flow path from all potential vulnerability-triggering statements.<n>We demonstrate that SliceLocator consistently performs well on four state-of-the-art GNN-based vulnerability detectors.
  arXiv  Detail & Related papers  (2024-01-05T10:15:04Z)
• Just-in-Time Detection of Silent Security Patches [7.840762542485285]
  Security patches can be em silent, i.e., they do not always come with comprehensive advisories such as CVEs. This lack of transparency leaves users oblivious to available security updates, providing ample opportunity for attackers to exploit unpatched vulnerabilities. We propose to leverage large language models (LLMs) to augment patch information with generated code change explanations.
  arXiv  Detail & Related papers  (2023-12-02T22:53:26Z)
• CompVPD: Iteratively Identifying Vulnerability Patches Based on Human Validation Results with a Precise Context [16.69634193308039]
  It is challenging to apply security patches in open source software timely because notifications of patches are often incomplete and delayed. We propose a multi-granularity slicing algorithm and an adaptive-expanding algorithm to accurately identify code related to the patches. We empirically compare CompVPD with four state-of-the-art/practice (SOTA) approaches in identifying vulnerability patches.
  arXiv  Detail & Related papers  (2023-10-04T02:08:18Z)
• REEF: A Framework for Collecting Real-World Vulnerabilities and Fixes [40.401211102969356]
  We propose an automated collecting framework REEF to collect REal-world vulnErabilities and Fixes from open-source repositories. We develop a multi-language crawler to collect vulnerabilities and their fixes, and design metrics to filter for high-quality vulnerability-fix pairs. Through extensive experiments, we demonstrate that our approach can collect high-quality vulnerability-fix pairs and generate strong explanations.
  arXiv  Detail & Related papers  (2023-09-15T02:50:08Z)
• VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements [62.93814803258067]
  This paper presents VELVET, a novel ensemble learning approach to locate vulnerable statements in source code. Our model combines graph-based and sequence-based neural networks to successfully capture the local and global context of a program graph. VELVET achieves 99.6% and 43.6% top-1 accuracy over synthetic data and real-world data, respectively.
  arXiv  Detail & Related papers  (2021-12-20T22:45:27Z)
• Anchor-Free Person Search [127.88668724345195]
  Person search aims to simultaneously localize and identify a query person from realistic, uncropped images. Most existing works employ two-stage detectors like Faster-RCNN, yielding encouraging accuracy but with high computational overhead. We present the Feature-Aligned Person Search Network (AlignPS), the first anchor-free framework to efficiently tackle this challenging task.
  arXiv  Detail & Related papers  (2021-03-22T07:04:29Z)
• CATCH: Context-based Meta Reinforcement Learning for Transferrable Architecture Search [102.67142711824748]
  CATCH is a novel Context-bAsed meTa reinforcement learning algorithm for transferrable arChitecture searcH. The combination of meta-learning and RL allows CATCH to efficiently adapt to new tasks while being agnostic to search spaces. It is also capable of handling cross-domain architecture search as competitive networks on ImageNet, COCO, and Cityscapes are identified.
  arXiv  Detail & Related papers  (2020-07-18T09:35:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.