Learning Graph-based Patch Representations for Identifying and Assessing Silent Vulnerability Fixes

• URL: http://arxiv.org/abs/2409.08512v1
• Date: Fri, 13 Sep 2024 03:23:11 GMT
• Title: Learning Graph-based Patch Representations for Identifying and Assessing Silent Vulnerability Fixes
• Authors: Mei Han, Lulu Wang, Jianming Chang, Bixin Li, Chunguang Zhang,
• Abstract summary: 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.
• Score: 5.983725940750908
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Software projects are dependent on many third-party libraries, therefore high-risk vulnerabilities can propagate through the dependency chain to downstream projects. Owing to the subjective nature of patch management, software vendors commonly fix vulnerabilities silently. Silent vulnerability fixes cause downstream software to be unaware of urgent security issues in a timely manner, posing a security risk to the software. Presently, most of the existing works for vulnerability fix identification only consider the changed code as a sequential textual sequence, ignoring the structural information of the code. In this paper, we propose GRAPE, a GRAph-based Patch rEpresentation that aims to 1) provide a unified framework for getting vulnerability fix patches representation; and 2) enhance the understanding of the intent and potential impact of patches by extracting structural information of the code. GRAPE employs a novel joint graph structure (MCPG) to represent the syntactic and semantic information of fix patches and embeds both nodes and edges. Subsequently, a carefully designed graph convolutional neural network (NE-GCN) is utilized to fully learn structural features by leveraging the attributes of the nodes and edges. Moreover, we construct a dataset containing 2251 silent fixes. For the experimental section, we evaluated patch representation on three tasks, including vulnerability fix identification, vulnerability types classification, and vulnerability severity classification. Experimental results indicate that, in comparison to baseline methods, GRAPE can more effectively reduce false positives and omissions of vulnerability fixes identification and provide accurate vulnerability assessments.

Related papers

• 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)
• Code Structure-Aware through Line-level Semantic Learning for Code Vulnerability Detection [44.29771620061153]
  We propose a novel network architecture based on pre-trained code models, which incorporates structural information awareness. We introduce a new network architecture, the Code Structure-Aware Network through Line-level Semantic Learning (CSLS), which integrates three key components: global vulnerability awareness, line-structural awareness, and sensitive-line awareness.
  arXiv  Detail & Related papers  (2024-07-26T17:15:58Z)
• FoC: Figure out the Cryptographic Functions in Stripped Binaries with LLMs [54.27040631527217]
  We propose a novel framework called FoC to Figure out the Cryptographic functions in stripped binaries. FoC-BinLLM outperforms ChatGPT by 14.61% on the ROUGE-L score. FoC-Sim outperforms the previous best methods with a 52% higher Recall@1.
  arXiv  Detail & Related papers  (2024-03-27T09:45:33Z)
• 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)
• The Vulnerability Is in the Details: Locating Fine-grained Information of Vulnerable Code Identified by Graph-based Detectors [33.395068754566935]
  VULEXPLAINER is a tool for locating vulnerability-critical code lines from coarse-level vulnerable code snippets. It can flag the vulnerability-triggering code statements with an accuracy of around 90% against eight common C/C++ vulnerabilities.
  arXiv  Detail & Related papers  (2024-01-05T10:15:04Z)
• 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)
• Silent Vulnerability-fixing Commit Identification Based on Graph Neural Networks [4.837912059099674]
  VFFINDER is a graph-based approach for automated silent vulnerability fix identification. VFFINDER distinguishes vulnerability-fixing commits from non-fixing ones using attention-based graph neural network models. Our results show that VFFINDER significantly improves the state-of-the-art methods by 272-420% in Precision, 22-70% in Recall, and 3.2X-8.2X in F1.
  arXiv  Detail & Related papers  (2023-09-15T07:51:39Z)
• VFFINDER: A Graph-based Approach for Automated Silent Vulnerability-Fix Identification [4.837912059099674]
  VFFINDER is a graph-based approach for automated silent vulnerability fix identification. It distinguishes vulnerability-fixing commits from non-fixing ones using attention-based graph neural network models. Our results show that VFFINDER significantly improves the state-of-the-art methods by 39-83% in Precision, 19-148% in Recall, and 30-109% in F1.
  arXiv  Detail & Related papers  (2023-09-05T05:55:18Z)
• 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)
• Software Vulnerability Detection via Deep Learning over Disaggregated Code Graph Representation [57.92972327649165]
  This work explores a deep learning approach to automatically learn the insecure patterns from code corpora. Because code naturally admits graph structures with parsing, we develop a novel graph neural network (GNN) to exploit both the semantic context and structural regularity of a program.
  arXiv  Detail & Related papers  (2021-09-07T21:24:36Z)
• Information Obfuscation of Graph Neural Networks [96.8421624921384]
  We study the problem of protecting sensitive attributes by information obfuscation when learning with graph structured data. We propose a framework to locally filter out pre-determined sensitive attributes via adversarial training with the total variation and the Wasserstein distance.
  arXiv  Detail & Related papers  (2020-09-28T17:55:04Z)

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.