Similar but Patched Code Considered Harmful -- The Impact of Similar but Patched Code on Recurring Vulnerability Detection and How to Remove Them

• URL: http://arxiv.org/abs/2412.20740v1
• Date: Mon, 30 Dec 2024 06:32:10 GMT
• Title: Similar but Patched Code Considered Harmful -- The Impact of Similar but Patched Code on Recurring Vulnerability Detection and How to Remove Them
• Authors: Zixuan Tan, Jiayuan Zhou, Xing Hu, Shengyi Pan, Kui Liu, Xin Xia,
• Abstract summary: We propose a programming language framework, Fixed Vulnerability Filter (FVF), to identify and filter such SBP instances in vulnerability detection. We apply FVF to 1,081 real-world software projects and construct a real-world SBP dataset containing 6,827 SBP functions.
• Score: 8.404849985552776
• License:
• Abstract: Identifying recurring vulnerabilities is crucial for ensuring software security. Clone-based techniques, while widely used, often generate many false alarms due to the existence of similar but patched (SBP) code, which is similar to vulnerable code but is not vulnerable due to having been patched. Although the SBP code poses a great challenge to the effectiveness of existing approaches, it has not yet been well explored. In this paper, we propose a programming language agnostic framework, Fixed Vulnerability Filter (FVF), to identify and filter such SBP instances in vulnerability detection. Different from existing studies that leverage function signatures, our approach analyzes code change histories to precisely pinpoint SBPs and consequently reduce false alarms. Evaluation under practical scenarios confirms the effectiveness and precision of our approach. Remarkably, FVF identifies and filters 65.1% of false alarms from four vulnerability detection tools (i.e., ReDeBug, VUDDY, MVP, and an elementary hash-based approach) without yielding false positives. We further apply FVF to 1,081 real-world software projects and construct a real-world SBP dataset containing 6,827 SBP functions. Due to the SBP nature, the dataset can act as a strict benchmark to test the sensitivity of the vulnerability detection approach in distinguishing real vulnerabilities and SBPs. Using this dataset, we demonstrate the ineffectiveness of four state-of-the-art deep learning-based vulnerability detection approaches. Our dataset can help developers make a more realistic evaluation of vulnerability detection approaches and also paves the way for further exploration of real-world SBP scenarios.

Related papers

• Are We Learning the Right Features? A Framework for Evaluating DL-Based Software Vulnerability Detection Solutions [3.204048014949849]
  This paper aims to provide the foundation for properly evaluating the research in this domain. We analyze vulnerability datasets for the syntactic and semantic features of code that contribute to vulnerability. We provide a novel, uniform representation to capture both sets of features, and use this representation to detect the presence of both vulnerability and spurious features in code.
  arXiv  Detail & Related papers  (2025-01-23T00:32:15Z)
• 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)
• Toward Improved Deep Learning-based Vulnerability Detection [2.482408071920832]
  Vulnerabilities in datasets have to be represented in a certain way, e.g., code lines, functions, or program slices within which the vulnerabilities exist. The detectors learn how base units can be vulnerable and then predict whether other base units are vulnerable. We have hypothesized that this focus on individual base units harms the ability of the detectors to properly detect those vulnerabilities that span multiple base units. We present our study and a framework that can be used to help DL-based detectors toward the proper inclusion of MBU vulnerabilities.
  arXiv  Detail & Related papers  (2024-03-05T14:57:28Z)
• 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)
• CrossDF: Improving Cross-Domain Deepfake Detection with Deep Information Decomposition [53.860796916196634]
  We propose a Deep Information Decomposition (DID) framework to enhance the performance of Cross-dataset Deepfake Detection (CrossDF) Unlike most existing deepfake detection methods, our framework prioritizes high-level semantic features over specific visual artifacts. It adaptively decomposes facial features into deepfake-related and irrelevant information, only using the intrinsic deepfake-related information for real/fake discrimination.
  arXiv  Detail & Related papers  (2023-09-30T12:30:25Z)
• DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection [55.70982767084996]
  A critical yet frequently overlooked challenge in the field of deepfake detection is the lack of a standardized, unified, comprehensive benchmark. We present the first comprehensive benchmark for deepfake detection, called DeepfakeBench, which offers three key contributions. DeepfakeBench contains 15 state-of-the-art detection methods, 9CL datasets, a series of deepfake detection evaluation protocols and analysis tools, as well as comprehensive evaluations.
  arXiv  Detail & Related papers  (2023-07-04T01:34:41Z)
• Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability Detection [17.761541379830373]
  DeepDFA is a dataflow analysis-inspired graph learning framework. It was trained in 9 minutes, 75x faster than the highest-performing baseline model. It detected 8.7 out of 17 vulnerabilities on average across folds and was able to distinguish between patched and buggy versions.
  arXiv  Detail & Related papers  (2022-12-15T19:49:27Z)
• 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)
• Spotting adversarial samples for speaker verification by neural vocoders [102.1486475058963]
  We adopt neural vocoders to spot adversarial samples for automatic speaker verification (ASV) We find that the difference between the ASV scores for the original and re-synthesize audio is a good indicator for discrimination between genuine and adversarial samples. Our codes will be made open-source for future works to do comparison.
  arXiv  Detail & Related papers  (2021-07-01T08:58:16Z)
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection [66.05992706105224]
  In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
  arXiv  Detail & Related papers  (2020-08-05T19:29:35Z)
• Autosploit: A Fully Automated Framework for Evaluating the Exploitability of Security Vulnerabilities [47.748732208602355]
  Autosploit is an automated framework for evaluating the exploitability of vulnerabilities. It automatically tests the exploits on different configurations of the environment. It is able to identify the system properties that affect the ability to exploit a vulnerability in both noiseless and noisy environments.
  arXiv  Detail & Related papers  (2020-06-30T18:49:18Z)

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.