Toward Effective Secure Code Reviews: An Empirical Study of Security-Related Coding Weaknesses
- URL: http://arxiv.org/abs/2311.16396v2
- Date: Thu, 9 May 2024 01:43:50 GMT
- Title: Toward Effective Secure Code Reviews: An Empirical Study of Security-Related Coding Weaknesses
- Authors: Wachiraphan Charoenwet, Patanamon Thongtanunam, Van-Thuan Pham, Christoph Treude,
- Abstract summary: We conducted an empirical case study in two large open-source projects, OpenSSL and PHP.
Based on 135,560 code review comments, we found that reviewers raised security concerns in 35 out of 40 coding weakness categories.
Some coding weaknesses related to past vulnerabilities, such as memory errors and resource management, were discussed less often than the vulnerabilities.
- Score: 14.134803943492345
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Identifying security issues early is encouraged to reduce the latent negative impacts on software systems. Code review is a widely-used method that allows developers to manually inspect modified code, catching security issues during a software development cycle. However, existing code review studies often focus on known vulnerabilities, neglecting coding weaknesses, which can introduce real-world security issues that are more visible through code review. The practices of code reviews in identifying such coding weaknesses are not yet fully investigated. To better understand this, we conducted an empirical case study in two large open-source projects, OpenSSL and PHP. Based on 135,560 code review comments, we found that reviewers raised security concerns in 35 out of 40 coding weakness categories. Surprisingly, some coding weaknesses related to past vulnerabilities, such as memory errors and resource management, were discussed less often than the vulnerabilities. Developers attempted to address raised security concerns in many cases (39%-41%), but a substantial portion was merely acknowledged (30%-36%), and some went unfixed due to disagreements about solutions (18%-20%). This highlights that coding weaknesses can slip through code review even when identified. Our findings suggest that reviewers can identify various coding weaknesses leading to security issues during code reviews. However, these results also reveal shortcomings in current code review practices, indicating the need for more effective mechanisms or support for increasing awareness of security issue management in code reviews.
Related papers
- Is Your AI-Generated Code Really Safe? Evaluating Large Language Models on Secure Code Generation with CodeSecEval [20.959848710829878]
Large language models (LLMs) have brought significant advancements to code generation and code repair.
However, their training using unsanitized data from open-source repositories, like GitHub, raises the risk of inadvertently propagating security vulnerabilities.
We aim to present a comprehensive study aimed at precisely evaluating and enhancing the security aspects of code LLMs.
arXiv Detail & Related papers (2024-07-02T16:13:21Z) - Leveraging Large Language Models for Efficient Failure Analysis in Game Development [47.618236610219554]
This paper proposes a new approach to automatically identify which change in the code caused a test to fail.
The method leverages Large Language Models (LLMs) to associate error messages with the corresponding code changes causing the failure.
Our approach reaches an accuracy of 71% in our newly created dataset, which comprises issues reported by developers at EA over a period of one year.
arXiv Detail & Related papers (2024-06-11T09:21:50Z) - How Far Have We Gone in Stripped Binary Code Understanding Using Large Language Models [51.527805834378974]
We propose a benchmark to evaluate the effectiveness of Large Language Models (LLMs) in binary code understanding.
Our evaluations reveal that existing LLMs can understand binary code to a certain extent, thereby improving the efficiency of binary code analysis.
arXiv Detail & Related papers (2024-04-15T14:44:08Z) - CodeAttack: Revealing Safety Generalization Challenges of Large Language Models via Code Completion [117.178835165855]
This paper introduces CodeAttack, a framework that transforms natural language inputs into code inputs.
Our studies reveal a new and universal safety vulnerability of these models against code input.
We find that a larger distribution gap between CodeAttack and natural language leads to weaker safety generalization.
arXiv Detail & Related papers (2024-03-12T17:55:38Z) - LLM-Powered Code Vulnerability Repair with Reinforcement Learning and
Semantic Reward [3.729516018513228]
We introduce a multipurpose code vulnerability analysis system textttSecRepair, powered by a large language model, CodeGen2.
Inspired by how humans fix code issues, we propose an instruction-based dataset suitable for vulnerability analysis with LLMs.
We identify zero-day and N-day vulnerabilities in 6 Open Source IoT Operating Systems on GitHub.
arXiv Detail & Related papers (2024-01-07T02:46:39Z) - Assessing the Security of GitHub Copilot Generated Code -- A Targeted
Replication Study [11.644996472213611]
Recent studies have investigated security issues in AI-powered code generation tools such as GitHub Copilot and Amazon CodeWhisperer.
This paper replicates the study of Pearce et al., which investigated security weaknesses in Copilot and uncovered several weaknesses in the code suggested by Copilot.
Our results indicate that, even with the improvements in newer versions of Copilot, the percentage of vulnerable code suggestions has reduced from 36.54% to 27.25%.
arXiv Detail & Related papers (2023-11-18T22:12:59Z) - Security Defect Detection via Code Review: A Study of the OpenStack and
Qt Communities [7.2944322548786715]
Security defects are not prevalently discussed in code review.
More than half of the reviewers provided explicit fixing strategies/solutions to help developers fix security defects.
Disagreement between the developer and the reviewer are the main causes for not resolving security defects.
arXiv Detail & Related papers (2023-07-05T14:30:41Z) - Transformer-based Vulnerability Detection in Code at EditTime:
Zero-shot, Few-shot, or Fine-tuning? [5.603751223376071]
We present a practical system that leverages deep learning on a large-scale data set of vulnerable code patterns.
We show that in comparison with state of the art vulnerability detection models our approach improves the state of the art by 10%.
arXiv Detail & Related papers (2023-05-23T01:21:55Z) - CodeLMSec Benchmark: Systematically Evaluating and Finding Security
Vulnerabilities in Black-Box Code Language Models [58.27254444280376]
Large language models (LLMs) for automatic code generation have achieved breakthroughs in several programming tasks.
Training data for these models is usually collected from the Internet (e.g., from open-source repositories) and is likely to contain faults and security vulnerabilities.
This unsanitized training data can cause the language models to learn these vulnerabilities and propagate them during the code generation procedure.
arXiv Detail & Related papers (2023-02-08T11:54:07Z) - Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance.
We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems.
We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
arXiv Detail & Related papers (2020-10-19T13:09:31Z) - Deep Just-In-Time Inconsistency Detection Between Comments and Source
Code [51.00904399653609]
In this paper, we aim to detect whether a comment becomes inconsistent as a result of changes to the corresponding body of code.
We develop a deep-learning approach that learns to correlate a comment with code changes.
We show the usefulness of our approach by combining it with a comment update model to build a more comprehensive automatic comment maintenance system.
arXiv Detail & Related papers (2020-10-04T16:49:28Z)
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.