The Java libraries JCA and JSSE offer cryptographic APIs to facilitate secure coding. When developers misuse some of the APIs, their code becomes vulnerable to cyber-attacks. To eliminate such vulnerabilities, people built tools to detect security-API misuses via pattern matching. However, most tools do not (1) fix misuses or (2) allow users to extend tools' pattern sets. To overcome both limitations, we created Seader-an example-based approach to detect and repair security-API misuses. Given an exemplar <insecure, secure>code pair, Seader compares the snippets to infer any API-misuse template and corresponding fixing edit. Based on the inferred info, given a program, Seader performs inter-procedural static analysis to search for security-API misuses and to propose customized fixes. For evaluation, we applied Seader to 28 <insecure, secure> codepairs; Seader successfully inferred 21 unique API-misuse templates and related fixes. With these <vulnerability, fix> patterns, we applied SEADER to a program benchmark that has 86 known vulnerabilities. Seader detected vulnerabilities with 95% precision, 72% recall, and82% F-score. We also applied Seader to 100 open-source projects and manually checked 77 suggested repairs; 76 of the repairs were correct. Seader can help developers correctly use security APIs.
CONTEXT: Applying vulnerability detection techniques is one of many tasks using the limited resources of a software project. OBJECTIVE: The goal of this research is to assist managers and other decision-makers in making informed choices about the use of software vulnerability detection techniques through an empirical study of the efficiency and effectiveness of four techniques on a Java-based web application. METHOD: We apply four different categories of vulnerability detection techniques \textendash~ systematic manual penetration testing (SMPT), exploratory manual penetration testing (EMPT), …
Java platform provides various APIs to facilitate secure coding. However, correctly using security APIs is usually challenging for developers who lack cybersecurity training. Prior work shows that many developers misuse security APIs; such misuses can introduce vulnerabilities into software, void security protections, and present security exploits to hackers. To eliminate such API-related vulnerabilities, this paper presents SEADER -- our new approach that detects and repairs security API misuses. Given an exemplar, insecure code snippet, and its secure counterpart, SEADER compares the snippet…
Due to the growing number of cyber attacks against computer systems, we need to pay special attention to the security of our software systems. In order to maximize the effectiveness, excluding the human component from this process would be a huge breakthrough. The first step towards this is to automatically recognize the vulnerable parts in our code. Researchers put a lot of effort into creating machine learning models that could determine if a given piece of code, or to be more precise, a selected function, contains any vulnerabilities or not. We aim at improving the existing models, building…
Large Language Models Cannot Reliably Detect Vulnerabilities in JavaScript: The First Systematic Benchmark and Evaluation
Lanzhou University（兰州大学）；Zhejiang University（浙江大学）；Beihang University（北京航空航天大学）；Technology Support Center of the Cyberspace Administration of China（国家互联网信息办公室技术支撑中心）；Fudan University（复旦大学）；Beijing University of Posts（北京邮电大学）
Several studies showed that misuses of cryptographic APIs are common in real-world code (e.g., Apache projects and Android apps). There exist several open-sourced and commercial security tools that automatically screen Java programs to detect misuses. To compare their accuracy and security guarantees, we develop two comprehensive benchmarks named CryptoAPI-Bench and ApacheCryptoAPI-Bench. CryptoAPI-Bench consists of 181 unit test cases that cover basic cases, as well as complex cases, including interprocedural, field sensitive, multiple class test cases, and path sensitive data flow of misuse …
K. N. Toosi University of Technology（肯恩·托基大学）；Ferdowsi University of Mashhad（马什哈德费尔多西大学）；Simon Fraser University（西蒙菲莎大学）；Amirkabir University of Technology（阿米尔卡比尔理工大学）
PatchIsland: Orchestration of LLM Agents for Continuous Vulnerability Repair
KAIST（韩国科学技术院）；POSTECH（浦项工科大学）；Georgia Institute of Technology（佐治亚理工学院）；Korea University（高丽大学）；Samsung SDS（三星SDS）；Samsung Electronics（三星电子）；ENKI WhiteHat；Microsoft（微软）
KTH Royal Institute of Technology（瑞典皇家理工学院）；Colorado State University（科罗拉多州立大学）
In this paper, we address the problem of automatic repair of software vulnerabilities with deep learning. The major problem with data-driven vulnerability repair is that the few existing datasets of known confirmed vulnerabilities consist of only a few thousand examples. However, training a deep learning model often requires hundreds of thousands of examples. In this work, we leverage the intuition that the bug fixing task and the vulnerability fixing task are related and that the knowledge learned from bug fixes can be transferred to fixing vulnerabilities. In the machine learning community, …
New York University（纽约大学）；University of Calgary（卡尔加里大学）
Human developers can produce code with cybersecurity bugs. Can emerging 'smart' code completion tools help repair those bugs? In this work, we examine the use of large language models (LLMs) for code (such as OpenAI's Codex and AI21's Jurassic J-1) for zero-shot vulnerability repair. We investigate challenges in the design of prompts that coax LLMs into generating repaired versions of insecure code. This is difficult due to the numerous ways to phrase key information - both semantically and syntactically - with natural languages. We perform a large scale study of five commercially available, b…
With one of the largest available collection of reusable packages, the JavaScript runtime environment Node.js is one of the most popular programming application. With recent work showing evidence that known vulnerabilities are prevalent in both open source and industrial software, we propose and implement a viable code-based vulnerability detection tool for Node.js applications. Our case study lists the challenges encountered while implementing our Node.js vulnerable code detector.
Computer security has been a concern for decades and artificial intelligence techniques have been applied to the area for nearly as long. Most of the techniques are being applied to the detection of attacks to running systems, but recent improvements in machine learning (for example, in natural language processing) have enabled the opportunity to process software and specifications to detect vulnerabilities in a system before it is deployed. This paper presents a survey of artificial intelligence techniques (including machine learning) to detect or repair security vulnerabilities before produc…
Users around the world rely on software-intensive systems in their day-to-day activities. These systems regularly contain bugs and security vulnerabilities. To facilitate bug fixing, data-driven models of automatic program repair use pairs of buggy and fixed code to learn transformations that fix errors in code. However, automatic repair of security vulnerabilities remains under-explored. In this work, we propose ways to improve code representations for vulnerability repair from three perspectives: input data type, data-driven models, and downstream tasks. The expected results of this work are…
Virtualization enables information and communications technology industry to better manage computing resources. In this regard, improvements in virtualization approaches together with the need for consistent runtime environment, lower overhead and smaller package size has led to the growing adoption of containers. This is a technology, which packages an application, its dependencies and Operating System (OS) to run as an isolated unit. However, the pressing concern with the use of containers is its susceptibility to security attacks. Consequently, a number of container scanning tools are avail…
The best practice to prevent Cross Site Scripting (XSS) attacks is to apply encoders to sanitize untrusted data. To balance security and functionality, encoders should be applied to match the web page context, such as HTML body, JavaScript, and style sheets. A common programming error is the use of a wrong type of encoder to sanitize untrusted data, leaving the application vulnerable. We present a security unit testing approach to detect XSS vulnerabilities caused by improper encoding of untrusted data. Unit tests for the XSS vulnerability are constructed out of each web page and then evaluate…
Two Timin': Repairing Smart Contracts With A Two-Layered Approach
Westborough High School（韦斯特伯勒高中）；Sunset High School（森塞特高中）；Granite Bay High School（格拉尼特贝高中）；Lakeside School（湖滨学校）；Bellarmine College Preparatory（贝拉明大学预科学校）；Robbinsville High School（罗宾斯维尔高中）；University of Illinois（伊利诺伊大学）
