Automatically detecting software vulnerabilities is an important problem that has attracted much attention from the academic research community. However, existing vulnerability detectors still cannot achieve the vulnerability detection capability and the locating precision that would warrant their adoption for real-world use. In this paper, we present a vulnerability detector that can simultaneously achieve a high detection capability and a high locating precision, dubbed Vulnerability Deep learning-based Locator (VulDeeLocator). In the course of designing VulDeeLocator, we encounter difficulties including how to accommodate semantic relations between the definitions of types as well as macros and their uses across files, how to accommodate accurate control flows and variable define-use relations, and how to achieve high locating precision. We solve these difficulties by using two innovative ideas: (i) leveraging intermediate code to accommodate extra semantic information, and (ii) using the notion of granularity refinement to pin down locations of vulnerabilities. When applied to 200 files randomly selected from three real-world software products, VulDeeLocator detects 18 confirmed vulnerabilities (i.e., true-positives). Among them, 16 vulnerabilities correspond to known vulnerabilities; the other two are not reported in the National Vulnerability Database (NVD) but have been "silently" patched by the vendor of Libav when releasing newer versions.
Despite the successes of machine learning (ML) and deep learning (DL) based vulnerability detectors (VD), they are limited to providing only the decision on whether a given code is vulnerable or not, without details on what part of the code is relevant to the detected vulnerability. We present IVDetect an interpretable vulnerability detector with the philosophy of using Artificial Intelligence (AI) to detect vulnerabilities, while using Intelligence Assistant (IA) via providing VD interpretations in terms of vulnerable statements. For vulnerability detection, we separately consider the vulnera…
The University of Adelaide（阿德莱德大学）；Cyber Security Cooperative Research Centre（网络安全合作研究中心）
Many studies have developed Machine Learning (ML) approaches to detect Software Vulnerabilities (SVs) in functions and fine-grained code statements that cause such SVs. However, there is little work on leveraging such detection outputs for data-driven SV assessment to give information about exploitability, impact, and severity of SVs. The information is important to understand SVs and prioritize their fixing. Using large-scale data from 1,782 functions of 429 SVs in 200 real-world projects, we investigate ML models for automating function-level SV assessment tasks, i.e., predicting seven Commo…
A fine-grained provenance-based access control policy model is proposed in this paper, in order to improve the express performance of existing model. This method employs provenance as conditions to determine whether a piece of data can be accessed because historical operations performed on data could reveal clues about its sensitivity and vulnerability. Particularly, our proposed work provides a four-valued decision set which allows showing status to match a restriction particularly. This framework consists of target policy, access control policy, and policy algebras. With the complete definit…
Reading or writing outside the bounds of a buffer is a serious security vulnerability that has been exploited in numerous occasions. These attacks can be prevented by ensuring that every buffer is only accessed within its specified bounds. In this paper we present Gandalf, a compiler-assisted hardware extension for the OpenRISC processor that thwarts all forms of memory based attacks including buffer overflows and over-reads.The feature associates lightweight base and bound capabilities to all pointer variables, which are checked at run time by the hardware. Gandalf is transparent to the user …
Huazhong University of Science and Technology（华中科技大学）；Shenzhen Huazhong University of Science and Technology Research Institute（深圳华中科技大学研究院）；University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）
Fine-grained software vulnerability detection is an important and challenging problem. Ideally, a detection system (or detector) not only should be able to detect whether or not a program contains vulnerabilities, but also should be able to pinpoint the type of a vulnerability in question. Existing vulnerability detection methods based on deep learning can detect the presence of vulnerabilities (i.e., addressing the binary classification or detection problem), but cannot pinpoint types of vulnerabilities (i.e., incapable of addressing multiclass classification). In this paper, we propose the f…
Beyond Fidelity: Explaining Vulnerability Localization of Learning-based Detectors
Peking University（北京大学）；University of Alberta（阿尔伯塔大学）；Huazhong University of Science and Technology（华中科技大学）；University of Queensland（昆士兰大学）；University of New South Wales（新南威尔士大学）
The wide acceptance of Internet of Things (IoT) for both household and industrial applications is accompanied by several security concerns. A major security concern is their probable abuse by adversaries towards their malicious intent. Understanding and analyzing IoT malicious behaviors is crucial, especially with their rapid growth and adoption in wide-range of applications. However, recent studies have shown that machine learning-based approaches are susceptible to adversarial attacks by adding junk codes to the binaries, for example, with an intention to fool those machine learning or deep …
Knowdit: Agentic Smart Contract Vulnerability Detection with Auditing Knowledge Summarization
Nanyang Technological University（南洋理工大学）；National Superior College for Engineers, Beihang University（北京航空航天大学高等工程学院）；Bitslab；The Chinese University of Hong Kong（香港中文大学）
Humboldt-Universität zu Berlin（柏林洪堡大学）；University of Paderborn（帕德博恩大学）；University of Bern（伯尔尼大学）
Context: Identifying potential vulnerable code is important to improve the security of our software systems. However, the manual detection of software vulnerabilities requires expert knowledge and is time-consuming, and must be supported by automated techniques. Objective: Such automated vulnerability detection techniques should achieve a high accuracy, point developers directly to the vulnerable code fragments, scale to real-world software, generalize across the boundaries of a specific software project, and require no or only moderate setup or configuration effort. Method: In this article, w…
Decoupling Reconnaissance and Exploitation: Measuring the Capability Boundaries of LLM-Based Web Penetration Testing
University of Science and Technology of China（中国科学技术大学）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州先进研究院）；Nanjing University of Science and Technology（南京理工大学）；Research Institute, Runjian Co., Ltd（润杰有限公司研究院）
MMJailBench: A Factorized Benchmark for Disentangling Multimodal Jailbreak Vulnerabilities
Tongji University（同济大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
