Search Results (5)

SQL injection (SQLI) black-box detection, which simulates external attack scenarios, is crucial for assessing vulnerabilities in real-world web applications. However, existing black-box detection methods rely on predefined rules to cover the most common SQLI cases, lacking diversity in vulnerability detection scheduling and payload, suffering from limited efficiency and accuracy. Large Language Models (LLMs) have shown significant advancements in several domains, so we developed SqliGPT, an LLM-powered SQLI black-box scanner that leverages the advanced contextual understanding and reasoning abilities of LLMs. Our approach introduces the Strategy Selection Module to improve detection efficiency and the Defense Bypass Module to address insufficient defense mechanisms. We evaluated SqliGPT against six state-of-the-art scanners using our SqliMicroBenchmark. Our evaluation results indicate that SqliGPT successfully detected all 45 targets, outperforming other scanners, particularly on targets with insufficient defenses. Additionally, SqliGPT demonstrated excellent efficiency in executing detection tasks, slightly underperforming Arachni and SQIRL on 27 targets but besting them on the other 18 targets. This study highlights the potential of LLMs in SQLI black-box detection and demonstrates the feasibility and effectiveness of LLMs in enhancing detection efficiency and accuracy. Full article

Black-box web application scanning has been a popular technique to detect Cross-Site Scripting (XSS) vulnerabilities without prior knowledge of the application. However, several limitations lead to low efficiency of current black-box scanners, including (1) the scanners waste time by repetitively visiting similar states, such as similar HTML forms of two different products, and (2) using a First-In-First-Out (FIFO) fuzzing order for the collected forms has led to low efficiency in detecting XSS vulnerabilities, as different forms have different potential possibilities of XSS vulnerability. In this paper, we present a state-sensitive black-box web application scanning method, including a filtering method for excluding similar states and a heuristic ranking method for optimizing the fuzzing order of forms. The filtering method excludes similar states by comparing readily available characteristic information that does not require visiting the states. The ranking method sorts forms based on the number of injection points since it is commonly observed that forms with a greater number of injection points have a higher probability of containing XSS vulnerabilities. To demonstrate the effectiveness of our scanning method, we implement it in our black-box web scanner and conduct experimental evaluations on eight real-world web applications within a limited scanning time. Experimental results demonstrate that the filtering method improves the code coverage about 17% on average and the ranking method helps detect 53 more XSS vulnerabilities. The combination of the filtering and ranking methods helps detect 81 more XSS vulnerabilities. Full article

Cross-site scripting (XSS) vulnerabilities are significant threats to web applications. The number of XSS vulnerabilities reported has increased annually for the past three years, posing a considerable challenge to web application maintainers. Black-box scanners are mainstream tools for security engineers to perform penetration testing and detect XSS vulnerabilities. Unfortunately, black-box scanners rely on crawlers to find input points of web applications and cannot guarantee all input points are tested. To this end, we propose a grey-box fuzzing method based on reinforcement learning, which can detect reflected and stored XSS vulnerabilities for Java web applications. We first use static analysis to identify potential input points from components (i.e., Java code, configuration files, and HTML files) of the Java web application. Then, an XSS vulnerability payload generation method is proposed, which is used together with the reinforcement learning model. We define the state, action, and reward functions of three reinforcement learning models for XSS vulnerability detection scenarios so that the fuzz loop can be performed automatically. To demonstrate the effectiveness of the proposed method, we compare it against four state-of-the-art web scanners. Experimental results show that our method finds all XSS vulnerabilities and has no false positives. Full article

With the Internet’s meteoric rise in popularity and usage over the years, there has been a significant increase in the number of web applications. Nearly all organisations use them for various purposes, such as e-commerce, e-banking, e-learning, and social networking. More importantly, web applications have become increasingly vulnerable to malicious attack. To find web vulnerabilities before an attacker, security experts use black-box web application vulnerability scanners to check for security vulnerabilities in web applications. Most studies have evaluated these black-box scanners against various vulnerable web applications. However, most tested applications are traditional (non-dynamic) and do not reflect current web. This study evaluates the detection accuracy of five black-box web application vulnerability scanners against one of the most modern and sophisticated insecure web applications, representing a real-life e-commerce. The tested vulnerabilities are injection vulnerabilities, in particular, structured query language (SQLi) injection, not only SQL (NoSQL), and server-side template injection (SSTI). We also tested the black-box scanners in four modes to identify their limitations. The findings show that the black-box scanners overlook most vulnerabilities in almost all modes and some scanners missed all the vulnerabilities. Full article

This study investigated the pore characterization of polyurethane (PU) foam as a necessary step in water filtration membrane fabrication. Porous material characterization is essential for predicting membrane performance, strength, durability, surface feel, and to understand the transport mechanisms using modeling and simulations. Most existing pore characterization techniques are relatively costly, time-consuming, subjective, and have cumbersome sample preparations. This study focused on using three relatively inexpensive imaging systems: a black box, Canon camera (EOS760D), and LaserJet scanner (M1132 MFP). Two standard, state-of-the-art imaging systems were used for comparison: a stereomicroscope and a scanning electron microscope. Digital images produced by the imaging systems were used with a MATLAB algorithm to determine the surface porosity, pore area, and shape factor of the polyurethane foam in an efficient manner. The results obtained established the compatibility of the image analysis algorithm with the imaging systems. The black box results were found to be more comparable to both the stereomicroscope and SEM systems than those of the Canon camera and scanner imaging systems. Indeed, the current research effort demonstrates the possibility of substrate characterization with inexpensive imaging systems. Full article