CSSXC: Context-sensitive Sanitization Framework for Web Applications against XSS Vulnerabilities in Cloud Environments

AbstractThis paper presents a context-sensitive sanitization based XSS defensive framework for the cloud environment. It discovers all the hidden injection points in HTML5-based web applications deployed on the platforms of cloud and sanitizes the XSS attack payloads injected in such points in a context sensitive manner. The identification of such injection points permits our technique to retrieve each possible web page of application, allowing a wider exploration and accelerating the process of applying the sanitizers on the untrusted variables of web application. The XSS attack mitigation capability of our framework was evaluated on web applications deployed for the cloud users in the cloud environment. The experimental results reveal that this technique detects the XSS attack payloads with minimum rate of false negatives and less runtime overhead

Code Injection Attacks on HTML5-based Mobile Apps

HTML5-based mobile apps become more and more popular, mostly because they are much easier to be ported across different mobile platforms than native apps. HTML5-based apps are implemented using the standard web technologies, including HTML5, JavaScript and CSS; they depend on some middlewares, such as PhoneGap, to interact with the underlying OS. Knowing that JavaScript is subject to code injection attacks, we have conducted a systematic study on HTML5-based mobile apps, trying to evaluate whether it is safe to rely on the web technologies for mobile app development. Our discoveries are quite surprising. We found out that if HTML5-based mobile apps become popular--it seems to go that direction based on the current projection--many of the things that we normally do today may become dangerous, including reading from 2D barcodes, scanning Wi-Fi access points, playing MP4 videos, pairing with Bluetooth devices, etc. This paper describes how HTML5-based apps can become vulnerable, how attackers can exploit their vulnerabilities through a variety of channels, and what damage can be achieved by the attackers. In addition to demonstrating the attacks through example apps, we have studied 186 PhoneGap plugins, used by apps to achieve a variety of functionalities, and we found that 11 are vulnerable. We also found two real HTML5-based apps that are vulnerable to the attacks.Comment: In Proceedings of the Third Workshop on Mobile Security Technologies (MoST) 2014 (http://arxiv.org/abs/1410.6674

Automated Dynamic Firmware Analysis at Scale: A Case Study on Embedded Web Interfaces

Embedded devices are becoming more widespread, interconnected, and web-enabled than ever. However, recent studies showed that these devices are far from being secure. Moreover, many embedded systems rely on web interfaces for user interaction or administration. Unfortunately, web security is known to be difficult, and therefore the web interfaces of embedded systems represent a considerable attack surface. In this paper, we present the first fully automated framework that applies dynamic firmware analysis techniques to achieve, in a scalable manner, automated vulnerability discovery within embedded firmware images. We apply our framework to study the security of embedded web interfaces running in Commercial Off-The-Shelf (COTS) embedded devices, such as routers, DSL/cable modems, VoIP phones, IP/CCTV cameras. We introduce a methodology and implement a scalable framework for discovery of vulnerabilities in embedded web interfaces regardless of the vendor, device, or architecture. To achieve this goal, our framework performs full system emulation to achieve the execution of firmware images in a software-only environment, i.e., without involving any physical embedded devices. Then, we analyze the web interfaces within the firmware using both static and dynamic tools. We also present some interesting case-studies, and discuss the main challenges associated with the dynamic analysis of firmware images and their web interfaces and network services. The observations we make in this paper shed light on an important aspect of embedded devices which was not previously studied at a large scale. We validate our framework by testing it on 1925 firmware images from 54 different vendors. We discover important vulnerabilities in 185 firmware images, affecting nearly a quarter of vendors in our dataset. These experimental results demonstrate the effectiveness of our approach

Web Application Weakness Ontology Based on Vulnerability Data

Web applications are becoming more ubiquitous. All manner of physical devices are now connected and often have a variety of web applications and web-interfaces. This proliferation of web applications has been accompanied by an increase in reported software vulnerabilities. The objective of this analysis of vulnerability data is to understand the current landscape of reported web application flaws. Along those lines, this work reviews ten years (2011 - 2020) of vulnerability data in the National Vulnerability Database. Based on this data, most common web application weaknesses are identified and their profiles presented. A weakness ontology is developed to capture the attributes of these weaknesses. These include their attack method and attack vectors. Also described is the impact of the weaknesses to software quality attributes. Additionally, the technologies that are susceptible to each weakness are presented, they include programming languages, frameworks, communication protocols, and data formats

Detecting Cross-Site Scripting Attacks Using Machine Learning

Cross-site scripting (XSS) is one of the most frequently occurring types of attacks on web applications, hence is of importance in information security. XSS is where the attacker injects malicious code, typically JavaScript, into the web application in order to be executed in the user’s browser. Identifying that a script is malicious is an important part of the defence of a web application. This paper investigates using SVM, k-NN and Random Forests to detect and limit these attacks, whether known or unknown, by building classifiers for JavaScript code. It demonstrated that using an interesting feature set combining language syntax and behavioural features results in classifiers that give high accuracy and precision on large real world data sets without restricting attention only to obfuscation