Web browsers are one of the most important enduser applications to browse, retrieve, and present Internet resources. Malicious or compromised resources may endanger Web users by hijacking web browsers to execute arbitrary malicious code in the victims' systems. Unfortunately, the widely-adopted Just-In-Time compilation (JIT) optimization technique, which compiles source code to native code at runtime, significantly increases this risk. By exploiting JIT compiled code, attackers can bypass all currently deployed defenses. In this paper, we systematically investigate threats against JIT compiled code, and the challenges of protecting JIT compiled code. We propose a general defense solution, JITScope, to enforce Control-Flow Integrity (CFI) on both statically compiled and JIT compiled code. Our solution furthermore enforces the W⊕X policy on JIT compiled code, preventing the JIT compiled code from being overwritten by attackers. We show that our prototype implementation of JITScope on the popular Firefox web browser introduces a reasonably low performance overhead, while defeating existing real-world control flow hijacking attacks.

Traditional remote-server-exploiting malware is quickly evolving and adapting to the new web-centric computing paradigm. By leveraging the large population of (insecure) web sites and exploiting the vulnerabilities at client-side modern (complex) browsers (and their extensions), web-based malware becomes one of the most severe and common infection vectors nowadays. While traditional malware collection and analysis are mainly focusing on binaries, it is important to develop new techniques and tools for collecting and analyzing web-based malware, which should include a complete web-based malicious logic to reflect the dynamic, distributed, multi-step, and multi-path web infection trails, instead of just the binaries executed at end hosts. This paper is a first attempt in this direction to automatically collect web-based malware scenarios (including complete web infection trails) to enable fine-grained analysis. Based on the collections, we provide the capability for offline "live" replay, i.e., an end user (e.g., an analyst) can faithfully experience the original infection trail based on her current client environment, even when the original malicious web pages are not available or already cleaned. Our evaluation shows that WebPatrol can collect/cover much more complete infection trails than state-of-the-art honeypot systems such as PHoneyC [11] and Capture-HPC [1]. We also provide several case studies on the analysis of web-based malware scenarios we have collected from a large national education and research network, which contains around 35,000 web sites.

A security analyst often needs to understand two runs of the same program that exhibit a difference in program state or output. This is important, for example, for vulnerability analysis, as well as for analyzing a malware program that features different behaviors when run in different environments. In this paper we propose a differential slicing approach that automates the analysis of such execution differences. Differential slicing outputs a causal difference graph that captures the input differences that triggered the observed difference and the causal path of differences that led from those input differences to the observed difference. The analyst uses the graph to quickly understand the observed difference. We implement differential slicing and evaluate it on the analysis of 11 real-world vulnerabilities and 2 malware samples with environment-dependent behaviors. We also evaluate it in an informal user study with two vulnerability analysts. Our results show that differential slicing successfully identifies the input differences that caused the observed difference and that the causal difference graph significantly reduces the amount of time and effort required for an analyst to understand the observed difference.