FirmHunter: State-Aware and Introspection-Driven Grey-Box Fuzzing towards IoT Firmware

IoT devices are exponentially increasing in all aspects of our lives. Via the web interfaces of IoT devices, attackers can control IoT devices by exploiting their vulnerabilities. In order to guarantee IoT security, testing these IoT devices to detect vulnerabilities is very important. In this work, we present FirmHunter, an automated state-aware and introspection-driven grey-box fuzzer towards Linux-based firmware images on the basis of emulation. It employs a message-state queue to overcome the dependency problem in test cases. Furthermore, it implements a scheduler collecting execution information from system introspection to drive fuzzing towards more interesting test cases, which speeds up vulnerability discovery. We evaluate FirmHunter by emulating and fuzzing eight firmware images including seven routers and one IP camera with a state-of-the-art IoT fuzzer FirmFuzz and a web application scanner ZAP. Our evaluation results show that (1) the message-state queue enables FirmHunter to parse the dependencies in test cases and find real-world vulnerabilities that other fuzzers cannot detect; (2) our scheduler accelerates the discovery of vulnerabilities by an average of 42%; and (3) FirmHunter is able to find unknown vulnerabilities.

Robot Hacking Games: China’s Competitions to Automate the Software Vulnerability Lifecycle

Software vulnerability discovery, patching, and exploitation—collectively known as the vulnerability lifecycle—is time consuming and labor intensive. Automating the process could significantly improve software security and offensive hacking. The Defense Advanced Research Projects Agency’s Cyber Grand Challenge supported teams of researchers from 2014 to 2016 that worked to create these tools. China took notice. In 2017, China hosted its first Robot Hacking Game, seeking to automate the software vulnerability lifecycle. Since then, China has hosted seven such competitions and the People’s Liberation Army has increased its role in hosting the games.

Bin2vec: learning representations of binary executable programs for security tasks

Tackling binary program analysis problems has traditionally implied manually defining rules and heuristics, a tedious and time consuming task for human analysts. In order to improve automation and scalability, we propose an alternative direction based on distributed representations of binary programs with applicability to a number of downstream tasks. We introduce Bin2vec, a new approach leveraging Graph Convolutional Networks (GCN) along with computational program graphs in order to learn a high dimensional representation of binary executable programs. We demonstrate the versatility of this approach by using our representations to solve two semantically different binary analysis tasks – functional algorithm classification and vulnerability discovery. We compare the proposed approach to our own strong baseline as well as published results, and demonstrate improvement over state-of-the-art methods for both tasks. We evaluated Bin2vec on 49191 binaries for the functional algorithm classification task, and on 30 different CWE-IDs including at least 100 CVE entries each for the vulnerability discovery task. We set a new state-of-the-art result by reducing the classification error by 40% compared to the source-code based inst2vec approach, while working on binary code. For almost every vulnerability class in our dataset, our prediction accuracy is over 80% (and over 90% in multiple classes).

Neutron: an attention-based neural decompiler

Decompilation aims to analyze and transform low-level program language (PL) codes such as binary code or assembly code to obtain an equivalent high-level PL. Decompilation plays a vital role in the cyberspace security fields such as software vulnerability discovery and analysis, malicious code detection and analysis, and software engineering fields such as source code analysis, optimization, and cross-language cross-operating system migration. Unfortunately, the existing decompilers mainly rely on experts to write rules, which leads to bottlenecks such as low scalability, development difficulties, and long cycles. The generated high-level PL codes often violate the code writing specifications. Further, their readability is still relatively low. The problems mentioned above hinder the efficiency of advanced applications (e.g., vulnerability discovery) based on decompiled high-level PL codes.In this paper, we propose a decompilation approach based on the attention-based neural machine translation (NMT) mechanism, which converts low-level PL into high-level PL while acquiring legibility and keeping functionally similar. To compensate for the information asymmetry between the low-level and high-level PL, a translation method based on basic operations of low-level PL is designed. This method improves the generalization of the NMT model and captures the translation rules between PLs more accurately and efficiently. Besides, we implement a neural decompilation framework called Neutron. The evaluation of two practical applications shows that Neutron’s average program accuracy is 96.96%, which is better than the traditional NMT model.