The recent paradigm shift introduced by the Internet of Things (IoT) has brought embedded systems into focus as a target for both security analysts and malicious adversaries. Typified by their lack of standardized hardware, diverse software, and opaque functionality, IoT devices present unique challenges to security analysts due to the tight coupling between their firmware and the hardware for which it was designed. In order to take advantage of modern program analysis techniques, such as fuzzing or symbolic execution, with any kind of scale or depth, analysts must have the ability to execute firmware code in emulated (or virtualized) environments. However, these emulation environments are rarely available and cumbersome to create through manual reverse engineering, greatly limiting the analysis of binary firmware.
In this work, we explore the problem of firmware re-hosting, the process by which firmware is migrated from its original hardware environment into a virtualized one. We show that an approach capable of creating virtual, interactive environments in an automated manner is a necessity to enable firmware analysis at scale. We present the first proof-of-concept system aiming to achieve this goal, called PRETENDER, which uses observations of the interactions between the original hardware and the firmware to automatically create models of peripherals, and allows for the execution of the firmware in a fully-emulated environment. Unlike previous approaches, these models are interactive, stateful, and transferable, meaning they are designed to allow the program to receive and process new input, a requirement of many analyses. We demonstrate our approach on multiple hardware platforms and firmware samples, and show that the models are flexible enough to allow for virtualized code execution, the exploration of new code paths, and the identification of security vulnerabilities.
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HyDiff: Hybrid Differential Software Analysis
Detecting regression bugs in software evolution, analyzing side-channels in programs and evaluating robustness in deep neural networks (DNNs) can all be seen as instances of differential software analysis, where the goal is to generate diverging executions of program paths. Two executions are said to be diverging if the observable program behavior differs, e.g., in terms of program output, execution time, or (DNN) classification. The key challenge of differential software analysis is to simultaneously reason about multiple program paths, often across program variants.
This paper presents HyDiff, the first hybrid approach for differential software analysis. HyDiff integrates and extends two very successful testing techniques: Feedback-directed greybox fuzzing for efficient program testing and shadow symbolic execution for systematic program exploration. HyDiff extends greybox fuzzing with divergence-driven feedback based on novel cost metrics that take into account the control flow graph of the program. Furthermore HyDiff extends shadow symbolic execution by applying four-way forking in a systematic exploration and still having the ability to incorporate concrete inputs in the analysis. HyDiff applies divergence revealing heuristics based on resource consumption and control-flow information to efficiently guide the symbolic exploration, which allows its efficient usage beyond regression testing applications. We introduce differential metrics such as output, decision and cost difference, as well as patch distance, to assist the fuzzing and symbolic execution components in maximizing the execution divergence.
We implemented our approach on top of the fuzzer AFL and the symbolic execution framework Symbolic PathFinder. We illustrate HyDiff on regression and side-channel analysis for Java bytecode programs, and further show how to use HyDiff for robustness analysis of neural networks.
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- Award ID(s):
- 1901136
- NSF-PAR ID:
- 10149224
- Date Published:
- Journal Name:
- Proceedings of the International Conference on Software Engineering
- ISSN:
- 1819-3781
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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