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This paper focuses on fuzzing document software or precisely, software that processes document files (e.g., HTML, PDF, and DOCX). Document software typically requires highly-structured inputs, which general-purpose fuzzing cannot handle well. We propose two techniques to facilitate fuzzing on document software. First, we design an intermediate document representation (DIR) for document files. DIR describes a document file in an abstract way that is independent of the underlying format. Reusing common SDKs, a DIR document can be lowered into a desired format without a deep understanding of the format. Second, we propose multi-level mutations to operate directly on a DIR document, which can more thoroughly explore the searching space than existing single-level mutations. Combining these two techniques, we can reuse the same DIR-based generations and mutations to fuzz any document format, without separately handling the target format and re-engineering the generation/mutation components.To assess utility of our DIR-based fuzzing, we applied it to 6 PDF and 6 HTML applications (48-hour) demonstrated superior performance, outpacing general mutation-based fuzzing (AFL++), ML-based PDF fuzzing (Learn&Fuzz), and structure-aware mutation-based fuzzing ((NAUTILUS) by 33.87%, 127.74%, and 25.17% in code coverage, respectively. For HTML, it exceeded AFL++ and generation-based methods (FreeDom and Domato) by 28.8% and 14.02%.
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Facilitating Parallel Fuzzing with Mutually-exclusive Task Distribution
Fuzz testing, or fuzzing, has become one of the de facto standard techniques for bug finding in the software industry. In general, fuzzing provides various inputs to the target program with the goal of discovering unhandled exceptions and crashes. In business sectors where the time budget is limited, software vendors often launch many fuzzing instances in parallel as a common means of increasing code coverage. However, most of the popular fuzzing tools — in their parallel mode — naively run multiple instances concurrently, without elaborate distribution of workload. This can lead different instances to explore overlapped code regions, eventually reducing the benefits of concurrency. In this paper, we propose a general model to describe parallel fuzzing. This model distributes mutually-exclusive but similarly-weighted tasks to different instances, facilitating concurrency and also fairness across instances. Following this model, we develop a solution, called AFL-EDGE, to improve the parallel mode of AFL, considering a round of mutations to a unique seed as a task and adopting edge coverage to define the uniqueness of a seed. We have implemented AFL-EDGE on top of AFL and evaluated the implementation with AFL on 9 widely used benchmark programs. It shows that AFL-EDGE can benefit the edge coverage of AFL. In a 24-hour test, the increase of edge coverage brought by AFL-EDGE to AFL ranges from 9.5% to 10.2%, depending on the number of instances. As a side benefit, we discovered 14 previously unknown bugs.
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- Award ID(s):
- 2031377
- PAR ID:
- 10298070
- Date Published:
- Journal Name:
- 17th EAI International Conference on Security and Privacy in Communication Networks
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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