FuzzFactory: domain-specific fuzzing with waypoints

Author:

Padhye Rohan1,Lemieux Caroline1,Sen Koushik1,Simon Laurent2,Vijayakumar Hayawardh2

Affiliation:

1. University of California at Berkeley, USA

2. Samsung Research, USA

Abstract

Coverage-guided fuzz testing has gained prominence as a highly effective method of finding security vulnerabilities such as buffer overflows in programs that parse binary data. Recently, researchers have introduced various specializations to the coverage-guided fuzzing algorithm for different domain-specific testing goals, such as finding performance bottlenecks, generating valid inputs, handling magic-byte comparisons, etc. Each such solution can require non-trivial implementation effort and produces a distinct variant of a fuzzing tool. We observe that many of these domain-specific solutions follow a common solution pattern. In this paper, we present FuzzFactory, a framework for developing domain-specific fuzzing applications without requiring changes to mutation and search heuristics. FuzzFactory allows users to specify the collection of dynamic domain-specific feedback during test execution, as well as how such feedback should be aggregated. FuzzFactory uses this information to selectively save intermediate inputs, called waypoints, to augment coverage-guided fuzzing. Such waypoints always make progress towards domain-specific multi-dimensional objectives. We instantiate six domain-specific fuzzing applications using FuzzFactory: three re-implementations of prior work and three novel solutions, and evaluate their effectiveness on benchmarks from Google's fuzzer test suite. We also show how multiple domains can be composed to perform better than the sum of their parts. For example, we combine domain-specific feedback about strict equality comparisons and dynamic memory allocations, to enable the automatic generation of LZ4 bombs and PNG bombs.

Publisher

Association for Computing Machinery (ACM)

Subject

Safety, Risk, Reliability and Quality,Software

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1. Directed or Undirected: Investigating Fuzzing Strategies in a CI/CD Setup (Registered Report);Proceedings of the 3rd ACM International Fuzzing Workshop;2024-09-13

2. Instiller: Toward Efficient and Realistic RTL Fuzzing;IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems;2024-07

3. FuzzInMem: Fuzzing Programs via In-memory Structures;Proceedings of the IEEE/ACM 46th International Conference on Software Engineering;2024-04-12

4. Fuzzing, Symbolic Execution, and Expert Guidance for Better Testing;IEEE Software;2024-01

5. SandPuppy: Deep-State Fuzzing Guided by Automatic Detection of State-Representative Variables;Lecture Notes in Computer Science;2024

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