SoK: Prudent Evaluation Practices for Fuzzing
arXiv:2405.10220 · doi:10.1109/SP54263.2024.00137
Abstract
Fuzzing has proven to be a highly effective approach to uncover software bugs over the past decade. After AFL popularized the groundbreaking concept of lightweight coverage feedback, the field of fuzzing has seen a vast amount of scientific work proposing new techniques, improving methodological aspects of existing strategies, or porting existing methods to new domains. All such work must demonstrate its merit by showing its applicability to a problem, measuring its performance, and often showing its superiority over existing works in a thorough, empirical evaluation. Yet, fuzzing is highly sensitive to its target, environment, and circumstances, e.g., randomness in the testing process. After all, relying on randomness is one of the core principles of fuzzing, governing many aspects of a fuzzer's behavior. Combined with the often highly difficult to control environment, the reproducibility of experiments is a crucial concern and requires a prudent evaluation setup. To address these threats to validity, several works, most notably Evaluating Fuzz Testing by Klees et al., have outlined how a carefully designed evaluation setup should be implemented, but it remains unknown to what extent their recommendations have been adopted in practice. In this work, we systematically analyze the evaluation of 150 fuzzing papers published at the top venues between 2018 and 2023. We study how existing guidelines are implemented and observe potential shortcomings and pitfalls. We find a surprising disregard of the existing guidelines regarding statistical tests and systematic errors in fuzzing evaluations. For example, when investigating reported bugs, ...
References in corpus (14)
- FairFuzz: Targeting Rare Branches to Rapidly Increase Greybox Fuzz Testing Coverage
- Magma: A Ground-Truth Fuzzing Benchmark
- MTFuzz: Fuzzing with a Multi-Task Neural Network
- Montage: A Neural Network Language Model-Guided JavaScript Engine Fuzzer
- DARWIN: Survival of the Fittest Fuzzing Mutators
- MobFuzz: Adaptive Multi-objective Optimization in Gray-box Fuzzing
- BeDivFuzz: Integrating Behavioral Diversity into Generator-based Fuzzing
- Fuzzing Symbolic Expressions
- Same Coverage, Less Bloat: Accelerating Binary-only Fuzzing with Coverage-preserving Coverage-guided Tracing
- : Rigorous and Efficient Directed Greybox Fuzzing
- Evaluating Synthetic Bugs
- Stateful Greybox Fuzzing
- IvySyn: Automated Vulnerability Discovery in Deep Learning Frameworks
- MINER: A Hybrid Data-Driven Approach for REST API Fuzzing
Cited by in corpus (5)
- fAmulet: Finding Finalization Failure Bugs in Polygon zkRollup
- SoK: A Literature and Engineering Review of Regular Expression Denial of Service (ReDoS)
- OpDiffer: LLM-Assisted Opcode-Level Differential Testing of Ethereum Virtual Machine
- ROSA: Finding Backdoors with Fuzzing
- PyPitfall: Dependency Chaos and Software Supply Chain Vulnerabilities in Python