· 2017
Directed Greybox Fuzzing
- 729citations
- 2017year
Short summary
Directed Greybox Fuzzing (DGF) efficiently generates inputs to reach specific program locations, outperforming existing undirected fuzzing and directed symbolic execution.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduces Directed Greybox Fuzzing (DGF) to guide fuzzing towards specific program locations.
- Employs a simulated annealing power schedule to allocate more 'energy' to promising input seeds.
- AFLGo implementation demonstrates superior efficiency compared to undirected greybox fuzzing and directed whitebox fuzzing.
- Successfully found 39 bugs in well-fuzzed projects, resulting in 17 CVEs, including applications in patch testing and crash reproduction.
AI-generated from the title and abstract; the full text is not read.
Abstract
Existing Greybox Fuzzers (GF) cannot be effectively directed, for instance, towards problematic changes or patches, towards critical system calls or dangerous locations, or towards functions in the stack-trace of a reported vulnerability that we wish to reproduce. In this paper, we introduce Directed Greybox Fuzzing (DGF) which generates inputs with the objective of reaching a given set of target program locations efficiently. We develop and evaluate a simulated annealing-based power schedule that gradually assigns more energy to seeds that are closer to the target locations while reducing energy for seeds that are further away. Experiments with our implementation AFLGo demonstrate that DGF outperforms both directed symbolic-execution-based whitebox fuzzing and undirected greybox fuzzing. We show applications of DGF to patch testing and crash reproduction, and discuss the integration of AFLGo into Google's continuous fuzzing platform OSS-Fuzz. Due to its directedness, AFLGo could find 39 bugs in several well-fuzzed, security-critical projects like LibXML2. 17 CVEs were assigned.
The authors' abstract, as published at the source., 2017 · DOI ↗
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Field: Software
SoftwareComputer Science