· 2016
Coverage-based Greybox Fuzzing as Markov Chain
- 623citations
- 2016year
Short summary
This paper models Coverage-based Greybox Fuzzing (CGF) as a Markov chain, revealing that prioritizing low-frequency paths significantly increases exploration efficiency.
AI-generated from the title and abstract; the full text is not read.
Key points
- CGF can be modeled as a Markov chain where states represent seeds and transitions represent path exploration.
- Most CGF tests focus on a few high-frequency paths, limiting exploration.
- Prioritizing low-frequency paths with increased 'energy' boosts exploration efficiency.
- A power schedule controls seed energy, which should be inversely proportional to stationary distribution density and increase with seed selection.
AI-generated from the title and abstract; the full text is not read.
Abstract
Coverage-based Greybox Fuzzing (CGF) is a random testing approach that requires no program analysis. A new test is generated by slightly mutating a seed input. If the test exercises a new and interesting path, it is added to the set of seeds; otherwise, it is discarded. We observe that most tests exercise the same few "high-frequency" paths and develop strategies to explore significantly more paths with the same number of tests by gravitating towards low-frequency paths. We explain the challenges and opportunities of CGF using a Markov chain model which specifies the probability that fuzzing the seed that exercises path i generates an input that exercises path j. Each state (i.e., seed) has an energy that specifies the number of inputs to be generated from that seed. We show that CGF is considerably more efficient if energy is inversely proportional to the density of the stationary distribution and increases monotonically every time that seed is chosen. Energy is controlled with a power schedule.
The authors' abstract, as published at the source., 2016 · DOI ↗
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Field: Software
SoftwareComputer Science