AIJon: Automated Generation of Annotations for Fuzzing
AIJON uses LLMs to auto-generate IJON-style fuzzing annotations, matching human-expert quality on the Magma benchmark.
Researchers replicate IJON experiments and propose AIJON, a system that uses LLMs to automatically generate expert-style annotations for coverage-guided fuzzing at scale. On the Magma benchmark, LLM-generated annotations performed comparably to human-generated ones, though annotation-based fuzzing was not strictly better than AFL++. The paper analyzes how annotations affect fuzzer energy distribution across campaigns.
- LLMs generate IJON-style annotations matching human-expert quality
- Evaluated on the Magma benchmark against an AFL++ baseline
- Annotation-based fuzzing did not strictly outperform AFL++
- Insights into annotation effects on fuzzer energy distribution
From the original article
1 min read at arxiv.orgModern fuzzers use code coverage as feedback to guide their exploration which has proven to be an effective strategy for driving exploration. However, this strategy overlooks inputs that may be interesting to the target program even without uncovering new code paths.
By Jayakrishna Menon Vadayath · arxiv.org
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