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
Full article231 words · extracted from arxiv.org · click to collapse
Modern 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. Fortunately, prior research has shown that annotations generated by human domain experts can provide additional feedback, guiding the fuzzer towards interesting parts of the program. In this paper, we replicate experiments presented in IJON and extend them to real-world vulnerability detection at scale. To mitigate the scalability challenge, imposed by the need for human domain expertise, we propose utilizing LLMs to automatically generate annotations. We demonstrate the applicability of LLMs for this purpose and observe that LLMs can generate annotations that perform comparably to human-generated annotations. Motivated by this finding, we design AIJON, a system that leverages LLMs to automatically generate IJON-style annotations. We evaluate AIJON on the Magma benchmark and surprisingly observe that annotation-based fuzzing does not perform strictly better than AFL++. We conduct several experiments to identify the cause of our results and identify key insights regarding the impact of annotations on fuzzing campaigns, including their effect on the energy distribution of the fuzzer. Notably, we observe that LLMs can generate annotations that achieve comparable results to human generated ones, thus opening the door for future research to perform further studies on the impact of annotations at scale.
Text extracted automatically; images, tables and formatting may be missing. Original: https://arxiv.org/abs/2609.18457