# IntelliGen: Automatic Driver Synthesis for FuzzTesting

arXiv: https://arxiv.org/abs/2103.00862 · HTML: https://mlsanity.com/paper/2103.00862/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2021.

## Built on by (most cited first, 3 of 3)

- [HarnessAgent: Scaling Automatic Fuzzing Harness Construction with Tool-Augmented LLM Pipelines](https://mlsanity.com/paper/2512.03420/) (2025)
- [Towards Reliable LLM-Driven Fuzz Testing: Vision and Road Ahead](https://mlsanity.com/paper/2503.00795/) (2025)
- [FuzzAgent: Multi-Agent System for Evolutionary Library Fuzzing](https://mlsanity.com/paper/2605.14431/) (2026)

## Abstract (arXiv)

Fuzzing is a technique widely used in vulnerability detection. The process usually involves writing effective fuzz driver programs, which, when done manually, can be extremely labor intensive. Previous attempts at automation leave much to be desired, in either degree of automation or quality of output. In this paper, we propose IntelliGen, a framework that constructs valid fuzz drivers automatically. First, IntelliGen determines a set of entry functions and evaluates their respective chance of exhibiting a vulnerability. Then, IntelliGen generates fuzz drivers for the entry functions through hierarchical parameter replacement and type inference. We implemented IntelliGen and evaluated its effectiveness on real-world programs selected from the Android Open-Source Project, Google's fuzzer-test-suite and industrial collaborators. IntelliGen covered on average 1.08X-2.03X more basic blocks and 1.36X-2.06X more paths over state-of-the-art fuzz driver synthesizers FUDGE and FuzzGen. IntelliGen performed on par with manually written drivers and found 10 more bugs.