# SoK: Where to Fuzz? Assessing Target Selection Methods in Directed Fuzzing

arXiv: https://arxiv.org/abs/2502.08341 · HTML: https://mlsanity.com/paper/2502.08341/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2025.

## Built on by (most cited first, 4 of 4)

- [Locus: Agentic Predicate Synthesis for Directed Fuzzing](https://mlsanity.com/paper/2508.21302/) (2025)
- [LLM-based Vulnerability Discovery through the Lens of Code Metrics](https://mlsanity.com/paper/2509.19117/) (2025)
- [Clutch Control: An Attention-based Combinatorial Bandit for Efficient Mutation in JavaScript Engine Fuzzing](https://mlsanity.com/paper/2510.12732/) (2025)
- [Program Structure-aware Language Models: Targeted Software Testing beyond Textual Semantics](https://mlsanity.com/paper/2604.17715/) (2026)

## Abstract (arXiv)

A common paradigm for improving fuzzing performance is to focus on selected regions of a program rather than its entirety. While previous work has largely explored how these locations can be reached, their selection, that is, the where, has received little attention so far. A common paradigm for improving fuzzing performance is to focus on selected regions of a program rather than its entirety. While previous work has largely explored how these locations can be reached, their selection, that is, the where, has received little attention so far. In this paper, we fill this gap and present the first comprehensive analysis of target selection methods for fuzzing. To this end, we examine papers from leading security and software engineering conferences, identifying prevalent methods for choosing targets. By modeling these methods as general scoring functions, we are able to compare and measure their efficacy on a corpus of more than 1,600 crashes from the OSS-Fuzz project. Our analysis provides new insights for target selection in practice: First, we find that simple software metrics significantly outperform other methods, including common heuristics used in directed fuzzing, such as recently modified code or locations with sanitizer instrumentation. Next to this, we identify language models as a promising choice for target selection. In summary, our work offers a new perspective on directed fuzzing, emphasizing the role of target selection as an orthogonal dimension to improve performance.