# The Impact of Magma: A Ground-Truth Fuzzing Benchmark

arXiv: https://arxiv.org/abs/2608.28016 · HTML: https://mlsanity.com/paper/2608.28016/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2026.

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

- [PatchBench: Evaluating AI Agents for Vulnerability Patching](https://mlsanity.com/paper/2609.04075/) (2026)
- [When LLM Decompilers Recompile More and Preserve Less](https://mlsanity.com/paper/2609.05370/) (2026)
- [CAROL: Context-Aware Online Learning for Fuzzer Scheduling](https://mlsanity.com/paper/2609.06772/) (2026)
- [VulValidate: Auditing Function-Level Vulnerability Labels with Executable Evidence](https://mlsanity.com/paper/2610.05103/) (2026)

## Abstract (arXiv)

Magma is an open-source and ground-truth fuzzing benchmark that enables uniform fuzzer evaluation and comparison. Magma was originally released with a research paper published at ACM SIGMETRICS 2021. This short paper explains the motivation, the design, and the impact of Magma, with a description of extensions to the original benchmark.