# Agentic Fuzzing: Opportunities and Challenges

arXiv: https://arxiv.org/abs/2605.10074 · HTML: https://mlsanity.com/paper/2605.10074/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 10 of its 73 references that are in the map.
- First posted on arXiv 11 May 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 10 of 10)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Single-Agent LLMs Outperform Multi-Agent Systems on Multi-Hop Reasoning Under Equal Thinking Token Budgets](https://mlsanity.com/paper/2604.02460/) (2026)
- [Prompt Fuzzing for Fuzz Driver Generation](https://mlsanity.com/paper/2312.17677/) (2023)
- [Multi-Agent Penetration Testing AI for the Web](https://mlsanity.com/paper/2508.20816/) (2025)
- [ELFuzz: Efficient Input Generation via LLM-driven Synthesis Over Fuzzer Space](https://mlsanity.com/paper/2506.10323/) (2025)
- [Superion: Grammar-Aware Greybox Fuzzing](https://mlsanity.com/paper/1812.01197/) (2018)
- [Automated Conformance Testing for JavaScript Engines via Deep Compiler Fuzzing](https://mlsanity.com/paper/2104.07460/) (2021)
- [Montage: A Neural Network Language Model-Guided JavaScript Engine Fuzzer](https://mlsanity.com/paper/2001.04107/) (2020)
- [JEST: N+1-version Differential Testing of Both JavaScript Engines and Specification](https://mlsanity.com/paper/2102.07498/) (2021)
- [Enhancing Security in Third-Party Library Reuse -- Comprehensive Detection of 1-day Vulnerability through Code Patch Analysis](https://mlsanity.com/paper/2411.19648/) (2024)

## Built on by (most cited first, 1 of 1)

- [Code-Augur: Agentic Vulnerability Detection via Specification Inference](https://mlsanity.com/paper/2606.18619/) (2026)

## Abstract (arXiv)

Fuzzers and static analyzers find many bugs but struggle with logic bugs in mature codebases. Triggering such a bug often requires multi-step reasoning that produces no distinctive execution feedback, and variants can appear across implementations too different for a single pattern to match. Recent LLM-assisted approaches help, but they use LLMs as auxiliaries rather than as the reasoning engine. We propose agentic fuzzing, a bug-finding approach seeded by historical bugs in which deep agents perform the reasoning directly. Given a reference bug, the agent analyzes its root cause, hypothesizes new scenarios elsewhere in the codebase that may share that cause, and verifies each hypothesis by generating and running proof-of-concept code. This lets the agent find variants that differ completely in trigger path or code structure from the reference. We identify three practical challenges in implementing agentic fuzzing: harness engineering, redundant investigations across seeds with similar root causes, and scheduling seeds in a large corpus. We address these in AFuzz through a four-stage agent pipeline, scenario coverage that deduplicates previously explored scenarios, and a DPP-MAP scheduler that orders seeds by diversity. We ran AFuzz on the V8 JavaScript engine for about one month, finding 40 bugs (including three duplicates), receiving a total 35,000 bounty, and being assigned two CVEs. AFuzz also found 19 bugs (including one duplicate) in SpiderMonkey and JavaScriptCore using the seeds from V8. However, agentic fuzzing is in its early stages with several remaining open problems we discuss in the paper. Still, we think it points to a promising direction for finding logic bugs.