AI-powered fuzzing with the GitHub Security Lab Taskflow Agent
In this blog post, I explain how to use the new fuzzing taskflow based on the GitHub Security Lab Taskflow Agent AI framework.
Marked important, but published more than 30 days ago, so it moved down to this band.
On this page11 sections
The editors' notes below — why it matters, the context, the learning path — are written in Chinese.
Record
The signal
#If you’re new to fuzzing and want to learn the fundamentals first, check out our Fuzzing 101 course at gh.io/fuzzing101 . Continuous fuzzing is not a magic solution that solves all your problems . Even projects that have been enrolled in OSS-Fuzz for years can still hide critical bugs, and the reason is almost always the same: someone needs to keep an eye on coverage, write new harnesses for the code that nobody is reaching, and triage the crashes that come out the other end. In other words, fuzzing still needs a human in the loop. So the natural question I kept asking myself was: how much of that human work can we actually hand over to an LLM agent? That is what led me to build the Fuzzing Taskflow, an autonomous fuzzing pipeline for C/C++ projects. You only need to point it at a GitHub repository, and it does the rest: it identifies the suitable entrypoints, analyzes the build system, writes the harnesses, runs AFL++, reads the coverage reports, improves the harnesses, triages every crash, and writes a vulnerability report for each unique bug, all without a human babysitting it. The Fuzzing Taskflow is built on top of the GitHub Security Lab Taskflow Agent , our framework for writing LLM-driven security automation, so the pipeline is expressed as a set of taskflows that an agent runs end to end. In this post, I’ll walk you through how it works and the design decisions behind it. Let’s get going! How to run it The simplest way to run it’s just to go to https://github.com/GitHubSecurityLab/seclab-taskflows-fuzzing and start a codespace. Then, run th…
Why now
Why it matters
#这是一份写得足够细的参考设计：哪些事交给模型判断，哪些事用确定的工具去做，什么时候停。它要解决的不是模糊测试本身，而是模糊测试一直以来扩展不开的那部分人工。
Background
Technical context
#模糊测试向程序喂大量变异出来的输入，靠覆盖率反馈去探索新的代码路径。测试桩是把被测函数包起来、让模糊测试器能调用它的一小段代码。结构化的文本输入很难靠按位变异撞出来，所以需要字典和针对格式的变异器。
Audience
Who should care
#Next
Learning path
#- 先读《工具使用与函数调用》：工具粒度怎样影响智能体的可靠性
- 读本站 Copilot 代码审查复盘：更好的工具为什么会让智能体更差
- 对照 DeepSeek DSec：攻防类任务为什么要更强的隔离
- 在一次性环境里拿一个小项目试跑，先看它的崩溃分类准不准
Network
Where it sits in the network
#How this signal connects to neighbouring technologies, skills and background knowledge. Follow any link to keep exploring.
Further thought
Follow-up questions
#- 你的流程里，哪些步骤应该由模型判断，哪些应该写成确定的工具？
- 让模型选定的命令在哪里运行，运行环境用完之后会被销毁吗？
- 崩溃分类的结论由谁复核，复核的成本比人工分类低多少？
Publication record
Featured in
#The briefings that carried this signal, newest first; the last row is the weekly review of its week. The tag is the section it ran in.
技术简报 - 2026-09-28
Worth trackingWeekly review · Sep 21 – Sep 27
Worth tracking
Sources