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GitHub Security Lab 推出 Fuzzing Taskflow：用 Agent 自动化 C/C++ 模糊测试流程
GitHub Security Lab 发布 Fuzzing Taskflow，将连续模糊测试中编写 harness、监控覆盖率、分类崩溃等人工环节交给 agent。用户指向一个 C 或 C++ 仓库后，该流程会识别入口点、分析构建、生成 harness、运行 AFL++ 模糊测试器、读取覆盖率，并为每个独立漏洞生成报告。官方博客给出了端到端流程说明。
AI推荐理由 GitHub Security Lab 官方账号介绍 Fuzzing Taskflow：将 C/C++ 仓库的入口点识别、构建分析、harness 编写、AFL++ 运行、覆盖率读取和漏洞报告生成交给 agent，属于可验证的官方工具发布，对 AI Agent 在安全测试落地有具体参考价值；但正文为推文摘要，未提供实测数据或效果验证，需保留其宣传性质。
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正文
Continuous fuzzing finds real bugs, but it still needs someone to write harnesses, watch coverage, and triage every crash.
The GitHub Security Lab built the Fuzzing Taskflow to give that loop to an agent. You point it at a C or C++ repository, and it identifies entrypoints, analyzes the build, crafts harnesses, runs the AFL++ fuzzer, reads coverage, and creates a vulnerability report for each unique bug.
Check out how the pipeline works end to end 🔍
https://github.blog/security/application-security/ai-powered-fuzzing-with-the-github-security-lab-taskflow-agent/