GitHub Security Lab debuts AI-driven fuzzing Taskflow for C/C++ repos

The new Taskflow can now fuzz C/C++ GitHub projects end to end with minimal setup, while GitHub urges running it only in disposable, non‑privileged environments.

GitHub Security Lab has released an AI-driven fuzzing Taskflow that can autonomously fuzz C/C++ GitHub repositories end‑to‑end, including harness generation, coverage analysis, crash triage, and per-bug reporting. The system is built on the GitHub Security Lab Taskflow Agent framework and uses Claude Sonnet 5 by default to drive decisions, while MCP tools handle concrete actions like running AFL++ and compiling harnesses. Developers invoke it via a simple script (run_fuzzing.sh) with an owner/repo slug, and the pipeline then installs required tools, clones the project, identifies key functions, and creates fuzz targets automatically. Each harness is compiled twice—once with AFL’s instrumentation for fuzzing and once with clang coverage flags—to enable a coverage‑driven feedback loop where the agent iteratively adjusts seeds, harness code, and AFL dictionaries until coverage plateaus. Because the agent can run arbitrary build commands without container isolation, GitHub strongly recommends executing it only in disposable, non‑privileged environments such as Codespaces or throwaway VMs.