Speaker
Description
Syzbot reports around 1,500 new fuzzer-detected findings in the Linux kernel each year. Even for straightforward bugs where the root cause is obvious, the manual effort of drafting, testing, and sending patches constitutes a significant effort on the kernel developers side.
To facilitate this process, we have launched an AI-assisted fix generation workflow in syzbot built around human-in-the-loop review:
* Two-Stage Pipeline: Candidate patches are sent to a moderation list where reviewers reply with natural-language feedback or commands (#syz reject, #syz upstream). The AI agent interprets comments, answers questions, and iterates on new versions while preserving tags like Reviewed-by or Tested-by.
* Human Accountability: When approved via the #syz upstream command, the human reviewer's Signed-off-by: tag is attached in compliance with Linux Kernel AI Coding Assistants guidelines before forwarding the patch to public mailing lists.
Status & Discussion: The system is in a POC stage and undergoing active development. As of early August 2026, 9 of the reported AI-generated patches have been merged upstream, 4 more are approved and waiting to be merged, and many more are under review/discussion.
In this session, we will focus on:
* System Overview: More detailed workflow description and how the system works under the hood.
* Lessons Learned: Based on the current experience, what went well and what needs more attention.
* Community Discussion: How to make the tool more useful and increase review efficiency.