How ABACI AI Agent Automates Linux Kernel Fuzzing, Bug Attribution, and Patch Generation

Overview

Traditional testing methods such as unit tests, static analysis, and manual code review struggle to keep up with the massive, rapidly evolving Linux kernel codebase. ABACI addresses this challenge by providing a fully automated, low‑intervention pipeline that covers fuzz testing, defect analysis, and automated patch generation, creating a closed loop of "discover → analyze → fix".

Automated Fuzzing Deployment

ABACI parses natural‑language test requests (e.g., "please test the latest linux‑next branch for the io_uring subsystem") and automatically performs the following steps:

Clones the target repository (

https://git.kernel.org/pub/scm/linux/kernel/git/next/linux-next.git

) and checks out the appropriate branch (default master).

Generates a tailored .config enabling CONFIG_KASAN, CONFIG_KCOV, CONFIG_UBSAN, block device drivers, and the io_uring subsystem.

Builds a QEMU image with suitable CPU, memory, and kernel command‑line options (e.g., console=ttyS0).

Creates a fuzz‑interface description file for the selected syscalls and launches the fuzzing job.

During execution, ABACI continuously monitors the fuzz process, uploads discovered crashes to a defect database, and schedules parallel analysis tasks.

Intelligent Defect Attribution

When a crash is found, ABACI performs a two‑phase bisect:

Cause Bisect : Marks the crashing commit as bad and an older clean commit as good, then runs git bisect to locate the introducing commit.

Fix Bisect : Searches the upstream repository for a commit that already fixes the issue (identified via Fixes: tags). If a matching fix is found, ABACI recommends it; otherwise, it falls back to a full fix‑bisect.

ABACI enhances bisect accuracy by using semantic analysis to recognize that the same underlying bug may manifest differently across kernel versions (e.g., KASAN use‑after‑free vs. a warning).

Human‑Machine Collaboration via DingTalk Bot

Key events—test start, crash detection, attribution results, and repair suggestions—are sent as structured cards to a DingTalk bot. Users can query the bot with natural language (e.g., "@ABACI show the latest test report") and receive real‑time updates, while still retaining the ability to manually intervene when needed.

Automated Patch Generation and Verification

ABACI’s repair workflow consists of the following steps:

Workspace Preparation : Recreates the exact build environment used during testing, including the kernel source, configuration, and toolchain.

Defect Verification : Reproduces the crash using the stored PoC to confirm the defect is still present.

Target Function Identification : Parses the crash log to pinpoint the vulnerable function(s) and extracts their source code, file path, and surrounding context.

Repair Pattern Retrieval : Searches a curated knowledge base of historical kernel fixes for patterns similar to the current defect.

Patch Generation : Feeds the function source, context, and retrieved patterns to a large language model, which outputs a rewritten version of the function rather than a raw diff.

Diff Creation : Replaces the original function with the LLM‑generated code, then uses git diff to produce a standard patch file ready for review.

Multi‑Stage Validation :

Static analysis with multiple tools to catch syntax or semantic issues.

PoC re‑execution to ensure the crash is eliminated.

Functional regression testing to verify no other kernel features are broken.

If any stage fails, ABACI feeds the diagnostic information back to the LLM for another iteration, repeating until a patch passes all checks or a maximum retry limit is reached.

Results and Impact

In a year‑long deployment, ABACI reduced manual effort from roughly 300 hours per kernel release to about 10 hours—a 96.6% efficiency gain. The end‑to‑end cycle from crash discovery to PR merge dropped from 5‑7 days to 1‑2 days. A concrete case involving a "general protection fault in coalesce_fill_reply" was automatically reproduced, bisected, matched to an upstream fix ( 74d6529b78f7), and delivered to developers via the DingTalk bot for quick cherry‑picking.

Conclusion

ABACI demonstrates that integrating LLM‑driven semantic reasoning with traditional fuzzing, bisect, static analysis, and CI/CD techniques can create a low‑intervention, sustainable security assurance platform for large‑scale system software such as the Linux kernel. While challenges remain in autonomous decision‑making and complex bug fixing, the approach provides a reusable blueprint for future intelligent maintenance of operating systems, compilers, and runtimes.