# All You Need Is a Fuzzing Brain: A Retrospective

Source: https://www.youtube.com/watch?v=QyDSX5wKGDw
Recap page: https://rapidrecap.app/video/QyDSX5wKGDw
Generated: 2025-09-26T16:08:45.757+00:00

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## Quick Overview

The "All You Need Is a Fuzzing Brain" team's retrospective reveals that large language models (LLMs) are the primary driver for their success in the AI-driven cybersecurity challenge, not traditional fuzzing, with LLMs contributing to 90% of their code generation and patch creation.

**Key Points:**
- The team "All You Need Is a Fuzzing Brain" emphasizes that LLMs, not fuzzing, formed the core of their AI cybersecurity system, with fuzzing playing a minimal role and even being absent in the semi-finals.
- LLMs were instrumental in generating Proof of Vulnerabilities (POVs) and patches, often achieving success in a multi-round chat with AI, where LLMs identified vulnerabilities and generated inputs, and then generated patches.
- Their system achieved remarkable speed, finding a vulnerability within five minutes by employing an aggressive, greedy basic strategy and running 96 worker nodes in parallel with 23 different strategies.
- The team open-sourced their AI cybersecurity system (CIS), including versions submitted for the AI-C finals and semi-finals, with a local Docker version available for desktop use.
- Building a reliable AI-based system proved highly challenging, with issues like race conditions, rate limits, and LLM hallucinations significantly impacting development and testing, costing them 20 hours in one instance due to a race condition.
- The team's patching strategy involved LLMs generating a full code patch rather than a diff file, which was then validated and converted to a diff file using `git diff` for reliable application.
- Despite significant LLM advancements, 23% of cybersecurity challenges remained undiscovered, highlighting the need for further LLM capability improvements, better evaluation datasets, and advanced debugging tools.

**Context:** The transcript features the "All You Need Is a Fuzzing Brain" team, composed of Texas A&M students and faculty, presenting a retrospective of their participation in an AI-driven cybersecurity challenge. Led by Professor Jeff Hang, the team discusses their system's architecture, strategies, and the surprising reliance on large language models (LLMs) rather than traditional fuzzing techniques, a core concept reflected in their team name's clever misdirection.

## Detailed Analysis

The "All You Need Is a Fuzzing Brain" team highlights that their success in an AI cybersecurity challenge was predominantly driven by large language models (LLMs), not traditional fuzzing. They open-sourced their AI cybersecurity system (CIS), noting that LLMs generated 90% of their code, including their final submission and semi-final versions. The team's strategy focused on using LLMs for both vulnerability discovery and patch generation through multi-round AI chats. They developed a fast system, identifying a vulnerability in under five minutes by running 96 worker nodes in parallel with 23 diverse strategies. Their patching approach involved LLMs generating full code modifications, which were then validated and converted to diff files. However, building a reliable AI system was a major hurdle, plagued by issues like race conditions (which cost them 20 hours of debugging), rate limits, and LLM hallucinations. Despite the advancements, they noted that a significant percentage of cybersecurity challenges remained undiscovered, emphasizing the need for continued LLM development, better training data, and improved debugging tools.

### Team Introduction

- Professor Jeff Hang, PhD student Zen, undergraduate Matthew Woodcock, graduate student Chin Xiao, and the eight-member Texas A&M team
- "All You Need Is a Fuzzing Brain" team
- AI-driven cybersecurity challenge retrospective

### System Overview

- Open-sourced CIS (AI Cybersecurity System)
- Final and semi-final versions available
- Local Docker version for desktop use
- Minimal fuzzing, emphasis on LLM 'brain'

### Key Strategy - LLM Reliance

- LLMs as the 'brain' of the system, not fuzzing
- Zero fuzzing in semi-finals, minimal in finals
- LLMs generate POV, inputs, and patches
- Multi-round chat with AI for vulnerability discovery and patching

### Performance and Speed

- Vulnerability found within five minutes
- Aggressive, greedy basic strategy for easy tasks
- 96 worker nodes running in parallel
- 23 different strategies executed concurrently

### Technical Components

- Web service (Go), strategies (Python)
- Static analysis service (CodeQL for Java) for reachability and call path analysis
- Worker nodes running hundreds of threads in parallel
- Submission service for deduplication and validation

### Patching Methodology

- LLM identifies target functions and generates full patched code
- Overrides original buggy function
- Validates patch by rebuilding and testing
- Generates diff file using `git diff` for application

### Advanced Strategy - AX Patch

- LLM patches code without explicit POV information
- Scans reachable functions and ranks top vulnerable functions based on CWEs
- Follows similar patching and validation steps

### Challenges and Surprises

- Dramatic improvement speed from LLMs (e.g., GPT-3.5 to 4)
- Building a reliable AI system is highly challenging
- Race conditions, rate limits, LLM hallucinations
- Debugging multi-language systems (Go, Python) is painful
- Patching can break overall program functionality
- Case study: 20-hour race condition debugging leading to false positive POVs
- 23% of cybersecurity challenges remain undiscovered