🤖 AI / Machine Learning16 min
AI-Powered Fuzzing: Using Neural Networks to Find Zero-Day Vulnerabilities
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Muhammad Asim Chattha
Software Developer & Cybersecurity Researcher
#Fuzzing#Neural Networks#Zero-Day#Security#Reinforcement Learning
Neural network-guided fuzzing represents the cutting edge of automated vulnerability discovery. This article explores how deep learning can dramatically improve fuzzing efficiency.
The Problem with Traditional Fuzzing
Coverage-guided fuzzers like AFL++ and libFuzzer rely on simple mutations and edge coverage feedback. While effective, they struggle with complex input formats and deep program states.
Neural Approaches
- **Generative Models**: Training GANs and VAEs on valid input corpora to generate structure-aware mutations
- **Reinforcement Learning**: Using coverage as a reward signal to train mutation policies
- **Gradient-Guided Fuzzing**: Computing gradients through the target to directly optimize for rare branches
- **Sequence Models**: LSTM and Transformer models to learn input grammar implicitly
Case Study: Fuzzing a PDF Parser
We demonstrate a complete pipeline using a modified AFL++ with a PyTorch-based mutation engine, achieving 3x more unique crashes than baseline AFL++ on MuPDF within 24 hours.