A neural network is a machine learning architecture consisting of interconnected processing nodes (artificial neurons) organized in layers. These networks learn to recognize patterns by adjusting connection weights during training, enabling tasks from image recognition to language generation. For Web3 security professionals, understanding neural network fundamentals is essential for auditing AI systems and identifying vulnerabilities in AI-powered protocols.

Neural Network Architecture

Neural networks typically consist of three layer types:

Input Layer: Receives raw data—text tokens, pixel values, numerical features. The input format determines what information the network can process.

Hidden Layers: Intermediate layers that transform inputs through weighted connections and activation functions. Deep networks have many hidden layers, enabling complex pattern recognition.

Output Layer: Produces the final result—class probabilities, generated text, predicted values. The output format matches the task requirements.

Information flows from input through hidden layers to output, with each connection having an associated weight that determines its influence.

How Neural Networks Learn

Training adjusts network weights to minimize prediction errors:

Forward Pass: Input data flows through the network, producing an output.

Loss Calculation: The output is compared to the desired result, computing an error measure (loss).

Backpropagation: Error gradients flow backward through the network, indicating how each weight contributed to the error.

Weight Update: Weights are adjusted to reduce the error, typically using optimization algorithms like gradient descent.

This process repeats over millions of examples until the network performs well on the training task.

Neural Networks in Web3

Neural networks power various Web3 applications:

Large Language Models: Transformer-based neural networks like GPT and Claude generate text, analyze code, and power AI assistants.

Smart Contract Analysis: Neural networks identify vulnerability patterns in contract code that rule-based analyzers might miss.

Fraud Detection: Networks recognize suspicious transaction patterns for exchange security and protocol monitoring.

Price Prediction: Trading systems use networks to forecast market movements based on historical data.

NFT Generation: Generative networks create artwork and content for NFT collections.

Security Vulnerabilities

Neural networks have inherent vulnerabilities relevant to security audits:

Training Poisoning: Malicious data injected during training can create backdoors that trigger specific behaviors when certain inputs appear.

Adversarial Examples: Carefully crafted inputs can cause misclassification—an image slightly modified to fool a classifier while appearing unchanged to humans.

Model Extraction: Attackers can query a model repeatedly to reconstruct its behavior, stealing proprietary models.

Membership Inference: Determining whether specific data was used in training, potentially leaking private information.

Hallucination: Networks confidently produce plausible-sounding but factually incorrect outputs, particularly problematic for decision-making systems.

Black Box Nature

Neural networks are notoriously difficult to interpret:

Opacity: With millions or billions of parameters, understanding why a network produces specific outputs is challenging.

Emergent Behavior: Complex behaviors emerge from training that weren't explicitly programmed, making prediction difficult.

Hidden Patterns: Networks may learn spurious correlations in training data that cause unexpected failures in deployment.

This opacity complicates security auditing—it's hard to verify that a network will behave safely across all possible inputs.

Auditing Neural Network Systems

When assessing AI systems in Web3:

Training Data Review: Examine data sources for potential poisoning or bias that could create vulnerabilities.

Input Validation: Test how the system handles adversarial, malformed, or unexpected inputs.

Output Verification: Implement checks on network outputs before they trigger on-chain actions.

Behavior Boundaries: Define and test acceptable behavior ranges, flagging anomalies.

Failure Modes: Understand how the network fails and ensure failures are safe.

Neural Networks vs Traditional Security

Traditional security often involves deterministic systems where behavior can be formally verified. Neural networks introduce probabilistic behavior that's harder to guarantee:

This fundamental difference requires adapted security approaches when neural networks are involved in critical Web3 infrastructure.