Tokenization in AI refers to the process of splitting input text into smaller units called tokens that language models can process. Unlike crypto tokenization, AI tokenization determines how models perceive text—whether "blockchain" is one token or three, how code is segmented, and where semantic boundaries lie. Understanding tokenization is crucial for Web3 AI security because token boundaries affect everything from context window usage to prompt injection vulnerabilities.

How Tokenization Works

LLMs don't process text character by character—they work with tokens, which are typically subwords:

1"Ethereum smart contracts" → ["Ether", "eum", " smart", " contracts"]
2"0x742d35Cc6634C0532925a" → ["0", "x", "742", "d", "35", "Cc", ...]

Common tokenization approaches:

Byte Pair Encoding (BPE): Iteratively merges frequent character pairs. "blockchain" might become ["block", "chain"] or stay as one token depending on training.

WordPiece: Similar to BPE but uses likelihood-based merging. Used by BERT-family models.

SentencePiece: Language-agnostic tokenization that works directly on raw text, handling any character set.

Tokenization and Model Perception

How text is tokenized fundamentally affects model behavior:

Token boundaries create perception boundaries: A model sees tokens, not characters. Unusual tokenization of code or addresses may cause misinterpretation.

Rare tokens get poorer representations: Tokens seen infrequently during training have worse embeddings, potentially causing errors on uncommon text.

Tokenization varies by language: English typically tokenizes efficiently; other languages or specialized domains (like Solidity code) may require many more tokens for equivalent content.

Security Implications

Tokenization creates specific attack surfaces:

Token Boundary Manipulation: Adversarial text crafted to tokenize in unexpected ways, bypassing filters that operate on the character level.

Token Smuggling: Hiding malicious content within token boundaries where it won't be detected by keyword-based safety systems.

Context Window Exploitation: Different tokenization of semantically equivalent content can dramatically change context window consumption.

Encoding Tricks: Using Unicode variations, homoglyphs, or special characters that tokenize differently than expected, evading detection.

Prompt Injection via Tokenization: Carefully crafted text that tokenizes into instruction-like sequences, triggering unintended model behaviors.

Tokenization in Web3 Applications

Web3 content presents unique tokenization challenges:

Addresses: Ethereum addresses tokenize into many small tokens, consuming context space and potentially fragmenting the model's understanding.

Code: Solidity syntax may not tokenize optimally if the tokenizer wasn't trained on smart contract code.

Technical Terms: Web3-specific terminology may be split into subwords, affecting comprehension.

Hex Data: Transaction data and bytecode create many tokens relative to their semantic content.

Token Counting Matters

Understanding token counts is essential for:

Cost Estimation: API pricing typically charges per token. Inefficient tokenization increases costs.

Context Management: Knowing how content tokenizes helps manage limited context windows.

Rate Limiting: Some systems limit tokens per minute, not characters.

Attack Surface Analysis: Token-based limits may be vulnerable to content that's short in characters but expensive in tokens (or vice versa).

Testing Tokenization Behavior

When auditing AI systems:

Tokenization Consistency: Verify that equivalent content tokenizes consistently.

Boundary Testing: Test inputs designed to exploit token boundaries.

Special Character Handling: Check how Unicode, control characters, and encoding variations tokenize.

Domain-Specific Content: Ensure Web3 terminology tokenizes appropriately.

Count Validation: Verify that token-based limits accurately reflect actual tokenization.

Best Practices

For secure AI system design:

• Understand your tokenizer and its behavior on domain-specific content
• Validate inputs at token level when token-based limits matter
• Test with adversarial tokenization designed to evade filters
• Consider tokenization when estimating context window usage
• Monitor for tokenization anomalies in production inputs

Tokenization sits at the interface between human-readable text and model processing, making it a critical consideration for both functionality and security in AI systems.