Tokenization security in the context of AI systems refers to protecting the token-level interface of language models from manipulation, injection, and exploitation. Since LLMs process text as sequences of tokens, understanding and securing this tokenization layer is critical for preventing attacks.

Security Concerns

• Prompt Injection: Attackers craft inputs with special token sequences that cause the model to interpret injected instructions as system-level commands, overriding intended behavior.
• Token Boundary Exploitation: Manipulating text so that the tokenizer splits it in unexpected ways, potentially bypassing content filters that operate on words rather than tokens.
• Special Token Injection: Inserting reserved special tokens (like <|endoftext|>, [INST], ) in user input that the model treats as control signals.
• Unicode/Encoding Attacks: Using invisible characters, zero-width joiners, homoglyphs, or unusual Unicode to create text that appears harmless but tokenizes into adversarial sequences.

Protection Strategies

• Input Sanitization: Strip or escape special tokens and control characters from user input before tokenization.
• Token Allow/Deny Lists: Block known adversarial token sequences or special tokens from appearing in user input.
• Delimiter Enforcement: Use robust delimiters between system prompts and user input that the model is trained to respect.
• Post-Tokenization Validation: Check the token sequence after tokenization for suspicious patterns before feeding it to the model.
• Encoding Normalization: Normalize Unicode to a canonical form (NFC/NFKC) before processing to prevent homoglyph and invisible character attacks.

Challenges

• Tokenizer-Model Coupling: Security filters must understand the specific tokenizer's behavior, as different tokenizers split text differently.
• Evolving Attacks: New token-level attacks are continuously discovered, requiring ongoing defense updates.

Tokenization security is a critical layer in the defense-in-depth strategy for LLM applications — it sits at the boundary between untrusted user input and the model's processing pipeline.