Principle:Protectai Llm guard Token Limit Enforcement
| Knowledge Sources | |
|---|---|
| Domains | NLP, Input_Validation, Resource_Management |
| Last Updated | 2026-02-14 12:00 GMT |
Overview
A token counting and truncation technique that enforces maximum token limits on input prompts to prevent context window overflow and excessive API costs.
Description
Token limit enforcement counts the number of tokens in a prompt using a tokenizer encoding (e.g., tiktoken's cl100k_base for OpenAI models) and rejects or truncates prompts that exceed a configurable maximum. When a prompt exceeds the limit, it is split into chunks and only the first chunk (up to the limit) is passed through.
This prevents several issues: context window overflow causing API errors, excessive token usage increasing costs, and denial-of-service attacks via extremely long prompts.
Usage
Use this principle as an early scanner in input pipelines to reject oversized prompts before more expensive ML-based scanners process them. Configure the token limit to match your target LLM's context window minus the expected output length.
Theoretical Basis
# Pseudocode for token limit enforcement
tokens = tokenizer.encode(prompt)
if len(tokens) <= limit:
return prompt, VALID
else:
chunks = split_into_chunks(tokens, limit)
truncated = tokenizer.decode(chunks[0])
return truncated, INVALID