How We Protect from Prompt Injection on Fireworks AI

Fireworks Training has introduced a feature called safe_tokenization to address vulnerabilities in prompt injection, particularly in large language models (LLMs) where user input might inadvertently be interpreted as control tokens. This issue arises because most open models, like those using HuggingFace tokenizers, render entire conversations into single strings, allowing user inputs that resemble control tokens to be misinterpreted, potentially altering the model's behavior. Fireworks' solution ensures that prompts maintain their intended structure by encoding user content in a way that prevents it from being mistaken as control tokens, thereby preserving the hierarchy of system instructions over user messages. This feature is designed to be cost-efficient, preserving user content without modification and ensuring consistency when user input does not contain control tokens. Available across multiple models in the Fireworks library, safe_tokenization is a key step in enhancing the security and reliability of AI deployments by maintaining the integrity of system prompts against adversarial inputs.

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