Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·May 21·6 min read
Prompt2Fingerprint: Plug-and-Play LLM Fingerprinting via Text-to-Weight Generation
TL;DR
Large language models (LLMs) lack built-in identity verification, making it hard to confirm which model generated a response or detect unauthorized copies. Prompt2Fingerprint converts natural language prompts directly into unique model weights (numerical parameters that define behavior), creating a plug-and-play fingerprinting system that embeds identifying signatures into any LLM without retraining.
✦ Why It Matters
Engineers can now verify LLM authenticity and detect model theft by embedding recoverable identity markers without expensive retraining.
Key Takeaways
- Implement fingerprinting on deployed models without retraining or architectural changes.
- Use natural language prompts to generate unique model weight signatures for IP protection.
- Verify model ownership by detecting embedded fingerprints across model copies.
- Maintain baseline model performance while adding security-critical identification markers.
Full Summary
LLMs are valuable intellectual property but vulnerable to theft and unauthorized deployment. Existing fingerprinting approaches—methods to embed unique identifiers into models—typically require expensive retraining or architectural changes.
Prompt2Fingerprint solves this by implementing text-to-weight generation: converting natural language prompts into direct modifications of model parameters (weights). The technique works as a plug-and-play module compatible with existing LLMs, meaning it integrates without redesigning the base model.
By embedding fingerprints through prompt-driven weight adjustments, the method creates verifiable ownership markers while preserving original model capabilities. Results demonstrate reliable fingerprint detection across model variants and sizes, with minimal performance degradation.
This approach enables practical IP protection for deployed models.