Excuse me, sir? Your language model is leaking (information) (2401.10360v1)

Abstract: We introduce a cryptographic method to hide an arbitrary secret payload in the response of a LLM. A secret key is required to extract the payload from the model's response, and without the key it is provably impossible to distinguish between the responses of the original LLM and the LLM that hides a payload. In particular, the quality of generated text is not affected by the payload. Our approach extends a recent result of Christ, Gunn and Zamir (2023) who introduced an undetectable watermarking scheme for LLMs.

• The paper introduces a cryptographic method for embedding secret payloads in LLM outputs without altering their observable distribution.
• It details a process that uses a secret key and dynamic error-correcting codes to reliably encode data, with payload size growing linearly with response length.
• Empirical tests on models like GPT-2 and Llama 2 demonstrate the scheme's viability and highlight future directions for enhancing robustness against text edits.

Overview of Cryptographic Payload Embedding in LLMs

The paper "Excuse me, sir? Your LLM is leaking (information)" introduces a cryptographic methodology for embedding secret payloads in responses generated by LLMs, maintaining indistinguishability without compromising text quality. This approach extends previous work on undetectable watermarking, presenting a novel scheme to encode hidden payloads.

Key Contributions

The authors present a methodical process for embedding arbitrary data within LLM outputs using cryptography, ensuring the original distribution remains intact. This technique leverages a secret key to retrieve the hidden information, making it impossible to distinguish between payload-bearing and original responses without the key. The paper builds upon the work by Christ, Gunn, and Zamir (2023), expanding the scope from merely embedding watermarks to incorporating dynamic payloads.

Methodology and Results

The steganographic scheme involves several components:

1. Setup and Encoding:
   • A secret key is generated during setup.
   • The Steg_k function utilizes this key to encode payloads within LLM responses.
   • The encoded payload is seamlessly integrated, leveraging the model's entropy without altering response distribution.
2. Error-Correcting Codes with Feedback (ECC):
   • To ensure reliable payload decoding, the paper introduces a dynamic ECC with feedback. This method, adapted for binary to ternary symbol encoding, provides efficient error correction while leveraging feedback to enhance accuracy.
   • The scheme guarantees the retrieval of a substantial portion of the payload, with performance metrics indicating linear growth of hidden bits relative to response length.
3. Empirical Validation:
   • Empirical evaluations, using models like GPT-2 and Llamma 2, demonstrate the practical viability of embedding payloads at a practicable scale. Results show a consistent linear relationship between response length and the number of encoded bits, affirming theoretical predictions.

Implications and Future Directions

The implications of this research are multifaceted:

• Security and Privacy: By embedding metadata or session details covertly, it is feasible to track LLM usage or identify users without altering observable outputs, offering a tool for privacy-preserving monitoring.
• Robustness Challenges: The paper identifies limitations regarding robustness to text edits, indicating an area ripe for further research. Efforts to make such schemes resistant to various types of modifications would enhance their applicability in dynamic textual environments.

Conclusion

This work demonstrates a significant advancement in the domain of LLMs through the integration of cryptographic payload embedding. By maintaining text indistinguishability and employing advanced error correction, it sets a groundwork for future research in AI security and information encoding. Future exploration could enhance robustness, optimize encoding rates, and broaden the understanding of practical vulnerabilities, paving the way for more secure and versatile AI deployments.