Imagine a world where sharing your cutting-edge AI model doesn't mean risking its theft. That's the promise of SLIP, a new technique designed to safeguard Large Language Models (LLMs). These powerful AI models, trained at tremendous cost, are increasingly deployed on less-secure environments like your phone or laptop, opening them up to potential intellectual property theft. SLIP offers a clever solution: it splits the LLM’s core components, placing the most valuable, information-rich parts under lock and key in a secure environment, like the cloud. The bulk of the computation happens on the less-secure device, but without access to the crucial “secret sauce.” How does it work? SLIP utilizes a mathematical technique called Singular Value Decomposition (SVD) to pinpoint the most sensitive parts of the LLM. This “secret sauce” is protected by a secure inference protocol, a kind of cryptographic handshake between the secure and insecure parts of the model during operation. This keeps the valuable IP safe while letting most of the work happen on the cheaper, more accessible device. Tests on popular LLMs like LLaMA 2, Phi-2, and GPT-2 show that SLIP can effectively protect the model's core intellectual property without affecting its performance. Even attempts to reconstruct the model using fine-tuning techniques have proven unsuccessful against properly configured SLIP deployments. While SLIP currently has limitations, such as the added latency caused by network communication between the secure and insecure environments, it offers a practical and efficient solution to safeguard LLM IP. Future research is aimed at optimizing decomposition strategies, further enhancing the security of this promising technology. This could lead to a new era of AI deployment, where sharing powerful models no longer carries the risk of losing control of your precious AI innovations.