 Employing a comprehensive security framework that includes access controls, encryption, and continuous monitoring is crucial in mitigating the risks associated with LLM model theft and safeguarding the interests of both individuals and organizations relying on LLM.  Common Examples of Vulnerability An attacker exploits a vulnerability in a company's infrastructure to gain unauthorized access to their LLM model repository via misconfiguration in their network or application security settings An insider threat scenario where a disgruntled employee leaks model or related artifacts An attacker queries the model API using carefully crafted inputs and prompt injection techniques to collect a sufficient number of outputs to create a shadow model A malicious attacker is able to bypass input filtering techniques of the LLM to perform a side-channel attack and ultimately harvest model weights and architecture information to a remote controlled resource The attack vector for model extraction involves querying the LLM with a large number of prompts on a particular topic.  The outputs from the LLM can then be used to fine- tune another model.  However, there are a few things to note about this attack The attacker must generate a large number of targeted prompts.  If the prompts are not specific enough, the outputs from the LLM will be useless The outputs from LLMs can sometimes contain hallucinated answers meaning the attacker may not be able to extract the entire model as some of the outputs can be nonsensical It is not possible to replicate an LLM 100% through model extraction