• Regularly monitor and review the LLM outputs.
• Cross-check the LLM output with trusted external sources or implement automatic validation mechanisms that can cross-verify the generated output against known facts or data.
• Enhance the model with fine-tuning or embeddings to improve output quality.
• Communicate the risks and limitations associated with using LLMs and build APIs and user interfaces that encourage responsible and safe use of LLMs.

10. Model theft

Model theft is when malicious actors access and exfiltrate entire LLM models or their weights and parameters so that they can create their own versions. This can result in economic or brand reputation loss, erosion of competitive advantage, unauthorized use of the model, or unauthorized access to sensitive information contained within the model.

For example, an attacker might get access to an LLM model repository via a misconfiguration in the network or application security setting, a disgruntled employee might leak a model. Attackers can also query the LLM to get enough question-and-answer pairs to create their own shadow clone of the model, or use the responses to fine tune their model. According to OWASP, it’s not possible to replicate an LLM 100% through this type of model extraction, but they can get close.

Attackers can use this new model for its functionality, or they can use it as a testing ground for prompt injection techniques which they can then use to break into the original model. As large language models become more prevalent and more useful, LLM thefts will become a significant security concern, OWASP says.

Preventative measures for this vulnerability include: