Privacy attacks on LLMs
In recent years, LLMs have revolutionized natural language understanding (NLU) and natural language generation (NLG), powering a wide range of applications from chatbots and virtual assistants to content recommendation systems and language translation services. However, the rapid advancement of these models has raised significant concerns about privacy and security. LLM applications have the potential to expose sensitive data, proprietary algorithms, or other confidential information through their output. This could lead to unauthorized access to sensitive data, IP, privacy infringements, and other security violations. As LLMs become increasingly prevalent in our digital landscape, there is a growing need for effective strategies to protect sensitive information and uphold user privacy.
As discussed in the earlier chapters, ML models are susceptible to privacy attacks, and there’s no exception for GenAI models (LLMs) either.
The following two recent...
