Summary
In conclusion, this chapter has provided an in-depth exploration into the world of Language Models (LLMs) and the critical considerations surrounding their use, particularly focusing on privacy and security aspects. We have covered key concepts such as prompt engineering and compared open-source versus closed-source LLMs. Additionally, we delved into AI standards and terminology of attacks, highlighting NIST’s guidelines and the OWASP Top 10 LLMs vulnerabilities.
Furthermore, we discussed various privacy attacks on LLMs, including real-world incidents of privacy leaks, membership inference attacks, and prompt injection attacks. These examples underscore the importance of robust privacy-preserving technologies in LLMs. We examined techniques like training LLMs using Differential Privacy with Private Transformer to mitigate privacy risks while maintaining model performance.
Overall, this chapter aims to empower readers with the knowledge and tools necessary to navigate...
