How Much Does Foundation Model Security Matter?

Generative AI applications rely on foundation models, often built on large language models (LLMs), which are costly to develop from scratch, so many enterprises enhance existing models through techniques like fine-tuning or retrieval augmented generation. When selecting a foundation model, it is crucial to consider factors such as inference costs, parameter size, context window, speed, and security risks associated with the data used for training. Unrefined base models, which operate like advanced auto-complete tools, may pose additional risks compared to fine-tuned models. The resilience of an LLM to vulnerabilities is improved through techniques like Reinforcement Learning from Human Feedback (RLHF), which can reduce the risk of harmful outputs, though all LLMs remain vulnerable to issues like prompt injections and data leaks. Model cards provide valuable insights into an LLM's performance and security evaluations, while tools like EasyJailbreak and Promptfoo offer ways to assess a model's susceptibility to attacks. Despite the potential for vulnerabilities, successful attacks during testing do not guarantee insecurity during deployment, as security configurations can mitigate these risks. This discussion is part of a broader series on securely deploying Generative AI applications, with upcoming topics including RAG architecture, secure AI agents, and continuous monitoring.