Adrian Johnson
TLDR:
- Nvidia has embraced generative AI (GenAI) revolution with large language models (LLMs).
- Nvidia’s security architect plans to share lessons learned in securing LLMs at Black Hat USA.
Article Summary:
Nvidia, known for its AI-accelerating chips, has delved into using its own large language models (LLMs) for various AI applications, including its NeMo platform. The company’s security architect, Richard Harang, will discuss practical LLM security at Black Hat USA and share insights gained from red-teaming their systems. While LLMs pose unique risks due to their privileged nature, existing cybersecurity strategies can be adapted to secure them effectively.
AI-generated applications bring familiar security concerns, but the attack surface of LLMs introduces new challenges. However, Harang emphasizes that essential security attributes like confidentiality, integrity, and availability still apply. The random nature of AI systems makes attacks less predictable, providing a defense advantage. Despite the increased risks of AI autonomy, Harang believes that security can be improved by integrating security principles from the start.
Businesses aiming to leverage agentic AI applications face potential risks from unexpected system behaviors. Harang acknowledges the industry’s ongoing learning curve in managing these risks, but remains optimistic about the solvability of AI security issues. He emphasizes the importance of security integration in AI development and envisions a future where AI can enhance information retrieval and analysis capabilities.
Overall, Nvidia’s embrace of LLMs and commitment to commonsense cybersecurity strategies highlight the evolving landscape of AI security and the importance of proactive security measures in safeguarding AI-powered applications.
