January 24, 2024 at 09:07AM
The heart of large language models (LLMs) is a black box, leading to risks from lack of transparency in AI decision-making. A report from BIML outlines 81 risks and aims to help security practitioners understand and address these challenges. NIST also emphasizes the need for a common language to discuss threats to AI. Defending LLMs is challenging, with attackers targeting model integrity and data.
From the meeting notes, here are the key takeaways:
1. The heart of all large language models (LLMs) is a black box, which means that the end users typically have limited information on how the data used to train the models was collected and cleaned.
2. The lack of visibility into how artificial intelligence (AI) makes decisions is the root cause of many risks posed by LLMs, as described in a report by the Berryville Institute of Machine Learning (BIML).
3. BIML’s report, “An Architectural Risk Analysis of Large Language Models,” aims to provide CISOs and other security practitioners with a way to identify risks associated with machine learning (ML) and AI models.
4. The US National Institute of Standards and Technology (NIST) has also released a paper focused on creating a common language for discussing threats to AI, particularly in the areas of predictive AI and generative AI systems.
5. The rapid adoption of AI models by businesses has made the security of these models increasingly important, leading to increased research interest in adversarial machine learning.
6. Many of the risks associated with LLMs are directly related to black-box issues, and the BIML researchers emphasize the need for better understanding and transparency in the design and implementation of LLM foundation models.
7. Defending LLMs is challenging, and current approaches are considered limited and incomplete, leading to a constant battle between attackers and defenders.
These takeaways highlight the critical need for greater transparency and understanding of the workings of large language models, as well as the ongoing challenges in defending against potential risks and attacks.