Team Research blog
In Part 2 of our series focusing on improving LLM security against prompt injection we’re doing a deeper dive into transformers, attention, and how these topics play a role in prompt injection attacks. This post aims to provide more under-the-hood context about why prompt injection attacks are effective, and why they’re so difficult to mitigate.
Many developers are leveraging LLMs without taking advantage of system roles, making their applications vulnerable by design. Security researches may be missing severe issues with prompt design and implementation by not testing the LLM APIs and focusing on the web user interfaces of LLM providers. Our latest blog post provides prescriptive advice to LLM application developers to help them minimize the security risk of their applications. It also helps security researchers focus on the issues that are important to developers of LLM applications. This post is the first in a series of two, where in future posts we’ll cover the concept of attention in transformer models.