"How AI Can Be Hacked With Prompt Injection: NIST Report"

In "Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations," the National Institute of Standards and Technology (NIST) defines different Adversarial Machine Learning (AML) tactics and cyberattacks, as well as provides guidance on how to mitigate and manage them. AML tactics gather information about how Machine Learning (ML) systems work in order to determine how they can be manipulated. That information is used to attack Artificial Intelligence (AI) and its Large Language Models (LLMs), enabling threat actors to evade security, bypass safeguards, and open up new avenues for exploitation. NIST delves into two types of prompt injection attacks: direct and indirect. Direct prompt injection occurs when a user enters a text prompt that causes the LLM to perform unintended or unauthorized actions. Indirect prompt injection occurs when an attacker poisons or degrades the data used by an LLM. This article continues to discuss NIST's guidance regarding prompt injection attacks.

Security Intelligence reports "How AI Can Be Hacked With Prompt Injection: NIST Report"

Submitted by grigby1