Introduction

Large Language Models (LLMs) have rapidly become integral to applications, but they come with some very interesting security pitfalls. Chief among these is prompt injection, where cleverly crafted inputs make an LLM bypass its instructions or leak secrets. Prompt injection in fact is so wildly popular that, OWASP now ranks prompt injection as the #1 AI security risk for modern LLM applications as shown in their OWASP GenAI top 10.

We’ve provided a higher-level overview about Prompt Injection in our other blog, so in this one we’ll focus on the concept with the technical audience in mind. Here we’ll explore how LLMs can be vulnerable at the architectural level and the sophisticated ways attackers exploit them. We’ll also examine effective defenses, from system prompt design to “sandwich” prompting techniques. We’ll also discuss a few tools that can help test and secure LLMs.

 

Introduction

Large Language Models (LLMs) have rapidly become integral to applications, but they come with some very interesting security pitfalls. Chief among these is prompt injection, where cleverly crafted inputs make an LLM bypass its instructions or leak secrets. Prompt injection in fact is so wildly popular that, OWASP now ranks prompt injection as the #1 AI security risk for modern LLM applications as shown in their OWASP GenAI top 10.

We’ve provided a higher-level overview about Prompt Injection in our other blog, so in this one we’ll focus on the concept with the technical audience in mind. Here we’ll explore how LLMs can be vulnerable at the architectural level and the sophisticated ways attackers exploit them. We’ll also examine effective defenses, from system prompt design to “sandwich” prompting techniques. We’ll also discuss a few tools that can help test and secure LLMs.

 

Source: SecurityInnovation
Source Link: https://blog.securityinnovation.com/securing-llms-against-prompt-injection-attacks