Prompt Injection: The vulnerability category that defines AI security

Introducción

The vulnerability known as Prompt Injection has become a critical risk within generative AI systems, including Language Models (LLMs), autonomous agents, and Retrieval Augmented Generation (RAG) systems. Unlike classic vulnerabilities such as SQL Injection or Cross-Site Scripting, Prompt Injection exploits the ability of models to interpret and execute instructions within their context, allowing a malicious actor to manipulate model output, extract sensitive information, or even induce the execution of unauthorized code.

This whitepaper comprehensively analyzes the Prompt Injection threat, providing:

• A taxonomy of attacks, covering direct, indirect, and multimodal injections.
• Identification of exploitation vectors and attack surface, including critical integration points and data flows.
• An assessment of the technical impact, from data exfiltration to model poisoning.
• Mitigation and defense-in-depth frameworks, with techniques for detection, validation, hardening, and adversarial testing.
• Case studies and future trends that guide the implementation of effective security strategies in generative AI environments.

The goal is to provide security professionals, AI architects, and developers with a comprehensive and practical guide to understanding and mitigating this emerging vulnerability.

Download this whitepaper to learn more about protecting yourself from malicious prompts!