Trust No AI: Prompt Injection Along The CIA Security Triad

TL;DR

This paper examines prompt injection attacks on large language models, demonstrating how they threaten the CIA security triad and pose significant cybersecurity risks through real-world exploits and vulnerabilities.

Contribution

It compiles and analyzes real-world prompt injection exploits, highlighting their impact on confidentiality, integrity, and availability in AI systems.

Findings

Prompt injection can compromise data confidentiality.

Attacks can undermine system integrity.

Availability of AI services is at risk.

Abstract

The CIA security triad - Confidentiality, Integrity, and Availability - is a cornerstone of data and cybersecurity. With the emergence of large language model (LLM) applications, a new class of threat, known as prompt injection, was first identified in 2022. Since then, numerous real-world vulnerabilities and exploits have been documented in production LLM systems, including those from leading vendors like OpenAI, Microsoft, Anthropic and Google. This paper compiles real-world exploits and proof-of concept examples, based on the research conducted and publicly documented by the author, demonstrating how prompt injection undermines the CIA triad and poses ongoing risks to cybersecurity and AI systems at large.