Trust No AI: Prompt Injection Along The CIA Security Triad

Fuente: arXiv
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Autor principal: Rehberger, Johann
Formato: Preprint
Publicado: 2024
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author Rehberger, Johann
author_facet Rehberger, Johann
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2412_06090
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Trust No AI: Prompt Injection Along The CIA Security Triad
Rehberger, Johann
Cryptography and Security
Artificial Intelligence
Machine Learning
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.
title Trust No AI: Prompt Injection Along The CIA Security Triad
topic Cryptography and Security
Artificial Intelligence
Machine Learning
url https://arxiv.org/abs/2412.06090