Research Report: AI Security is a LangSec Problem

The rapid development of Artificial Intelligence (AI) systems, and particularly Large Language Models (LLMs), has already started changing how software is written in industry. In this work, we categorize two important features of modern AI systems - structured outputs and tool-use - and explain how the security of each is, inherently, a LangSec problem. We provide anecdotal evidence from the San Francisco startup ecosystem to illustrate how companies are currently using, deploying, and securing AI systems with these features. Based on these observations and our analysis of current practices, we identify three concrete research directions where the LangSec community can contribute to securing both the parsing of LLM outputs and the safe deployment of LLM-powered tools. This work should be read as a call-to-action for the LangSec community to tackle outstanding, and growing, security problems catalyzed by AI.

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Publication Details

Published
2025-05-15
DOI
https://doi.org/10.1109/spw67851.2025.00011
Primary Topic
Machine Learning and Data Classification
Type
article
Field-Weighted Citation Impact
0.00
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article

Research Report: AI Security is a LangSec Problem

Max von Hippel, Evan Miyazono
Machine Learning and Data Classification
article

Research Report: AI Security is a LangSec Problem

Max von Hippel, Evan Miyazono
article en

Abstract

The rapid development of Artificial Intelligence (AI) systems, and particularly Large Language Models (LLMs), has already started changing how software is written in industry. In this work, we categorize two important features of modern AI systems - structured outputs and tool-use - and explain how the security of each is, inherently, a LangSec problem. We provide anecdotal evidence from the San Francisco startup ecosystem to illustrate how companies are currently using, deploying, and securing AI systems with these features. Based on these observations and our analysis of current practices, we identify three concrete research directions where the LangSec community can contribute to securing both the parsing of LLM outputs and the safe deployment of LLM-powered tools. This work should be read as a call-to-action for the LangSec community to tackle outstanding, and growing, security problems catalyzed by AI.

Atlas Scientific (United States) (US)
Openalex Percentile: Top 92%
Machine Learning and Data Classification
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