Securing LLM Deployment: Challenges, Risks, and Best Practices

Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks such as text generation, summarization, and sentiment analysis. However, their deployment raises significant security concerns, including data privacy risks, adversarial manipulation, and ethical considerations. This article explores the security risks of LLM deployment, with a specific focus on generating and evaluating tweets using OpenAI APIs. It examines existing security frameworks, highlights major vulnerabilities, and proposes best practices for mitigating threats associated with LLM deployment.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-03-13
DOI
https://doi.org/10.5281/zenodo.15020047
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
0.00
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article

Securing LLM Deployment: Challenges, Risks, and Best Practices

Zenodo (CERN European Organization for Nuclear Research)
Adversarial Robustness in Machine Learning
article

Securing LLM Deployment: Challenges, Risks, and Best Practices

article en

Abstract

Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks such as text generation, summarization, and sentiment analysis. However, their deployment raises significant security concerns, including data privacy risks, adversarial manipulation, and ethical considerations. This article explores the security risks of LLM deployment, with a specific focus on generating and evaluating tweets using OpenAI APIs. It examines existing security frameworks, highlights major vulnerabilities, and proposes best practices for mitigating threats associated with LLM deployment.

Zenodo (CERN European Organization for Nuclear Research)
Apple (Israel) (IL), Master's College (US), Institute of Information Security (JP), SBS CyberSecurity (United States) (US), Apple (United States) (US)
Openalex Percentile: Top 77%
Adversarial Robustness in Machine Learning
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