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.
Institutions
- Apple (Israel) (IL)
- Master's College (US)
- Institute of Information Security (JP)
- SBS CyberSecurity (United States) (US)
- Apple (United States) (US)
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