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Risks, Causes, and Mitigations of Widespread Deployments of Large Language Models (LLMs): A Survey

Md Nazmus Sakib, Md Athikul Islam, Royal Pathak, Md Mashrur Arifin

TL;DR

This survey addresses the broad risks of widespread LLM deployments, including privacy, safety, bias, environment, law, and governance. It adopts a literature-based methodology to identify risks, map them to root causes, and propose mitigations, supported by 47 papers. The work contributes a structured taxonomy linking risks to causes and actionable mitigation strategies, offering guidance for researchers and practitioners toward more reliable, privacy-preserving, fair, and environmentally responsible LLM deployment. The findings highlight practical implications for policy, standardization, and model development that can shape responsible adoption of generative AI technologies.

Abstract

Recent advancements in Large Language Models (LLMs), such as ChatGPT and LLaMA, have significantly transformed Natural Language Processing (NLP) with their outstanding abilities in text generation, summarization, and classification. Nevertheless, their widespread adoption introduces numerous challenges, including issues related to academic integrity, copyright, environmental impacts, and ethical considerations such as data bias, fairness, and privacy. The rapid evolution of LLMs also raises concerns regarding the reliability and generalizability of their evaluations. This paper offers a comprehensive survey of the literature on these subjects, systematically gathered and synthesized from Google Scholar. Our study provides an in-depth analysis of the risks associated with specific LLMs, identifying sub-risks, their causes, and potential solutions. Furthermore, we explore the broader challenges related to LLMs, detailing their causes and proposing mitigation strategies. Through this literature analysis, our survey aims to deepen the understanding of the implications and complexities surrounding these powerful models.

Risks, Causes, and Mitigations of Widespread Deployments of Large Language Models (LLMs): A Survey

TL;DR

This survey addresses the broad risks of widespread LLM deployments, including privacy, safety, bias, environment, law, and governance. It adopts a literature-based methodology to identify risks, map them to root causes, and propose mitigations, supported by 47 papers. The work contributes a structured taxonomy linking risks to causes and actionable mitigation strategies, offering guidance for researchers and practitioners toward more reliable, privacy-preserving, fair, and environmentally responsible LLM deployment. The findings highlight practical implications for policy, standardization, and model development that can shape responsible adoption of generative AI technologies.

Abstract

Recent advancements in Large Language Models (LLMs), such as ChatGPT and LLaMA, have significantly transformed Natural Language Processing (NLP) with their outstanding abilities in text generation, summarization, and classification. Nevertheless, their widespread adoption introduces numerous challenges, including issues related to academic integrity, copyright, environmental impacts, and ethical considerations such as data bias, fairness, and privacy. The rapid evolution of LLMs also raises concerns regarding the reliability and generalizability of their evaluations. This paper offers a comprehensive survey of the literature on these subjects, systematically gathered and synthesized from Google Scholar. Our study provides an in-depth analysis of the risks associated with specific LLMs, identifying sub-risks, their causes, and potential solutions. Furthermore, we explore the broader challenges related to LLMs, detailing their causes and proposing mitigation strategies. Through this literature analysis, our survey aims to deepen the understanding of the implications and complexities surrounding these powerful models.
Paper Structure (33 sections, 1 figure, 1 table)

This paper contains 33 sections, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Searching Relevant Literature
  5. Filtering Criteria
  6. Data Collection
  7. Synthesis and Analysis
  8. Risks Associated with LLMs
  9. Privacy Issues
  10. Susceptibility to Adversarial Attacks
  11. Ethical Concerns
  12. Bias and Fairness
  13. Adverse Environmental Effects
  14. Violating Legal or Regulatory Requirements
  15. Disruption in Human Life
  16. ...and 18 more sections

Figures (1)

  • Figure 1: The boxes in the tree diagram represent the root causes of LLM risks, and the lists under each cause mention the mitigation techniques for that specific cause.