mlsanityData to 7 Oct 2026

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

arXiv 2408.04643 ↗First posted 1 Aug 2024cs.CL cs.LGcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 42 of its 64 references that are in the map. First posted on arXiv 1 Aug 2024.

Builds on

42 of 64 references.

References · 42

  1. 2020Scaling Laws for Neural Language Modelscited by 6,411
  2. 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,339
  3. 2022Quantifying Memorization Across Neural Language Modelscited by 755
  4. 2019Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailmentcited by 663
  5. 2020BERT-ATTACK: Adversarial Attack Against BERT Using BERTcited by 488
  6. 2023"Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Modelscited by 457
  7. 2023Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyondcited by 409
  8. 2021Large Language Models Can Be Strong Differentially Private Learnerscited by 405
  9. 2023A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Uglycited by 380
  10. 2023Challenges and Applications of Large Language Modelscited by 251
  11. 2023Don't Make Your LLM an Evaluation Benchmark Cheatercited by 194
  12. 2023AI Deception: A Survey of Examples, Risks, and Potential Solutionscited by 185
30 more
  1. 2023GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Modelscited by 174
  2. 2024The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)cited by 159
  3. 2019Reducing Sentiment Bias in Language Models via Counterfactual Evaluationcited by 148
  4. 2023From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacycited by 137
  5. 2024Attacks, Defenses and Evaluations for LLM Conversation Safety: A Surveycited by 124
  6. 2024Rethinking Interpretability in the Era of Large Language Modelscited by 110
  7. 2019Large-Scale Multi-Label Text Classification on EU Legislationcited by 104
  8. 2020InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspectivecited by 92
  9. 2024On Protecting the Data Privacy of Large Language Models (LLMs): A Surveycited by 91
  10. 2024Rethinking LLM Memorization through the Lens of Adversarial Compressioncited by 91
  11. 2020Shapley explainability on the data manifoldcited by 88
  12. 2020Do We Need Zero Training Loss After Achieving Zero Training Error?cited by 61
  13. 2021Certified Robustness to Text Adversarial Attacks by Randomized [MASK]cited by 57
  14. 2024Investigating Bias in LLM-Based Bias Detection: Disparities between LLMs and Human Perceptioncited by 55
  15. 2023Unveiling Security, Privacy, and Ethical Concerns of ChatGPTcited by 53
  16. 2023Bias Testing and Mitigation in LLM-based Code Generationcited by 53
  17. 2021Model Extraction and Adversarial Transferability, Your BERT is Vulnerable!cited by 51
  18. 2024Disclosure and Mitigation of Gender Bias in LLMscited by 47
  19. 2023Privacy-Preserving Parameter-Efficient Fine-Tuning for Large Language Model Servicescited by 43
  20. 2023A Survey on ChatGPT: AI-Generated Contents, Challenges, and Solutionscited by 34
  21. 2022Fewer Errors, but More Stereotypes? The Effect of Model Size on Gender Biascited by 33
  22. 2022Just Fine-tune Twice: Selective Differential Privacy for Large Language Modelscited by 32
  23. 2023How to Protect Copyright Data in Optimization of Large Language Models?cited by 26
  24. 2023Quantifying and Analyzing Entity-level Memorization in Large Language Modelscited by 18
  25. 2024Efficient and Green Large Language Models for Software Engineering: Literature Review, Vision, and the Road Aheadcited by 17
  26. 2024Green AI: Exploring Carbon Footprints, Mitigation Strategies, and Trade Offs in Large Language Model Trainingcited by 15
  27. 2020SHIELD: Defending Textual Neural Networks against Multiple Black-Box Adversarial Attacks with Stochastic Multi-Expert Patchercited by 13
  28. 2023A Comprehensive Survey of Attack Techniques, Implementation, and Mitigation Strategies in Large Language Modelscited by 13
  29. 2024PoliTune: Analyzing the Impact of Data Selection and Fine-Tuning on Economic and Political Biases in Large Language Modelscited by 11
  30. 2022Mitigating Gender Bias in Machine Translation through Adversarial Learningcited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 7; 2026: 1.

8 papers.

  1. 2025LLMs in Software Security: A Survey of Vulnerability Detection Techniques and Insightscited by 44
  2. 2025The Scales of Justitia: A Comprehensive Survey on Safety Evaluation of LLMscited by 24
  3. 2025Beyond Words: Exploring Cultural Value Sensitivity in Multimodal Modelscited by 14
  4. 2025Towards Alignment-Centric Paradigm: A Survey of Instruction Tuning in Large Language Modelscited by 5
  5. 2025AURA: An Agent Autonomy Risk Assessment Frameworkcited by 2
  6. 2025Differential Robustness in Transformer Language Models: Empirical Evaluation Under Adversarial Text Attackscited by 1
  7. 2025ForgeDAN: An Evolutionary Framework for Jailbreaking Aligned Large Language Modelscited by 0
  8. 2026GRACE: Graph-Grounded Reflective Agent Copilot Engine for Expert-in-the-Loop Knowledge Expansioncited by 0