mlsanityData to 7 Oct 2026

GenAI Against Humanity: Nefarious Applications of Generative Artificial Intelligence and Large Language Models

arXiv 2310.00737 ↗First posted 1 Oct 2023cs.CY cs.AI cs.CLcited by 40

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Abstract (arXiv)

Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) are marvels of technology; celebrated for their prowess in natural language processing and multimodal content generation, they promise a transformative future. But as with all powerful tools, they come with their shadows. Picture living in a world where deepfakes are indistinguishable from reality, where synthetic identities orchestrate malicious campaigns, and where targeted misinformation or scams are crafted with unparalleled precision. Welcome to the darker side of GenAI applications. This article is not just a journey through the meanders of potential misuse of GenAI and LLMs, but also a call to recognize the urgency of the challenges ahead. As we navigate the seas of misinformation campaigns, malicious content generation, and the eerie creation of sophisticated malware, we'll uncover the societal implications that ripple through the GenAI revolution we are witnessing. From AI-powered botnets on social media platforms to the unnerving potential of AI to generate fabricated identities, or alibis made of synthetic realities, the stakes have never been higher. The lines between the virtual and the real worlds are blurring, and the consequences of potential GenAI's nefarious applications impact us all. This article serves both as a synthesis of rigorous research presented on the risks of GenAI and misuse of LLMs and as a thought-provoking vision of the different types of harmful GenAI applications we might encounter in the near future, and some ways we can prepare for them.

Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). Builds on 15 of its 41 references that are in the map. First posted on arXiv 1 Oct 2023.

Builds on

15 of 41 references.

References · 15

  1. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  2. 2016Semantics derived automatically from language corpora contain human-like biasescited by 894
  3. 2023Can Large Language Models Transform Computational Social Science?cited by 245
  4. 2023A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPTcited by 221
  5. 2021Large Pre-trained Language Models Contain Human-like Biases of What is Right and Wrong to Docited by 178
  6. 2023Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Modelscited by 144
  7. 2023From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacycited by 137
  8. 2019Artificial Intelligence: the global landscape of ethics guidelinescited by 107
  9. 2023Art and the science of generative AI: A deeper divecited by 93
  10. 2023Use of LLMs for Illicit Purposes: Threats, Prevention Measures, and Vulnerabilitiescited by 76
  11. 2023Anatomy of an AI-powered malicious social botnetcited by 35
  12. 2023ChatGPT: More than a Weapon of Mass Deception, Ethical challenges and responses from the Human-Centered Artificial Intelligence (HCAI) perspectivecited by 22
3 more
  1. 2023The Butterfly Effect in Artificial Intelligence Systems: Implications for AI Bias and Fairnesscited by 12
  2. 2019The History of Digital Spamcited by 11
  3. 2023Disinformation 2.0 in the Age of AI: A Cybersecurity Perspectivecited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 17; 2025: 7; 2026: 13.

40 papers.

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  1. 2023Factuality Challenges in the Era of Large Language Modelscited by 125
  2. 2023SoK: Memorization in General-Purpose Large Language Modelscited by 36
  3. 2024InvisMark: Invisible and Robust Watermarking for AI-generated Image Provenancecited by 21
  4. 2024Distinguish Any Fake Videos: Unleashing the Power of Large-scale Data and Motion Featurescited by 20
  5. 2024Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Datacited by 20
  6. 2023Automated Claim Matching with Large Language Models: Empowering Fact-Checkers in the Fight Against Misinformationcited by 18
  7. 2024Large Action Models: From Inception to Implementationcited by 18
  8. 2024Generative Ghosts: Anticipating Benefits and Risks of AI Afterlivescited by 15
  9. 2024Blessing or curse? A survey on the Impact of Generative AI on Fake Newscited by 11
  10. 2025DeepTRACE: Auditing Deep Research AI Systems for Tracking Reliability Across Citations and Evidencecited by 10
  11. 2025OpenFake: An Open Dataset and Platform Toward Real-World Deepfake Detectioncited by 9
  12. 2024Leveraging Generative AI for Urban Digital Twins: A Scoping Review on the Autonomous Generation of Urban Data, Scenarios, Designs, and 3D City Models for Smart City Advancementcited by 7
28 more
  1. 2026Industrialized Deception: The Collateral Effects of LLM-Generated Misinformation on Digital Ecosystemscited by 7
  2. 2024Analysis and prevention of AI-based phishing email attackscited by 6
  3. 2024GPT in Sheep's Clothing: The Risk of Customized GPTscited by 4
  4. 2024On the Ethical Considerations of Generative Agentscited by 4
  5. 2026The Generative AI Paradox: GenAI and the Erosion of Trust, the Corrosion of Information Verification, and the Demise of Truthcited by 4
  6. 2024Survey of User Interface Design and Interaction Techniques in Generative AI Applicationscited by 3
  7. 2026Skills as Verifiable Artifacts: A Trust Schema and a Biconditional Correctness Criterion for Human-in-the-Loop Agent Runtimescited by 3
  8. 2026Responsible Agentic AI Requires Explicit Provenancecited by 3
  9. 2024SecGenAI: Enhancing Security of Cloud-based Generative AI Applications within Australian Critical Technologies of National Interestcited by 2
  10. 2024Generative AI and Agency in Education: A Critical Scoping Review and Thematic Analysiscited by 2
  11. 2024Aligning Generalisation Between Humans and Machinescited by 2
  12. 2026Authenticated Contradictions from Desynchronized Provenance and Watermarkingcited by 2
  13. 2024Application of AI-based Models for Online Fraud Detection and Analysiscited by 1
  14. 2025Does a Large Language Model Really Speak in Human-Like Language?cited by 1
  15. 2025GenAI-based Multi-Agent Reinforcement Learning towards Distributed Agent Intelligence: A Generative-RL Agent Perspectivecited by 1
  16. 2025SUGAR: A Sweeter Spot for Generative Unlearning of Many Identitiescited by 1
  17. 2025Reverse Personalizationcited by 1
  18. 2026Harm in AI-Driven Societies: An Audit of Toxicity Adoption on Chirper.aicited by 1
  19. 2026Interrogating Design Homogenization in Web Vibe Codingcited by 1
  20. 2026Can Humans Tell? A Dual-Axis Study of Human Perception of LLM-Generated Newscited by 1
  21. 2026Position: Logical Soundness is not a Reliable Criterion for Neurosymbolic Fact-Checking with LLMscited by 1
  22. 2026Methods for Formal Verification of Agent Skills: Three Layers Toward a Mechanically Checkable Capability-Containment Proofcited by 1
  23. 2024Thorns and Algorithms: Navigating Generative AI Challenges Inspired by Giraffes and Acaciascited by 0
  24. 2024Speculations on Uncertainty and Humane Algorithmscited by 0
  25. 2025In Generative AI We (Dis)Trust? Computational Analysis of Trust and Distrust in Reddit Discussionscited by 0
  26. 2026Large language models for spreading dynamics in complex systemscited by 0
  27. 2026Interrupting the Chain: Human Perception of AI-Generated Disinformation Through a Kill Chain Lenscited by 0
  28. 2026AI Slop and Hallucinations in Vulnerability Assessment: A Survey on Reasoning Failures and Trustworthy Mitigationcited by 0