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

arXiv: https://arxiv.org/abs/2310.00737 · HTML: https://mlsanity.com/paper/2310.00737/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 41 references that are in the map.
- First posted on arXiv 1 Oct 2023.

## Builds on (most cited first, 15 of 15)

- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [Can Large Language Models Transform Computational Social Science?](https://mlsanity.com/paper/2305.03514/) (2023)
- [A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT](https://mlsanity.com/paper/2303.04226/) (2023)
- [Large Pre-trained Language Models Contain Human-like Biases of What is Right and Wrong to Do](https://mlsanity.com/paper/2103.11790/) (2021)
- [Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models](https://mlsanity.com/paper/2304.03738/) (2023)
- [From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy](https://mlsanity.com/paper/2307.00691/) (2023)
- [Artificial Intelligence: the global landscape of ethics guidelines](https://mlsanity.com/paper/1906.11668/) (2019)
- [Art and the science of generative AI: A deeper dive](https://mlsanity.com/paper/2306.04141/) (2023)
- [Use of LLMs for Illicit Purposes: Threats, Prevention Measures, and Vulnerabilities](https://mlsanity.com/paper/2308.12833/) (2023)
- [Anatomy of an AI-powered malicious social botnet](https://mlsanity.com/paper/2307.16336/) (2023)
- [ChatGPT: More than a Weapon of Mass Deception, Ethical challenges and responses from the Human-Centered Artificial Intelligence (HCAI) perspective](https://mlsanity.com/paper/2304.11215/) (2023)
- [The Butterfly Effect in Artificial Intelligence Systems: Implications for AI Bias and Fairness](https://mlsanity.com/paper/2307.05842/) (2023)
- [The History of Digital Spam](https://mlsanity.com/paper/1908.06173/) (2019)
- [Disinformation 2.0 in the Age of AI: A Cybersecurity Perspective](https://mlsanity.com/paper/2306.05569/) (2023)

## Built on by (most cited first, 30 of 42)

- [Factuality Challenges in the Era of Large Language Models](https://mlsanity.com/paper/2310.05189/) (2023)
- [SoK: Memorization in General-Purpose Large Language Models](https://mlsanity.com/paper/2310.18362/) (2023)
- [InvisMark: Invisible and Robust Watermarking for AI-generated Image Provenance](https://mlsanity.com/paper/2411.07795/) (2024)
- [Distinguish Any Fake Videos: Unleashing the Power of Large-scale Data and Motion Features](https://mlsanity.com/paper/2405.15343/) (2024)
- [Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Data](https://mlsanity.com/paper/2406.13843/) (2024)
- [Automated Claim Matching with Large Language Models: Empowering Fact-Checkers in the Fight Against Misinformation](https://mlsanity.com/paper/2310.09223/) (2023)
- [Large Action Models: From Inception to Implementation](https://mlsanity.com/paper/2412.10047/) (2024)
- [Generative Ghosts: Anticipating Benefits and Risks of AI Afterlives](https://mlsanity.com/paper/2402.01662/) (2024)
- [Blessing or curse? A survey on the Impact of Generative AI on Fake News](https://mlsanity.com/paper/2404.03021/) (2024)
- [DeepTRACE: Auditing Deep Research AI Systems for Tracking Reliability Across Citations and Evidence](https://mlsanity.com/paper/2509.04499/) (2025)
- [OpenFake: An Open Dataset and Platform Toward Real-World Deepfake Detection](https://mlsanity.com/paper/2509.09495/) (2025)
- [Leveraging 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 Advancement](https://mlsanity.com/paper/2405.19464/) (2024)
- [Industrialized Deception: The Collateral Effects of LLM-Generated Misinformation on Digital Ecosystems](https://mlsanity.com/paper/2601.21963/) (2026)
- [Analysis and prevention of AI-based phishing email attacks](https://mlsanity.com/paper/2405.05435/) (2024)
- [The Generative AI Paradox: GenAI and the Erosion of Trust, the Corrosion of Information Verification, and the Demise of Truth](https://mlsanity.com/paper/2601.00306/) (2026)
- [GPT in Sheep's Clothing: The Risk of Customized GPTs](https://mlsanity.com/paper/2401.09075/) (2024)
- [Survey of User Interface Design and Interaction Techniques in Generative AI Applications](https://mlsanity.com/paper/2410.22370/) (2024)
- [On the Ethical Considerations of Generative Agents](https://mlsanity.com/paper/2411.19211/) (2024)
- [Authenticated Contradictions from Desynchronized Provenance and Watermarking](https://mlsanity.com/paper/2603.02378/) (2026)
- [Skills as Verifiable Artifacts: A Trust Schema and a Biconditional Correctness Criterion for Human-in-the-Loop Agent Runtimes](https://mlsanity.com/paper/2605.00424/) (2026)
- [Responsible Agentic AI Requires Explicit Provenance](https://mlsanity.com/paper/2605.17169/) (2026)
- [SecGenAI: Enhancing Security of Cloud-based Generative AI Applications within Australian Critical Technologies of National Interest](https://mlsanity.com/paper/2407.01110/) (2024)
- [Generative AI and Agency in Education: A Critical Scoping Review and Thematic Analysis](https://mlsanity.com/paper/2411.00631/) (2024)
- [Aligning Generalisation Between Humans and Machines](https://mlsanity.com/paper/2411.15626/) (2024)
- [Application of AI-based Models for Online Fraud Detection and Analysis](https://mlsanity.com/paper/2409.19022/) (2024)
- [Does a Large Language Model Really Speak in Human-Like Language?](https://mlsanity.com/paper/2501.01273/) (2025)
- [GenAI-based Multi-Agent Reinforcement Learning towards Distributed Agent Intelligence: A Generative-RL Agent Perspective](https://mlsanity.com/paper/2507.09495/) (2025)
- [SUGAR: A Sweeter Spot for Generative Unlearning of Many Identities](https://mlsanity.com/paper/2512.06562/) (2025)
- [Reverse Personalization](https://mlsanity.com/paper/2512.22984/) (2025)
- [Harm in AI-Driven Societies: An Audit of Toxicity Adoption on Chirper.ai](https://mlsanity.com/paper/2601.01090/) (2026)

## 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.