mlsanityData to 7 Oct 2026

The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation

arXiv 1802.07228 ↗First posted 20 Feb 2018cs.AI cs.CR cs.CYcited by 159

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Cited by 27 in LLM safety and security, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

This report surveys the landscape of potential security threats from malicious uses of AI, and proposes ways to better forecast, prevent, and mitigate these threats. After analyzing the ways in which AI may influence the threat landscape in the digital, physical, and political domains, we make four high-level recommendations for AI researchers and other stakeholders. We also suggest several promising areas for further research that could expand the portfolio of defenses, or make attacks less effective or harder to execute. Finally, we discuss, but do not conclusively resolve, the long-term equilibrium of attackers and defenders.

Key facts. Cited by 159 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Feb 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 19; 2022: 14; 2023: 34; 2024: 34; 2025: 29; 2026: 29.

159 papers.

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  1. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,422
  2. 2024HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusalcited by 1,330
  3. 2023Towards Automated Circuit Discovery for Mechanistic Interpretabilitycited by 700
  4. 2023Are aligned neural networks adversarially aligned?cited by 305
  5. 2021Unsolved Problems in ML Safetycited by 291
  6. 2023The Stable Signature: Rooting Watermarks in Latent Diffusion Modelscited by 289
  7. 2023Challenges and Applications of Large Language Modelscited by 251
  8. 2021Alignment of Language Agentscited by 167
  9. 2023Model evaluation for extreme riskscited by 167
  10. 2024The Emerged Security and Privacy of LLM Agent: A Survey with Case Studiescited by 127
  11. 2023Regulating ChatGPT and other Large Generative AI Modelscited by 102
  12. 2024Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systemscited by 94
38 more
  1. 2022The Fallacy of AI Functionalitycited by 88
  2. 2024A Survey of Attacks on Large Vision-Language Models: Resources, Advances, and Future Trendscited by 82
  3. 2023Use of LLMs for Illicit Purposes: Threats, Prevention Measures, and Vulnerabilitiescited by 76
  4. 2022Frido: Feature Pyramid Diffusion for Complex Scene Image Synthesiscited by 75
  5. 2025Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agentscited by 66
  6. 2024On the Societal Impact of Open Foundation Modelscited by 64
  7. 2023The Gradient of Generative AI Release: Methods and Considerationscited by 63
  8. 2022X-Risk Analysis for AI Researchcited by 60
  9. 2024Introducing v0.5 of the AI Safety Benchmark from MLCommonscited by 60
  10. 2021Improved Transformer for High-Resolution GANscited by 59
  11. 2022Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of Foundation Modelscited by 56
  12. 2022SparCL: Sparse Continual Learning on the Edgecited by 55
  13. 2022Synthetic Disinformation Attacks on Automated Fact Verification Systemscited by 44
  14. 2025Agentic Web: Weaving the Next Web with AI Agentscited by 43
  15. 2021The Dangers of Underclaiming: Reasons for Caution When Reporting How NLP Systems Failcited by 38
  16. 2021The Threat of Offensive AI to Organizationscited by 37
  17. 2023Anatomy of an AI-powered malicious social botnetcited by 35
  18. 2024A survey of synthetic data augmentation methods in computer visioncited by 34
  19. 2025AI-Generated Video Detection via Perceptual Straighteningcited by 34
  20. 2023Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectivescited by 31
  21. 2021Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flowscited by 30
  22. 2024Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectivescited by 30
  23. 2023Parents and Children: Distinguishing Multimodal DeepFakes from Natural Imagescited by 29
  24. 2022Diagnosing and Fixing Manifold Overfitting in Deep Generative Modelscited by 28
  25. 2022CaloMan: Fast generation of calorimeter showers with density estimation on learned manifoldscited by 28
  26. 2024LaWa: Using Latent Space for In-Generation Image Watermarkingcited by 27
  27. 2021Information Directed Reward Learning for Reinforcement Learningcited by 24
  28. 2024Two Types of AI Existential Risk: Decisive and Accumulativecited by 21
  29. 2024On the Essence and Prospect: An Investigation of Alignment Approaches for Big Modelscited by 21
  30. 2023What does it take to catch a Chinchilla? Verifying Rules on Large-Scale Neural Network Training via Compute Monitoringcited by 20
  31. 2024Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Datacited by 20
  32. 2023The Ethical Implications of Generative Audio Models: A Systematic Literature Reviewcited by 18
  33. 2023Protecting Society from AI Misuse: When are Restrictions on Capabilities Warranted?cited by 15
  34. 2022Current and Near-Term AI as a Potential Existential Risk Factorcited by 14
  35. 2023Challenges and Remedies to Privacy and Security in AIGC: Exploring the Potential of Privacy Computing, Blockchain, and Beyondcited by 14
  36. 2024Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Researchcited by 14
  37. 2021Why and How Governments Should Monitor AI Developmentcited by 13
  38. 2021EvilModel 2.0: Bringing Neural Network Models into Malware Attackscited by 13

50 most cited of 159 All 159 (JSON)