mlsanityData to 7 Oct 2026

Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods

arXiv 2210.07321 ↗First posted 13 Oct 2022cs.CL cs.CR cs.CYcited by 75

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Abstract (arXiv)

Machine generated text is increasingly difficult to distinguish from human authored text. Powerful open-source models are freely available, and user-friendly tools that democratize access to generative models are proliferating. ChatGPT, which was released shortly after the first edition of this survey, epitomizes these trends. The great potential of state-of-the-art natural language generation (NLG) systems is tempered by the multitude of avenues for abuse. Detection of machine generated text is a key countermeasure for reducing abuse of NLG models, with significant technical challenges and numerous open problems. We provide a survey that includes both 1) an extensive analysis of threat models posed by contemporary NLG systems, and 2) the most complete review of machine generated text detection methods to date. This survey places machine generated text within its cybersecurity and social context, and provides strong guidance for future work addressing the most critical threat models, and ensuring detection systems themselves demonstrate trustworthiness through fairness, robustness, and accountability.

Key facts. Cited by 75 papers in the map (data through 7 Oct 2026). Builds on 81 of its 217 references that are in the map. First posted on arXiv 13 Oct 2022.

Builds on

81 of 217 references.

References · 81

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Citing papers per year: 2023: 20; 2024: 28; 2025: 20; 2026: 7.

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  1. 2023A Watermark for Large Language Modelscited by 667
  2. 2024LLM Evaluators Recognize and Favor Their Own Generationscited by 651
  3. 2024Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Textcited by 255
  4. 2024Security and Privacy Challenges of Large Language Models: A Surveycited by 179
  5. 2023GPT detectors are biased against non-native English writerscited by 175
  6. 2023Can LLM-Generated Misinformation Be Detected?cited by 173
  7. 2023On the Reliability of Watermarks for Large Language Modelscited by 170
  8. 2024Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviewscited by 160
  9. 2024Mapping the Increasing Use of LLMs in Scientific Paperscited by 112
  10. 2023A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directionscited by 101
  11. 2023OUTFOX: LLM-Generated Essay Detection Through In-Context Learning with Adversarially Generated Examplescited by 80
  12. 2023Three Bricks to Consolidate Watermarks for Large Language Modelscited by 80
38 more
  1. 2024Detecting Multimedia Generated by Large AI Models: A Surveycited by 73
  2. 2024On the Societal Impact of Open Foundation Modelscited by 64
  3. 2024Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systemscited by 63
  4. 2023On the Creativity of Large Language Modelscited by 57
  5. 2023Prompt Engineering for Healthcare: Methodologies and Applicationscited by 52
  6. 2024Watermarking Makes Language Models Radioactivecited by 52
  7. 2024SoK: Watermarking for AI-Generated Contentcited by 43
  8. 2023GPT-who: An Information Density-based Machine-Generated Text Detectorcited by 38
  9. 2023Anatomy of an AI-powered malicious social botnetcited by 35
  10. 2024Training-free LLM-generated Text Detection by Mining Token Probability Sequencescited by 33
  11. 2023Assessing Language Model Deployment with Risk Cardscited by 30
  12. 2024Detecting AI-Generated Text: Factors Influencing Detectability with Current Methodscited by 23
  13. 2023Model Leeching: An Extraction Attack Targeting LLMscited by 22
  14. 2023AI vs. Human -- Differentiation Analysis of Scientific Content Generationcited by 21
  15. 2023The Age of Synthetic Realities: Challenges and Opportunitiescited by 15
  16. 2024Rag and Roll: An End-to-End Evaluation of Indirect Prompt Manipulations in LLM-based Application Frameworkscited by 14
  17. 2023Detecting ChatGPT: A Survey of the State of Detecting ChatGPT-Generated Textcited by 10
  18. 2024Exploring Vulnerabilities and Protections in Large Language Models: A Surveycited by 10
  19. 2024Prompt Obfuscation for Large Language Modelscited by 10
  20. 2024ThreatModeling-LLM: Automating Threat Modeling using Large Language Models for Banking Systemcited by 10
  21. 2023Towards an Understanding and Explanation for Mixed-Initiative Artificial Scientific Text Detectioncited by 8
  22. 2026Detecting LLM-Generated Text with Performance Guaranteescited by 8
  23. 2026Learn-to-Distance: Distance Learning for Detecting LLM-Generated Textcited by 8
  24. 2023AuthentiGPT: Detecting Machine-Generated Text via Black-Box Language Models Denoisingcited by 5
  25. 2024Deciphering Textual Authenticity: A Generalized Strategy through the Lens of Large Language Semantics for Detecting Human vs. Machine-Generated Textcited by 5
  26. 2024SoK: Prompt Hacking of Large Language Modelscited by 5
  27. 2024TransitGPT: A Generative AI-based framework for interacting with GTFS data using Large Language Modelscited by 5
  28. 2025Aero-LLM: A Distributed Framework for Secure UAV Communication and Intelligent Decision-Makingcited by 5
  29. 2024Using Machine Learning to Distinguish Human-written from Machine-generated Creative Fictioncited by 4
  30. 2025Robustness Assessment and Enhancement of Text Watermarking for Google's SynthIDcited by 4
  31. 2023Detection of Fake Generated Scientific Abstractscited by 3
  32. 2025BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarkscited by 3
  33. 2025Automating Prompt Leakage Attacks on Large Language Models Using Agentic Approachcited by 3
  34. 2025Watermarking Degrades Alignment in Language Models: Analysis and Mitigationcited by 3
  35. 2025Detecting Post-generation Edits to Watermarked LLM Outputs via Combinatorial Watermarkingcited by 3
  36. 2025On the Detectability of LLM-Generated Text: What Exactly Is LLM-Generated Text?cited by 3
  37. 2026Overview of PAN 2026: Voight-Kampff Generative AI Detection, Text Watermarking, Multi-Author Writing Style Analysis, Generative Plagiarism Detection, and Reasoning Trajectory Detectioncited by 3
  38. 2023An Information-Theoretic Approach for Detecting Edits in AI-Generated Textcited by 2

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