mlsanityData to 7 Oct 2026

Seeing Seeds Beyond Weeds: Green Teaming Generative AI for Beneficial Uses

arXiv 2306.03097 ↗First posted 30 May 2023cs.HC cs.AIcited by 8

On the map: LLM safety and security → Prompt injection and agent security

Abstract (arXiv)

Large generative AI models (GMs) like GPT and DALL-E are trained to generate content for general, wide-ranging purposes. GM content filters are generalized to filter out content which has a risk of harm in many cases, e.g., hate speech. However, prohibited content is not always harmful -- there are instances where generating prohibited content can be beneficial. So, when GMs filter out content, they preclude beneficial use cases along with harmful ones. Which use cases are precluded reflects the values embedded in GM content filtering. Recent work on red teaming proposes methods to bypass GM content filters to generate harmful content. We coin the term green teaming to describe methods of bypassing GM content filters to design for beneficial use cases. We showcase green teaming by: 1) Using ChatGPT as a virtual patient to simulate a person experiencing suicidal ideation, for suicide support training; 2) Using Codex to intentionally generate buggy solutions to train students on debugging; and 3) Examining an Instagram page using Midjourney to generate images of anti-LGBTQ+ politicians in drag. Finally, we discuss how our use cases demonstrate green teaming as both a practical design method and a mode of critique, which problematizes and subverts current understandings of harms and values in generative AI.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Placed in Prompt injection and agent security, one of 12 directions in LLM safety and security. Builds on 17 of its 56 references that are in the map. First posted on arXiv 30 May 2023.

Builds on

17 of 56 references.

Closest ancestors · 2

Same direction, newest first

  1. 2022Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned23 Aug
  2. 2022Red Teaming Language Models with Language Models7 Feb

Other references · 15

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  4. 2020The Pile: An 800GB Dataset of Diverse Text for Language Modelingcited by 2,346
  5. 2021Ethical and social risks of harm from Language Modelscited by 957
  6. 2022Ignore Previous Prompt: Attack Techniques For Language Modelscited by 760
  7. 2021Persistent Anti-Muslim Bias in Large Language Modelscited by 376
  8. 2020Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claimscited by 163
  9. 2022GitHub Copilot AI pair programmer: Asset or Liability?cited by 150
  10. 2022Programming Is Hard -- Or at Least It Used to Be: Educational Opportunities And Challenges of AI Code Generationcited by 97
  11. 2022Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programmingcited by 91
  12. 2022Automated Repair of Programs from Large Language Modelscited by 71
3 more
  1. 2022Human-AI Collaboration Enables More Empathic Conversations in Text-based Peer-to-Peer Mental Health Supportcited by 68
  2. 2022Handling and Presenting Harmful Text in NLP Researchcited by 43
  3. 2023Assessing Language Model Deployment with Risk Cardscited by 30

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 4; 2025: 3.

8 papers.

  1. 2023Can LLM-Generated Misinformation Be Detected?cited by 173
  2. 2024Roleplay-doh: Enabling Domain-Experts to Create LLM-simulated Patients via Eliciting and Adhering to Principlescited by 72
  3. 2024Operationalizing a Threat Model for Red-Teaming Large Language Models (LLMs)cited by 44
  4. 2024Securing Large Language Models: Threats, Vulnerabilities and Responsible Practicescited by 36
  5. 2025Guardians and Offenders: A Survey on Harmful Content Generation and Safety Mitigation of LLMcited by 8
  6. 2025Un-Straightening Generative AI: How Queer Artists Surface and Challenge the Normativity of Generative AI Modelscited by 6
  7. 2024Can LLMs be Fooled? Investigating Vulnerabilities in LLMscited by 5
  8. 2025TN-Eval: Rubric and Evaluation Protocols for Measuring the Quality of Behavioral Therapy Notescited by 3