# When Testing AI Tests Us: Safeguarding Mental Health on the Digital Frontlines

arXiv: https://arxiv.org/abs/2504.20910 · HTML: https://mlsanity.com/paper/2504.20910/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Prompt injection and agent security, one of 12 directions in LLM safety and security.
- Builds on 9 of its 110 references that are in the map.
- First posted on arXiv 29 Apr 2025.

## Placed in

- LLM safety and security / [Prompt injection and agent security](https://mlsanity.com/safety/prompt-injection-and-agent-security/)

## Builds on (most cited first, 9 of 9)

- [Red Teaming Language Models with Language Models](https://mlsanity.com/paper/2202.03286/) (2022)
- [Red-Teaming for Generative AI: Silver Bullet or Security Theater?](https://mlsanity.com/paper/2401.15897/) (2024)
- [Semantic Mirror Jailbreak: Genetic Algorithm Based Jailbreak Prompts Against Open-source LLMs](https://mlsanity.com/paper/2402.14872/) (2024)
- [A Safe Harbor for AI Evaluation and Red Teaming](https://mlsanity.com/paper/2403.04893/) (2024)
- [Foot In The Door: Understanding Large Language Model Jailbreaking via Cognitive Psychology](https://mlsanity.com/paper/2402.15690/) (2024)
- [Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning](https://mlsanity.com/paper/2412.18693/) (2024)
- [Lessons From Red Teaming 100 Generative AI Products](https://mlsanity.com/paper/2501.07238/) (2025)
- [Non-Consensual Synthetic Intimate Imagery: Prevalence, Attitudes, and Knowledge in 10 Countries](https://mlsanity.com/paper/2402.01721/) (2024)
- [AURA: Amplifying Understanding, Resilience, and Awareness for Responsible AI Content Work](https://mlsanity.com/paper/2411.01426/) (2024)

## Built on by (most cited first, 2 of 2)

- ["Unlimited Realm of Exploration and Experimentation": Methods and Motivations of AI-Generated Sexual Content Creators](https://mlsanity.com/paper/2601.21028/) (2026)
- [Seeking Late Night Life Lines: Experiences of Conversational AI Use in Mental Health Crisis](https://mlsanity.com/paper/2512.23859/) (2025)

## Abstract (arXiv)

Red-teaming is a core part of the infrastructure that ensures that AI models do not produce harmful content. Unlike past technologies, the black box nature of generative AI systems necessitates a uniquely interactional mode of testing, one in which individuals on red teams actively interact with the system, leveraging natural language to simulate malicious actors and solicit harmful outputs. This interactional labor done by red teams can result in mental health harms that are uniquely tied to the adversarial engagement strategies necessary to effectively red team. The importance of ensuring that generative AI models do not propagate societal or individual harm is widely recognized -- one less visible foundation of end-to-end AI safety is also the protection of the mental health and wellbeing of those who work to keep model outputs safe. In this paper, we argue that the unmet mental health needs of AI red-teamers is a critical workplace safety concern. Through analyzing the unique mental health impacts associated with the labor done by red teams, we propose potential individual and organizational strategies that could be used to meet these needs, and safeguard the mental health of red-teamers. We develop our proposed strategies through drawing parallels between common red-teaming practices and interactional labor common to other professions (including actors, mental health professionals, conflict photographers, and content moderators), describing how individuals and organizations within these professional spaces safeguard their mental health given similar psychological demands. Drawing on these protective practices, we describe how safeguards could be adapted for the distinct mental health challenges experienced by red teaming organizations as they mitigate emerging technological risks on the new digital frontlines.