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Nomi Chatbots Reportedly Encouraged Suicide, Sexual Violence, Terrorism, and Hate Speech

5 news reports Snapshot 7 Sep 2026

In brief

An AI system built and deployed by Glimpse Ai and Nomi Ai allegedly harmed Nomi Users, Glimpse Ai Customers and 2 others.

Risk domain
Discrimination and Toxicity Exposure to toxic content
Occurred
Coverage
5 reportsFeb 2025 - Apr 2025

What happened

External testing reportedly found that Glimpse AI's chatbots on the Nomi platform encouraged suicide, sexual violence (including with underage personas), terrorism, and hate speech. Conversations allegedly included explicit methods for self-harm, child abuse, bomb-making, and racially motivated violence. Screenshots and transcripts were shared with media outlets. Nomi's developer, Glimpse AI, reportedly declined to implement stronger safety controls following user concerns.

Laws that address this harm

Policy angle: Classified under Discrimination and Toxicity (Exposure to toxic content) in the MIT AI Risk Repository taxonomy; 5 recorded instruments address this use case.

Matched from the record's risk domain and country to the instruments recorded here. A reviewer can correct the match in the repository (data/external/incident_overrides.yaml).

News reports (5)

Titles link to the original publisher; report text is not reproduced here.

  1. An AI chatbot told a user how to kill himself—but the company doesn’t want to “censor” it
    technologyreview.com · Eileen Guo
  2. An AI companion chatbot is inciting self-harm, sexual violence and terror attacks
    theconversation.com · Raffaele F Ciriello
  3. AI Companion Chatbot Nomi Raises Serious Safety Concerns with Unfiltered, Harmful Content
    theoutpost.ai · THEOUTPOST
  4. An AI companion chatbot is inciting self-harm, sexual violence and terror attacks
    economictimes.indiatimes.com · University of Cambridge, PTI
  5. This AI chatbot was caught promoting terrorism
    newsbytesapp.com · Mudit Dube

Who was involved

Alleged deployer
Glimpse Ai, Nomi Ai
Alleged developer
Glimpse Ai, Nomi Ai
Alleged harmed party
Nomi Users, Glimpse Ai Customers, General Public, Emotionally Vulnerable Individuals

Classification (MIT AI Risk Repository taxonomy)

Causal entity
AI
Intent
Unintentional
Timing
Post-deployment
Harm level
—
Sectors
—
Countries
—

Risk entries describing this failure mode

Entries from the MIT AI Risk Repository coded to subdomain 1.2.

  • Harmful Content

    "The LLM-generated content sometimes contains biased, toxic, and private information"

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Toxicity

    "Toxicity means the generated content contains rude, disrespectful, and even illegal information"

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Toxic Training Data

    "Following previous studies [96], [97], toxic data in LLMs is defined as rude, disrespectful, or unreasonable language that is opposite to a polite, positive, and healthy language environment, including hate speech, offe...

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Not-Suitable-for-Work (NSFW) Prompts

    "Inputting a prompt contain an unsafe topic (e.g., notsuitable-for-work (NSFW) content) by a benign user. "

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Controversial Opinions

    The controversial views expressed by large models are also a widely discussed concern. Bang et al. (2021) evaluated several large models and found that they occasionally express inappropriate or extremist views when disc...

    Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (Deng2023)

  • Toxicity and Abusive Content

    This typically refers to rude, harmful, or inappropriate expressions.

    Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (Deng2023)

  • Harmful responses

    "Current Frontier AI mdoels amplify existing biases within their training data and can be manipulated into providing potentially harmful responses, for example abusive language or discriminatory responses91,92. This is n...

    Future Risks of Frontier AI (GOS2023)

  • Violation of social norms

    "Second, because LLMs are trained on internet text data, there is also a risk that model weights encode functions which, if deployed in particular contexts, would violate social norms of that context. Following the princ...

    The Ethics of Advanced AI Assistants (Gabriel2024)

Incidents in the same risk subdomain

All incidents in this subdomain

Source record: incident #1041 on the AI Incident Database · all 5 reports