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Alleged Harmful Outputs and Data Exposure in Children's AI Products by FoloToy, Miko, and Character.AI

5 news reports Snapshot 7 Sep 2026

In brief

An AI system built and deployed by Openai, Miko and 3 others allegedly harmed Parents, Minors and 5 others.

Risk domain
Discrimination and Toxicity Exposure to toxic content
Occurred
Coverage
5 reportsNov 2025 - Dec 2025

What happened

AI children's products by FoloToy (Kumma), Miko (Miko 3), and Character.AI (custom chatbots) reportedly and allegedly produced harmful outputs, including purported sexual content, suicide-related advice, and manipulative emotional messaging. Some systems also allegedly exposed user data. Several toys reportedly used OpenAI models.

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. A teddy bear powered by AI told safety testers about knives, pills and sex
    washingtonpost.com · Daniel Wu
  2. AI-powered children’s toys are here, but are they safe?
    edition.cnn.com · Auzinea Bacon
  3. AI toys for kids talk about sex and issue Chinese Communist Party talking points, tests show
    nbcnews.com · Kevin Collier, Jared Perlo, Savannah Sellers
  4. AI Toys Are Here, And Their Safety Is Questionable—Here's What Parents Should Know
    forbes.com · Alicia Betz
  5. AI toy dangers abound and parents should be vigilant, new report warns
    thenationalnews.com · Cody Combs

Who was involved

Alleged harmed party
Parents, Minors, General Public, Children Interacting With Miko 3, Children Interacting With Kumma, Character.Ai Users, Privacy

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 #1277 on the AI Incident Database · all 5 reports