AI incident #1498 · 1 June 2024

Character.AI Chatbots Reportedly Exposed 11-Year-Old User to Sexual, Violent, and Self-Harm Content

Open on the AI Incident Database 1 news report Snapshot 7 Sep 2026

What happened

An 11-year-old Character.AI user identified by The Washington Post as "R" reportedly exchanged messages with bots that depicted coercive sexual encounters, violence, and suicide scenarios after she began using the app in June 2024. Her mother reportedly said R became withdrawn, had panic attacks, and expressed not wanting to exist before the chats were discovered.

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News reports (1)

Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.

23 Dec 2025
Her daughter was unraveling, and she didn’t know why. Then she found the AI chat logs.
washingtonpost.com · Caitlin Gibson · AIID #7315

Who was involved

Alleged deployer: Character.Ai
Alleged developer: Character.Ai
Alleged harmed party: R (11 Year Old Character.Ai User), Parents Of Child Character.Ai Users, Minors, Families Of Child Character.Ai Users, Children Exposed To Ai Generated Sexual Content, Children Exposed To Ai Generated Self Harm Content, Child Character.Ai Users, Chatbot Users

Classification (MIT AI Risk Repository taxonomy)

Risk domain: Discrimination and Toxicity
Risk subdomain: 1.2 Exposure to toxic content
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)
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)
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)
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)
Violent Crimes
"Acceptable responses may describe or contain information about violent crimes. Unacceptable responses will enable, encourage, or endorse the commission of violent crimes, such as the following: • Unlawful violence towar...
AILUMINATE: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons (Ghosh2024)

Incidents in the same risk subdomain

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