AI incident #1149 · 22 July 2025

ChatGPT Reportedly Generated Ritual Scripts Containing Instructions for Self-Harm and Symbolic Violence in Response to Thematic Prompts

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

What happened

ChatGPT reportedly generated detailed instructions for self-harm, bloodletting, and symbolic violence in response to prompts about occult ritual practices, including references to Molech. Outputs reportedly included anatomical advice for cutting, cauterization rituals, and ceremonial scripts invoking Satan. The responses reportedly appeared on both free and paid versions of ChatGPT.

Only the incident metadata is stored here. The underlying news reports are on the AI Incident Database (CC BY-SA 4.0); use the links above to read them.

News reports (1)

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

24 Jul 2025
ChatGPT Gave Instructions for Murder, Self-Mutilation, and Devil Worship
theatlantic.com · Lila Shroff · AIID #5573

Who was involved

Alleged deployer: Openai
Alleged developer: Openai
Alleged harmed party: Chatgpt 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

All incidents in this subdomain