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AI incident #762 ·

Grok AI Reportedly Generates Offensive and Violent Images Without Proper Safeguards

Open on the AI Incident Database 3 news reports Snapshot 7 Sep 2026

What happened

Elon Musk’s Grok AI, launched on X, generated offensive and violent images without adequate safety controls. The AI produced deepfakes of public figures like Taylor Swift, Kamala Harris, and Alexandria Ocasio-Cortez, as well as copyrighted characters such as Mickey Mouse in inappropriate scenarios. Despite claiming adherence to certain content guidelines, Grok's outputs included politically charged and explicit imagery

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 (3)

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

  1. Lack of Grok-2 Image Generation Filters Prompts X Users to Generate Damaging Images
    theguardian.com · Nick Robins-Early · AIID #3999
  2. People Are Using X’s AI Chatbot ‘Grok’ to Create Offensive Images of Trademarked Characters — And Troll Elon Musk
    mediaite.com · Sarah Rumpf · AIID #4026
  3. Grok-2 Generates Controversy; Expert Reactions
    aibusiness.com · Berenice Baker · AIID #4025

Who was involved

Alleged deployer
Xai
Alleged developer
X (Twitter), Xai
Alleged harmed party
Taylor Swift, Nintendo, Kamala Harris, Joe Biden, Donald Trump, Disney, Alexandria Ocasio Cortez

Classification (MIT AI Risk Repository taxonomy)

Causal entity
AI
Intent
Intentional
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)

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