AI incident #957 · 28 February 2025

Alleged Instagram Algorithm Malfunction Floods Users' Reels Feeds with Violent and Graphic Content

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

What happened

An alleged Instagram algorithm malfunction caused users' Reels feeds to be overwhelmed with violent and distressing content. Many reported seeing deaths, extreme brutality, and other graphic material in rapid succession, often without prior engagement with similar content. The sudden exposure caused psychological distress for many users, with minors and vulnerable individuals particularly affected by the graphic content. Meta confirmed an AI failure was responsible and apologized.

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.

28 Feb 2025
Meta apologises over flood of gore, violence and dead bodies on Instagram
theguardian.com · Dan Milmo · AIID #4807

Who was involved

Alleged deployer: Meta
Alleged developer: Meta
Alleged harmed party: Minors, Meta Users, Instagram 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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