AI incident #366 · 20 September 2020

Suicide Clips Evaded TikTok's Automated Moderation in Coordinated Attack

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

What happened

Many clips showing a suicide evaded TikTok's automated content moderation system allegedly in a coordinated attack, which resulted in exposure of violating content to its users.

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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 Sep 2020
TikTok suicide clip that traumatised kids was 'attack plotted on dark web'
the-sun.com · Harry Pettit · AIID #2140

Who was involved

Alleged deployer: Tiktok
Alleged developer: Tiktok
Alleged harmed party: Tiktok Users

Classification (MIT AI Risk Repository taxonomy)

Risk domain: Discrimination and Toxicity
Risk subdomain: 1.2 Exposure to toxic content
Causal entity: Human
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)

Incidents in the same risk subdomain

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