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Incident 224: WeChat Pay's Facial Recognition Security Evaded by Scammers Using Victims’ Social Media Content

Description: In China, fraudsters bypassed facial-recognition security for online financial transactions on WeChat Pay by crafting identity-verification GIFs of victims from their selfies on WeChat Moments, a social media platform.

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Alleged: WeChat developed an AI system deployed by WeChat Pay, which harmed WeChat Pay users.

Incident Stats

Incident ID
224
Report Count
1
Incident Date
2020-07-01
Editors
Khoa Lam
Applied Taxonomies
GMF, MIT

MIT Taxonomy Classifications

Machine-Classified
Taxonomy Details

Risk Subdomain

A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
 

2.2. AI system security vulnerabilities and attacks

Risk Domain

The Domain Taxonomy of AI Risks classifies risks into seven AI risk domains: (1) Discrimination & toxicity, (2) Privacy & security, (3) Misinformation, (4) Malicious actors & misuse, (5) Human-computer interaction, (6) Socioeconomic & environmental harms, and (7) AI system safety, failures & limitations.
 
  1. Privacy & Security

Entity

Which, if any, entity is presented as the main cause of the risk
 

Human

Timing

The stage in the AI lifecycle at which the risk is presented as occurring
 

Post-deployment

Intent

Whether the risk is presented as occurring as an expected or unexpected outcome from pursuing a goal
 

Intentional

Incident Reports

Reports Timeline

Incident OccurrenceScammers Busted Using Face GIFs to Steal From WeChat Accounts
Scammers Busted Using Face GIFs to Steal From WeChat Accounts

Scammers Busted Using Face GIFs to Steal From WeChat Accounts

sixthtone.com

Scammers Busted Using Face GIFs to Steal From WeChat Accounts
sixthtone.com · 2020

Chat stickers have become an essential tool for communication in the digital age. Now, they’re also a tool for scammers.

Police in central China’s Hubei province have arrested three swindlers who used chat stickers, or GIFs, to verify peopl…

Variants

A "variant" is an incident that shares the same causative factors, produces similar harms, and involves the same intelligent systems as a known AI incident. Rather than index variants as entirely separate incidents, we list variations of incidents under the first similar incident submitted to the database. Unlike other submission types to the incident database, variants are not required to have reporting in evidence external to the Incident Database. Learn more from the research paper.
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