GCD AI RADAR
incidents/AIR-2020-0030

Camera Hijack and Facial Recognition Evasion for Tax Fraud

Attack on AI4 Confirmed real-world lossAdversarial evasion

Perpetrators bypassed a government facial recognition authentication system using camera hijacking to commit major tax fraud. The scheme resulted in significant financial losses totaling 77 million dollars.

Details

type
Attack on AI
An AI model, agent or AI-powered product was the thing exploited, abused or leaked.
categories
  • Adversarial evasion
    Inputs crafted to make a model misclassify or miss detections (perturbations, evasion of AI-based defences).
owasp llm top 10
no entry for these categories
mitre atlas
Both rows are derived from the type and categories (OWASP 2025 edition, ATLAS release 2026.09), not classified per record. How
severity
4 · Confirmed real-world loss
Financial loss, PHI/PII breach, safety impact or regulatory action.
targets
Enterprise deployment
products
Shanghai government tax office facial recognition
incident date
Jan 2020 (approx.)
first reported
3 Dec 2020
origin
MITRE ATLAS
Derived from MITRE ATLAS (Apache 2.0, © MITRE).
classifier confidence
0.95 · published at or above 0.80; see the methodology
tags
facial-recognition · camera-hijack · fraud
record
created 2026-10-18 06:55Z · updated 2026-10-18 06:55Z · json

Sources · 2

  1. 2020-12-03
    Camera Hijack Attack on Facial Recognition System
    MITRE ATLAS · atlas.mitre.org/studies/AML.CS0004
  2. 2020-12-03
    Faces are the next target for fraudsters
    The Wall Street Journal · wsj.com/articles/faces-are-the-next-target-for-fraudsters-11625662828

Titles and links belong to their publishers. The summary above is written by GCD AI RADAR in its own words; data is CC BY-SA 4.0.

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