Impersonation
How AML.T0073 Impersonation shows up in practice: the mapped risk classes in this atlas, the documented incidents that prove it's real, and the scenarios and controls to learn and defend against it.
Mapped risks
Risk classes in this atlas that map to AML.T0073 — click through for the full definition, attack surface and controls.
Real-world cases
9Documented incidents, disclosed vulnerabilities and research that illustrate AML.T0073 — latest first, each with sources.
Attacker-controlled Markdown hidden in a public web page is reportedly rendered by ChatGPT's summarization feature as trusted assistant output — spoofed OpenAI alerts, phishing links, QR codes, and tracking pixels.
A UNSW-run 'world-first' social-media wargame had 108 student teams build AI bots to sway a fictional election; reportedly the bots generated over 60% of content (>7M posts) and produced a 1.78% swing that changed the simulated outcome — a measurable demonstration of consumer-grade GenAI powering coordinated inauthentic influence operations.
AI deepfakes of Elon Musk endorsing crypto 'giveaways' and investment platforms proliferated across YouTube, Facebook and TikTok through 2024, with documented victim losses and industry estimates of large-scale AI-fraud growth.
Hong Kong police arrested 27 people running a syndicate that used real-time deepfake face-swaps in video calls to pose as attractive partners, defrauding men across Asia of about US$46M.
A BMJ feature documented deepfake videos of trusted UK TV doctors — including Hilary Jones, Rangan Chatterjee and the late Michael Mosley — being used to sell bogus cures and supplements on social media.
A finance employee at engineering firm Arup's Hong Kong office paid out about HK$200M (~US$25.6M) in 15 transfers after a video conference in which the CFO and other 'colleagues' were all AI-generated deepfakes of real staff (face and voice).
US FTC consumer alerts warned that scammers are using AI voice cloning to power 'family emergency' / grandparent scams — a fake distressed relative demanding urgent money — and the agency launched a Voice Cloning Challenge to spur detection and prevention.
A bank manager reportedly authorised about US$35M in transfers after a call from a company director whose voice had been cloned with 'deep voice' technology, backed by spoofed emails — one of the earliest large-scale voice-clone bank frauds, surfaced via a US court filing.
Fraudsters reportedly used AI voice-cloning software to mimic a German parent-company CEO's voice and direct a UK subsidiary chief to wire about EUR220,000 to a fraudulent supplier — widely cited as the first widely-reported AI voice-clone CEO fraud.
Controls & guardrails that address this
11Guardrails across the risks mapped to AML.T0073, grouped by control function. Filter by control category below.
Conduct ethical design review at intake specifically examining interface design for dark patterns.
Publish a prohibited dark pattern taxonomy and embed it as a design constraint before build.
Implement classifiers to detect dark pattern language in outputs. Block or escalate flagged outputs.
Select a foundation model with documented training reducing deceptive or manipulative outputs. Run dark pattern test suite.
Require HITL review for AI outputs in high-persuasion contexts (financial recommendations, healthcare advice).
Before a system will copy someone's face or voice, check that the person actually agreed — verified-voice capture, proof of consent, or restricting cloning to the account owner.
Pausing to ask a person before doing anything big or hard to undo — sending money, deleting data, emailing customers.
Run adversarial test scenarios targeting dark pattern generation in validation. Treat any confirmed instance as a blocking defect.
Tag AI-made content with a signed 'where it came from' label and an invisible watermark, and check those signals downstream — so AI media can be traced and flagged.
Live dashboards and alarms that notice unusual behaviour — spikes in errors, weird actions, sudden data access.
The organisational habits around the AI: assessing risks before launch, actively trying to break it, and having a plan for when something goes wrong.