Record summary
A quick snapshot of what this page covers.
Attack context
How this AI attack works in practice.
- ATLAS ID
- AML.T0021
- ATT&CK external ID
- T1585
- Priority score
- 130
Mitigations
Defenses that may help against this attack.
Case studies
Examples from public reports and exercises.
ProKYC: Deepfake Tool for Account Fraud Attacks
Cato CTRL security researchers have identified ProKYC, a deepfake tool being sold to cybercriminals as a method to bypass Know Your Customer (KYC) verification on financial service applications such as cryptocurrency exchanges. ProKYC can create fake identity documents and generate deepfake selfie videos, two key pieces of biometric data used during KYC verification. The tool helps cybercriminals defeat facial recognition and liveness checks to create fraudulent accounts.
The procedure below describes how a bad actor could use ProKYC’s service to bypass KYC verification.
Live Deepfake Image Injection to Evade Mobile KYC Verification
Facial biometric authentication services are commonly used by mobile applications for user onboarding, authentication, and identity verification for KYC requirements. The iProov Red Team demonstrated a face-swapped imagery injection attack that can successfully evade live facial recognition authentication models along with both passive and active liveness verification on mobile devices. By executing this kind of attack, adversaries could gain access to privileged systems of a victim or create fake personas to create fake accounts on banking or cryptocurrency apps.
Organization Confusion on Hugging Face
threlfall_hax, a security researcher, created organization accounts on Hugging Face, a public model repository, that impersonated real organizations. These false Hugging Face organization accounts looked legitimate so individuals from the impersonated organizations requested to join, believing the accounts to be an official site for employees to share models. This gave the researcher full access to any AI models uploaded by the employees, including the ability to replace models with malicious versions. The researcher demonstrated that they could embed malware into an AI model that provided them access to the victim organization's environment. From there, threat actors could execute a range of damaging attacks such as intellectual property theft or poisoning other AI models within the victim's environment.
ClearviewAI Misconfiguration
Clearview AI makes a facial recognition tool that searches publicly available photos for matches. This tool has been used for investigative purposes by law enforcement agencies and other parties.
Clearview AI's source code repository, though password protected, was misconfigured to allow an arbitrary user to register an account. This allowed an external researcher to gain access to a private code repository that contained Clearview AI production credentials, keys to cloud storage buckets containing 70K video samples, and copies of its applications and Slack tokens. With access to training data, a bad actor has the ability to cause an arbitrary misclassification in the deployed model. These kinds of attacks illustrate that any attempt to secure ML system should be on top of "traditional" good cybersecurity hygiene such as locking down the system with least privileges, multi-factor authentication and monitoring and auditing.
Camera Hijack Attack on Facial Recognition System
This type of camera hijack attack can evade the traditional live facial recognition authentication model and enable access to privileged systems and victim impersonation.
Two individuals in China used this attack to gain access to the local government's tax system. They created a fake shell company and sent invoices via tax system to supposed clients. The individuals started this scheme in 2018 and were able to fraudulently collect $77 million.
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