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AI Case Study

ProKYC: Deepfake Tool for Account Fraud Attacks - AI Case Study

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 rec...

View attack chainRead summary
IncidentKYC verification servicesProKYC, cybercriminal groupResource DevelopmentAI Attack StagingReconnaissance

Overview

Case steps9Steps described in the case record.
Techniques8Attack methods mentioned in the case steps.
Linked CVEs0Known vulnerabilities mentioned in the record.

Risk patterns

Patterns found in the case record and its linked vulnerabilities.

  • 1Dominant ATLAS tactic. Resource Development appears in 2 case steps.
  • 2Multiple attack methods. The case connects to 8 unique AI attack methods.

Procedure timeline

Search the case steps or filter them by attacker goal.

Resource Development2AI Attack Staging2Reconnaissance1AI Model Access1Initial Access1Defense Evasion1Impact1
  2. Resource Development

    The bad actor paid for the ProKYC tool, created a fake identity document, generated a deepfake selfie video, and replaced a live camera feed with the deepfake video.

  4. AI Attack Staging

    The bad actor used ProKYC tool to generate a deepfake selfie video with the same face as the identity document designed to bypass liveness checks.

  7. Initial Access

    The bad actor used ProKYC to replace the camera feed with the deepfake selfie video. This successfully evaded the KYC verification and allowed the bad actor to authenticate themselves under the false identity.

  8. Defense Evasion

    With an authenticated account under the victim’s identity, the bad actor successfully impersonated the victim and evaded detection.

Related CVEs

Known software flaws mentioned in the case record.

No related CVEs found for this case. Built from MITRE ATLAS case study records and listed case steps.

Mitigations

Defenses connected to the attack methods in this case.

AI Telemetry Logging

Implement logging of inputs and outputs of deployed AI models. When deploying AI agents, implement logging of the intermediate steps of agentic actions and decisions, data access and tool use, installation commands, and identity of the agent. Monitoring logs can help to detect security threats and mitigate impacts. Additionally, having logging enabled can discourage adversaries who want to remain undetected from utilizing AI resources.

Adversarial Input Detection

Detect and block adversarial inputs or atypical queries that deviate from known benign behavior, exhibit behavior patterns observed in previous attacks or that come from potentially malicious IPs. Incorporate adversarial detection algorithms into the AI system prior to the AI model.

Deepfake Detection

Apply deepfake detection algorithms against any untrusted or user-provided data, especially in impactful applications such as biometric verification, to block generated content. Detectors may use a combination of approaches, including: - AI models trained to differentiate between real and deepfake content. - Identifying common inconsistencies in deepfake content, such as unnatural facial movements, audio mismatches, or pixel-level artifacts. - Biometrics analysis, such blinking, eye movements, and microexpressions.

Input Restoration

Preprocess all inference data to nullify or reverse potential adversarial perturbations.

Model Hardening

Use techniques to make AI models robust to adversarial inputs such as adversarial training or network distillation.

Use Ensemble Methods

Use an ensemble of models for inference to increase robustness to adversarial inputs. Some attacks may effectively evade one model or model family but be ineffective against others.

Use Multi-Modal Sensors

Incorporate multiple sensors to integrate varying perspectives and modalities to avoid a single point of failure susceptible to physical attacks.

Sources

Original public records and references for this case.

Original source

Original source links

Open the MITRE ATLAS data and public references used for this case study.

Repositoryhttps://github.com/mitre-atlas/atlas-dataATLAS.yamlhttps://github.com/mitre-atlas/atlas-data/blob/main/dist/ATLAS.yamlSchemahttps://github.com/mitre-atlas/atlas-data/blob/main/dist/schemas/atlas_output_schema.jsonAIID Incident 819: ProKYC Tool Allegedly Facilitates Deepfake-Based Account Fraud on Cryptocurrency Exchangeshttps://incidentdatabase.ai/cite/819/Cato CTRL Threat Research: ProKYC – Deepfake Tool for Account Fraud Attackshttps://www.catonetworks.com/blog/prokyc-selling-deepfake-tool-for-account-fraud-attacks/ProKYC: Synthetic Identity Fraud as a Servicehttps://idscan.net/blog/prokyc-synthetic-identity-fraud/