QR Code Attacks

Stop image-based attacks that use QR codes to trick your end users.

Get a Demo

Trusted by more than 3,000 customers—including 25% of the Fortune 500

Problem

The Rising Threat of Quishing Attacks

Attackers are now using malicious QR codes in credential phishing and invoice payment fraud attacks. In fact, Abnormal customer data shows that 17% of all attacks bypassing native security controls contain a QR code. These malicious QR codes often link to what appears to be a legitimate website, such as Google or Microsoft login pages, and prompt recipients to enter their login credentials, which are then exploited by the attacker.

Solution

How Abnormal Detects Malicious QR Codes

Parses links embedded in images such as QR codes and feeds the extracted signals to the AI-native detection engine.
Employs NLP/NLU to detect fraudulent topics, tone, and sentiment, including urgency and formality, which are commonly associated with QR code-based attacks.
Detects unusual behavioral signals by understanding normal business relationships and communication patterns.

Why abnormal

An Abnormal Approach to Stopping QR Code Attacks

Learns the behavior of every identity in the organization by analyzing tens of thousands of contextual signals to establish a baseline.
Deploys AI models to detect novel attacks by identifying abnormal behavior and QR codes that lead to malicious links.
Automatically remediates QR code attacks before employees can view or engage with them.