CWE-1039: Automated Recognition Mechanism with Inadequate Detection or Handling of Adversarial Input Perturbations

When techniques such as machine learning are used to automatically classify input streams, and those classifications are used for security-critical decisions, then any mistake in classification can introduce a vulnerability that allows attackers to cause the product to make the wrong security decision. If the automated mechanism is not developed or "trained" with enough input data, then attackers may be able to craft malicious input that intentionally triggers the incorrect classification.

Targeted technologies include, but are not necessarily limited to:

• automated speech recognition
• automated image recognition

For example, an attacker might modify road signs or road surface markings to trick autonomous vehicles into misreading the sign/marking and performing a dangerous action.