Inadequate Detection or Handling of Adversarial Input Perturbations in Automated Recognition Mechanism

When machine learning models are deployed for security-critical tasks—such as autonomous vehicle perception, content moderation, or fraud detection—their classification errors become direct security flaws. Attackers can exploit weaknesses in the model's training or design by creating adversarial inputs (e.g., subtly perturbed images, malicious audio clips, or jailbreak prompts for LLMs) to force misclassification, bypass safeguards, or disrupt services. This is especially dangerous in systems where automated recognition directly triggers actions without human oversight. Preventing these attacks requires robust adversarial training, continuous testing with malicious inputs, and implementing input validation layers. Managing this at scale across multiple AI components is difficult; an ASPM like Plexicus can help you inventory, track, and prioritize these model vulnerabilities alongside traditional code flaws in your entire application stack.