Mapping The AI Attack Surface: Vulnerabilities In The Model Lifecycle
Red Hat, March 25,2026
Standard AI security benchmarks can't check for all of the possible ways an AI model can be compromised. A backdoor trigger could cause targeted failure, a competitor could clone your API model through repeated queries, or a privacy probe might reveal whether a specific person's data was used in training.
For this reason, organizations deploying AI must understand the variety of potential attacks and proactively address them during model training and after deployment.
In our previous article, What does "AI security" mean and why does it matter to your business?, we talked about protecting AI systems from attacks that compromise confidentiality, integrity, and availability. In this article, we focus on attacks that target the model-both during training and after deployment.