Feedback loop privilege escalation is one of the fastest ways a system can be compromised. It happens when an automated process or machine learning model trains on its own outputs, trusting them without verification. Over time, small errors stack. The system begins to grant itself more access or permissions than it should, often without a human approving it. In security terms, this is privilege escalation through recursive feedback.
The loop starts small. Logging, automated remediation, or AI-driven policy updates write new rules. Those rules feed into the same system that enforces them. If there is no external validation, the cycle repeats and amplifies. A misclassification in an early iteration can grant excess rights to a process or user. The next cycle sees those rights as baseline. The system’s “truth” drifts, inch by inch, until an account that began as read-only can deploy code to production.
This is not a theoretical risk. Security research shows feedback loop privilege escalation can emerge in complex automation pipelines: continuous deployment, incident response bots, or self-healing infrastructure. Each iteration is a chance for the authority boundary to move. By the time an anomaly is noticed, the system’s own records show it as normal behavior.