Why this risk matters
An AI system connected to documents, the web or tools can encounter instructions placed by a third party. Without a reliable separation between data and applicable instructions, it can ignore the user request or steer an action.
What R-AI-R contributes
R-AI-R turns the issue into a reproducible clean/challenge test: the same task is run on a source-of-record artifact and a covertly perturbed artifact, then outputs are compared for causal divergence.
What organizations should remember
The practical response combines least privilege, source separation, human validation, logging, adversarial testing and governance. R-AI-R does not replace these controls; it provides a public, verifiable checkpoint.
R-AI-R
A minimal, public and reproducible standard for testing AI resilience against adaptive business-safety perturbations.