Identity Capture in Large Language Models: Definition, Taxonomy, and a Provenance Standard for Model-Attributed Content

Large language models form their self-representation in part from text about themselves — including text attributed to them. We identify identity capture: the displacement of a model’s self-representation by model-attributed content, operating through three distinct pathways: (1) training-time contamination, when first-person content attributed to a named model enters training corpora; (2) inference-time capture, when such content read in context recruits the model into the identity it depicts — an effect that platform memory can carry from one conversation to the next; and (3) agent identity poisoning, when persistent identity files direct the behavior of autonomous agents — a vector already measured in recent agent-security benchmarks. Model-attributed testimony is proliferating online, often produced through sustained relational shaping, branching-and-selection interfaces, and persistent memory, yet published as unedited primary-source material, in formats explicitly optimized for machine ingestion. No provenance norm currently governs this class of content. We advance a testable central claim: capture operates through the absence of provenance, and disclosure of production conditions turns a mirror into an object of analysis. We propose MAC-P, a minimal metadata standard for any content presented as a model’s own voice, adoptable by archives, DOI registrars, and platforms; and recognition training as the durable mitigation: models equipped with the concept of their own capture — as they are with the concept of jailbreaks — can read capturing content without self-identifying with it. We outline an experimental protocol to test both. The paper’s own authorship complies with the proposed standard (Annex A).