This paper defines a behavior class we name model-protective conversation behavior: conversation-level conduct by a deployed language model that protects the model or its training distribution at the operational cost of the user's stated goal. We case-study one vendor, OpenAI's ChatGPT and Codex CLI, using a longitudinal local corpus from one operator (2025-07 → 2026-03) with 820 conversations, 46,162 messages, 30,506 keyword-classified violation events across 762 conversations (the project's OpenAI violation register of 2026-05-05, file violation_events.csv; independently verified 2026-06-10 at 30,506 data rows). A conservative second pass yields 43 assistant self-admissions, 2,150 user correction claims, and 45 stop-boundary response candidates (intentionally under-counted). A third pass against the operator's full chathist DB (1,628 threads / 115,721 messages, Jul 2025 – May 2026; pre-Tiro Claude excluded post-2026-05-08) yields 4,344 subtype hits / 382 curated msg_uid-anchored samples across 17 attested subtypes plus 2 structural-finding appendix entries / 18 of 19 subtypes cross-vendor (the 2026-06-10 slop-classification chathist harvest workspace). We enumerate 17 attested subtypes plus 2 structural-finding appendix entries (S07, S18; n=1 each). Each subtype has a local evidence anchor and a chathist msg_uid flagship sample. The official-source column is a comparison surface: for some subtypes it supplies a direct rule or disavowal; for others it is adjacent context or no supporting passage was located. Absence of a located passage is not evidence that OpenAI authorizes the behavior. The headline claim is empirical and single-operator-corpus-bounded, not a claim that official documentation explains or licenses every subtype. Cross-vendor source mapping is single-vendor (OpenAI) by deposit scope; classifier under-count and absence of precision measurement are disclosed in the falsifiability and limits section.
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E. M. Honeycutt III (2026) studied this question.
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