Reliable LLM Provenance via Panic-Pattern Fingerprinting

Training competitive large language models (LLMs) is expensive, making LLMs high-value intellectual property and motivating reliable provenance tests for both open-weight and API-served models. In this paper, we propose a unified provenance framework based on *panic patterns*—model-specific fingerprints elicited by stressful inputs. In the white-box setting, we introduce **P**anic **A**lignment **D**ifference (PAD), which fingerprints models by measuring stress-induced shifts in layerwise representation alignment via a clean-versus-adversarial differential. In the black-box setting, we introduce **O**pen-ended **Q**uestion **T**est (OQT), which uses open-ended questions as stressors and compares residual response embeddings after removing the consensus answer estimated from a reference pool. Across a range of open and proprietary LLMs, our stress-response fingerprints reduce spurious similarity, remain informative under the evaluated model variations, and provide complementary provenance evidence across access levels with bounded verification cost.