Key result
LLM EKG detects cross-provider model swaps in ~83% of cases and identifies system prompt tampering.
Signal-based monitoring using statistical features can reliably detect LLM model swaps and prompt tampering, providing a low-cost method for auditing commercial APIs.
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Enables low-cost LLM auditing in clinical cardiology workflows; confirms effectiveness of signal-based statistical monitoring for commercial APIs.
We present the first systematic cross-provider behavioral audit of commercial LLM APIs using signal-based monitoring. Using LLM EKG, an open-source tool that extracts 16 statistical features from model outputs without semantic analysis, we test 8 models across 4 providers (OpenAI, Anthropic, xAI, Google) under identical conditions. Key findings: (1) each model produces a statistically distinct behavioral fingerprint; (2) cross-provider model swaps are reliably detected (83.3% of directed pairs at 3σ); (3) system prompt tampering is detected in 7/9 conditions (z-scores up to 17.96); (4) temperature manipulation within normal ranges is NOT detectable; (5) GPT-4o-mini and Grok-3 are statistically indistinguishable despite different providers. Total cost: $0.20. Fully reproducible. All data, code, and baselines included.
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Carmen Esteban (2026) studied this question. LLM EKG, a signal-based monitoring tool, reliably detects cross-provider model swaps in 83.3% of cases and system prompt tampering in 7 of 9 conditions with z-scores up to 17.96.
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