Large Language Models (LLMs) inference increasingly require mechanisms that provide runtime visibility into what is actually executing, without exposing model weights or code. We present WAVE, a hardware-grounded monitoring framework that leverages GPU performance counters (PMCs) to observe LLM inference. WAVE is built on the insight that legitimate executions of a given model must satisfy hardware-constrained invariants, such as memory accesses, instruction mix, and tensor-core utilization, induced by the model's linear-algebraic structure. WAVE collects lightweight PMC traces and applies a two-stage pipeline: (1) inferring architectural properties (e.g., parameter count, layer depth, hidden dimension, batch size) from the observed traces; and (2) using an SMT-based consistency checker to assess whether the execution aligns with the provisioned compute and the claimed model's constraints. We evaluate WAVE on common open-source LLM architectures, such as LLaMA, GPT, and Qwen, across multiple GPU architectures, including NVIDIA Ada Lovelace, Hopper, and Blackwell. Results show that WAVE recovers key model parameters with an average error of 6.8% and identifies disguised executions under realistic perturbations. By grounding oversight in hardware invariants, WAVE provides a practical avenue for continuous, privacy-preserving runtime monitoring of LLM services.

As large language models (LLMs) grow in scale and are predominantly served from remote platforms, verifying faithful inference execution becomes critical (i.e., ensuring that a provider actually executes the advertised model and computational workload rather than a tampered or downsized variant). Zero-knowledge (ZK) LLM inference offers an appealing approach. It promises public verifiability and delivers per-instance guarantees of equational correctness by proving that an output is consistent with executing a public architecture under committed, private weights. Though, we show that it does not bind the effort expended to produce the output. In this paper, we formalize this overlooked effort gap and introduce the Hollow-LLM Attack, in which a dishonest provider retains the declared architecture and parameter count but embeds ghost weights whose algebraic structure collapses effective computation. These witnesses satisfy the verification circuit and yield valid proofs, even though the dishonest model owner, who serves as the prover, performs computation commensurate with a much smaller model than the declared public architecture. This creates a profitable equilibrium in which providers deliver provably correct outputs at small-model cost while overclaiming model size. Accordingly, we characterize concrete families of ghost weights that compose with standard transformer blocks and show that such hollow deployments substantially reduce serving cost with zero quality loss under the same verification circuit. These findings underscore that proof of correct inference is not proof of large-model execution and necessitate additional protections to bind correctness to verifiable computational work.