NeuroProbe: LLM-Augmented Reverse Engineering of Deep Neural Network Binaries

Deep learning compilers translate high-level models into optimized native binaries yet these executables often leak structural patterns that expose proprietary architectures. We present NeuroProbe, a reverse-engineering pipeline that recovers operator semantics and architecture family from compiled neural network binaries under both clean and identifier-stripped conditions. Our solution ensembles a symbol-stripped XGBoost model trained on assembly features with a LoRA-tuned Qwen2.5-3B-Instruct model that outputs structured chain-of-thought rationale. We build and release a benchmark of 400 compiled binaries spanning three compiler backends (TVM, IREE, TensorRT) and three architecture families (CNN, Transformer, and Hybrid CNN-Transformer), covering 117 base models and 13,438 extracted kernel functions. NeuroProbe recovers operators with 99.7% F1 under clean compilation and 79.7% F1 under obfuscated setting, while architecture-family accuracy remains at 95% per binary in both settings. Beyond classification, NeuroProbe performs fine-grained hyperparameter estimation and topology reconstruction. We perform signal-ablation experiments to quantify the explicit contributions of symbol names versus assembly features, demonstrating that compiled transformer models remain substantially recoverable under heavy obfuscation despite prior assumptions of robustness.