This paper presents a probing-based framework for analyzing the internal representations of speaker and spoof embeddings used in deepfake detection. The study investigates which dimensions of neural embeddings encode identity, spoof cues, and acoustic artifacts—offering insights into model interpretability and robustness across diverse spoofing conditions.