Researchers introduced SYNAPSE, a framework for analyzing and perturbing the internal behavior of Transformer models without retraining the base model. SYNAPSE extracts per-layer representations, trains a lightweight linear probe to rank neurons, and applies forward-hook interventions during inference. This allows for controlled experiments on internal representations, enabling the measurement of weaknesses, stability patterns, and label-specific sensitivities across tasks and architectures. SYNAPSE reveals consistent internal organization across domains, with task-relevant information encoded in overlapping neuron subsets, and highlights vulnerability profiles that can guide the development of more robust and interpretable models. The framework's ability to analyze and stress-test internal representations without altering the original model makes it a valuable tool for improving the trustworthiness and reliability of AI systems.