Privacy of physiological data collected by a network of embedded sensors on human body is an important issue to be considered. Physiological signal-based security is a light weight solution which eliminates the need for security key storage and complex exponentiation computation in sensors. An important concern is whether such security measures are vulnerable to attacks, where the attacker is in close proximity to a Body Sensor Network (BSN) and senses physiological signals through non-contact processes such as electromagnetic coupling. Recent studies show that when two individuals are in close proximity, the electrocardiogram (ECG) of one person gets coupled to the electroencephalogram (EEG) of the other, thus indicating a possibility of proximity-based security attacks. This paper proposes a model-driven approach to proximity-based attacks on security using physiological signals and evaluates its feasibility. Results show that a proximity-based attack can be successful even without the exact reconstruction of the physiological data sensed by the attacked BSN. Our results show that with a 30 second handshake we can break PSKA with an average probability of 0.3 (0.24 minimum and 0.5 maximum).