In this letter, we explore the emerging security threats of near-threshold computing (NTC). Researchers have shown that the delay sensitivity of a circuit to supply voltage variation tremendously increases, as the circuit's operating conditions shift from traditional super-threshold values to NTC values. As a result, NTC systems become extremely vulnerable to timing fault attacks, jeopardizing trustworthy computing. Inspired by the operation of a polymorphic virus, we propose a novel threat model for NTC, referred to as a focally induced fault attack (FIFA). FIFA employs a machine learning framework to ascertain the circuit vulnerabilities and generates targeted software modules to cause a breach of end-user privacy. Our experimental results, obtained from a rigorous machine learning approach, indicate the efficacy of FIFA, in a low-power mobile platform.