  Many of today's machine learning (ML) systems are not built from scratch, but
are compositions of an array of {\em modular learning components} (MLCs). The
increasing use of MLCs significantly simplifies the ML system development
cycles. However, as most MLCs are contributed and maintained by third parties,
their lack of standardization and regulation entails profound security
implications.
  In this paper, for the first time, we demonstrate that potentially harmful
MLCs pose immense threats to the security of ML systems. We present a broad
class of {\em logic-bomb} attacks in which maliciously crafted MLCs trigger
host systems to malfunction in a predictable manner. By empirically studying
two state-of-the-art ML systems in the healthcare domain, we explore the
feasibility of such attacks. For example, we show that, without prior knowledge
about the host ML system, by modifying only 3.3{\textperthousand} of the MLC's
parameters, each with distortion below $10^{-3}$, the adversary is able to
force the misdiagnosis of target victims' skin cancers with 100\% success rate.
We provide analytical justification for the success of such attacks, which
points to the fundamental characteristics of today's ML models: high
dimensionality, non-linearity, and non-convexity. The issue thus seems
fundamental to many ML systems. We further discuss potential countermeasures to
mitigate MLC-based attacks and their potential technical challenges.
