The proliferation of Internet of Things devices has driven the adoption of collaborative inference (CI) for efficiently operating deep neural networks (DNNs) on resource-limited devices. However, this paradigm introduces vulnerabilities to bit-flip attacks, a form of fault injection that manipulates critical network parameters and compromises model integrity. In this paper, we design and evaluate a targeted bit-flip attack mechanism that strategically disrupts collaborative inference by flipping bits in model parameters deployed on IoT devices. We also analyze the impact of bit-flip attacks on model accuracy and reliability, providing insights into the susceptibility of different DNN layers. Experimental results reveal that flipping less than 0.02% of model parameters can cause up to a 40% accuracy degradation in DNN models, highlighting the urgent need for robust security measures in CI frameworks.