At inference time, deep neural networks are susceptible to backdoor attacks, which can produce attacker-controlled outputs when inputs contain carefully crafted triggers. Existing defense methods often focus on specific attack types or incur high costs, such as data cleaning or model fine-tuning. In contrast, we argue that it is possible to achieve effective and generalizable defense without removing triggers or incurring high model-cleaning costs. From the attacker’s perspective and based on characteristics of vulnerable neuron activation anomalies, we propose an Adaptive Feature Injection (AFI) method for black-box backdoor detection. AFI employs a pre-trained image encoder to extract multi-level deep features and constructs a dynamic weight fusion mechanism for precise identification and interception of poisoned samples. Specifically, we select the control samples with the largest feature differences from the clean dataset via feature-space analysis, and generate blended sample pairs with the test sample using dynamic linear interpolation. The detection statistic is computed by measuring the divergence $G(x)$ in model output responses. We systematically evaluate the effectiveness of AFI against representative backdoor attacks, including BadNets, Blend, WaNet, and IAB, on three benchmark datasets: MNIST, CIFAR-10, and ImageNet. Experimental results show that AFI can effectively detect poisoned samples, achieving average detection rates of 95.20%, 94.15%, and 86.49% on these datasets, respectively. Compared with existing methods, AFI demonstrates strong cross-domain generalization ability and robustness to unknown attacks.