Photo Response Non-Uniformity (PRNU) noise serves as a sensor-level fingerprint in camera-based authentication and source attribution systems. Rather than degrading or suppressing PRNU patterns as in prior work, we introduce a novel transfer attack that injects PRNU noise from one device into images from another source or generated by AI. This enables manipulated images to falsely pass forensic source verification checks, posing a new class of threat to PRNU-based authentication. Our method achieves an average 85.5% compromise rate, validated using both a custom PRNU injection pipeline and the commercial forensic tool (Amped Authenticate). We further propose two mitigation techniques to detect such spoofing, revealing critical limitations in current image forensics pipelines.