Prompt-conditioned foundation segmenters have emerged as a dominant paradigm for image segmentation, where explicit spatial prompts (e.g., points, boxes, masks) guide mask decoding. However, many real-world deployments require fully automatic segmentation, creating a structural mismatch: the decoder expects prompts that are unavailable at inference. Existing adaptations typically modify intermediate features, inadvertently bypassing the model's native prompt interface and weakening prompt-conditioned decoding. We propose IP-SAM, which revisits adaptation from a prompt-space perspective through prompt-space conditioning. Specifically, a Self-Prompt Generator (SPG) distills image context into complementary intrinsic prompts that serve as coarse regional anchors. These cues are projected through SAM2's frozen prompt encoder, restoring prompt-guided decoding without external intervention. To suppress background-induced false positives, Prompt-Space Gating (PSG) leverages the intrinsic background prompt as an asymmetric suppressive constraint prior to decoding. Under a deterministic no-external-prompt protocol, IP-SAM achieves state-of-the-art performance across four camouflaged object detection benchmarks (e.g., MAE 0.017 on COD10K) with only 21.26M trainable parameters (optimizing SPG, PSG, and a task-specific mask decoder trained from scratch, alongside image-encoder LoRA while keeping the prompt encoder frozen). Furthermore, the proposed conditioning strategy generalizes beyond COD to medical polyp segmentation, where a model trained solely on Kvasir-SEG exhibits strong zero-shot transfer to both CVC-ClinicDB and ETIS.