We present EdiTTS, an off-the-shelf speech editing methodology based on score-based generative modeling for text-to-speech synthesis. EdiTTS allows targeted, granular editing of audio, both in terms of content and pitch, without the need for any additional training, task-specific optimization, or architectural modifications to the score-based model backbone. Specifically, we apply coarse yet deliberate perturbations in the Gaussian prior space to induce desired behavior from the diffusion model, while applying masks and softening kernels to ensure that iterative edits are only applied to the target region. Listening tests demonstrate that EdiTTS is capable of reliably generating natural-sounding audio that satisfies user-imposed requirements.