Latent-space monitoring techniques have shown promise as defenses against LLM attacks. These defenses act as scanners to detect harmful activations before they lead to undesirable actions. This prompts the question: can models execute harmful behavior via inconspicuous latent states? Here, we study such obfuscated activations. Our results are nuanced. We show that state-of-the-art latent-space defenses---such as activation probes and latent OOD detection---are vulnerable to obfuscated activations. For example, against probes trained to classify harmfulness, our obfuscation attacks can reduce monitor recall from 100% down to 0% while still achieving a 90% jailbreaking success rate. However, we also find that certain probe architectures are more robust than others, and we discover the existence of an obfuscation tax: on a complex task (writing SQL code), evading monitors reduces model performance. Together, our results demonstrate white-box monitors are not robust to adversarial attack, while also providing concrete suggestions to alleviate, but not completely fix, this weakness.