Executive Summary We conducted a comparative study of the built-in guardrails offered by three major cloud-based large language model (LLM) platforms. We examined how each platform's guardrails handle a broad range of prompts, from benign queries to malicious instructions. This examination included evaluating both false positives (FPs), where safe content is erroneously blocked, and false negatives (FNs), where harmful content slips through these guardrails.