Imagine a world where seemingly harmless words can cripple AI, blocking access for legitimate users. This isn't science fiction; it's a new type of denial-of-service (DoS) attack targeting large language models (LLMs) like ChatGPT. Researchers have discovered that malicious actors can exploit vulnerabilities in LLM safeguards—the very mechanisms designed to protect us from harmful content—to trigger false positives. By injecting short, carefully crafted adversarial prompts into user requests, attackers can fool the safeguards into thinking safe content is unsafe, effectively shutting down access. This attack is particularly insidious because the adversarial prompts are often short, seemingly innocuous strings of characters, easily hidden within user configurations or injected via software vulnerabilities. The research reveals that these attacks can be highly effective, blocking over 97% of user requests in some cases. This poses a significant threat to the reliability and availability of LLM services, especially in critical sectors like finance and healthcare. While current mitigation techniques like random perturbation and resilient optimization exist, they often come at the cost of reducing the effectiveness of the safeguards themselves. The challenge lies in finding a balance between robust protection and maintaining functionality. This new attack vector emphasizes the need for a renewed focus on the robustness and security of LLM safeguards, not just against malicious content generation (jailbreaking), but also against these more subtle DoS attacks that threaten to disrupt access to these increasingly essential tools.