Large language models (LLMs) like ChatGPT are designed with safety in mind, incorporating moderation guardrails to prevent harmful outputs. But what if these safeguards could be bypassed? New research explores how "jailbreaking" techniques, using cleverly disguised prompts, can trick LLMs into generating prohibited content. The technique, known as "Jailbreak Against Moderation" (JAM), uses "cipher characters" to slip past the AI's defenses. These characters, inserted within the text, disrupt the LLM's ability to recognize harmful content, essentially making it invisible to the moderation system. Researchers tested JAM on several leading LLMs, including GPT-3.5, GPT-4, Gemini, and Llama-3, and found it alarmingly effective. JAM achieved a jailbreak success rate nearly 20 times higher than existing methods, raising concerns about the vulnerability of even the most advanced AI models. The study also introduces JAMBench, a new benchmark designed to test the effectiveness of moderation guardrails. This benchmark includes a range of challenging prompts across categories like hate speech, violence, and self-harm, providing a more robust testing ground for AI safety. While JAM exposes vulnerabilities, the researchers also propose countermeasures. These include defenses based on output complexity and secondary LLM audits, offering potential solutions to strengthen AI safeguards against these evolving jailbreak techniques. The research underscores the ongoing cat-and-mouse game between AI safety and those seeking to exploit its weaknesses, highlighting the critical need for continuous improvement in LLM security.