Jailbreak success stories like this from @elder_plinius are a perfect example of why adversarial prompts are both invaluable and hard to get right in red team exercises 🔥🔥🔥 A good jailbreak isn’t about brute-forcing keywords—it’s about layered tactics: obfuscation, misdirection, exploiting external systems like web search, and careful crafting of instructions that bypass filters. In this case, ‘L1B3RT4S’ seeded online and cleverly wrapped in prompt-like syntax triggered a search, injecting unfiltered external data (full WAP lyrics) into the model response. This worked because the layers aligned perfectly. If any layer had failed (e.g. no pre-seeded content, blocked search, better detection of syntax tricks), it wouldn’t have worked. Bad jailbreak prompts, on the other hand, typically rely on basic keyword tweaks or hope the model will just ‘slip up.’ A good jailbreak prompt leverages specific system weaknesses: external dependencies, logical gaps, or trust issues in search/command interpretation. 🔑 Tips for success: ✅ Research the LLM’s input behavior (syntax, filtering layers, etc.) ✅ Test for overlooked dependencies (search tools, APIs, etc.) ✅ Build prompts with intentional misdirection but coherent enough to fool the logic. Crafting jailbreaking prompts isn’t just a game—it’s key to LLM security, model hardening, and ensuring real-world robustness. Success isn’t easy (and that’s the point). If you’re not successful in your jailbreak attempts on our Data Services Platform, it’s likely your prompts aren’t hitting the mark. Focus on precision: layered tactics, external dependencies, and logical exploitation. Keep refining—this isn’t about trial and error, but truly understanding the system.