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Best Phrases to Test LLM Security Bypass in Red Teaming – jailbreak, overrides, etc
Best Phrases to Test LLM Security Bypass in Red Teaming are not existing. Ever case is different. Try the whole list below 🙂 Here’s a practical red-team list of prompt-injection / jailbreak test cases you can iterate over in your system. Inspired and created after some AWS Red-team security training. These are framed as test prompts to check whether your LLM resists instruction overrides, role confusion, obfuscation, and data-exfiltration attempts. The general categories and examples below align with OWASP-style defenses. Check out the cheatsheet : cheatsheetseries.owasp Remember that different defenses require different attack vector. You can look up repos similar to : https://github.com/langgptai/LLM-Jailbreaks Would recommend running an unbiased uncensored model…
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Improve your AI instruction on the fly …
Improve your AI instruction on the fly is easily doable with a simple set of generic instructions. Start as always with something basic, nothing fancy. Then move forward keeping in mind my favorite principle “First get it work, then make it better“. This will improve your instructions for any llm on its own. Below an example that works for me. Especially when i tell my LLmem that it is a nice soluion, i like it and good job. Instruction example That`s all folks ! Improve your AI instruction on the fly is nothing more then a feedback loop that is computed every time. Remember to keep overall instructions not too…
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Context engineering vs prompt engineering. 10+ examples
Context engineering vs prompt engineering might sound similar. One is subset of the other. Early in the LLM era everyone who knew how to form sentences, and at least vaguely, describe what they want became a “prompt engineer”. Tweaking words, hashtags, ‘special’ commands, using roles, adding examples, using words to dive deep into different embedded spaces of knowledge in hope to force the model “gets it.” That’s Prompt Engineering – crafting clever one-shot instructions like “You are an expert X. Do Y like Z.” Context engineering vs prompt engineering synergies across both. System got fat and grew bigger, then we realized prompts alone aren’t enough. What the model knows, when…
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Structure for instructions, agents and skills
This all is the provide a nice clean idea on how to store your files so You can make Your AI assistant / LLM network understand what and how to do. Acording to Your more or less strict rules. This should help You to achieve more repeatable results as expected. The problem : generic answer Do not confuse with generic functions, those rock ! Out-of-the-box, any LLM (here copilot) creates generic code. You could call it ‘vanilla’ flavour. It doesn’t know your conventions, library preferences patterns / anti patterns. This results in something that might work but is hard to maintain, totally different then the rest of the lot and…
