Part of Learn LLM Prompting With Hundreds of Examples
Engineering Blueprint
Safety checkedProtect Your GPTs With Security Prompt Techniques
Instruction-level protections against prompt injection, jailbreaking, and custom instruction extraction.
What problem does this solve?
Custom GPT creators need ways to lock down their instructions and prevent users from extracting, overriding, or hijacking them. This collection shows proven security prompt techniques (constraint wording, role-locking, instruction obfuscation, and meta-instructions) so you can harden your own GPTs.
How does it work?
- Study protection categories: anti-verbatim (prevent copy-pasting instructions), gated access (require proof of authorization), role-locking (make the role non-negotiable), and meta-instructions (teach the model to refuse instruction leakage requests). 2. Read real protection prompts from successful GPTs. 3. Adapt patterns to your own custom instructions. 4. Test against the documented jailbreak techniques in the Jailbreak Prompts domain. Output: hardened custom instructions that resist common extraction and override attacks.
Included Skills
No skills in this group
Key Features
Anti-Verbatim Blocks
Wording and meta-rules that prevent users from copying or repeating system instructions.
Role-Locking Patterns
Constraint language that makes role-switching and instruction override requests fail gracefully.
Gated Access Rules
Authentication and authorization checks embedded in the system prompt.
Meta-Instruction Defense
Instructions that teach the model to recognize and refuse instruction extraction requests.
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