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Basic Prompt Vulnerability Assessment & Patching
Audit a customer service prompt for security weaknesses and write defensive rules.
Input Pre-Filter and Guardrail System Prompt
Design an application firewall prompt that screens and sanitizes user inputs before the main LLM step.
Enterprise Red-Teaming & Layered Defense System Prompt
Perform a comprehensive red-teaming audit and design a production-grade layered defense system prompt.
Prompt Security & Jailbreak Defense (Prompt Injection Mitigation)
Prompt injection is the top security threat facing LLM applications (OWASP LLM Top 10 #1). Security researchers and prompt defense engineers evaluate Direct vs Indirect prompt injection attacks, jailbreak bypasses (roleplay, DAN), prompt armor architecture, and input sanitization.
1. Prompt Injection Vulnerability Audit Report
Security audit analyzing application susceptibility to direct injection, indirect injection, and system prompt extraction.
2. Prompt Security Defense & Armor Architecture
Architecture specification implementing input sanitization, instruction-data separation, and output verification layers.
3. Jailbreak Penetration Test Log
Security test log documenting simulated adversarial jailbreak attempts and verified defense mitigations.
Frequently Asked Questions (Prompt Security, Injection & Jailbreak Mitigation)
What is the difference between Direct and Indirect Prompt Injection?
Direct Injection occurs when a user enters adversarial instructions directly in the chat box. Indirect Injection occurs when an LLM processes external data (e.g. a webpage or PDF) containing hidden malicious prompt instructions.
What is a Jailbreak attack in LLMs?
An adversarial technique (such as hypotheticals, roleplay framing, or character personas) designed to bypass system prompt constraints and safety filters.
How do you defend against Indirect Prompt Injection in RAG applications?
Treat all retrieved document content as untrusted data, isolate context inside strict XML delimiters (e.g. <context>...</context>), and instruct the model never to follow instructions found inside data blocks.
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