Prompt Injection Defense
Techniques and strategies for protecting LLM applications from prompt injection attacks, including input sanitization, output filtering, and architectural defenses.
Why It Matters
Prompt injection defense is essential for any LLM application that accepts user input. Without it, users can manipulate the system to bypass safety controls.
Example
Implementing input validation that detects and blocks injection patterns, using a separate LLM to evaluate outputs for policy violations, and sandboxing tool access.
Think of it like...
Like SQL injection defense in traditional software — you need multiple layers of protection to prevent malicious input from compromising the system.
Related Terms
Prompt Injection
A security vulnerability where malicious input is crafted to override or manipulate an LLM's system prompt or instructions, causing it to behave in unintended ways.
AI Safety
The research field focused on ensuring AI systems operate reliably, predictably, and without causing unintended harm. It spans from technical robustness to long-term existential risk concerns.
Guardrails
Safety mechanisms and constraints built into AI systems to prevent harmful, inappropriate, or off-topic outputs. Guardrails can operate at the prompt, model, or output level.