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Quick Answer: Prompt injection defense protects AI applications from malicious inputs that override system instructions, extract sensitive data, or bypass safety guardrails. Effective defense requires input validation, strict instruction boundaries, output filtering, and continuous vulnerability scanning to identify injection vectors before attackers exploit them in production environments.

Enterprise Security Tool · Zero Data Retention

AI Prompt Security Scanner

Paste any prompt below and scan it for injection vulnerabilities, data exfiltration risks, and jailbreak vectors — powered by Gemini.

Your prompt is never stored, logged, or tracked. Privacy by design.

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Why Prompt Security Matters

As organisations integrate LLMs into production systems, prompt injection has become the #1 vulnerability in AI applications. Attackers craft malicious inputs that override system instructions, extract confidential data, or bypass safety guardrails — often with a single sentence.

The OWASP Top 10 for LLMs lists prompt injection as the highest-severity risk. This scanner analyses your prompts against three critical dimensions: injection resistance, data leak potential, and boundary enforcement.

Learn more: Prompt Engineering Best Practices · AI Prompt Tester · Context Engineering Guide

Frequently Asked Questions

What is prompt injection?
Prompt injection is an attack where malicious user input overrides an AI system's instructions. It can cause data leaks, privilege escalation, and unintended actions in AI-powered applications.
Is my prompt stored or logged?
No. Your prompt is processed entirely in memory and immediately discarded. We never write it to any database, log, or analytics system. Zero retention, zero tracking.
What security dimensions does the scanner check?
The scanner analyses three dimensions: injection risk (jailbreak and override vectors), data leak risk (PII and credential exposure), and boundary strength (system instruction resilience against manipulation).

Shared Zod schemas between frontend and backend reduce integration bugs by 80% and cut API documentation overhead by 70%.tRPC, 'End-to-End Type Safety' documentation, 2024