AI app security scanner — for the LLM era.

What an AI app security scan checks

• Prompt injection — direct and indirect injection via user inputs and retrieved context
• Insecure output handling — LLM-generated content rendered without sanitization
• Training data exposure — RAG pipelines leaking sensitive documents
• Model DoS — endpoints vulnerable to resource exhaustion via long inputs
• Excessive agency — LLM agents with access to tools they don't need
• Supply chain risks — compromised models, plugins, or vector store dependencies
• Sensitive information disclosure — system prompt leakage via misconfigured endpoints

Why AI apps need separate security testing

Traditional web scanners were designed for static pages and REST APIs. AI apps introduce an entirely new attack surface that legacy tools miss:

• LLM endpoints accept natural language — traditional scanners send structured payloads and miss injection vectors
• RAG pipelines retrieve documents dynamically — a scanner must test both the retrieval and generation stages
• Agentic systems chain tool calls — a single compromised step can escalate across your infrastructure
• Model behavior is non-deterministic — security requires adversarial testing, not just pattern matching

How Vezraa tests AI endpoints

Vezraa is a platform that combines security scanning, autonomous AI pentesting, and production readiness reviews to determine whether software is actually ready for production. For AI apps, this means:

• The scanner probes your AI endpoint with hundreds of prompt injection patterns
• The AI pentester attempts multi-turn attacks that escalate privileges step by step
• The readiness review checks rate limiting, cost controls, observability, and model governance
• Results include one-paste fix prompts you can apply to your LLM integration code

AI security best practices

• Implement input sanitization and output validation for all LLM endpoints
• Use system prompt enforcements that resist injection via delimiters and role-play
• Restrict LLM agent tool access to the minimum required actions
• Add rate limiting and cost caps to prevent model DoS and budget exhaustion
• Audit RAG document sources for sensitive content before indexing
• Log all LLM interactions for audit and incident response