Security in AI Applications

AI Applications Are Uniquely Vulnerable

Traditional web security protects against well-defined attack patterns. AI applications add a new attack surface: the model itself. The language model can be manipulated through carefully crafted input — a class of attack that did not exist before LLMs.

Prompt Injection

Prompt injection is the AI equivalent of SQL injection. An attacker crafts input designed to override your system prompt and make the model behave in unintended ways.

Prevention strategies:

1. Input sanitization — strip known injection patterns
2. Output validation — verify response doesn't contain sensitive data
3. Structural prompting — separate instructions from user input clearly

API Key Security

AI API keys (OpenAI, Anthropic, Google) are high-value attack targets. A compromised key can generate thousands of API calls and destroy your budget in hours.

Rules:

• NEVER call AI APIs from the client — this exposes your API key to anyone who views page source
• ALWAYS proxy through your backend
• Authenticate first, then call the AI API
• Cap token usage with max_tokens

Token Budget Attacks

Sending extremely long inputs burns through API credits rapidly. Enforce input length limits and per-user token budgets.

RAG Security: Document Access Control

When your AI reads documents (Retrieval-Augmented Generation), ensure document access controls are enforced at the retrieval layer. Filter by userId before semantic search — never retrieve documents from other users even if semantically relevant.

Key Takeaways

• Prompt injection is the AI equivalent of SQL injection — sanitize inputs, validate outputs, use structural prompting
• AI API keys must never appear in client-side code — always proxy through your backend
• Enforce input length limits and per-user token budgets to prevent cost attacks
• RAG systems must enforce document access controls at retrieval time
• Log AI interactions for audit, abuse detection, and compliance