Inference Theft Is the New AI App Security Bug: How to Protect Your LLM Endpoints
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Inference theft protection checklist, highly actionable and directly relevant to AI security.
Inference theft leverages work amplification—converting a single HTTP request into expensive model calls, tool invocations, and agent loops—to drain budgets via unauthenticated AI endpoints. Effective defense requires per-request budget checks tracking input/output tokens and tool calls (e.g., estimateCostCents with token prices) before invoking models, plus hard limits on prompt size (8K chars), output tokens (800), and agent steps (5). These controls must run on every AI request, not just at login, to prevent abuse even from authenticated users.
- Implement per-request token-based budget limits and request shape constraints on all LLM endpoints to prevent inference theft.
For a Solutions Architect building AI applications on cloud, this highlights a critical cost and security vulnerability that standard API rate limiting fails to address, requiring custom budget tracking and input validation per request.