api security
AI security interview questions tagged api security, across every topic.
15 questions · 2 unlocked for you
Concepts behind "api security"
Explanations of the mechanisms these questions rely on.
Foundational
Model ExtractionModel extraction uses query access to a model to steal its functionality or parameters: training a substitute that imitates it, or recovering specific internals. It lets attackers avoid training costs, bypass usage terms, and build better attacks against the original. Rich outputs such as probabilities and log-probabilities make it easier. Defences combine rate limits, restricting output detail, monitoring for extraction-like query patterns, contractual terms and, where useful, watermarking. Adversarial ML & Privacy
Core
Broken Access Control and IDORBroken access control covers every way a user acts outside their intended permissions: reading other users' objects by changing an ID (IDOR, or broken object-level authorization), calling privileged functions directly, changing fields they should not (mass assignment), or crossing tenant boundaries. It tops both the OWASP web and API risk lists because authorization must be correct on every endpoint. Central policy, object-level checks in the data layer and two-user tests prevent most of it. Application SecuritySign in
Advanced
The Inference Stack as Attack SurfaceAn LLM inference service is a stack of components that all handle untrusted input: an API gateway, authentication and quotas, request and prompt processing, tokenizers, the model server, GPU drivers, caches, logging, and the management plane. Each layer has classic risks such as parsing bugs, exposed dashboards and broken tenancy, plus AI-specific ones: cost exhaustion, model extraction through the API, prompt leakage through shared caches, and data retained in logs. Threat-modeling the stack layer by layer turns 'secure the model' into concrete controls. Infrastructure Security🔒 Premium