agents
AI security interview questions tagged agents, across every topic.
67 questions · 9 unlocked for you
Concepts behind "agents"
Explanations of the mechanisms these questions rely on.
Foundational
The Confused DeputyA confused deputy is a program that holds authority and is tricked into using it on behalf of a less privileged party. The problem was named in 1988 and reappears in OAuth proxies, cloud services that act for customers, and, most sharply, in AI agents: a model holding a user's or a service's credentials can be steered by text from anyone. The fix is to make each action carry the authority of the party that requested it, not the deputy's own. Identity & Access
Foundational
Server-Side Request ForgeryServer-side request forgery (SSRF) happens when an application fetches a URL that an attacker can influence, letting the attacker use the server's network position to reach internal services, cloud metadata endpoints and admin interfaces. String checks on URLs are easy to bypass. Robust defences validate the resolved IP address, connect to that exact address, re-check redirects, prefer allowlists, and route outbound fetches through an egress proxy. LLM tools that fetch web pages are SSRF primitives by design. Application Security
Foundational
Sandboxing Untrusted CodeAgents that write and run code, notebooks that execute model output, and platforms that run customer code all execute code nobody reviewed. A sandbox makes that safe by combining a strong isolation boundary with an environment that has nothing valuable in it: no credentials, minimal files, no network or tightly allowlisted egress, resource and time limits, and disposal after use. Isolation answers whether code can escape; the environment answers what it can do without escaping. Infrastructure Security
Foundational
Tool Calling and the Agent LoopTool calling produces proposed operations for an application or provider-hosted executor. Inspect the input contract, trusted identity, resource permissions, task scope, resource admission and actual completion. Delegation and schema compliance help constrain the path but do not replace its remaining checks. How LLM Systems Work
Foundational
Indirect Prompt InjectionIndirect prompt injection carries attempted task redirection inside content an application processes. Map the source writer, ingestion path, model influence and effect policy. Learn to test retrieval and consequences separately, preserve unknown outcomes and account for derived copies without overstating risk. Prompt Injection
Advanced
Threat Modeling AI SystemsThreat modeling an LLM application uses the same data-flow diagram and STRIDE walk as any system, with new elements drawn in: the context assembler, retrieval stores, the tool layer, memory, and the model and its supply chain. Two adjustments make it work. Annotate every input to the context with its author, because any author can steer the model. And classify every control as deterministic or probabilistic, because only deterministic controls can carry a boundary. Security Foundations🔒 Premium