API Security Checklist (Built for AI Agents & MCP)

Harden REST, GraphQL, gRPC, and MCP servers with a practical, copy-pasteable API security checklist.

Interactive Checklist

Identity & Auth

Rotate API keys and secrets regularly

Enforce MFA for admin endpoints

Use short-lived tokens with refresh mechanism

Implement task-scoped, identity-bound tokens for AI agents

Access & Policy

Define granular RBAC policies

Use tool allow-lists for AI agents

Apply rate limits per user and endpoint

Configure strict CORS policies

Data Protection

Encrypt sensitive data at rest and in transit

Sanitize PII from logs and error messages

Implement data loss prevention (DLP) controls

Validate and sanitize all inputs

Endpoint Hygiene

Version your APIs explicitly

Document and sunset deprecated endpoints

Disable unnecessary HTTP methods

Tie detection to your code changes

Observability & Response

Log all authentication and authorization events

Monitor for anomalous access patterns

Track cross-session behavior for fraud detection

Set up real-time security alerts

AI Agents & MCP

Isolate MCP server execution environments

Guard against prompt injection attacks

Validate all tool chain executions

Implement runtime controls for autonomous actions

MCP & AI Agents: New Risk Landscape

AI agents and Model Context Protocol (MCP) servers introduce fundamentally new security challenges. Unlike traditional APIs serving human users, AI agents execute autonomous tool chains, process untrusted prompts, and make decisions without direct oversight. This creates novel attack surfaces: prompt injection can manipulate agent behavior, tool chains can be exploited to chain seemingly harmless operations into damaging sequences, and agents may inadvertently exfiltrate sensitive data through their outputs. Traditional security controls designed for human interaction patterns often miss these agentic threats. You need runtime controls, task-scoped permissions, and cross-session behavioral tracking to detect and prevent misuse.

Agent Architecture Flow

User

AI Agent

MCP Server

Tools/APIs

Data

Why Generic Checklists Fail

•Coarse controls: Traditional security tools apply blanket rules that don't account for the nuanced, context-dependent behavior of AI agents.
•Blind to agentic misuse: Static WAFs and API gateways can't detect when an agent chains legitimate operations into malicious workflows.
•Lacks cross-session context: Most tools analyze requests in isolation, missing sophisticated attacks that unfold across multiple sessions.

How Cerberus Helps

•Semantic endpoint understanding tied to code changes: Cerberus analyzes your codebase to understand what each endpoint actually does, updating risk models automatically when code changes, with no manual retraining required.
•Agent/MCP awareness with task-scoped, identity-bound tokens and tool allow-lists: Issue short-lived tokens tied to specific tasks and identities, with granular tool permissions that prevent agents from accessing unauthorized capabilities.
•Cross-session tracking for fraud and anomaly detection: Build behavioral profiles across sessions to detect sophisticated attacks, account takeovers, and gradual data exfiltration.

Traditional WAF vs Application-level Detection

Capability	Traditional WAF	Cerberus
Code awareness	None	Updates with every deployment
Agent detection	Basic rate limiting	Task-scoped tokens + tool allow-lists
Context window	Single request	Cross-session behavioral tracking
False positives	High (generic rules)	Low (semantic understanding)
Setup time	Weeks of rule tuning	Under 5 minutes (auto-learns from code)

Frequently Asked Questions

How does this differ from OWASP API Security Top 10?

OWASP provides excellent foundational guidance, but it predates the AI agent era. Our checklist extends those principles with specific controls for MCP servers, autonomous agents, prompt injection, and tool chain validation, threats that didn't exist when OWASP's list was created.

What are the biggest risks from MCP and AI agents?

The top risks are: (1) prompt injection attacks that manipulate agent behavior, (2) unauthorized tool chain execution where agents combine legitimate operations maliciously, (3) data exfiltration through agent outputs, and (4) lack of oversight for autonomous decisions.

How do task-scoped tokens improve security?

Task-scoped tokens bind each agent action to a specific user identity and narrow task context with an expiration time. Unlike static API keys that grant broad, indefinite access, these tokens automatically limit blast radius and enable precise attribution when something goes wrong.

What does cross-session anomaly detection catch?

It identifies patterns like: gradual privilege escalation, account takeover from new locations, unusual data access sequences, and slow-burn data exfiltration that single-request analysis would miss entirely.

How does DLP work for API responses?

Data loss prevention for APIs scans outbound responses for sensitive patterns (PII, credentials, proprietary data) before they leave your infrastructure. If detected, the system can sanitize, block, or alert, preventing accidental leaks through agent outputs or compromised endpoints.

Is this checklist relevant for startups or just enterprises?

Both. Startups should prioritize the "Identity & Auth" and "AI Agents & MCP" sections to build secure foundations from day one. It's far cheaper than retrofitting later. Enterprises need the full checklist, especially "Observability & Response" for compliance and incident response.