This AI agents 2026 mapper charts your AI stack's attack surface: tick every AI component you run and get a visual map of where attackers actually get in.
It is an interactive checklist that maps where attackers get into AI agent stacks in 2026 — customer-facing chatbots, coding agents with shell access, MCP servers, RAG over internal docs, agents with persistent memory and more. Each component contributes a weighted exposure score out of 100, with the real attack research behind every risk so you know why each item matters.
Map your AI agent security posture: tick every AI component you run — get a visual map of where attackers actually get in, with the real attack research behind each risk.
Every point where your AI setup can be attacked: the models, the prompts, the tools agents can call, the data they touch, and the agents themselves. Tick your components and the map shows where attackers actually get in.
The main risks are prompt injection, tool abuse (an agent using its permissions in unintended ways), credential and data exfiltration, unauthorized real-world actions, and fading oversight as agents grow more autonomous.
Use least-privilege permissions, require human approval for irreversible actions, enforce egress allowlists at the network layer rather than in the prompt, keep tamper-evident logs of every agent action, and maintain a working kill switch.
An AI agent security framework is a structured set of practices for deploying AI agents safely — permissions, oversight, logging, and incident response. Recent examples include NIST, NSA and CISA's October 2026 joint practices and CERT-In's "AI Agents as Attack Vectors" advisory.