What does a credible AI security vendor's threat model look like for agentic AI systems in 2026?

A credible AI security threat model for agentic AI systems in 2026 focuses on autonomous execution risk, not only harmful text generation. The security perimeter has shifted from what a model says to what an agent can do: invoking tools, accessing sensitive resources, executing multi-step workflows, and interacting with external systems without human approval.

Point-in-time model safety evaluations are insufficient because agentic threats are behavioral and relational. A single prompt may appear harmless until it triggers a risky tool call, escalates permissions through a multi-step workflow, or manipulates another agent in a connected system.

A complete threat model for agentic AI systems should cover:

• Prompt injection: external inputs that redirect agent behavior or override system instructions

• Tool poisoning: compromised or manipulated tools that cause agents to take unintended actions

• MCP trust boundary failures: unauthorized access or abuse of MCP-connected resources

• Excessive permissions: agents operating with broader access than their task requires

• Agent hijacking: attacker-controlled inputs that take over autonomous decision-making

• Context and memory poisoning: corrupted context windows that alter agent reasoning

• Unsafe action chaining: multi-step workflows that produce harmful outcomes through individually benign steps

• Data exfiltration: sensitive information extracted through agent outputs or tool interactions

• Rogue autonomous behavior: agents executing unintended actions outside defined operational boundaries

• Cross-agent manipulation: one agent influencing or compromising another in a multi-agent system

Akto's threat model is operationalized through the AI Agent Context Graph, which maps how agents, prompts, permissions, tools, and resources interact in production. ARGUS, Akto's runtime agent monitoring product, monitors agent behavior inline and detects when actions deviate from expected patterns. Agent Probe continuously validates whether prompts can trigger risky tool calls, bypass intended permissions, or manipulate multi-step workflows using more than 4,000 test cases mapped to real agentic attack paths.