AI Platforms:

Threat Modeling

Guides on mapping AI trust boundaries, agent permissions, and targets

5 guides • Created Mar 18, 2026 • Updated Mar 29, 2026

5 guides

Identifying High-Value Targets in AI Systems

Techniques for identifying critical assets in AI systems, including models, training data, APIs, and inference pipelines to prioritize security controls.

Mar 18, 2026 • 5 min read

Single Points of Failure in Multi-Agent Workflows

Identifies SPOF risks in multi-agent AI architectures, covering orchestrator compromise, shared memory poisoning, and cascading failure vectors across agent trust boundaries.

Mar 18, 2026 • 4 min read

Identifying Trust Boundaries in AI Architectures

Explores trust boundary identification in AI system architectures, covering data flows, model endpoints, and inter-component trust zones for effective threat modeling.

Mar 18, 2026 • 4 min read

Mapping A2A Communication Flows and Inter-Agent Trust

Analyzes agent-to-agent communication patterns, trust boundaries, and authentication mechanisms to identify attack surfaces in multi-agent AI architectures.

Mar 18, 2026 • 3 min read

Mapping Agent Permissions, Tool Access, and Memory

Analyze and document AI agent permission boundaries, tool access scopes, and memory persistence to identify privilege escalation and data leakage risks.

Mar 18, 2026 • 5 min read

Threat modeling for AI systems maps the attack surface before exploitation begins. AI-enabled environments introduce trust boundaries that do not exist in traditional architectures: the boundary between user input and model context, between retrieved content and trusted instructions, between agent tool calls and backend service execution. Identifying where those boundaries are enforced , and where they are not , drives the exploitation strategy.

A useful AI threat model answers four questions: what AI components are deployed and how are they connected, what data flows through the AI layer and where does it come from, what actions can the AI system perform on connected infrastructure, and what happens if the model is manipulated. The answers determine which attack surfaces are in scope and which techniques from the exploitation sections are applicable.