MITRE ATLAS is a knowledge base of adversary tactics and techniques for AI systems, useful for modeling AI attack chains, threat scenarios, and detection priorities.

AI Threat Modeling Tool for LLMs, RAG & Agentic AI

Q: How do you secure agentic AI?

Securing agentic AI requires control over tool permissions, memory integrity, identity, approval gates, sandboxing, logging, policy enforcement, and kill switches for autonomous behavior.

Q: How is AI threat modeling different from traditional threat modeling?

AI threat modeling expands beyond classic application boundaries to include prompts, model behavior, retrieval, training data, downstream tool use, autonomy, and AI governance risks.

Q: What is OWASP LLM Top 10?

The OWASP LLM Top 10 is a risk framework describing common security issues in large language model applications, including prompt injection, insecure output handling, excessive agency, and training data exposure.

Q: How do you assess RAG security?

RAG security assessment reviews retrieval trust, document provenance, context poisoning, access controls, prompt injection exposure, output filtering, and data leakage paths across the full pipeline.

Q: What are AI attack chains?

AI attack chains are multi-step abuse paths where several weak controls combine, such as retrieval poisoning followed by prompt injection and then tool misuse or data exfiltration.

Q: How do you model AI risk?

Model AI risk by identifying business impact, attacker goals, system architecture, data sensitivity, trust boundaries, autonomy levels, and mapped mitigations across technical and governance domains.

What Is AI Threat Modeling?

AI threat modeling extends traditional threat modeling to prompts, retrieval pipelines, agents, memory, tool use, model dependencies, and downstream AI-driven actions. It is essential for LLM threat modeling, RAG security assessment, MCP security, and broader AI risk management.

Threat Modeling for Real AI Systems

Analyze production-style AI architectures instead of isolated prompts. Model runtime behavior, autonomy, external tools, memory, and downstream attack paths.

Chain-Based Risk Discovery

Correlate multiple weak controls into AI attack chains so teams can see how medium findings become serious multi-step exploitation paths.

Framework-Aware Security Mapping

Map findings to OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF, ISO 27001, SOC 2, GDPR, HIPAA, and the EU AI Act.

Built for More Than Just LLMs

The platform supports multiple AI architectures and dynamically adjusts questions, controls, and attack paths based on the selected system and exposure level.

AI threat modeling coverage matrix across LLMs, RAG, MCP, agentic AI, and machine learning systems

Threat Modeling for Agentic AI Systems

Assess agentic AI security across autonomous agents, browser-use agents, multi-agent systems, persistent memory, tool execution, external APIs, and code execution workflows.

Human-in-the-loop controls
Agent memory integrity
Inter-agent authentication
Code execution sandboxing
Supply-chain review
Runtime kill switches

Agentic AI security controls for autonomous agents and multi-agent threat modeling

AI runtime behavior analysis for AI attack surface analysis and operational controls

Runtime Behavior & Operational Controls

Analyze runtime exposure including direct user access, autonomous execution, downstream integrations, external systems, tool usage, and real-time processing.

Direct user interaction analysis
External inference exposure
Tool and plugin execution review
Human override validation
Operational risk visibility

AI Security Assessment Workflow

The assessment adapts based on architecture selection, runtime behavior, and business impact.

Project & Architecture Context

Capture deployment stage, business impact, AI type, model patterns, supply-chain exposure, and regulated domains.

Data & Runtime Exposure

Evaluate sensitive data handling, runtime outputs, external integrations, governance controls, and downstream systems.

LLM & Prompt Security

Analyze prompt injection exposure, hallucination controls, system prompt leakage risk, moderation gaps, and output handling.

AI security assessment workflow for data handling and runtime behavior analysis

LLM threat modeling controls for prompt injection, model safety, and output handling

OWASP LLM Top 10 Mapping

The assistant helps translate architecture context into actionable controls aligned with the OWASP LLM Top 10, including prompt injection, insecure output handling, training data exposure, excessive agency, and supply chain issues.

Prompt & Retrieval Risks

Model prompt injection, indirect prompt injection, retrieval poisoning, and unsafe context loading across RAG systems.

Agency & Tool Risk

Assess excessive permissions, untrusted tool invocation, browser automation misuse, and agent escalation paths.

Data & Output Risk

Review system prompt leakage, sensitive output exposure, untrusted transformations, and model governance gaps.

MITRE ATLAS Coverage

Use MITRE ATLAS to turn architecture review into mapped adversary techniques and AI attack chains that teams can communicate to engineering and leadership.

MITRE ATLAS and OWASP LLM Top 10 coverage for AI risk assessment and threat modeling

Attack Chains & Correlated Findings

Findings are not treated as isolated issues. The engine correlates weak controls into deterministic attack paths.

AI attack chain overview for LLM and RAG security assessment

Detailed AI attack chain analysis showing correlated findings and exploit paths

Executive Risk Reporting

Generate security reporting understandable by AppSec teams, AI governance leaders, architects, risk stakeholders, and developers.

Application Security teams
AI governance teams
Security leadership
Risk and compliance stakeholders
Developers and AI engineers

AI threat modeling executive summary for leadership and governance reporting

Prioritized Risks & Quick Wins

Risks are ranked based on likelihood, impact, exploitability, and missing control correlation.

Top AI security risks dashboard for AI governance and risk management prioritization

Detailed AI threat analysis with MITRE ATLAS mapping and mitigation guidance

Detailed Threat Intelligence

Every finding includes technical and governance context.

Reasoning and exploitability analysis
Abuse-case narratives
Attack path mapping
MITRE ATLAS references
NIST impact categories
Practical mitigation guidance
Quick remediation wins

Technical Deep Dive

Google rewards educational depth, and security teams need real methodology. These sections are designed for both.

How Threat Modeling for AI Differs From Traditional Approaches

Classic threat modeling focuses on services, identities, and network trust. AI systems add prompts, model behavior, retrieval trust, autonomy, tool permissioning, evaluation loops, and data provenance.

RAG Security Assessment Methodology

Review source trust, retrieval authorization, document provenance, prompt injection resistance, output guardrails, and sensitive data handling across the full retrieval pipeline.

Enterprise Usage Scenarios

Use the platform for AI architecture reviews, security sign-off before launch, third-party AI risk reviews, governance reporting, and post-incident AI attack surface analysis.

Agentic AI Workflow Review

Document memory stores, autonomy levels, approval steps, browser automation, code execution boundaries, identity assumptions, and kill-switch design.

AI Threat Modeling FAQ

Answers for high-intent searches around AI threat modeling, RAG security assessment, and AI governance.

What is AI threat modeling?

AI threat modeling identifies assets, trust boundaries, attack paths, and mitigations specific to AI systems such as LLMs, RAG pipelines, MCP integrations, agentic AI, and classical machine learning workflows.

How do you secure agentic AI?

Secure agentic AI by limiting permissions, validating tool execution, hardening memory, enforcing approvals, sandboxing code execution, logging decisions, and adding kill switches for unsafe autonomous behavior.

What is MITRE ATLAS?

MITRE ATLAS is a framework of adversary tactics and techniques for AI systems. It helps security teams structure attack scenarios, map AI abuse paths, and communicate findings consistently.

How is AI threat modeling different from traditional threat modeling?

It expands the model to include prompts, model behavior, retrieval, AI autonomy, data lineage, and governance risks rather than only network paths, identities, and software components.

What is OWASP LLM Top 10?

The OWASP LLM Top 10 outlines common security risks in LLM applications, including prompt injection, insecure output handling, excessive agency, system prompt leakage, and supply chain weaknesses.

How do you assess RAG security?

Assess RAG security by reviewing document trust, retrieval permissions, context poisoning, prompt injection resilience, output filtering, and sensitive data exposure across the ingestion and response lifecycle.

What are AI attack chains?

AI attack chains are linked sequences where one weak control enables another, such as document poisoning leading to prompt injection and then tool misuse or sensitive data access.

How do you model AI risk?

Model AI risk by combining business impact, system architecture, attacker goals, data sensitivity, autonomy level, and mapped mitigations into a structured AI risk assessment.

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