AI Threat Modeling Assistant

A deterministic AI/ML threat modeling engine that identifies security risks across LLMs, traditional machine learning systems, and agentic AI workflows.

🌐 Live Tool: https://aimlthreats.com

Unlike LLM-based tools, this engine uses a rule-based approach to produce consistent, explainable, and audit-friendly threat models.

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Why this tool?

AI systems introduce new and evolving attack surfaces:

• Prompt injection
• Data poisoning
• Model extraction
• Adversarial inputs
• Agentic misuse and tool abuse

Most teams struggle to:

• Identify relevant threats for their AI architecture
• Map risks to compliance frameworks
• Understand how to test and validate security controls

This tool bridges that gap using deterministic logic instead of black-box AI.

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Key Design Principle

This tool is rule-based, not generative AI.

Why?

• No hallucinated threats
• Fully explainable logic
• Consistent outputs
• Suitable for audits and compliance
• Deterministic behavior across runs

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⚙️ Features

• ✅ Supports multiple AI types:

  • LLMs
  • Traditional ML
  • Agentic AI systems

• 🔍 Threat identification based on system inputs

• 🧩 Mapping to security frameworks:

  • OWASP Top 10 (LLM & ML)
  • MITRE ATLAS
  • NIST AI RMF (CIA + Abuse)

• 🔗 Attack chain detection (deterministic patterns)

• 🧪 “How to Test” guidance for each finding

• 📊 Severity scoring with contextual adjustments

• 📄 Clean, structured report output

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How it works

1. User provides system details:

   • AI type (LLM / ML / Agentic)
   • Data sensitivity
   • Exposure (public/internal)
   • Controls (validation, logging, etc.)

2. Rules engine evaluates:

   • Threat conditions
   • Control gaps
   • Risk scoring

3. Findings are:

   • Mapped to OWASP / MITRE / NIST
   • Grouped into deterministic attack chains (when applicable)

4. Output includes:

   • Severity (Low / Medium / High / Critical)
   • Description of risk
   • How to test
   • Mitigation guidance

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Example Output

• Scenario:

• AI Type: LLM
• Exposure: Public
• Input Validation: No
• Output Filtering: No
• Sensitive Data: Yes

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Findings:

• Missing Input Validation → High
• Missing Output Filtering → High
• Sensitive Data Exposure Risk → High

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Attack Chain (Triggered):

Chain: Untrusted Input → Unsafe Processing → Data Exposure