Identify attack vectors in your AI architecture before attackers do. From LLMs to edge devices, we research how your systems can be broken—and how to prevent it.
AI systems introduce attack vectors that traditional security assessments miss. Prompt injection, model manipulation, sensor spoofing, and agentic autonomy risks require specialized threat modeling that understands both the AI technology and real-world attack techniques. We analyze your planned or existing AI architecture to identify vulnerabilities before they become exploits.
From cloud LLMs to physical AI devices, we model threats across the full spectrum of AI deployments
Prompt injection, jailbreaking, data leakage, model manipulation, and supply chain attacks on foundation models and fine-tuned systems.
Tool calling exploits, excessive agency risks, multi-agent coordination vulnerabilities, and autonomous decision-making failures.
On-device model security, hardware attack surfaces, resource-constrained defenses, and update mechanism vulnerabilities.
Sensor spoofing, control system hijacking, safety-critical failures, and physical-world adversarial attacks.
Document ingestion attacks, knowledge base poisoning, retrieval manipulation, and context window exploitation.
Chatbots, copilots, and AI-enhanced products. Model API security, user input handling, and output validation.
A structured approach combining traditional threat modeling with AI-specific analysis
Deep dive into your AI system architecture, data flows, integration points, and deployment environment.
Systematic identification of AI-specific threats using STRIDE, MITRE ATLAS, and our proprietary AI threat taxonomy.
Evaluate likelihood and impact of each threat considering your specific context, threat actors, and business criticality.
Design security controls and architectural changes that address identified risks within your constraints.
Actionable artifacts that integrate into your security and development processes
Comprehensive documentation of identified threats, attack trees, and risk ratings specific to your AI architecture.
Visual mapping of all entry points, trust boundaries, and potential attack vectors across your AI system.
Prioritized list of security controls and design changes with implementation guidance and effort estimates.
Derived security requirements that can be integrated into your development process and acceptance criteria.
Before building, understand the security implications of your AI architecture choices and design security in from the start.
Shipping AI to devices in uncontrolled environments? Identify physical and logical attack vectors before production.
Connecting AI to sensitive systems? Map the risks of LLM access to internal data, APIs, and business processes.
When AI failures have physical consequences, threat modeling is essential for identifying safety-security intersections.
Threat modeling is most powerful when combined with our other services
Whether you're designing a new AI system or securing an existing deployment, threat modeling gives you the roadmap to build secure.