AI Threat Modeling for Secure Systems

The five core steps are: define the system and its business context; map architecture, assets, data flows, and trust boundaries; identify credible threats and attack paths; evaluate likelihood and impact to prioritize risks; and select, assign, and validate mitigations. For AI systems, this also means examining prompts, retrieval sources, model outputs, agent tools, permissions, and third-party integrations.

AI threat modeling is a structured assessment of how an attacker could misuse or compromise an AI system. It identifies valuable assets, entry points, trust boundaries, and realistic attack paths across LLMs, RAG pipelines, agents, tools, APIs, and cloud services. The result is a prioritized plan for security controls, targeted testing, and remediation before risks become incidents.

LLM applications accept untrusted natural-language input and can connect to sensitive data, documents, APIs, or tools. Threat modeling identifies where prompt injection, system-prompt leakage, unsafe output handling, unauthorized retrieval, or tool misuse could occur. It helps teams design guardrails and permissions around real deployment conditions rather than relying solely on model-provider safeguards or generic application-security controls.

Yes. RAG threat modeling examines the full retrieval path: user queries, document ingestion, metadata, embeddings, vector databases, access controls, retrieval filters, and generated responses. Key scenarios include cross-tenant document retrieval, access-control bypass, vector database poisoning, embedding inversion, and indirect prompt injection hidden in retrieved content. These findings inform targeted RAG security testing and practical safeguards.

Agent threat modeling maps the agent’s goals, instructions, connected tools, credentials, approvals, delegated agents, and data channels. It tests scenarios such as tool-call injection, goal hijacking, indirect prompt injection, privilege escalation through agent chaining, and data exfiltration through legitimate tools. High-impact actions such as payments, record deletion, or external messaging receive added scrutiny and recommended guardrails.

A useful AI threat model includes an architecture and data-flow view, identified assets and trust boundaries, prioritized threat scenarios, affected components, likely impact, recommended controls, and ownership for remediation. Vynox Security can connect these scenarios to adversarial validation, OWASP LLM Top 10 coverage, evidence screenshots, CVSS-scored findings where applicable, and developer-ready reproduction guidance for confirmed vulnerabilities.

Timing depends on the number of models, pipelines, integrations, and environments in scope. Focused AI and LLM penetration tests typically deliver in 5–7 business days, while RAG and agent assessments commonly take 10–15 business days. Comprehensive AI red teaming can take 3–5 weeks because it models and validates multi-step adversarial scenarios across the entire system.

Yes. AI threat modeling can provide a defensible basis for risk treatment, security testing priorities, and documentation. Vynox maps relevant findings and evidence to SOC 2 and ISO 27001 control requirements, and supports EU AI Act Article 15 adversarial-testing needs for high-risk AI systems. It is security-testing evidence, not a substitute for an independent certification audit.