Traditional threat modeling wasn’t built for the non-deterministic nature of AI. As LLMs become deeply integrated into enterprise workflows—accessing sensitive data and executing system commands—the potential for exploitation grows exponentially. AONIQ’s LLM Threat Modeling service provides a rigorous, architectural deep-dive into your AI ecosystem. We identify where your models are vulnerable, how your data could be leaked, and where your logic could be subverted, long before a single line of malicious prompt is ever written.
We evaluate how your system handles untrusted input. Our experts map out potential bypasses where attackers could "jailbreak" the model to ignore safety guardrails or leak the original system instructions.
LLMs often have access to vast repositories of internal data. We model the boundaries between the model, the vector database (RAG), and the end-user to ensure sensitive information—like PII or proprietary trade secrets—never exits the secure environment.
An LLM’s output is often treated as "trusted" by downstream systems. We identify risks where model outputs could lead to Cross-Site Scripting (XSS), Remote Code Execution (RCE), or unauthorized API calls within your internal network.
From third-party model providers to corrupted training sets, we assess the "upstream" risks. We model the impact of model poisoning and the security of your integration with external AI platforms.
Architecture Review: Mapping the data flow between users, the LLM, plugin integrations, and backend databases.
Adversarial Persona Development: Defining who would attack your AI and what their specific objectives would be (e.g., data theft, service disruption, or reputation damage).
Attack Path Analysis: Step-by-step walkthroughs of how a simple chat interface could be leveraged to gain unauthorized system access.
Remediation Roadmap: A prioritized list of architectural changes and guardrails—such as output sanitization and robust “human-in-the-loop” triggers.
Secure by Design: Fix architectural flaws when they are cheapest to solve—during the design phase.
Non-Deterministic Defense: Move beyond static rules to create a resilient framework that accounts for the “unpredictability” of LLM behavior.
Regulatory Compliance: Align with emerging standards like the OWASP Top 10 for LLMs and the NIST AI Risk Management Framework.
