Threat Modeling for AI: The Case for an Asset-Centric Approach
This addresses security problems for organizations deploying AI agents with autonomous capabilities, though it appears incremental as an adaptation of existing threat modeling approaches.
The paper tackles the security challenges of integrated AI agents that can autonomously execute code and interact with external systems by introducing an asset-centric threat modeling methodology, which enables defenders to systematically identify vulnerabilities impacting critical AI assets across distributed infrastructures.
Recent advances in AI are transforming AI's ubiquitous presence in our world from that of standalone AI-applications into deeply integrated AI-agents. These changes have been driven by agents' increasing capability to autonomously make decisions and initiate actions, using existing applications; whether those applications are AI-based or not. This evolution enables unprecedented levels of AI integration, with agents now able to take actions on behalf of systems and users -- including, in some cases, the powerful ability for the AI to write and execute scripts as it deems necessary. With AI systems now able to autonomously execute code, interact with external systems, and operate without human oversight, traditional security approaches fall short. This paper introduces an asset-centric methodology for threat modeling AI systems that addresses the unique security challenges posed by integrated AI agents. Unlike existing top-down frameworks that analyze individual attacks within specific product contexts, our bottom-up approach enables defenders to systematically identify how vulnerabilities -- both conventional and AI-specific -- impact critical AI assets across distributed infrastructures used to develop and deploy these agents. This methodology allows security teams to: (1) perform comprehensive analysis that communicates effectively across technical domains, (2) quantify security assumptions about third-party AI components without requiring visibility into their implementation, and (3) holistically identify AI-based vulnerabilities relevant to their specific product context. This approach is particularly relevant for securing agentic systems with complex autonomous capabilities. By focusing on assets rather than attacks, our approach scales with the rapidly evolving threat landscape while accommodating increasingly complex and distributed AI development pipelines.