Stop Your Legacy Infrastructure from Hijacking Your AI Agents

Earlier this month, I spoke at the Gartner Security & Risk Management Summit about a blind spot most security programs are still not accounting for - how attackers are circumventing AI security programs by using legacy infrastructure to hijack AI agents. AI adoption is moving faster than security programs can account for. Roughly 71% of organizations are piloting AI agents across their

The article reports that attackers are increasingly hijacking AI agents indirectly via legacy infrastructure, exploiting weaknesses in older servers, IAM/AD configurations, cloud storage, and misconfigured identity relationships instead of attacking the AI models directly.[1][3][10] It describes how AI agents inherit the permissions and exposures of these legacy systems, creating end-to-end attack paths where issues like unpatched application servers, misconfigured Active Directory, and stolen cloud keys can be chained to reach AI knowledge bases and tools.[1][3][10] From a CyberSE.AI perspective, this illustrates a high-risk pattern of AI agent abuse driven by inadequate identity, access, and exposure management around agents and their dependencies, requiring redesign of agent access models with least privilege, zero trust principles, and strong isolation of AI-related assets.[1][3][4] Practically, organizations should map and continuously test attack paths from legacy components into AI agents, harden identities and permissions, and adopt ongoing red teaming and architectural reviews to ensure AI agents cannot be used as a powerful pivot into sensitive data and systems.[1][2]