Lava Security and OWASP Take Aim at the Security Gaps Emerging in AI Infrastructure

New York, USA, November 11th, 2026, FinanceWire

Artificial intelligence is changing the economics and architecture of computing infrastructure. As organizations demand greater GPU capacity, specialized data centers and neocloud providers are expanding to support increasingly complex workloads. But the infrastructure powering this growth also introduces security challenges that existing approaches may not fully address.

Unlike traditional enterprise environments, modern AI data centers bring together thousands of GPUs, high-speed networking technologies, specialized management controllers, shared storage, and complex orchestration systems. Much of this technology was originally designed for trusted high-performance computing environments. Its growing use in commercial environments, where unrelated customers may share physical infrastructure, creates additional security considerations.

Lava Security is working to bring greater structure to that discussion. The company announced that its Top 10 Neocloud and AI Data Center Security Risks framework has become an official project of the Open Worldwide Application Security Project (OWASP), establishing a community-driven initiative focused on identifying and addressing vulnerabilities across AI infrastructure.

A New Security Challenge for Neocloud Providers

One of the central challenges involves the level of access customers receive in bare-metal neocloud environments. Organizations renting servers may receive root access, giving them considerable control over the systems running their workloads.

However, that access can introduce risks extending beyond an individual customer's deployment. For example, unrestricted access to management interfaces could allow a tenant to modify hardware boot configurations that remain in place even after an operating system reinstall. If those settings are not reset before the server is reassigned, the next customer could inherit a compromised environment.

Such scenarios demonstrate why securing AI infrastructure requires attention to components that sit beneath conventional software environments.

"AI infrastructure introduces attack surfaces that traditional security approaches may not adequately address. Organizations need to understand what happens below the operating system, particularly when hardware, firmware, and management interfaces are shared across customers," said Michael Katchinskiy, Head of Security Research at Lava Security. "This framework helps make those risks easier to identify and provides practical guidance for addressing them."

Examining the Entire Infrastructure Environment

Developed by Yakir Kadkoda, CTO and Co-Founder of Lava Security, and Michael Katchinskiy, the framework examines security risks across hardware, firmware, networking, management, and operational layers.

Its coverage includes compromised server firmware, vulnerabilities in high-speed networking technologies such as InfiniBand and RDMA, inadequate tenant isolation, and insecure out-of-band management interfaces.

Additional categories address supply chain threats, data center facility systems, shared storage and AI artifacts, incomplete security certifications, centralized operational services, and delays in vulnerability patching.

Each category includes potential attack scenarios, real-world implications, and practical mitigation recommendations. This approach allows infrastructure providers and operators to evaluate their environments, identify security gaps, and prioritize improvements. Organizations purchasing GPU cloud capacity can also use the framework to better understand the security controls protecting their workloads.

Moving Security Research into an Open Initiative

The framework was initially published in July 2026, drawing on disclosed vulnerabilities, incident reports, technical documentation, and Lava Security's research into AI infrastructure security.

Its development incorporated feedback from more than 20 security and infrastructure practitioners representing neocloud providers, hardware OEMs, security companies, universities, and research institutions.

The transition to OWASP creates an opportunity to expand that participation and establish a shared reference for an industry where security knowledge remains distributed across different stakeholders.

"Securing AI infrastructure requires looking at the entire environment, not just individual components. Hardware, networking, management systems, and workloads are interconnected, and risks in one area can affect the security of the broader infrastructure," said Yakir Kadkoda, CTO and Co-Founder of Lava Security. "Developing this framework within OWASP gives the community an opportunity to contribute its expertise and continuously improve how these risks are understood and addressed."

An Industry Resource That Can Evolve

The project will continue developing through an open process on GitHub. Security researchers, infrastructure engineers, hardware vendors, and operators can review the framework, challenge existing assumptions, propose changes, and contribute additional research or mitigation recommendations. Contributors will be credited in future releases.

The initiative is intended to evolve alongside AI infrastructure, including new GPU architectures, networking technologies, management systems, and deployment models.

Interested participants can explore the official OWASP project page or contribute through the GitHub repository. By placing the framework within an established open security initiative, Lava Security is helping create a common starting point for evaluating the infrastructure risks accompanying the expansion of artificial intelligence.

Contact