When China summoned Nvidia over “serious security vulnerabilities” in its H20 AI chip, it was more than a routine regulatory meeting. It signaled that AI hardware is now firmly on the front lines of national security strategy. The allegations included claims of hardware backdoors, a narrative amplified by U.S. lawmakers pushing for location-tracking and telemetry features in advanced AI chips sold abroad.
Nvidia denied embedding kill switches, spyware, or remote access mechanisms. But for CISOs, the truth matters less than the fallout. Even the perception of compromised hardware can stall supply chains, trigger regulatory probes, and weaken market confidence.
Here’s the real risk: at the hardware level, Remote Code Execution (RCE) can bypass every software safeguard, operating systems, hypervisors, you name it, giving attackers privileged, persistent control. Yet most corporate risk frameworks are still overwhelmingly software-focused.
Governments aren’t helping much either.
AI policy conversations often revolve around data integrity, statistical accuracy, and model reliability. Those matter, but they leave a gaping blind spot in hardware-level threats. That blind spot extends into the enterprise. In a KPMG survey, AI risk discussions overwhelmingly focused on software, barely touching chip provenance, hardware vulnerabilities, or supply chain security.
For CISOs, the takeaway is clear: treat chip-level threats with the same rigor as application and network security.
Your AI Chip Supply Chain Has a Nationality
AI chip production is both a technical process and a geopolitical chessboard. Every stage, from design to fabrication, sits in a handful of jurisdictions, each carrying its own set of risks.
Right now, Nvidia owns the advanced GPU design space, producing the processors that drive everything from large-scale AI training to autonomous vehicles and hyperscale data centers. But once designed, there’s only one place they can be manufactured at scale is at TSMC in Taiwan. That fabrication process depends on extreme ultraviolet (EUV) lithography systems, machines so complex that only ASML in the Netherlands can build them
The supply chain stretches further upstream to raw materials. Rare-earth metals, semiconductor-grade neon gas, and high-purity silicon often come from politically sensitive regions, including China, Congo, Australia, and Ukraine. When war in Ukraine disrupted neon gas exports, it showed just how fragile this supply chain really is.
This means that a single diplomatic dispute, export ban, or regional conflict can ripple straight into your AI infrastructure.
Why AI Chips Are Attractive Targets
The true value of AI chips isn’t just in raw performance; it’s in the strategic leverage they offer. Their computing muscle powers massive AI workloads, making them mission-critical for everything from breakthrough commercial innovations to advanced military operations.
In sectors like energy, healthcare, and transportation, a single compromised chip could trigger data theft, system manipulation, or large-scale service disruptions. With individual units costing tens of thousands of dollars, they’re not just valuable assets; they’re prime targets for theft, diversion, and black-market resale.
Then there’s the dual-use dilemma. The same GPU driving cancer research could just as easily power autonomous weapons or mass-surveillance systems. That overlap means every AI hardware purchase is also a national security decision, whether the buyer intends it or not.
Not Paranoia, But Preparedness
Recent security advisories have flagged high-severity flaws in GPUs and CPUs from major vendors like Nvidia and AMD, the same chips driving AI training and inference in critical industries. These weaknesses in hardware root-of-trust and confidential computing create attack surfaces that no software patch can fully mitigate.
Then there’s the supply chain problem. Malicious substitution — where chips from restricted regions are disguised as compliant — has been caught in multiple sectors. These components can pass visual checks and functional tests, yet still harbor hidden vulnerabilities or embedded exploits.
One proposed fix from the Institute for AI Policy and Strategy (IAPS) is a logic-based watermark built directly into chip designs to verify origin without relying on foundry cooperation.
In theory, it’s tamper-resistant. In practice, adoption is slow. As Mike Borza, principal security technologist at Synopsys, points out: “Vendors are reluctant to accept security features that might add latency, increase power consumption, or reduce throughput.”
Given this, CISOs cannot rely solely on vendors or policymakers. They must bake hardware security into procurement, deployment, and lifecycle policies. And treat chip verification and provenance as core to enterprise risk governance.
Questions CISOs Should Ask AI Vendors to Confirm Chip Security and Compliance
When subscribing to or buying an AI software, you’re also inheriting the hardware vulnerabilities used to build it. Asking the right questions can expose security gaps, compliance issues, and long-term risks before they’re embedded in your infrastructure.
Here’s the minimum due diligence every CISO and procurement team should cover:
- What security certifications do the chips have?
Look for FIPS 140-3, Common Criteria, or ISO/IEC 27001. These signal independent verification against recognized security standards. No certifications? No baseline assurance.
- Have the chips been tested for hardware backdoors or undocumented features?
Backdoors can enable shutdowns, tracking, or unauthorized data access. Demand test reports proving deep security audits have been done.
- Where are the chips designed, manufactured, and assembled?
Every step in the supply chain is a potential exposure point. Mapping locations helps assess jurisdictional risks and geopolitical dependencies.
- What controls are in place to prevent firmware tampering?
Even the best hardware can be undermined by malicious firmware. Ask for evidence of secure boot, code-signing, and update validation.
- Do the chips include any remote management or telemetry features?
Remote access can be useful in some cases, but risky if abused. Get clear answers on what’s collected, where it’s sent, and who controls shutdown or monitoring.
- How do you handle vulnerability disclosures for these chips?
Vendors should have a transparent, rapid patching process with public CVE reporting. Delays or secrecy increase your exposure.
- Are the chips compliant with all relevant export control laws?
Non-compliance with US, EU, or other export laws (ITAR, EAR) can trigger supply disruptions, fines, or product recalls.
- What is the chip’s expected lifecycle and end-of-life (EOL) plan?
AI hardware lifespans are shortening, but enterprise systems often run for years. If a chip becomes unsupported too soon, you could be forced into insecure, outdated hardware.
- Can we perform independent third-party security audits on the chips?
Trust is good. Verification is better. External audits can confirm there are no hidden functions or vulnerabilities.
Building a Sustainable AI Hardware Security Strategy
AI hardware security is a continuous process that extends from vendor selection to end-of-life decommissioning. Treating it as a living discipline ensures that risks are identified and addressed before they can undermine operations.
This means:
- Weaving supply chain risk assessments directly into procurement workflows
- Applying traceability frameworks such as logic-based watermarking to verify origin and integrity
- Enforcing independent hardware audits to catch vulnerabilities that may escape vendor disclosure.
Organizations that embed this proactive approach into their governance frameworks gain a lasting advantage. They are better equipped to respond quickly to shifting regulations, adapt to supply chain disruptions, and maintain operational resilience even when geopolitical pressures mount.
