AI security
Ping-Based Location Verification: 3 Deployment Scenarios
Countering 'Dark Compute' and Illicit Proliferation: A Review of AI Chip Location Verification Methods.
Kristian Rönn
CEO
May 30, 2025
Ping-Based Location Verification: 3 Deployment Scenarios
Introduction: The Critical Need for Technology Enabled Location Verification
In today's rapidly evolving artificial intelligence landscape, verifying the physical location of high-performance computing hardware has become a paramount national security concern. Advanced AI chips, particularly those manufactured by industry leaders like Nvidia and AMD, have become highly sought-after components for nations looking to accelerate their AI capabilities. The illegal smuggling of these chips through shell companies and intermediary jurisdictions threatens to undermine export controls designed to protect national interests and global security.
The concept of "dark compute" - unmonitored computational resources that could enable malicious actors to develop dangerous capabilities - represents a significant threat to national security. Just as the international community monitors the proliferation of enriched uranium to prevent nuclear weapons development, there is an urgent need to implement robust technology-enabled verification systems for advanced computing hardware.
Current export control regulations require end-user checks to verify compliance with licenses. It is untenable to imagine that humans can adequately and accurately account for every exported AI chip.
The Current Threat Landscape
Recent intelligence indicates that hundreds of thousands of advanced AI chips are being illegally smuggled into restricted jurisdictions through elaborate networks of shell companies operating in countries like Malaysia and Singapore. These chips then power AI development that circumvents international safeguards and regulations. For example, reports suggest that the Chinese DeepSeek model was trained using tens of thousands of illegally imported Nvidia AI chips.
This circumvention has far-reaching consequences beyond direct security threats. When DeepSeek announced its capabilities, U.S. technology stocks reportedly experienced a $1 trillion decrease in value, demonstrating the economic impact of unauthorized AI development. Current regulations include a License Exception for Low-Power Performance (LPP), which exempts orders below the equivalent of 1,700 Nvidia H100 chips from certain controls. This creates a significant loophole that adversaries could exploit by establishing multiple shell companies to import chips below this threshold before aggregating them for large-scale AI supercomputing.
To address these critical challenges, three primary approaches to location verification have emerged. Each offers distinct advantages and limitations that make them suitable for different deployment scenarios.
1. Co-located Approach: Maximum Security for High-Risk Environments
Detailed Implementation
The co-located approach establishes a secure verification system within the physical confines of the datacenter itself. This system consists of three primary components:
Attestation Agent: Embedded within the Trusted Execution Environment (TEE) of each AI chip, this agent securely communicates with the endorser server without the possibility of tampering.
Endorser Server: A third-party controlled server housed within the datacenter's Local Area Network (LAN). This server is contained in a tamper-proof enclosure with specialized security features such as motion sensors, tamper-evident seals, and continuous monitoring systems.
The communication between these components occurs entirely within the datacenter's network, with the endorser server periodically sending cryptographically signed attestation reports during physical audits.
Advantages | Limitations |
✓ Airgapped Compatibility: Uniquely suited for Security Level 5 (SL5) datacenters that operate in complete isolation from external networks, essential for facilities handling classified information. | X Vulnerability to Physical Tampering: Despite tamper-proof enclosures, the endorser server remains physically accessible to datacenter personnel, creating potential attack vectors including electromagnetic analysis, side-channel attacks, and counterfeit hardware replacement. |
✓ Precise Location Verification: With near-instantaneous communication between chips and the verification system, location can be verified down to specific rack or server level, making spoofing virtually impossible. | X Audit Complexity and Cost: Auditors must conduct regular physical inspections. Their duties include securely downloading attestation records to a portable drive for off-site analysis and verifying that the endorser server has not been tampered with. |
✓ Sensor Integration: Enables connection to datacenter-specific sensors monitoring power usage, cooling systems, network interfaces, and hardware performance counters, creating a robust profile of legitimate activity. | X Single Point of Failure Risk: If the endorser server is compromised or malfunctions, the entire verification system for that facility could be affected. |
✓ Enforcement Mechanisms: Enables graduated response including processing throttling, workload isolation, emergency power termination, and even cooling system deactivation for severe violations. |
Ideal Use Cases
The co-located approach is best suited for:
Military and intelligence agency facilities developing AI for national security applications
Critical infrastructure protected by national security laws
Research facilities working on cutting-edge AI capabilities with dual-use potential
Government-operated supercomputing centers with strict security requirements
2. Network Approach: Cost-Effective Solution for Lower-Risk Scenarios
Detailed Implementation
The network approach leverages a distributed network of landmark servers operated by trusted third parties. This implementation includes:
Attestation Agent: Similar to the co-located approach, this component resides within the TEE of each AI chip.
Landmark Server Network: A geographically distributed network of trusted servers that communicate with the attestation agents. These landmarks can include servers in secure facilities like embassies, telecommunications hubs, certified datacenters, and government installations.
The verification process triangulates the chip's location by measuring network latency to multiple landmark servers, creating a unique network "fingerprint" that is difficult to falsify.
Advantages | Limitations |
✓ Economic Efficiency: Eliminates the need for additional hardware at each datacenter, reducing implementation costs, maintenance expenses, and administrative overhead. | X Location Accuracy Constraints: Network routing complexities, congestion, and inherent limitations in distance measurement make precise location verification challenging, especially within 500km of restricted jurisdictions. |
✓ Minimal Operational Impact: Simple software deployment to existing systems with no modifications to datacenter infrastructure and reduced compliance burden for legitimate operators. | X Incompatible with Airgapped Environments: Cannot function in high-security facilities that operate without external network connections. |
X Limited Enforcement Options: Restricted to software-level controls without direct access to hardware systems, reducing response capabilities for serious violations. |
Ideal Use Cases
The network approach is best suited for:
Small-scale deployments with only a handful of AI-chips.
Cloud service providers offering AI acceleration to vetted customers
Academic and research institutions in low-risk jurisdictions
Commercial AI deployments with lower security requirements
Geographically isolated facilities far from restricted territories
3. Hybrid Approach: Balanced Security for Most Enterprise Scenarios
Detailed Implementation
The hybrid approach combines the strengths of both previous methods, creating a robust verification system that balances security and practicality:
Attestation Agent: Similar to the co-located and network approach, this component resides within the TEE of each AI chip.
Co-located Endorser: A tamper-resistant server installed within the datacenter's LAN, similar to the co-located approach.
Landmark Server Network: Unlike the co-located approach, which requires frequent physical inspections to ensure the endorser hasn't been tampered with, this hybrid deployment scenario allows the endorser server to communicate with a broader network of landmark servers and other data center endorsers. This creates multiple layers of verification, ultimately requiring fewer physical inspections.
This approach implements a trust chain where the co-located endorser verifies the AI chips, while the network of landmarks continuously verifies the endorser itself.
Advantages | Limitations |
✓ Multilayered Security Architecture: Implements defense-in-depth with local verification for precise location confirmation and network verification to ensure the endorser itself hasn't been compromised. | X Not completely airgapped: Unlike the pure co-located solution, a hybrid approach can’t be completely airgapped, which means that it might not be suitable for military-grade security. |
✓ Reduced Audit Frequency: Continuous remote attestation supplements in-person inspections, with automated anomaly detection triggering targeted audits only when necessary. | X Moderate Cost Increase: While less expensive than frequent physical audits, still requires initial hardware investment, ongoing maintenance, and security systems to protect the co-located endorser. |
✓ Operational Flexibility: Adaptable to different security requirements with configurable trust levels, graceful degradation during connectivity issues, and adjustable verification frequency. | |
✓ Enhanced Forensic Capabilities: Creates comprehensive audit trails with correlation between local and network data, historical pattern analysis, and evidence preservation for potential legal proceedings. |
Ideal Use Cases
The hybrid approach is optimal for:
Enterprise AI deployments with significant security requirements
Datacenters in jurisdictions where export controls are a concern
Facilities with intermittent network connectivity
Systems processing sensitive but not classified information
Commercial entities working with government contracts
Comparative Metrics
Metric | Co-located Approach | Network Approach | Hybrid Approach |
Implementation Cost | High (hardware + audits) | Low (software only) | Moderate (hardware + reduced audits) |
Location Accuracy | Extremely high (facility-specific) | Moderate (regional) | High (facility-specific) |
Security Level | Maximum | Basic | Enhanced |
Audit Requirements | Frequent physical audits | Minimal to none | Occasional physical audits |
Network Dependency | None (works in airgapped environments) | High (requires reliable internet) | Moderate (can function with intermittent connectivity) |
Tamper Resistance | High (physical enclosure) | Limited (software-based) | Enhanced (physical + network verification) |
Enforcement Capabilities | Comprehensive (hardware-level) | Limited (software-level) | Substantial (hardware + software) |
Sensor Integration | Extensive | None | Moderate to extensive |
Scalability | Limited by physical installation | Highly scalable | Moderately scalable |
Operational Impact | Moderate | Minimal | Low to moderate |
Border Proximity Suitability | Excellent | Poor within 500km of borders | Good |
Recommended Deployment Size | Large facilities (1000+ chips) | Small deployments (<500 chips) | Medium to large deployments (500+ chips) |
Conclusion
The proliferation of advanced AI chips to unauthorized entities represents a significant national security challenge comparable to the spread of nuclear technology. Implementing robust location verification systems is an essential component of a comprehensive strategy to prevent "dark compute" from enabling dangerous capabilities in the hands of malicious actors.
Each of the three approaches offers distinct advantages that make them suitable for different deployment scenarios based on security requirements, budget constraints, and operational considerations. The co-located approach provides maximum security for critical facilities, the network approach offers cost-effective verification for lower-risk deployments, and the hybrid approach strikes an optimal balance for most enterprise scenarios.
As AI capabilities continue to advance, the importance of these verification systems will only increase. By implementing appropriate location verification based on risk assessment, organizations can help ensure that advanced computing resources remain in responsible hands while maintaining compliance with evolving regulatory frameworks.
