MithrilAI Corp. provides cryptographic‑level hardware defenses for AI chips, using randomized multi‑party computation, masking, shuffling, and fault‑injection resilience to make individual computations intractable to attackers while preserving functionality. Their scalable architecture protects large neural networks from side‑channel and fault attacks with minimal performance overhead, enabling semiconductor manufacturers and system integrators to deliver secure AI hardware for defense, aerospace, autonomous driving, cybersecurity, and other safety‑critical applications.
Funding
Funding not disclosed
Founders
Product
Problem
AI hardware used in safety‑critical and defense applications is vulnerable to side‑channel attacks, model theft, and fault injection, which can compromise confidentiality, integrity, and reliability of machine‑learning workloads.
Solution
mithrilAI provides cryptographic‑level hardware defenses that make individual computations in AI chips computationally intractable for attackers while preserving required functionality. Their approach combines randomized multi‑party computation, hardware masking, shuffling, and fault‑injection resilience to protect large neural networks against power analysis, electromagnetic, and other physical side‑channel attacks. The technology is implemented as a scalable architecture that maintains high throughput and low overhead, enabling secure deployment of AI models in military, autonomous‑driving, cybersecurity, and critical‑infrastructure systems. By integrating these defenses at the silicon level, mithrilAI helps hardware vendors deliver AI products that meet stringent safety and security requirements.
Target Audience
Primary customers are semiconductor manufacturers and system integrators developing AI chips for defense, aerospace, autonomous driving, cybersecurity, and other safety‑critical domains.
Features
- Randomized multi‑party computation embedded in hardware to render individual operations intractable to adversaries
- Integrated masking and shuffling techniques that protect against differential power analysis and electromagnetic side‑channel attacks
- Fault‑injection resilience mechanisms to maintain correct inference despite malicious perturbations
- Scalable architecture designed for large, complex neural networks with minimal performance overhead
- Custom‑tuned hardware modules that can be incorporated into semiconductor designs and system‑integrator workflows
- Support for both military and civilian safety‑critical AI applications, including aviation, autonomous vehicles, and critical infrastructure