SoFI: Security Property-Driven Vulnerability Assessments of ICs Against Fault-Injection Attacks

Fault-injection attacks have become a major concern for hardware designs, primarily due to their powerful capability in tampering with critical locations in a device to cause violation of its integrity, confidentiality, and availability. Researchers have proposed a number of physical and architectural countermeasures against fault-injection attacks; however, these techniques usually come with large overhead and design efforts making them difficult to use in practice. In addition, the current electronic design automation (EDA) tools are not fully equipped to support vulnerability assessment against fault-injection attacks at the design-time for secure hardware development. To perform a design-time (i.e., presilicon) evaluation of such attacks, a designer should be aware of various security vulnerabilities and must perform a tedious manual design review, which is time-consuming and hard to ensure effectiveness. Therefore, it is very important to develop an automatic assessment framework to identify the most security-critical locations in a design to fault-injection attacks and place emphasis on protecting those locations. In this article, we propose an automated framework for fault-injection vulnerability assessment of designs at gate-level, while considering the design-specific security properties (SPs) using novel models and metrics. The proposed framework identifies the faults that can violate the SPs of the design. As a result, applying local countermeasures will be more effective and the protection overhead will be reduced significantly. Our experimental results on the SP of AES, RSA, and SHA implementations show that the security threat from fault-injection attacks can be significantly mitigated by protecting the identified critical locations, which are less than 0.6% of the design.

Automated summary

Fault-injection attacks pose a significant threat to hardware designs, but the current countermeasures are often costly and lack automation. Therefore, it is crucial to develop an automated framework to identify the most critical security locations in a design and reduce the impact of such attacks.