Intro to AICAD4Sec Workshop
Security vulnerabilities in hardware designs are catastrophic as once fabricated, they are nearly impossible to patch. Modern SoCs (Systems-on-Chip) face threats like side-channel leakage, information leakage, access control violations, and malicious functionality, jeopardizing the foundational integrity of chips and systems. These vulnerabilities circumvent software-level defenses, creating urgent challenges for hardware security. Ensuring the security of hardware designs is challenging due to their huge complexity, aggressive time to markets, and the variety of attacks introduced against hardware designs. Moreover, it is very costly for a design house to keep many security experts with in-depth design knowledge with diverse security implications. So, the semiconductor industry are looking for a set of metrics, reusable security solutions, and automatic computer-aided design (CAD) tools to aid analysis, identifying, root-causing, and mitigating SoC security problems. Artificial Intelligence (AI) is revolutionizing the landscape of Computer-Aided Design (CAD) and Electronic Design Automation (EDA), providing unprecedented opportunities to tackle these challenges. AI-driven tools have the potential to analyze complex SoC designs at multiple abstraction levels, automatically detect vulnerabilities, and even predict potential attack vectors. By leveraging advanced AI models, including large language models (LLMs) and machine learning algorithms, we can now accelerate the identification of root causes, assess risks, and recommend security countermeasures. The inclusion of AI in CAD/EDA for security addresses these issues in innovative ways: (1) Enhanced Vulnerability Detection: AI can detect patterns and anomalies in massive design spaces that are otherwise unfeasible for human analysis; (2) Contextual Adaptability: AI models evolve to address emerging threats by learning from diverse security challenges across the industry; and (3) Proactive Security: AI tools not only identify vulnerabilities but also recommend pre-silicon countermeasures, ensuring security integration throughout the design flow.
Building on the resounding success of the 1st (inauguration) and 2nd CAD4Sec workshops, co-located with the Design Automation Conference 2022 (DAC’59) and Design Automation Conference 2023 (DAC’60), respectively, and drawing ~100 attendees each year, the 3rd iteration aims to embrace the transformative intersection of AI, CAD, and hardware security. Now rebranded as AICAD4Sec, this workshop aims to drive innovation at the nexus of AI-driven solutions and hardware design security. The ultimate vision of AICAD4Sec is to establish a cutting-edge platform that fosters collaboration, showcases advancements, and sets the roadmap for secure, AI-enabled hardware design. Specifically, (i) Engaging experts from industry leaders like Google, Microsoft, Synopsys, and ARM, alongside academia and government agencies such as DARPA and AFRL; (ii) Showcasing the latest breakthroughs in AI-enhanced CAD tools for security; (iii) Facilitating practical demonstrations of AI-driven solutions in hardware security by both industries/academia; and (iv) Hosting a dynamic panel discussion on the evolving role of AI, with a particular focus on large language models and their implications for secure SoC design.
Building on the foundation of its predecessors, the 3rd AICAD4Sec workshop will feature technical presentations, case studies, and metrics discussions that underscore the role of AI in enhancing CAD workflows. In continuation to the 1st and 2nd CAD4Sec workshops, the 3rd AICAD4Sec workshop will contain several technical talks on the scope of metrics and CAD as the following:
- CAD Tools for Side-Channel Vulnerability Assessment (Power, Timing, and Electromagnetic Leakage)
- AI-Powered Tools for Pre-Silicon Vulnerability Mitigation and Countermeasure Suggestions
- Security-Oriented Equivalency Checking and Property Validation
- Large Language Models for Security-Aware Design Automation
- Fault Injection Analysis and Countermeasure Integration in CAD
- ML-Enhanced Threat Detection Across Design Abstractions
- CAD for Secure Packaging and Heterogeneous Integration
- AI-Augmented Detection of Malicious Functionality in Hardware Designs
- Assessment of Physical Probing and Reverse Engineering Risks
- AI-Enabled Security Verification for Emerging SoC Architectures