Deepali Garg

Zain Ul Abideen, Deepali Garg, Lawrence Pileggi et al.

Universal Circuits (UCs) offer a promising approach to hardware Intellectual Property (IP) obfuscation, leveraging cryptographic principles to hide both structure and function in a programmable logic fabric. Their adaptability makes them especially suitable for the globalized Integrated Circuit (IC) supply chain, where security against threats like reverse engineering is crucial. Despite the potential, UC security remains largely unexplored. This work evaluates UC security against state-of-the-art oracle-guided (OG) and oracle-less (OL) attacks. Results show near-random success rates (approx 50%) for OG attacks whereas OL attacks display minimal structural leakage. Collectively, these findings confirm the feasibility of UCs for IP protection.

Joseph Sweeney, Deepali Garg, Lawrence Pileggi

The outsourced manufacturing of integrated circuits has increased the risk of intellectual property theft. In response, logic locking techniques have been developed for protecting designs by adding programmable elements to the circuit. These techniques differ significantly in both overhead and resistance to various attacks, leaving designers unable to discern their efficacy. To overcome this critical impediment for the adoption of logic locking, we propose two metrics, key corruption and minimum corruption, that capture the goals of locking under different attack scenarios. We develop a flow for approximating these metrics on generic locked circuits and evaluate several locking techniques.

Wei Song, Xuezixiang Li, Sadia Afroz et al.

Modern commercial antivirus systems increasingly rely on machine learning to keep up with the rampant inflation of new malware. However, it is well-known that machine learning models are vulnerable to adversarial examples (AEs). Previous works have shown that ML malware classifiers are fragile to the white-box adversarial attacks. However, ML models used in commercial antivirus products are usually not available to attackers and only return hard classification labels. Therefore, it is more practical to evaluate the robustness of ML models and real-world AVs in a pure black-box manner. We propose a black-box Reinforcement Learning (RL) based framework to generate AEs for PE malware classifiers and AV engines. It regards the adversarial attack problem as a multi-armed bandit problem, which finds an optimal balance between exploiting the successful patterns and exploring more varieties. Compared to other frameworks, our improvements lie in three points. 1) Limiting the exploration space by modeling the generation process as a stateless process to avoid combination explosions. 2) Due to the critical role of payload in AE generation, we design to reuse the successful payload in modeling. 3) Minimizing the changes on AE samples to correctly assign the rewards in RL learning. It also helps identify the root cause of evasions. As a result, our framework has much higher black-box evasion rates than other off-the-shelf frameworks. Results show it has over 74\%--97\% evasion rate for two state-of-the-art ML detectors and over 32\%--48\% evasion rate for commercial AVs in a pure black-box setting. We also demonstrate that the transferability of adversarial attacks among ML-based classifiers is higher than the attack transferability between purely ML-based and commercial AVs.