Graph Neural Networks for Hardware Vulnerability Analysis -- Can you Trust your GNN?

The participation of third-party entities in the globalized semiconductor supply chain introduces potential security vulnerabilities, such as intellectual property piracy and hardware Trojan (HT) insertion. Graph neural networks (GNNs) have been employed to address various hardware security threats, owing to their superior performance on graph-structured data, such as circuits. However, GNNs are also susceptible to attacks. This work examines the use of GNNs for detecting hardware threats like HTs and their vulnerability to attacks. We present BadGNN, a backdoor attack on GNNs that can hide HTs and evade detection with a 100% success rate through minor circuit perturbations. Our findings highlight the need for further investigation into the security and robustness of GNNs before they can be safely used in security-critical applications.Comment: Will be presented at 2023 IEEE VLSI Test Symposium (VTS

FPGA-Patch: Mitigating Remote Side-Channel Attacks on FPGAs using Dynamic Patch Generation

We propose FPGA-Patch, the first-of-its-kind defense that leverages automated program repair concepts to thwart power side-channel attacks on cloud FPGAs. FPGA-Patch generates isofunctional variants of the target hardware by injecting faults and finding transformations that eliminate failure. The obtained variants display different hardware characteristics, ensuring a maximal diversity in power traces once dynamically swapped at run-time. Yet, FPGA-Patch forces the variants to have enough similarity, enabling bitstream compression and minimizing dynamic exchange costs. Considering AES running on AMD/Xilinx FPGA, FPGA-Patch increases the attacker's effort by three orders of magnitude, while preserving the performance of AES and a minimal area overhead of 14.2%.Comment: 6 page

IsoLock: Thwarting Link-Prediction Attacks on Routing Obfuscation by Graph Isomorphism

Logic locking/obfuscation secures hardware designs from untrusted entities throughout the globalized semiconductor supply chain. Machine learning (ML) recently challenged the security of locking: such attacks successfully captured the locking-induced, structural design modifications to decipher obfuscated gates. Although routing obfuscation eliminates this threat, more recent attacks exposed new vulnerabilities, like link formation, breaking such schemes. Thus, there is still a need for advanced, truly learning-resilient locking solutions. Here we propose IsoLock, a provably-secure locking scheme that utilizes isomorphic structures which ML models and other structural methods cannot discriminate. Unlike prior work, IsoLock’s security promise neither relies on re-synthesis nor on dedicated sub-circuits. Instead, IsoLock introduces isomorphic key-gate structures within the design via systematic routing obfuscation. We theoretically prove the security of IsoLock against modeling attacks. Further, we lock ISCAS-85 and ITC-99 benchmarks and launch state-of-the-art ML attacks, SCOPE and MuxLink, as well as the Redundancy and SAAM attacks, which only decipher an average of 0–6% of the key, well confirming the resilience of IsoLock. All in all, IsoLock is proposed to break the cycle of “cat and mouse” in locking and attack studies, through a provably-secure locking approach against structural ML attacks

ScanSAT: Unlocking Obfuscated Scan Chains

While financially advantageous, outsourcing key steps such as testing to potentially untrusted Outsourced Semiconductor Assembly and Test (OSAT) companies may pose a risk of compromising on-chip assets. Obfuscation of scan chains is a technique that hides the actual scan data from the untrusted testers; logic inserted between the scan cells, driven by a secret key, hide the transformation functions between the scan- in stimulus (scan-out response) and the delivered scan pattern (captured response). In this paper, we propose ScanSAT: an attack that transforms a scan obfuscated circuit to its logic- locked version and applies a variant of the Boolean satisfiability (SAT) based attack, thereby extracting the secret key. Our empirical results demonstrate that ScanSAT can easily break naive scan obfuscation techniques using only three or fewer attack iterations even for large key sizes and in the presence of scan compression