The modern supply chain ecosystem exposes hardware intellectual property (IP) blocks to diverse confidentiality attacks aimed at reverse engineering (RE), piracy, or the extraction of design secrets. An emerging and potent design solution for IP protection against these attacks, particularly against RE, is the fine-grained redaction of security-critical logic and replacing the redacted logic with lookup tables (LUTs). The LUTs are then programmed in-field, similar to FPGAs, using protected bitstreams, thereby preventing untrusted foundries or test/assembly facilities from mounting RE attacks. The LUT-based redaction paradigm incurs a substantial hardware cost, with area overhead ranging from 70x to 100x and delay overhead from 2x to 5x, while also often necessitating significant alterations to the commercial tool flow for design, verification, and testing. In this work, we propose PROM, a robust fine-grain redaction technique inspired by structured ASIC, that aims to address the limitations of LUT-based redaction with novel overhead optimizations. The redacted security-critical logic is implemented using a library of custom-design PROM cells that are optimized to minimize overheads compared to state-of-the-art redaction techniques while providing strong protection against various RE attacks. We evaluated the proposed redaction technique across a range of open-source benchmarks, achieving robust security with average overheads of 1.42x in area and 1.09x in delay, demonstrating its efficiency and practicality.

Hardware Trojans are malicious modifications in digital designs that can be inserted by untrusted supply chain entities. Hardware Trojans can give rise to diverse attack vectors such as information leakage (e.g. MOLES Trojan) and denial-of-service (rarely triggered bit flip). Such an attack in critical systems (e.g. healthcare and aviation) can endanger human lives and lead to catastrophic financial loss. Several techniques have been developed to detect such malicious modifications in digital designs, particularly for designs sourced from third-party intellectual property (IP) vendors. However, most techniques have scalability concerns (due to unsound assumptions during evaluation) and lead to large number of false positive detections (false alerts). Our framework (SALTY) mitigates these concerns through the use of a novel Graph Neural Network architecture (using Jumping-Knowledge mechanism) for generating initial predictions and an Explainable Artificial Intelligence (XAI) approach for fine tuning the outcomes (post-processing). Experiments show > 98% True Positive Rate (TPR) and True Negative Rate (TNR), significantly outperforming state-of-the-art techniques across a large set of standard benchmarks.

Due to the globalization of the semiconductor supply chain and the adoption of the zero trust model, hardware Trojan attacks pose significant security threats introduced by untrusted entities. Hardware Trojans relate to malicious modification of a design before fabrication, resulting in unintended functional or side-channel behavior, such as causing a Denial of Service (DoS) attack or leaking sensitive information. Detecting hardware Trojans in fabricated silicon chips is extremely challenging primarily due to the vast possible attack space. Directed test generation towards activation (i.e., trigger) and/or manifestation (e.g., observation of payload) of the viable Trojans with conventional post-manufacturing Automatic Test Pattern Generation (ATPG) process is known to be practically infeasible. Hence, researchers have explored statistical test techniques for detecting arbitrary instances of Trojan attacks through post-silicon functional testing. However, existing statistical test solutions lack in effective trigger and payload coverage and suffer from scalability issues. In this paper, we propose LATENT, a scalable payload-aware statistical test pattern generation technique for high-coverage Trojan detection leveraging the power of existing functional ATPG solutions. Our experimental study on large population of randomly inserted Trojans in a suite of open-source designs shows promising results in both trigger and Trojan coverage.

System-on-chip (SoC) developers increasingly rely on pre-verified hardware intellectual property (IP) blocks often acquired from untrusted third-party vendors. These IPs might contain hidden malicious functionalities or hardware Trojans that may compromise the security of the fabricated SoCs. Lack of golden or reference models and vast possible Trojan attack space form some of the major barriers in detecting hardware Trojans in these third-party IP (3PIP) blocks. Recently, supervised machine learning (ML) techniques have shown promising capability in identifying nets of potential Trojans in 3PIPs without the need for golden models. However, they bring several major challenges. First, they do not guide us to an optimal choice of features that reliably covers diverse classes of Trojans. Second, they require multiple Trojan-free/trusted designs to insert known Trojans and generate a trained model. Even if a set of trusted designs are available for training, the suspect IP can have an inherently very different structure from the set of trusted designs, which may negatively impact the verification outcome. Third, these techniques only identify a set of suspect Trojan nets that require manual intervention to understand the potential threat. In this article, we present VIPR, a systematic machine learning (ML)-based trust verification solution for 3PIPs that eliminates the need for trusted designs for training. We present a comprehensive framework, associated algorithms, and a tool flow for obtaining an optimal set of features, training a targeted machine learning model, detecting suspect nets, and identifying Trojan circuitry from the suspect nets. We evaluate the framework on several Trust-Hub Trojan benchmarks and provide a comparative analysis of detection performance across different trained models, selection of features, and post-processing techniques. We demonstrate promising Trojan detection accuracy for VIPR with up to 92.85% reduction in false positives by the proposed post-processing algorithm.

Field Programmable Gate Arrays (FPGAs) provide a flexible compute platform for quick prototyping or hardware acceleration in diverse application domains. However, similar to the global semiconductor life-cycle in the modern supply chain, FPGA-based product development includes processes and interactions with potentially untrusted parties outside the traditional scrutiny of a completely in-house development cycle. An untrusted party/software can maliciously alter hardware intellectual property (IP) blocks mapped to an FPGA device during various stages of the FPGA life-cycle. Such malicious alterations, also known as hardware Trojans, have garnered significant research into their detection and prevention in the context of application-specific integrated circuit (ASIC) design flow. However, Trojan attacks in FPGAs have not enjoyed this same attention. Designers often rely on mapping ASIC-specific solutions and benchmarks to the FPGA domain, leaving much of the FPGA-specific Trojan space uncovered. The distinctive business model and architectural configurations of FPGAs also present unique Trojan attack opportunities for adversaries. To this end, we introduce a framework to automatically explore the hardware Trojan attack space in FPGA netlists, which can insert different FPGA-specific Trojans in a netlist enabling rapid exploration of potential Trojan attacks in an FPGA design: soft-template, monolithic and distributed dark silicon. The dark silicon Trojans use the under-utilized input space in FPGA primitives and other optimizations to realize Trojans with effectively zero area, delay, and power footprint. We generate over 1300 Trojan-inserted benchmarks using the introduced FPGA Trojan classes, and compare their impact on utilization, delay, and power and evaluate their stealthiness against Trojan detection.

The need for metrics for quantifying trustworthiness of electronic hardware against diverse threats on its integrity and confidentiality has greatly increased due to the increasing reliance on the untrusted global supply chain. Hardware Trojans, or malicious design alterations, has emerged as a major threat to hardware integrity and garnered significant interest in recent times due to its catastrophic potential. Effective protection against hardware Trojan attacks, however, requires well-defined metrics, which fall into two broad classes: 1) measure of a design’s vulnerability to Trojan insertion and 2) measure of effectiveness of a defense solution—a design or verification/test approach—against Trojan attacks, which is often represented as Trojan coverage. The former is important to assess the level of difficulty an adversary would encounter to insert a hard-to-detect Trojan. Previous efforts have assigned vulnerability as a function of the number of suspect nets identified in a design or are required to enumerate a subspace of Trojans. However, these values simplify the problem of hardware Trojan insertion and leave much of the subsequent analysis regarding the viable Trojan space unmeasured. In this article, we address this critical gap by presenting Trojan vulnerability factor (TVF), a metric for quantifying a design’s vulnerability to Trojan insertion via maximal clique analysis. With such analysis, we can frame the threat to more accurately represent the Trojan behavior and quantify the level of effort required for a designer to cover these Trojan triggers without needing to directly consider Trojan trigger sizes. We also introduce soft thresholding to account for suspect nets, which lie at the boundary of a design. Experimental results highlight the benefits of the proposed approach over existing Trojan vulnerability metrics. Finally, we demonstrate scalability to large designs through partitioning and clique sampling-based estimations.