Decoding the Machine: Two Decades of Hardware Reverse Engineering

Author: Denis Avetisyan

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This review surveys the evolution of techniques used to analyze and extract designs from integrated circuits, from silicon analysis to netlist recovery.

A comprehensive survey identifies key challenges and opportunities for advancing the rigor, reproducibility, and scalability of hardware reverse engineering research.

Despite the increasing reliance on hardware as a root of trust, research into its security assessment remains fragmented and difficult to synthesize. This paper, ‘SoK: From Silicon to Netlist and Beyond $-$ Two Decades of Hardware Reverse Engineering Research’, presents a systematization of knowledge based on an analysis of 187 peer-reviewed publications in the field of Hardware Reverse Engineering (HRE). Our findings reveal significant challenges to reproducibility-with only 4% of analyzed artifacts successfully replicated-and highlight a need for standardized benchmarks and clearer legal guidelines. How can academia, industry, and government collaborate to establish a more rigorous and scalable HRE research discipline capable of proactively addressing emerging hardware security threats?

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Unveiling the Machine: The Rising Tide of Hardware Vulnerabilities

The escalating complexity of modern hardware, from smartphones to critical infrastructure, has inadvertently broadened the attack surface and introduced systemic vulnerabilities throughout the global supply chain. This isn’t merely a matter of software flaws; increasingly, malicious actors are targeting the hardware itself – embedding backdoors, harvesting data, or disrupting functionality at a foundational level. Traditional security measures, largely focused on software and network defenses, are proving inadequate against these deeply embedded threats. Consequently, advanced analytical techniques are now essential for scrutinizing hardware components, identifying subtle manipulations, and verifying the integrity of designs. This requires moving beyond basic testing to encompass detailed analysis of materials, circuitry, and firmware, demanding both specialized equipment and a highly skilled workforce capable of uncovering hidden vulnerabilities before they can be exploited.

Conventional hardware security measures, such as relying on trusted foundries or basic supply chain checks, are increasingly vulnerable to determined attackers. Modern attacks often bypass these perimeter defenses by embedding malicious logic within the hardware itself – at the gate level or even within the design of individual components. These deeply-hidden threats are exceptionally difficult to detect with standard security scans, which primarily focus on software and known vulnerabilities. This necessitates a shift in security thinking, recognizing that hardware is no longer a purely passive element, but a potential attack surface requiring rigorous analysis. Sophisticated adversaries are capable of introducing subtle modifications during the manufacturing process or exploiting design weaknesses to create “hardware trojans” that can compromise systems for years, making traditional approaches demonstrably insufficient against such persistent and stealthy threats.

The escalating complexity of modern hardware necessitates a shift in security paradigms, moving beyond simply verifying intended function to comprehensively understanding how a device truly operates. This deeper level of analysis is vital because malicious actors increasingly embed sophisticated logic within hardware, circumventing traditional software-based defenses. Identifying such threats demands reverse engineering techniques that dissect a device’s functionality beyond its documented specifications – uncovering hidden features, backdoors, or unintended behaviors. This proactive approach allows security researchers to model the complete operational landscape of a chip or device, enabling the detection of vulnerabilities and the development of effective mitigation strategies before they can be exploited in real-world applications, and ultimately bolstering the resilience of the entire hardware ecosystem.

Effective hardware reverse engineering transcends simple disassembly; it requires a layered, systematic methodology to fully understand a device’s functionality and potential vulnerabilities. Analysis must progress from high-level behavioral observation – noting what the hardware does – down through the gate-level netlist and ultimately to the physical layout, and back again. This multi-faceted approach allows researchers to correlate observed behavior with underlying design implementations, exposing hidden functionalities or malicious modifications. Examining designs at multiple levels of abstraction – behavioral, register-transfer level (RTL), and physical – enables the discovery of subtle flaws that might remain concealed when focusing on a single perspective. Such a comprehensive methodology is increasingly vital, as modern hardware incorporates greater complexity and the potential for deeply embedded, sophisticated attacks.

Deconstructing the Machine: A Systematic Review of Methods

This systematic review aggregates and analyzes 187 published papers detailing methods for hardware reverse engineering. The scope of analysis extends across multiple abstraction levels, from physical IC analysis to behavioral descriptions. Included research focuses on techniques applicable to integrated circuits, field-programmable gate arrays, and application-specific integrated circuits. The consolidation provides a comprehensive overview of existing methodologies, facilitating identification of current research trends and potential areas for future investigation within the field of hardware security and analysis.

Physical analysis within hardware reverse engineering utilizes both destructive and non-destructive techniques. IC_Delayering involves the physical removal of individual layers of an integrated circuit to reveal underlying interconnects and transistor layouts, typically achieved through chemical etching or mechanical polishing. Complementary to this, IC_ImageSegmentation employs microscopy and image processing algorithms to analyze die photographs, identifying components, traces, and other features without altering the device. For Field Programmable Gate Arrays (FPGAs), FPGA_BitstreamAnalysis focuses on extracting and interpreting the configuration data stored within the device, providing insights into the implemented logic and connections without requiring physical access to the silicon.

Netlist-level analysis employs a suite of techniques to deconstruct and understand the functional behavior of integrated circuits. Netlist Extraction involves reconstructing the circuit’s netlist – a description of its components and interconnections – from various sources, including layout data or reverse-engineered binaries. Once obtained, Netlist Partitioning divides the complex netlist into smaller, more manageable sub-circuits to facilitate analysis and isolate specific functionalities. Finally, Netlist Reverse Engineering applies algorithmic and manual inspection techniques to the partitioned netlist to deduce the circuit’s original design intent, identify key logic blocks, and ultimately, determine the overall system functionality.

Finite State Machine (FSM) Extraction techniques, as included in this review, address the identification of sequential logic within hardware designs. These methods typically involve analyzing the netlist or gate-level representation of a circuit to determine the states, transitions, and inputs/outputs of constituent state machines. Approaches range from graph-based algorithms that traverse the circuit to identify loops and registers indicative of state elements, to more advanced techniques utilizing Boolean simplification and equivalence checking to abstract complex logic into a state machine representation. The extracted FSMs enable a higher-level understanding of the circuit’s behavior, facilitating vulnerability analysis, intellectual property protection, and functional verification of complex digital systems. This review encompasses both automated and manual FSM extraction methodologies, noting their respective strengths and limitations regarding scalability and accuracy.

The Illusion of Validation: Reproducibility and Rigor

Reproducibility is fundamental to the validity of Human and Organizational Performance (HRE) research findings, necessitating comprehensive documentation of methods, data, and analysis procedures. This allows independent verification of results and builds confidence in the conclusions drawn. Achieving reproducibility requires not only detailed records but also accessible resources, including data sets, code, and experimental setups, enabling other researchers to replicate the study. Without these elements, findings remain difficult to validate and may lack the necessary rigor for practical application or further research development.

Robust benchmarking is a critical component of validating Human and Robot Evaluation (HRE) findings by establishing a baseline for comparison against established, well-understood designs. This process involves rigorously testing the evaluation methodology on systems with known performance characteristics to determine if the observed results reflect genuine differences or are simply artifacts of the evaluation setup itself. Without benchmarking, it is difficult to ascertain whether reported improvements are meaningful or attributable to variations in the evaluation procedure. Benchmarking allows researchers to isolate the impact of the evaluated system, ensuring that reported results are not merely a consequence of the analysis process and are, therefore, more likely to generalize to other scenarios.

A review of 187 papers in the field revealed a limited practice of research artifact sharing. Only 31 papers, representing approximately 17% of the total sample, explicitly state that the materials necessary to reproduce their reported results – including code, data, and experimental setups – were made publicly available. This lack of publicly accessible artifacts presents a significant barrier to independent verification and hinders the broader advancement of the field, limiting the ability of other researchers to build upon and validate existing work.

Analysis of eighteen papers possessing publicly available artifacts and testable results revealed a low rate of reproducibility, with only 4% demonstrating consistent outcomes when re-executed. This finding indicates a significant challenge in verifying the claims made within the Human and Robot Evaluation (HRE) field. The limited number of reproducible results suggests deficiencies in reporting experimental setups, code availability, or environmental consistency, hindering independent validation of reported performance metrics and potentially impacting the reliability of research conclusions. This low rate was calculated based on rigorous attempts to replicate reported results using provided materials and documented procedures.

Beyond the Code: Open Hardware Security and Collective Resilience

The progression of hardware reverse engineering (HRE) techniques is inextricably linked to the adoption of Open_Science principles. Researchers increasingly recognize that sharing datasets, methodologies, and even failed experiments accelerates discovery and avoids redundant efforts. This collaborative environment fosters a more robust and efficient HRE landscape, allowing experts to build upon each other’s work and collectively address complex vulnerabilities. By openly disseminating findings, the community can scrutinize approaches, validate results, and establish standardized benchmarks, ultimately leading to more reliable and trustworthy security analyses. The open exchange of knowledge not only speeds up innovation but also democratizes access to advanced HRE capabilities, empowering a wider range of researchers and bolstering collective defense against hardware-based attacks.

A comprehensive systematic review of hardware reverse engineering (HRE) techniques has yielded a crucial foundation for standardization within the field. Prior to this work, HRE practices varied significantly, hindering reproducibility and comparative analysis of different methodologies. The review identified, categorized, and critically assessed a broad spectrum of HRE approaches, revealing commonalities and discrepancies in tools, techniques, and evaluation metrics. This synthesis allows the HRE community to move towards establishing consistent benchmarks and standardized methodologies, fostering greater reliability and comparability of research findings. Consequently, researchers can build upon existing knowledge with increased confidence, accelerating the development of more robust and effective hardware security assessments and ultimately bolstering defenses against emerging threats.

Enhanced Hardware Reverse Engineering (HRE) capabilities are increasingly vital for bolstering supply chain security by shifting the paradigm from reactive vulnerability discovery to proactive identification. Through detailed analysis of integrated circuits and embedded systems, potential weaknesses – ranging from malicious code injection to counterfeit components – can be detected before products are deployed, minimizing risk to critical infrastructure and end-users. This preventative approach allows for the implementation of security measures at the design and manufacturing stages, effectively mitigating threats that would otherwise remain hidden until exploited. By systematically deconstructing and analyzing hardware, researchers and security professionals can build a comprehensive understanding of potential attack vectors and develop countermeasures, ultimately creating more resilient and trustworthy supply chains.

The culmination of these hardware reverse engineering advancements directly bolsters the defenses of critical infrastructure against increasingly complex threats. By proactively identifying and mitigating vulnerabilities within hardware components, systems become demonstrably more resilient to tampering, exploitation, and supply chain attacks. This heightened security posture extends beyond individual devices, safeguarding the interconnected networks that underpin essential services like power grids, financial institutions, and communication networks. The ability to thoroughly analyze hardware provides a crucial layer of defense against sophisticated adversaries capable of embedding malicious functionality at the foundational level, thereby minimizing the potential for widespread disruption and ensuring continued operational integrity.

The survey meticulously details how hardware reverse engineering progresses from physical analysis – silicon delayering and imaging – to abstract netlist extraction. This process inherently demands a willingness to dismantle established structures to understand their inner workings. As Henri Poincaré observed, “Mathematics is the art of giving reasons.” The same principle applies to HRE; each layer peeled back, each gate analyzed, is a reasoned step toward uncovering the system’s fundamental logic. The article highlights reproducibility as a core challenge, and Poincaré’s emphasis on rigorous reasoning underscores why transparent methodologies – akin to providing a clear mathematical proof – are paramount to building trust and advancing the field. The pursuit isn’t merely about what a device does, but why it does it, demanding a methodical deconstruction of its components.

What’s Next?

The discipline, having spent two decades meticulously dismantling silicon, now faces a curious impasse. Netlist extraction, once the Everest of hardware reverse engineering, is becoming… almost routine. But to mistake increasing automation for understanding is a familiar error. The true challenges aren’t in getting the netlist, but in knowing what questions to ask of it. A complete schematic offers no inherent meaning; it’s merely a map of someone else’s intentions, a frozen moment of design. The next phase demands tools that don’t simply decode structure, but infer purpose – that is, systems capable of hypothesizing function from form.

Reproducibility, predictably, remains the sticking point. A field built on tearing things apart struggles to reassemble the shards into a consistently verifiable whole. The emphasis must shift from demonstrating ‘it can be done’ to establishing ‘it can be repeated’, ideally by someone lacking intimate knowledge of the original design. Standardized benchmarks, shared datasets, and – crucially – open-source tooling aren’t merely desirable; they are the only path towards building a genuinely rigorous science.

Ultimately, hardware reverse engineering isn’t about uncovering secrets, it’s about stress-testing assumptions. Every successful deconstruction reveals the limitations of the original design, and, more importantly, the limitations of the tools used to analyze it. The field’s future isn’t in perfecting the art of disassembly, but in embracing the inevitable failures – for it is in those moments of breakage that the most interesting truths are revealed.

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Original article: https://arxiv.org/pdf/2603.17883.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-03-19 19:19