
Analyzing Hardware Security: The Tools Powering RESCALE’s Dynamic Hardware…
Detecting and mitigating hardware vulnerabilities requires specialized tools capable of extracting, processing, and analyzing physical security risks within cryptographic and FPGA-based systems. The Dynamic Hardware Analyzer (DHA) in RESCALE is built around a suite of tools designed to identify side-channel leakage, detect exploitable vulnerabilities, and assess security risks in hardware implementations. These tools focus on trace collection, preprocessing, and attack simulations, ensuring that supply chain components undergo rigorous security validation before deployment.
Extracting Side-Channel Data with the Trace Collection Tools
To detect hardware vulnerabilities, the first step is capturing physical traces from the target system. The Trace Collection Layer of the Dynamic Hardware Analyzer includes several specialized tools that record power consumption, electromagnetic emissions, and timing variations during cryptographic operations. These traces serve as the foundation for further analysis.
One of the primary tools in this layer is FlexLECO, an FPGA-based leakage assessment framework that performs large-scale trace collection and real-time monitoring. Unlike conventional power measurement tools, FlexLECO integrates directly with FPGA implementations, enabling high-speed acquisition of side-channel signals while minimizing measurement interference. Additionally, the system leverages ChipWhisperer, a well-known open-source platform designed for side-channel attack research. ChipWhisperer captures power consumption fluctuations during cryptographic operations, allowing security analysts to detect unintended leakage paths.
Another key tool in the trace collection phase is the use of on-chip FPGA sensors, which monitor transient power variations and signal fluctuations at an extremely fine-grained level. These sensors allow for low-noise trace acquisition, making it possible to detect even minimal leakage that could expose cryptographic keys or other sensitive data.
Processing and Filtering Traces with the Preprocessing Software Library
Once traces are collected, they must undergo preprocessing to filter out noise and highlight relevant leakage information. The Trace Preprocessing Software Library plays a crucial role in this phase by applying signal filtering, normalization, and correlation analysis techniques. This ensures that collected traces are clean, structured, and ready for in-depth security assessment.
This software suite applies statistical tests such as Test Vector Leakage Assessment (TVLA) to determine whether the captured traces exhibit unintended data-dependent variations. TVLA is the primary statistical method used in RESCALE for evaluating cryptographic leakage. It helps identify leakage points where cryptographic implementations may be revealing sensitive information. These points appear as deviations in signal quantification, which can be statistically analyzed to determine the likelihood of an exploitable vulnerability.

The AES Leakage Plot above illustrates the results of a TVLA leakage test. The graph visualizes quantification over time samples, with several identified peaks that indicate points of significant leakage. These leakage points suggest areas where cryptographic keys or sensitive operations might be unintentionally exposed, requiring further investigation to assess their exploitability. The presence of strong deviations in power consumption signals at specific time samples confirms that certain cryptographic operations may be leaking data in a manner detectable through side-channel analysis.
Detecting Security Weaknesses with the SCA Script Software Library
After preprocessing, the SCA Script Software Library is used to conduct security evaluations and attack simulations. This toolset applies correlation power analysis (CPA), machine-learning-based template attacks, and non-specific (NS) assessments to determine whether an attacker could extract sensitive information from the collected traces.
The CPA method is particularly effective in identifying linear relationships between power consumption and cryptographic key bits, making it a widely used attack technique against AES, RSA, and other encryption standards. In contrast, machine-learning-based template attacks leverage pre-trained statistical models to recognize leakage patterns, even in the presence of noise or countermeasures.
The NS assessment approach is used to detect leakage without assuming a specific attack model. This technique helps analysts identify general leakage trends before applying targeted cryptographic attack models like CPA. NS assessment is particularly useful in discovering unexpected security weaknesses that might not align with traditional attack strategies, ensuring a broader evaluation of side-channel resilience.

The SCA Trace Analyzer Flow diagram illustrates the relationship between preprocessing, penetration testing, and reporting within the Dynamic Hardware Analyzer. At the core of this system, the Trace Preprocessing Software Library processes collected traces, applying signal filtering and statistical assessments. The SCA Script Software Library then runs advanced side-channel attack models, including SCA penetration testing assessments, which attempt to exploit detected vulnerabilities. The results are structured into NS reports and SCA pentest reports, feeding into broader supply chain security validation efforts.
Assessing Multi-Tenant FPGA Security Risks
One of the unique challenges addressed by the Dynamic Hardware Analyzer is the security of multi-tenant FPGA environments. When multiple users share the same FPGA fabric, one user may unintentionally or maliciously extract information from another user’s operations, leading to data leakage risks.
To analyze this threat, the DHA integrates tools that simulate cross-tenant leakage scenarios, measuring how shared hardware resources expose unintended data flows. These tests assess whether attackers can infer cryptographic computations performed by another tenant, helping to enforce stricter FPGA isolation policies.
Generating Security Reports for Supply Chain Validation
The final step in the hardware security analysis process is structuring the collected findings into a standardized security report. The results from FlexLECO, ChipWhisperer, and the SCA Script Software Library are aggregated into the Dynamic Supply Chain Component Guarantee (DSCG). This structured document provides a comprehensive assessment of detected vulnerabilities, categorizing them based on severity, exploitability, and recommended mitigations.
The CDX Validator processes these reports to ensure they adhere to standard security assessment frameworks. Once validated, the DSCG is integrated into the RESCALE Management Module, contributing to supply chain security assurance and risk mitigation strategies.
Conclusion
The Dynamic Hardware Analyzer in RESCALE is powered by a sophisticated suite of tools designed to capture, process, and analyze side-channel leakage data. FlexLECO and ChipWhisperer provide high-resolution trace collection, while on-chip FPGA sensors enable real-time monitoring of leakage effects. The Trace Preprocessing Software Library ensures high-quality signal processing, while the SCA Script Software Library applies advanced CPA, machine-learning, and non-specific analysis techniques to detect vulnerabilities.
By aggregating results into the DSCG, the module provides structured security assessments that contribute to supply chain risk management. As hardware security threats continue to evolve, automated security testing tools remain essential for ensuring that cryptographic and FPGA-based systems remain resistant to side-channel attacks and unintended leakage risks.