arXiv subjects

Sahan Sanjaya

Publications and source records attributed to Sahan Sanjaya.

7 recordsLinked to original sources

arXiv2026

PowerFuzz: Power-Based Black-Box Firmware Fuzzing

Fuzzing is widely used for software and hardware verification, offering an effective alternative to random testing. While gray-box fuzzers benefit from full visibility into the system under test and can leverage execution feedback such as branch coverage, these approaches are not applicable when verifying systems whose firmware or binaries are not publicly available. In such scenarios, obtaining coverage information for guiding the fuzzer becomes infeasible. In this paper, we introduce PowerFuzz, a statistical black-box fuzzing framework that leverages power side-channel measurements as a substitute for binary instrumentation, requiring no internal visibility into the target firmware. A central challenge in black-box firmware fuzzing is determining the executed branches during test execution. To address this challenge, we use power traces to identify branches utilizing a sliding window followed by a growing window full-trace correlation method. This approach also enables the construction of a high-level control-flow graph of the black-box firmware, which we utilize to drive the fuzzer to unexplored execution paths. Extensive evaluation using three embedded hardware platforms and ten firmware benchmarks demonstrates that PowerFuzz can provide branch coverage comparable (within 13.5%) to gray-box fuzzers while significantly outperforming (up to 22%) state-of-the-art black-box fuzzers.

cs.CR↗

arXiv2026

Defending Quantum Classifiers against Adversarial Perturbations through Quantum Autoencoders

Machine learning models can learn from data samples to carry out various tasks efficiently. When data samples are adversarially manipulated, such as by insertion of carefully crafted noise, it can cause the model to make mistakes. Quantum machine learning models are also vulnerable to such adversarial attacks, especially in image classification using variational quantum classifiers. While there are promising defenses against these adversarial perturbations, such as training with adversarial samples, they face practical limitations. For example, they are not applicable in scenarios where training with adversarial samples is either not possible or can overfit the models on one type of attack. In this paper, we propose an adversarial training-free defense framework that utilizes a quantum autoencoder to purify the adversarial samples through reconstruction. Moreover, our defense framework provides a confidence metric to identify potentially adversarial samples that cannot be purified the quantum autoencoder. Extensive evaluation demonstrates that our defense framework can significantly outperform state-of-the-art in prediction accuracy (up to 68%) under adversarial attacks.

quant-ph↗

arXiv2026

Controlled Steering-Based State Preparation for Adversarial-Robust Quantum Machine Learning

Quantum machine learning (QML) provides a promising framework for leveraging quantum-mechanical effects in learning tasks. However, its vulnerability to adversarial perturbations remains a major challenge for practical deployment. In QML systems, small perturbations applied to classical inputs can propagate through the quantum encoding stage and distort the resulting quantum state, thereby degrading model performance. In this work, we propose a defense mechanism that replaces the conventional quantum encoding stage of a QML model with passive steering-based controlled state preparation, which guides the encoded state toward a controlled intermediate state. By tuning the steering strength and the number of steering iterations, the proposed method suppresses the influence of adversarial perturbations while maintaining high clean accuracy and improving adversarial accuracy. Experimental results demonstrate that the passive steering-based defense consistently improves adversarial accuracy across different QML models and datasets under gradient-based adversarial attacks, achieving adversarial accuracy improvements of up to 40.19%.

quant-ph↗

arXiv2026

Sleep Reveals the Nonce: Breaking ECDSA using Sleep-Based Power Side-Channel Vulnerability

Security of Elliptic Curve Digital Signature Algorithm (ECDSA) depends on the secrecy of the per-signature nonce. Even partial nonce leakage can expose the long-term private key through lattice-based cryptanalysis. In this paper, we introduce a previously unexplored power side-channel vulnerability that exploits sleep-induced power spikes to extract ECDSA nonces. Unlike conventional power-based side-channel attacks, this vulnerability leverages power fluctuations generated during processor context switches invoked by sleep functions. These fluctuations correlate with nonce-dependent operations in scalar multiplication, enabling nonce recovery even under constant-time and masked implementations. We evaluate the attack across multiple cryptographic libraries, RustCrypto, BearSSL, and GoCrypto, and processor architectures, including ARM and RISC-V. Our experiments show that subtle variations in the power envelope during sleep-induced context switches provide sufficient leakage for practical ECDSA nonce extraction, recovering 20 bits of the nonce. These results establish sleep-induced power spikes as a practical cross-platform side-channel threat and highlight the need to reconsider design choices in cryptographic systems.

cs.CR↗

arXiv2025

Application-Specific Power Side-Channel Attacks and Countermeasures: A Survey

Side-channel attacks try to extract secret information from a system by analyzing different side-channel signatures, such as power consumption, electromagnetic emanation, thermal dissipation, acoustics, time, etc. Power-based side-channel attack is one of the most prominent side-channel attacks in cybersecurity, which rely on data-dependent power variations in a system to extract sensitive information. While there are related surveys, they primarily focus on power side-channel attacks on cryptographic implementations. In recent years, power-side channel attacks have been explored in diverse application domains, including key extraction from cryptographic implementations, reverse engineering of machine learning models, user behavior data exploitation, and instruction-level disassembly. In this paper, we provide a comprehensive survey of power side-channel attacks and their countermeasures in different application domains. Specifically, this survey aims to classify recent power side-channel attacks and provide a comprehensive comparison based on application-specific considerations.

cs.CR↗

arXiv2025

SleepWalk: Exploiting Context Switching and Residual Power for Physical Side-Channel Attacks

Context switching is utilized by operating systems to change the execution context between application programs. It involves saving and restoring the states of multiple registers and performing a pipeline flush to remove any pre-fetched instructions, leading to a higher instantaneous power consumption compared to typical program execution. In this paper, we introduce a physical power side-channel leakage source that exploits the power spike observed during a context switch, triggered by the inbuilt sleep function of the system kernel. We observed that this power spike directly correlates with both the power consumption during context switching and the residual power consumption of the previously executed program. Notably, the persistence of residual power signatures from previous workloads extends the scope of this side-channel beyond extracting the data in registers during the context switch. Unlike traditional approaches that require analyzing full power traces, applying complex preprocessing, or relying on external synchronization triggers, this novel technique leverages only the amplitude of a single power spike, significantly simplifying the attack. We developed a power model to illustrate the feasibility of mounting end-to-end side-channel attacks using the sleep-induced power spikes. Experimental evaluation demonstrates that our framework can successfully perform cryptographic key recovery for both AES and SIKE implementations on Broadcom BCM2711.

cs.CR↗

arXiv2024

Information Leakage through Physical Layer Supply Voltage Coupling Vulnerability

Side-channel attacks exploit variations in non-functional behaviors to expose sensitive information across security boundaries. Existing methods leverage side-channels based on power consumption, electromagnetic radiation, silicon substrate coupling, and channels created by malicious implants. Power-based side-channel attacks are widely known for extracting information from data processed within a device while assuming that an attacker has physical access or the ability to modify the device. In this paper, we introduce a novel side-channel vulnerability that leaks data-dependent power variations through physical layer supply voltage coupling (PSVC). Unlike traditional power side-channel attacks, the proposed vulnerability allows an adversary to mount an attack and extract information without modifying the device. We assess the effectiveness of PSVC vulnerability through three case studies, demonstrating several end-to-end attacks on general-purpose microcontrollers with varying adversary capabilities. These case studies provide evidence for the existence of PSVC vulnerability, its applicability for on-chip as well as on-board side-channel attacks, and how it can eliminate the need for physical access to the target device, making it applicable to any off-the-shelf hardware. Our experiments also reveal that designing devices to operate at the lowest operational voltage significantly reduces the risk of PSVC side-channel vulnerability.

cs.CR↗