iThermTroj: Exploiting Intermittent Thermal Trojans in Multi-Processor System-on-Chips

• URL: http://arxiv.org/abs/2507.05576v1
• Date: Tue, 08 Jul 2025 01:24:28 GMT
• Title: iThermTroj: Exploiting Intermittent Thermal Trojans in Multi-Processor System-on-Chips
• Abstract summary: We introduce Intermittent Thermal Trojans (iThermTroj) that exploit the chips' thermal information in a random time-triggered manner.<n>Compared to existing methods, our approach improves the attack detection rate by 29.4%, 17.2%, and 14.3%.<n>Our method increases the full protection resolution to 0.8 degrees Celsius, meaning that any temperature manipulations exceeding $pm 0.8$ degrees will be detected with 100% accuracy.
• Score: 0.562479170374811
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Thermal Trojan attacks present a pressing concern for the security and reliability of System-on-Chips (SoCs), especially in mobile applications. The situation becomes more complicated when such attacks are more evasive and operate sporadically to stay hidden from detection mechanisms. In this paper, we introduce Intermittent Thermal Trojans (iThermTroj) that exploit the chips' thermal information in a random time-triggered manner. According to our experiments, iThermTroj attack can easily bypass available threshold-based thermal Trojan detection solutions. We investigate SoC vulnerabilities to variations of iThermTroj through an in-depth analysis of Trojan activation and duration scenarios. We also propose a set of tiny Machine Learning classifiers for run-time anomaly detection to protect SoCs against such intermittent thermal Trojan attacks. Compared to existing methods, our approach improves the attack detection rate by 29.4\%, 17.2\%, and 14.3\% in scenarios where iThermTroj manipulates up to 80\%, 60\%, and 40\% of SoC's thermal data, respectively. Additionally, our method increases the full protection resolution to 0.8 degrees Celsius, meaning that any temperature manipulations exceeding $\pm 0.8$ degrees will be detected with 100\% accuracy.

Related papers

• Chypothermia: Clock Freezing for Static Side-channel Attacks [14.726177440082715]
  Static side-channel attacks, which exploit halted-clock conditions, pose an increasing threat to chip security.<n>In this work, we demonstrate that exposing a chip to cryogenic temperatures interferes with on-chip mixed-signal components.<n>We develop Chypothermia, an attack that disables the target clock sensor, the clock generation circuit, and the voltage sensors, while preserving the secret data.
  arXiv  Detail & Related papers  (2026-09-10T12:12:12Z)
• PATCH-FFT: Unmasking Dormant Hardware Trojans with Patch-Based Frequency-Domain Transformers [0.5156484100374058]
  Hardware Trojans embedded by malicious entities in integrated circuits can covertly leak sensitive information through power side channels.<n>This paper introduces a patch-based Transformer architecture for detecting dormant hardware Trojans through frequency-domain analysis of power traces.
  arXiv  Detail & Related papers  (2026-07-26T02:38:38Z)
• Early Detection of Hardware Trojans Using Neural Controlled Differential Equations and Analysis of Power Traces [1.2234742322758416]
  Evolving Hardware Trojans pose a serious threat to modern digital systems by evading traditional detection.<n>We propose a novel approach for hardware Trojan detection and prediction using Neural Controlled Differential Equations (NCDEs) and analysis of power traces.
  arXiv  Detail & Related papers  (2026-07-26T02:27:59Z)
• Detecting Hardware Trojans in Microprocessors via Hardware Error Correction Code-based Modules [49.1574468325115]
  Hardware Trojans (HTs) enable attackers to execute unauthorized software or gain illicit access to privileged operations.<n>This manuscript introduces a hardware-based methodology for detecting runtime HT activations using Error Correction Codes (ECCs) on a RISC-V microprocessor.
  arXiv  Detail & Related papers  (2025-06-18T12:37:14Z)
• MATTER: Multi-stage Adaptive Thermal Trojan for Efficiency & Resilience degradation [1.1534313664323634]
  This research introduces a new attack method called the Multi-stage Adaptive Thermal Trojan for Efficiency and Resilience Degradation (MATTER)<n>MATTER takes advantage of weaknesses in Dynamic Thermal Management (DTM) systems by manipulating temperature sensor interfaces.<n>Our experiments show that this attack can degrade DTM performance by as much as 73%, highlighting serious vulnerabilities in modern mobile devices.
  arXiv  Detail & Related papers  (2024-11-29T19:39:48Z)
• An AI-Enabled Side Channel Power Analysis Based Hardware Trojan Detection Method for Securing the Integrated Circuits in Cyber-Physical Systems [7.333490062088133]
  One of the stealthiest threats is the insertion of a hardware trojan into an IC. Trojans can severely compromise system safety and security. This paper presents a non-invasive method for hardware trojan detection based on side-channel power analysis.
  arXiv  Detail & Related papers  (2024-11-19T18:39:20Z)
• Towards Practical Fabrication Stage Attacks Using Interrupt-Resilient Hardware Trojans [4.549209593575401]
  We introduce a new class of hardware trojans called interrupt-resilient trojans (IRTs) IRTs can successfully address the problem of non-deterministic triggering in CPUs. We show that our design allows for seamless integration during fabrication stage attacks.
  arXiv  Detail & Related papers  (2024-03-15T19:55:23Z)
• Attention-Enhancing Backdoor Attacks Against BERT-based Models [54.070555070629105]
  Investigating the strategies of backdoor attacks will help to understand the model's vulnerability. We propose a novel Trojan Attention Loss (TAL) which enhances the Trojan behavior by directly manipulating the attention patterns.
  arXiv  Detail & Related papers  (2023-10-23T01:24:56Z)
• Rethinking the Reverse-engineering of Trojan Triggers [13.588758147243828]
  Deep Neural Networks are vulnerable to Trojan (or backdoor) attacks. Existing reverse-engineering methods only consider input space constraints. We observe that both input-space and feature-space Trojans are associated with feature space hyperplanes.
  arXiv  Detail & Related papers  (2022-10-27T02:25:18Z)
• Game of Trojans: A Submodular Byzantine Approach [9.512062990461212]
  We provide an analytical characterization of adversarial capability and strategic interactions between the adversary and detection mechanism. We propose a Submodular Trojan algorithm to determine the minimal fraction of samples to inject a Trojan trigger. We show that the adversary wins the game with probability one, thus bypassing detection.
  arXiv  Detail & Related papers  (2022-07-13T03:12:26Z)
• Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free [126.15842954405929]
  Trojan attacks threaten deep neural networks (DNNs) by poisoning them to behave normally on most samples, yet to produce manipulated results for inputs attached with a trigger. We propose a novel Trojan network detection regime: first locating a "winning Trojan lottery ticket" which preserves nearly full Trojan information yet only chance-level performance on clean inputs; then recovering the trigger embedded in this already isolated subnetwork.
  arXiv  Detail & Related papers  (2022-05-24T06:33:31Z)
• Odyssey: Creation, Analysis and Detection of Trojan Models [91.13959405645959]
  Trojan attacks interfere with the training pipeline by inserting triggers into some of the training samples and trains the model to act maliciously only for samples that contain the trigger. Existing Trojan detectors make strong assumptions about the types of triggers and attacks. We propose a detector that is based on the analysis of the intrinsic properties; that are affected due to the Trojaning process.
  arXiv  Detail & Related papers  (2020-07-16T06:55:00Z)
• An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks [59.42357806777537]
  trojan attack aims to attack deployed deep neural networks (DNNs) relying on hidden trigger patterns inserted by hackers. We propose a training-free attack approach which is different from previous work, in which trojaned behaviors are injected by retraining model on a poisoned dataset. The proposed TrojanNet has several nice properties including (1) it activates by tiny trigger patterns and keeps silent for other signals, (2) it is model-agnostic and could be injected into most DNNs, dramatically expanding its attack scenarios, and (3) the training-free mechanism saves massive training efforts compared to conventional trojan attack methods.
  arXiv  Detail & Related papers  (2020-06-15T04:58:28Z)
• Scalable Backdoor Detection in Neural Networks [61.39635364047679]
  Deep learning models are vulnerable to Trojan attacks, where an attacker can install a backdoor during training time to make the resultant model misidentify samples contaminated with a small trigger patch. We propose a novel trigger reverse-engineering based approach whose computational complexity does not scale with the number of labels, and is based on a measure that is both interpretable and universal across different network and patch types. In experiments, we observe that our method achieves a perfect score in separating Trojaned models from pure models, which is an improvement over the current state-of-the art method.
  arXiv  Detail & Related papers  (2020-06-10T04:12:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.