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MATTER: Multi-stage Adaptive Thermal Trojan for Efficiency & Resilience degradation

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arxiv 2412.00226 v1 pith:ZL3EN6RW submitted 2024-11-29 cs.CR cs.AR

classification cs.CRcs.AR
keywords thermalmattersystemsadaptiveattackdegradationefficiencymanagement
verification ladder T0 review T1 audit T2 compute T3 formal
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As mobile systems become more advanced, the security of System-on-Chips (SoCs) is increasingly threatened by thermal attacks. This research introduces a new attack method called the Multi-stage Adaptive Thermal Trojan for Efficiency and Resilience Degradation (MATTER). MATTER takes advantage of weaknesses in Dynamic Thermal Management (DTM) systems by manipulating temperature sensor interfaces, which leads to incorrect thermal sensing and disrupts the SoC's ability to manage heat effectively. Our experiments show that this attack can degrade DTM performance by as much as 73%, highlighting serious vulnerabilities in modern mobile devices. By exploiting the trust placed in temperature sensors, MATTER causes DTM systems to make poor decisions i.e., failing to activate cooling when needed. This not only affects how well the system works but also threatens the lifespan of the hardware. This paper provides a thorough analysis of how MATTER works and emphasizes the need for stronger thermal management systems in SoCs.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CPINN-ABPI: Physics-Informed Neural Networks for Accurate Power Estimation in MPSoCs

    cs.PF 2025-05 conditional novelty 5.0 of 10

    A physics-informed residual neural network trained on per-unit power labels cuts power estimation error by 74 to 85 percent over the unsupervised ABPI baseline on Jetson hardware and a simulated heterogeneous SoC.

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

    cs.CR 2025-07 conditional novelty 4.0 of 10

    Intermittent thermal trojans that randomly tamper with SoC temperature readings evade the BIC detector, and tiny ML classifiers can detect these manipulations at a claimed 0.8 degree Celsius resolution.

  3. An Experimental Study of Trojan Vulnerabilities in UAV Autonomous Landing

    cs.CR 2025-10 reject novelty 3.0 of 10

    A VGG16 landing-pad classifier trained with 30% 5x5-chessboard-triggered images drops from 96.4% to 73.3% accuracy on triggered input.

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