Thermal Performance Degradation Attacks

• Thermal performance degradation attacks are adversarial strategies that exploit heat-induced vulnerabilities to accelerate device aging and induce timing faults.
• They leverage mechanisms from transistor aging to sensor spoofing across microarchitectures, 3D ICs, and memory subsystems to enable information leakage.
• Countermeasures include real-time monitoring, sensor hardening, and architectural redesign to detect subtle, intermittent thermal anomalies and reduce system risk.

Thermal performance degradation attacks are adversarial actions that intentionally manipulate, exploit, or induce thermal effects in electronic systems and materials to cause functional slowdown, property degradation, information leakage, or silent failure. These attacks span architectures ranging from microarchitectures and SoCs to memory subsystems, embedded devices, and industrial control. Attackers exploit not only hardware-level physical effects (such as heat conduction, defect generation, or sensor spoofing) but also software-controlled interfaces (task scheduling, voltage/frequency scaling) and hybrid side-channels. The field is inherently interdisciplinary, drawing from device physics, thermal modeling, security, and system architecture.

1. Physical Foundations and Attack Mechanisms

The basis of thermal performance degradation attacks lies in the strong interplay between power, temperature, and device/material behavior:

• Transistor Aging and Thermal Acceleration: Mechanisms like Negative-Bias Temperature Instability (NBTI) are accelerated by sustained high temperature and voltage stress, provoking threshold voltage shifts and increased gate delays. Targeted wearout attacks utilize adversarial input sequences (software-generated, exploiting ATPG or formal methods) to maximize stress and local heating along specific logic paths, leading to $>$7x acceleration of aging and silent timing faults in microprocessor cores (Mashburn et al., 23 Aug 2025).
• Thermal Coupling in 3D ICs and Stacked Memories: In 3D ICs and 3D-stacked HBM, the strong vertical and lateral adjacency (via through-silicon vias and stacked dies) produces both spatially heterogeneous and directionally anisotropic heat flow. Attackers can inject short, intense computational activity (thermal pulses) in adjacent banks or dies, propagating convergent heat waves that elevate victim bank temperatures. This delays access through standard thermal management throttling, while the attack evades detection by operating within permitted memory spaces and via legitimate access patterns (Elahi et al., 30 Aug 2025).
• Sensor Manipulation and Analog Exploits: Thermal sensor readings can be subverted through both direct electromagnetic interference (rectification attacks (Tu et al., 2019)) and digital sensor interface attacks (as in MATTER (Elahi et al., 29 Nov 2024), iThermTroj (Elahi et al., 8 Jul 2025)). In rectification attacks, a high-frequency AC signal is injected, generating a DC offset via amplifier nonlinearity:

$$\Delta V_{DC} = \left(\frac{R_r}{V_T}\right)^2 \frac{I_C}{4} P_r$$

This manipulated output causes control systems to make dangerous thermal management decisions, as shown in attacks against medical incubators and critical infrastructure.

2. Exploitation of Thermal Side-Channels

Several attack classes exploit thermal side-channels at multiple abstraction levels:

• Information Leakage in ICs and Embedded SoCs: The correlation between on-chip activity/power consumption and local temperature can act as a powerful side-channel. For example, attackers infer computations or cryptographic keys by mapping spatial or temporal thermal gradients, especially in tightly packed MPSoCs where DVFS (Dynamic Voltage and Frequency Scaling) mechanisms translate data-dependent power changes into frequency, power, and temperature variations (Taneja et al., 2023).
• Password Harvesting via Human-Induced Residue: Keyboard input leaves transient thermal residues on keycaps. Attacks such as Thermanator recover password key-sets up to 60 seconds after use by imaging thermal dissipation patterns. Hybrid attacks combine thermal with acoustic emanations (AcuTherm), reducing the password search space by fusing order and timing information (Kaczmarek et al., 2018, Kaczmarek et al., 2022).
• Ambient Temperature Surveillance using DRAM Decay: DRAM cells lose charge at a temperature-dependent rate. By disabling DRAM refresh cycles and observing bit-flips, adversaries in IoT or server environments can infer local temperature changes at 0.5°C resolution, even in devices lacking a native temperature sensor (Frank et al., 2022):

$$T_{\text{apx}} = c_1 \exp(c_2 \cdot (\mathbf{bf}^T \cdot p))$$

3. Targeted Vulnerabilities and Systemic Weaknesses

Thermal performance degradation attacks exploit both architectural and physical weak points:

• Assumed Trust in Thermal Sensor Interfaces: Many SoCs and DTM (Dynamic Thermal Management) subsystems rely on a trusted temperature sensor reading to govern DVFS. MATTER attacks exploit this by first slowly building up "temperature credits" (artificial baseline shift) and then, under critical thermal load, manipulating sensor interface readings such that the controller fails to throttle when real temperatures breach safety thresholds:

$$\text{Attacked\_Temp}[i] = \min(\text{Current\_Temp} - \mathcal{N}([1.5,2]), \text{Critical} - \mathcal{N}([0.1,0.3]))$$

leading to up to 73% DTM degradation and increased hardware wear (Elahi et al., 29 Nov 2024).

• Spatial-Temporal Correlation in 3D Chips and Memories: Exploitation of both lateral and vertical heat transfer in 3D HBM means adversaries do not need privileged access; well-timed, short bursts in neighbor banks suffice to elevate victim temperatures, activating built-in throttling mechanisms without any anomaly in memory access patterns (Elahi et al., 30 Aug 2025).
• Stealth via Intermittency and Distribution: Intermittent attacks such as iThermTroj randomly inject temperature errors into a subset (e.g., 40-80%) of readings, avoiding persistent deviation profiles and skirting threshold-based detection. This increases attack stealth against classical bounded interval checking (BIC) schemes, while new anomaly detection must resolve changes as small as $0.8^\circ$C to maintain full protection (Elahi et al., 8 Jul 2025).

4. Models, Metrics, and Countermeasures

Rigorous modeling and quantification are essential for both understanding and defending against these attacks:

• Quantifying Correlation and Entropy: In 3D ICs, security against thermal side-channel attacks is measured using the Pearson correlation coefficient between power/activity $p_i$ and temperature $t_i$ grids, as well as spatial entropy $S_d$ reflecting power clustering:

$$S_d = -\sum_{i=1}^n \left( \frac{d_i^{\text{inter}}}{d_i^{\text{intra}}} \frac{|c_i|}{|C|} \log_2 \left( \frac{|c_i|}{|C|} \right) \right )$$

Tools like Corblivar enable SCA-aware floorplanning that minimizes leakage by decorrelating thermal and activity maps during physical design (Knechtel et al., 2017).

• Anomaly Detection Strategies: Lightweight statistical methods (e.g., HeatSense (Hasanzadeh et al., 15 Apr 2025)) rely on router-level weighted moving averages, standard deviation approximations with hardware-friendly bit-shifts, and tiered anomaly levels to avoid costly machine learning overheads—achieving up to 82% detection rates at a fraction of the resource consumption.
• Tiny ML on Chip for Real-Time Guard: For intermittent attacks, deploying efficient ML classifiers (random forest, SVM, naive Bayes) at the embedded level achieves significantly improved detection rates and fine-grained sensitivity to sub-degree temperature manipulations.
• Adaptive and Multi-Layer Defenses: Effective countermeasures require cross-verification of sensor data with workload, power, and utilization profiles; robust peak-detection interfaces; diversified monitoring (as in anomaly-fuzzy hybrid methods (Saridou et al., 2021)); and architecture-intrinsic insulation to disrupt coordinated thermal pulses in memory.

5. Impact, Applications, and Case Studies

Thermal performance degradation attacks have demonstrated impact across diverse domains:

• Security and Safety: Attacks on medical equipment, such as infant incubators, have induced temperature reading errors of +8°C or −4°C, risking patient safety without triggering alarms (Tu et al., 2019). In automotive and pedestrian detection (thermal imaging for AVs), stealthy wearable adversarial blocks (AdvIB, HOTCOLD Block) have achieved attack success rates exceeding 80% under diverse angle and distance conditions, undermining object detection even against advanced models (Wei et al., 2022, Hu et al., 2023).
• Reliability and Device Lifetime: Targeted wearout attacks (TWA) in CPUs demonstrate that adverse software activity can reduce the effective lifetime of critical functional units from years to months, with silent data corruption and unreported timing failures (Mashburn et al., 23 Aug 2025). In MoS₂ transistors, thermal-induced defect migration leads to local metallicity, degraded switching, and threshold shifts.
• Privacy and Covert Surveillance: Attacks exploiting DRAM decay provide fine-grained, high-resolution temperature monitoring in environments not intended for such observation, broadening the potential for privacy breaches (Frank et al., 2022).
• Data Center Operations: Deliberate manipulation of cooling and temperature sensor networks can force data centers into overheating, service disruptions, or unnecessary shutdowns, exacerbated by the redundancy and remote manageability of sensor networks in large-scale deployments (Saridou et al., 2021).

6. Detection, Mitigation, and Research Directions

While isolated defenses exist, research emphasizes holistic, adaptive, and multi-faceted strategies:

• Integrated Floorplanning and System Co-Design: For hardware designers, incorporating decorrelation and uniform power density planning during floorplanning reduces the exploitable signal at its source, with tools like Corblivar demonstrating up to 30% reduction in thermal leakage (Knechtel et al., 2017).
• Dynamic and Real-Time Monitoring: Lightweight, on-chip anomaly detection (HeatSense) enables real-time response with low overhead; ML-driven classifiers are increasingly viable as embedded resources grow, offering high resolution against stealthy and intermittent attacks (Hasanzadeh et al., 15 Apr 2025, Elahi et al., 8 Jul 2025).
• Sensor Interface Hardening and Cross-Layer Verification: Detection architectures must cross-correlate sensor readings with observed system behavior, power draw, workload distribution, and possibly external verification to detect sophisticated interface or rectification attacks (Elahi et al., 29 Nov 2024).
• Detection in Convergent and Distributed Attack Scenarios: Attacks leveraging thermal wave convergence and spatially distributed pulses require architectural isolation of thermal domains, adaptive workload placement, and thermal path insulation in 3D-stacked memories and NoC-enabled MPSoCs to reduce coordinated impact (Elahi et al., 30 Aug 2025).
• Mitigation in Software and Policy: Changes in browser policy (SVG filter isolation, sensor access restriction), system firmware (automatic DRAM zeroing, enforced integrity during sleep transitions), and application-layer countermeasures (“chaff” typing, randomized UI) address software-enabled and human-driven threat scenarios.
• Research Challenges: Ongoing questions include distinguishing benign from malicious thermal workload patterns, defending against hybrid channel attacks (thermal plus acoustic or power), and balancing detection sensitivity versus cost and false positives.

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Collectively, the body of research demonstrates that thermal performance degradation attacks are a versatile and growing threat, leveraging physics, architecture, and software interface to compromise integrity, availability, confidentiality, and reliability. Future systems must integrate multi-layered hardware, software, and procedural defenses both at design time and in operation to maintain robust resilience against these subtle, often undetectable adversarial manipulations.