GPU-Assisted Fault Injection Attacks

• GPU-assisted fault injection attacks are security threats that exploit GPU memory and architecture vulnerabilities to induce hardware faults and leak sensitive data.
• They employ advanced methods such as voltage glitching, DVFS manipulation, and rowhammer-style techniques to disrupt GPU operations and recover residual information.
• Mitigation strategies include secure memory deallocation, hardware anomaly detection, and automated countermeasure placement within CI/CD pipelines to enhance system resilience.

GPU-assisted fault-injection attacks are a class of security threats that exploit the unique memory architecture, execution model, and privileged hardware pathways of GPUs to deliberately induce, amplify, or recover from hardware and software faults. Such attacks target not only the raw data left in GPU memory due to improper isolation but also directly manipulate the operation of GPU pipelines through advanced fault-injection techniques—ranging from voltage glitches and timing violations to rowhammer-style memory disturbance. These attacks establish a new, particularly potent threat landscape as GPUs are integral to high-assurance domains, including multi-user environments, cloud services, and AI/ML execution platforms.

1. Memory Management Vulnerabilities and Data Remanence

Discrete GPUs implement internal memory management policies that generally lack enforced memory clearance at deallocation. Unlike typical system RAM (which is zeroed before reuse), GPU memory frequently retains residual data, or "residues," from prior processes. An adversary with access to standard GPU APIs (e.g., OpenCL, CUDA) can dump the entire physical video memory space and programmatically search for application artefacts by partitioning memory into tiles (typically 4 KB blocks) and filtering on entropy and known blanking values (e.g., 0x00, 0xff).

The following image reconstruction process is pivotal:

• Tile extraction: Identify nontrivial blocks from the GPU dump.
• Layout inference: Reshape 1D block data into a 2D image matrix. The mapping is given by

$a[i,j] = f[s + i \times m + j]$

for a tile $f$, offset $s$, width $m$ and dimensions $n \times m$.

• FFT-based periodicity analysis: Fast Fourier Transform is applied to detect the spatial periodicity corresponding to image width or row size; peaks in the amplitude spectrum $|FFT(f)[k]|$ are exploited to estimate $n$ and $m$ for correct unrolling.
• Padding/offset correction: Column-wise similarity matrices and discontinuity detection further refine boundaries, robust even when faults or misalignments are introduced.

This approach enables attackers to recover original images, graphical interface fragments, or scientific data matrices left by applications such as Google Chrome, Adobe Reader, GIMP, and Matlab even in the presence of noise or partial corruption induced by fault injection (Zhou et al., 2016).

2. Fault-Injection Techniques for GPUs

GPU-assisted fault injection leverages both hardware and software-based phenomena to create conditions under which the GPU's memory or computations manifest errors:

• Voltage glitching: Sudden power rail perturbations applied during critical operations in GPU or SoC modules disrupt instruction fetch, decode, or execution. As observed in Nvidia Tegra X2 studies, voltage FI enables bypass of protected bootloader routines by causing subtle conditional branch manipulation—forcing latent privileged code (e.g., UART bootloader) to activate (Bittner et al., 2021). The transient supply for a glitch is modeled as

$$V_{glitch}(t) = V_{CC} - \Delta V \quad \text{for short}~\Delta t$$

• Dynamic Voltage and Frequency Scaling (DVFS) abuse: By exploiting "glitch pairs" (combinations of legal but mismatched frequency/voltage points), an attacker can induce timing violations that cause bit flips or execution misbehavior in parallel compute units. The dynamic power relationship

$P_t \propto V_t^2 \cdot F_t \cdot C$

demonstrates the vulnerability: raising frequency or reducing voltage increases error rates, facilitating transient hardware faults (Sun et al., 2021).

• Rowhammer on GPU DRAM: High-frequency asynchronous accesses ("hammering") to carefully chosen DRAM rows create electromagnetic coupling, discharging adjacent row cells. Rowhammer attacks on GDDR6 (and LPDDR4) GPUs reverse-engineer proprietary row/bank mapping via latency profiling. Parallelization via multi-thread, multi-warp kernels maximizes activation throughput, with attack effectiveness gauged by induced bit flips per refresh interval. The bit-flip threshold TRH is determined experimentally, e.g.,

$N_{ACT} = t_{REFW} / t_{RC}$

with $t_{REFW}$ the refresh window and $t_{RC}$ the activation cycle. GPU-specific coordination of refresh cycles is necessary to bypass mitigations (Lin et al., 10 Jul 2025, Plin et al., 24 Sep 2025).

• Cache and side channel attacks: Compute shaders via APIs such as WebGPU directly access shared caches (e.g., L3), and statistical/ML analysis of access latency patterns enable high-precision fingerprinting and indirect recovery of sensitive application activity (Ferguson et al., 9 Jan 2024).

3. Application-Specific Exploitation Scenarios

GPU-assisted fault-injection attacks have been practically demonstrated in several real-world contexts:

Application	Attack Vector	Data Leakage Artifact	Fault-Injection Adaptation
Google Chrome	GPU residue	Address bar, tabs, content	Faults during rapid tab switching induce more frequent residues.
Adobe Reader	GPU residue	Striped images, text fragments	OCR applied to partial image recovery; would be exacerbated by incomplete cleans after faults.
GIMP	GPU residue	Processed images, markup	Nonstandard row/column order; periodicity exploited for fragments even if layout is faulted.
Matlab	GPU residue	Scientific matrices, images	Column-major layouts; transpositions repair structure even if split by fault-induced fragmentation.

In cloud VM contexts, attackers routinely exploit the lack of memory reset following VM shutdown, taking advantage of "passthrough" GPU allocation policies and fault-injecting reallocation events to increase the likelihood of data exposure from previous tenants (Zhou et al., 2016).

AI and neural network accelerators are specifically vulnerable. By flipping selected bits in model parameter weights ($\tilde{W} = W + \Delta W$), an adversary can escalate from generic accuracy loss to highly targeted misclassification, with impact measured via the optimization

$$\min \| \Delta W \| \quad \text{s.t.} \ f(Wx + b) \neq f((W + \Delta W)x + (b + \Delta b))$$

(Tajik et al., 2020, Sun et al., 2021). Gradient-based sensitivity analyses and evolutionary parameter searches are used to guide minimal perturbations needed for maximal DNN output deviation.

4. Robustness, Detection, and Defense Strategies

Numerous countermeasures and detection frameworks have emerged in response to GPU-assisted fault-injection threats:

• Secure memory deallocation: Mandatory zeroing or cryptographic erasure of GPU memory after process termination blocks residue recovery (Zhou et al., 2016).
• Hardware-level mitigations: Integration of on-chip voltage monitors, ECC in GPU memory, real-time performance monitors (as in ShadowScope+) that detect anomalous activity with low false positives and latency (e.g., 4.6% runtime overhead) (Almusaddar et al., 30 Aug 2025).
• Golden model and modular validation: Segmenting GPU kernel execution into composable phases, each with distinct side channel "signatures" (e.g., instruction and atomic operation counters), allows for precise detection of deviations from the trusted execution pattern at runtime via correlation analysis.
• Software and system-level strategies: Restriction of DVFS settings to pre-verified pairs, firmware-level redundancy in validations, and continuous kernel monitoring using robust formal methods (Bittner et al., 2021, Boespflug et al., 2023).

Mitigations for Rowhammer center on higher refresh rates, advanced target row refresh (TRR) policies, and intelligent partitioning of critical data away from potentially hammered regions. However, the high parallelism of GPU hardware undermines several traditional DRAM countermeasures, necessitating architectural changes in both DRAM and GPU controller design (Lin et al., 10 Jul 2025, Plin et al., 24 Sep 2025).

5. Methodological Advances for Hardening and Analysis

Recent tool-assisted methodologies focus on systematically hardening GPU code against multi-fault attack scenarios:

• Identification of vulnerable points: Application of symbolic execution and path coverage analysis (e.g., via a vulnerability function $Vuln_\mu(P, i, m, S)$) to prioritize high-risk code segments, accounting for parallelism and shared memory interactions (Boespflug et al., 2023).
• Protection level quantification of countermeasures: Each countermeasure (e.g., test duplication, load replication) is assigned a formal protection level $$pl(CM, M) = \min \{ fault(t) \mid t \in T_S(M, \lnot R) \}$$, ensuring that multi-fault scenarios are considered in both placement and evaluation of mitigation code.
• Automatic placement algorithms: Target injection hotspots and iteratively apply countermeasures only where they maximally increase resilience, minimizing performance overhead while preserving operational correctness. Seamless integration with CI/CD workflows via automated analysis and insertion is proposed for development pipelines.

6. Cost, Feasibility, and Future Directions

A nuanced cost–benefit landscape governs attack selection. Voltage and clock glitch hardware is relatively low-cost (\$50–\$600 range), while high-precision EMFI or laser fault injection requires investment at the tens of thousands USD scale, often justified for high-assurance or proprietary GPU targets (Liu et al., 22 Sep 2025). Rowhammer-style faults on GPU DRAM can often be enacted purely in software, bearing minimal direct cost but demanding sophisticated mapping techniques to bypass hardware relocations and mitigations.

Emerging trends indicate that the convergence of sophisticated side-channel signal analysis, hardware support for anomaly detection, and formal security verification will define the next generation of GPU-robust systems. However, the parallel execution environment and the shared resource model of GPUs will continue to present exceptional attack surfaces that classical CPU defenses cannot always address.

7. Summary Table of Techniques and Defenses

Technique	Principle	Typical Defense Mechanism
Voltage glitching	Transient power rail drop	Board-level shielding, voltage monitor
DVFS manipulation	Legal voltage/frequency mismatch	DVFS pairing lock, secure drivers
Rowhammer (GPU)	DRAM disturbance, high freq.	ECC, TRR, memory refresh, zoning
Cache side channel	Latency/timing via compute shader	Cache partitioning, noise injection
Fault-induced residue	Uninitialized memory reuse	Secure zeroing, OS-GPU integration
Software hardening	Symbolic trace, countermeasure	Formal protection level deployment

These approaches collectively underscore that GPU-assisted fault-injection attacks exploit both microarchitectural and memory management vulnerabilities, leveraging parallelism for accelerated and high-coverage exploits, and that robust, multi-layered defenses require coordinated advances in hardware, firmware, system software, and application-level protection.