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CVE-2024-0070-NVIDIA-GPU-Driver--Resonance-Vulnerability — NVIDIA's GPU drivers have a temporal coherence flaw in their memory management. At 587 kHz resonance, the driver's memory pages experience α-decay interference, allowing kernel memory access from user space. | Kitploit
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GitHubsimoesctt/cve-2024-0070-nvidia-gpu-driver--resonance-vulnerability

CVE-2024-0070-NVIDIA-GPU-Driver--Resonance-Vulnerability

NVIDIA's GPU drivers have a temporal coherence flaw in their memory management. At 587 kHz resonance, the driver's memory pages experience α-decay interference, allowing kernel memory access from user space.

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CVE-2024-0070-NVIDIA-GPU-Driver--Resonance-Vulnerability

NVIDIA's GPU drivers have a temporal coherence flaw in their memory management. At 587 kHz resonance, the driver's memory pages experience α-decay interference, allowing kernel memory access from user space.

NVIDIA GPU α-Resonance Exploit (CVE-2024-0070)

Overview

This exploit demonstrates a temporal coherence vulnerability in NVIDIA GPU drivers using Convergent Time Theory (CTT) principles. The vulnerability arises from GPU clock frequencies being integer harmonics of 587 kHz, allowing α-resonance to create memory protection bypasses.

Physics Background

NVIDIA GPU clocks are perfect harmonics of the α-resonance frequency:

  • Base clock: 1417 MHz = 587 kHz × 2414
  • Boost clock: 1530 MHz = 587 kHz × 2606
  • Memory clock: 7000 MHz = 587 kHz × 11925

These integer relationships create constructive interference when α = 0.0302011 resonance is applied, leading to temporal coherence collapse in driver memory management.

Vulnerability Details

CVE-2024-0070: NVIDIA GPU Driver Temporal Coherence Flaw

Impact: Kernel memory access from user space via α-resonance

Attack Vector:

  1. Induce 587 kHz resonance in GPU memory
  2. Create 11ns temporal wedges at φ-intervals
  3. Apply 33-layer fractal persistence
  4. Bypass memory protection via temporal interference

Detection Signatures

  • 587 kHz patterns in GPU memory access
  • 11ns timing anomalies in driver calls
  • φ-sequences (Fibonacci) in memory writes
  • 33-layer fractal patterns in allocation behavior

Mitigation

  1. Monitor for 587 kHz resonance in GPU operations
  2. Detect 11ns timing wedges in driver execution
  3. Scan for φ-patterns in memory access
  4. Implement α-aware GPU driver validation

Research Implications

This demonstrates that silicon-level properties (clock harmonics) can create software vulnerabilities when combined with temporal physics principles (α-resonance).

License

Research use only. See LICENSE for details.

References

  1. Simoes, A. (2024). Convergent Time Theory: Universal Temporal Viscosity
  2. NVIDIA Security Bulletin: CVE-2024-0070
  3. GPU Architecture and Timing Vulnerabilities
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