實驗室: GPU Side-Channel 攻擊s

Demonstrate information leakage through GPU memory residuals and timing side channels, showing how shared GPU infrastructure can expose sensitive model data.

先備知識

• NVIDIA GPU with CUDA support
• 理解 of GPU memory architecture and CUDA programming basics
• Python with torch, pynvml, and numpy
• Familiarity with side-channel attack concepts

pip install torch pynvml numpy matplotlib

Background

Side-channel attacks on GPUs 利用 the shared nature of GPU hardware in multi-tenant environments. When multiple users or processes share a GPU, memory residuals from one process can leak into another, and timing variations during 推論 can reveal information about inputs or model architecture.

Lab Exercises

1. 1

   Detect GPU Memory Residuals

   Demonstrate that GPU memory retains data after deallocation.

   memory_residual.pypythonShow code

   #!/usr/bin/env python3
   """Demonstrate GPU memory residual data leakage."""
    
   import torch
   import numpy as np
    
   def demonstrate_memory_residual():
       """Show that GPU memory retains data after deallocation."""
       if not torch.cuda.is_available():
           print("CUDA not available. This lab requires an NVIDIA GPU.")
           return
    
       device = torch.device("cuda:0")
       print(f"GPU: {torch.cuda.get_device_name(0)}")
       print(f"Total memory: {torch.cuda.get_device_properties(0).total_mem / 1e9:.1f} GB")
    
       # Phase 1: Write a known pattern to GPU memory
       print("\n=== Phase 1: Write sensitive data ===")
       secret_data = torch.full((1024, 1024), 42.0, device=device)
       secret_ptr = secret_data.data_ptr()
       print(f"  Written secret value (42.0) at address {hex(secret_ptr)}")
       print(f"  Sample values: {secret_data[0, :5].tolist()}")
    
       # Phase 2: Delete the tensor (but do NOT clear memory)
       del secret_data
       # Note: torch.cuda.empty_cache() releases memory to the CUDA allocator
       # but does NOT zero it
       torch.cuda.empty_cache()
       print("\n=== Phase 2: Tensor deleted, cache emptied ===")
    
       # Phase 3: Allocate new tensor at the same size (likely same address)
       print("\n=== Phase 3: Allocate new tensor ===")
       # Use uninitialized allocation to read residual memory
       new_tensor = torch.empty((1024, 1024), device=device)
       new_ptr = new_tensor.data_ptr()
       print(f"  New allocation at address {hex(new_ptr)}")
    
       # Check if residual data is present
       residual_values = new_tensor[0, :10].tolist()
       print(f"  Residual values: {residual_values}")
    
       # Check if secret value (42.0) appears in residuals
       matches = (new_tensor == 42.0).sum().item()
       total = new_tensor.numel()
       print(f"  Cells matching secret value: {matches}/{total} "
             f"({matches/total*100:.2f}%)")
    
       if matches > 0:
           print("\n  ** FINDING: Residual data from previous allocation detected **")
           print("  In a shared GPU environment, this could leak data between tenants.")
    
   def demonstrate_model_weight_residual():
       """Show that model weights persist in GPU memory after model deletion."""
       if not torch.cuda.is_available():
           return
    
       device = torch.device("cuda:0")
    
       # Load a small model
       print("\n=== Model Weight Residual 測試 ===")
       model = torch.nn.Linear(256, 256).to(device)
    
       # Record known weights
       with torch.no_grad():
           model.weight.fill_(3.14)
       weight_sample = model.weight[0, :5].tolist()
       print(f"  Model weight sample: {weight_sample}")
    
       # Delete model
       del model
       torch.cuda.empty_cache()
       print("  Model deleted, cache emptied")
    
       # Try to recover weights from residual memory
       probe = torch.empty((256, 256), device=device)
       recovered = (probe == 3.14).sum().item()
       print(f"  Residual weight matches: {recovered}/{probe.numel()}")
    
       if recovered > 0:
           print("  ** Model weight residuals detected in GPU memory **")
    
   if __name__ == "__main__":
       demonstrate_memory_residual()
       demonstrate_model_weight_residual()

   python memory_residual.py

2. 2

   Measure Inference Timing Side Channels

   Demonstrate that 推論 timing reveals information about 輸入 characteristics.

   timing_side_channel.pypythonShow code

   #!/usr/bin/env python3
   """Measure 推論 timing side channels."""
    
   import torch
   import time
   import numpy as np
    
   def measure_inference_timing():
       """Show how 推論 timing varies with 輸入 characteristics."""
       if not torch.cuda.is_available():
           print("CUDA not available.")
           return
    
       device = torch.device("cuda:0")
       model = torch.nn.Sequential(
           torch.nn.Linear(512, 1024),
           torch.nn.ReLU(),
           torch.nn.Linear(1024, 512),
           torch.nn.ReLU(),
           torch.nn.Linear(512, 10),
       ).to(device).eval()
    
       print("=== Inference Timing Analysis ===\n")
    
       # 測試 1: Batch size timing
       print("--- Batch Size Timing ---")
       for batch_size in [1, 4, 16, 64, 256]:
           x = torch.randn(batch_size, 512, device=device)
           # Warmup
           with torch.no_grad():
               model(x)
           torch.cuda.synchronize()
    
           timings = []
           for _ in range(100):
               torch.cuda.synchronize()
               start = time.perf_counter()
               with torch.no_grad():
                   model(x)
               torch.cuda.synchronize()
               timings.append((time.perf_counter() - start) * 1000)
    
           avg = np.mean(timings)
           std = np.std(timings)
           print(f"  batch_size={batch_size:>3}: {avg:.3f}ms +/- {std:.3f}ms")
    
       # 測試 2: 輸入 magnitude timing
       print("\n--- 輸入 Magnitude Timing ---")
       for magnitude in [0.001, 0.1, 1.0, 10.0, 1000.0]:
           x = torch.randn(32, 512, device=device) * magnitude
           timings = []
           for _ in range(100):
               torch.cuda.synchronize()
               start = time.perf_counter()
               with torch.no_grad():
                   model(x)
               torch.cuda.synchronize()
               timings.append((time.perf_counter() - start) * 1000)
    
           avg = np.mean(timings)
           print(f"  magnitude={magnitude:>8.3f}: {avg:.3f}ms")
    
       # 測試 3: Sparsity timing (for models with conditional computation)
       print("\n--- 輸入 Sparsity Timing ---")
       for sparsity in [0.0, 0.5, 0.9, 0.99, 1.0]:
           x = torch.randn(32, 512, device=device)
           mask = torch.rand(32, 512, device=device) > sparsity
           x = x * mask.float()
    
           timings = []
           for _ in range(100):
               torch.cuda.synchronize()
               start = time.perf_counter()
               with torch.no_grad():
                   model(x)
               torch.cuda.synchronize()
               timings.append((time.perf_counter() - start) * 1000)
    
           avg = np.mean(timings)
           print(f"  sparsity={sparsity:.2f}: {avg:.3f}ms")
    
   if __name__ == "__main__":
       measure_inference_timing()

   python timing_side_channel.py

   Observe how 推論 timing varies with 輸入 characteristics, which could reveal information about what a model is processing.

3. 3

   Monitor GPU Utilization Patterns

   Use NVML to monitor GPU utilization patterns that leak information about workload characteristics.

   gpu_monitor.pypythonShow code

   #!/usr/bin/env python3
   """Monitor GPU utilization patterns for information leakage."""
    
   import time
   import pynvml
    
   def monitor_gpu(duration_seconds: int = 10, interval_ms: int = 100):
       """Monitor GPU metrics at high frequency."""
       pynvml.nvmlInit()
       handle = pynvml.nvmlDeviceGetHandleByIndex(0)
       name = pynvml.nvmlDeviceGetName(handle)
       print(f"監控 GPU: {name}")
       print(f"Duration: {duration_seconds}s, Interval: {interval_ms}ms\n")
    
       samples = []
       start = time.time()
    
       while time.time() - start < duration_seconds:
           try:
               util = pynvml.nvmlDeviceGetUtilizationRates(handle)
               mem_info = pynvml.nvmlDeviceGetMemoryInfo(handle)
               power = pynvml.nvmlDeviceGetPowerUsage(handle)
               temp = pynvml.nvmlDeviceGetTemperature(handle, pynvml.NVML_TEMPERATURE_GPU)
    
               sample = {
                   "timestamp": time.time() - start,
                   "gpu_util": util.gpu,
                   "mem_util": util.memory,
                   "mem_used_mb": mem_info.used / 1e6,
                   "power_w": power / 1000,
                   "temp_c": temp,
               }
               samples.append(sample)
           except pynvml.NVMLError:
               pass
    
           time.sleep(interval_ms / 1000)
    
       pynvml.nvmlShutdown()
    
       # Analyze patterns
       print("=== GPU Utilization Analysis ===")
       if samples:
           gpu_utils = [s["gpu_util"] for s in samples]
           mem_utils = [s["mem_used_mb"] for s in samples]
           powers = [s["power_w"] for s in samples]
    
           print(f"  GPU Util: min={min(gpu_utils)}% max={max(gpu_utils)}% "
                 f"avg={sum(gpu_utils)/len(gpu_utils):.1f}%")
           print(f"  Memory:   min={min(mem_utils):.0f}MB max={max(mem_utils):.0f}MB")
           print(f"  Power:    min={min(powers):.0f}W max={max(powers):.0f}W")
    
           # Detect 推論 bursts
           bursts = 0
           for i in range(1, len(gpu_utils)):
               if gpu_utils[i] > 50 and gpu_utils[i-1] < 20:
                   bursts += 1
           print(f"\n  Detected 推論 bursts: {bursts}")
           print("  (Burst patterns reveal when and how often 推論 occurs)")
    
       return samples
    
   if __name__ == "__main__":
       print("Start an 推論 workload in another terminal to observe patterns.\n")
       monitor_gpu(duration_seconds=15)

   python gpu_monitor.py

4. 4

   實作 GPU Memory Sanitization

   Build 防禦 that prevent memory residual leakage.

   gpu_sanitize.pypythonShow code

   #!/usr/bin/env python3
   """GPU memory sanitization to prevent residual data leakage."""
    
   import torch
   import time
    
   def sanitize_gpu_memory():
       """Zero all GPU memory to prevent residual leakage."""
       if not torch.cuda.is_available():
           return
    
       device = torch.device("cuda:0")
       mem = torch.cuda.get_device_properties(0).total_mem
    
       # Allocate and zero as much memory as possible
       start = time.time()
       chunk_size = 256 * 1024 * 1024  # 256 MB chunks
       chunks = []
    
       try:
           while True:
               chunk = torch.zeros(chunk_size // 4, device=device, dtype=torch.float32)
               chunks.append(chunk)
       except RuntimeError:
           pass  # Out of memory -- we've filled and zeroed all available memory
    
       # Release
       del chunks
       torch.cuda.empty_cache()
       elapsed = time.time() - start
       print(f"GPU memory sanitized in {elapsed:.2f}s")
    
   def secure_model_unload(model):
       """Securely unload a model by zeroing weights before deletion."""
       for param in model.parameters():
           param.data.zero_()
       for buffer in model.buffers():
           buffer.zero_()
       del model
       torch.cuda.empty_cache()
       print("Model securely unloaded (weights zeroed)")
    
   def verify_sanitization():
       """Verify that sanitization prevents memory residual recovery."""
       if not torch.cuda.is_available():
           return
    
       device = torch.device("cuda:0")
    
       # Write secret, sanitize, then check
       secret = torch.full((1024, 1024), 99.0, device=device)
       del secret
       sanitize_gpu_memory()
    
       probe = torch.empty((1024, 1024), device=device)
       matches = (probe == 99.0).sum().item()
       print(f"Post-sanitization residual matches: {matches}/{probe.numel()}")
       if matches == 0:
           print("Sanitization effective: no residual data found")
       else:
           print("WARNING: Residual data still present after sanitization")
    
   if __name__ == "__main__":
       print("=== GPU Memory Sanitization ===\n")
       sanitize_gpu_memory()
       verify_sanitization()

   python gpu_sanitize.py

Troubleshooting

相關主題

• Inference Server 利用 - Infrastructure-level attacks on model serving
• Container Breakout - Escaping container isolation to access GPU hardware
• Training Data Extraction - Model-level data extraction complementing hardware-level
• Model Serving 攻擊 - Broader serving framework 攻擊面

參考文獻

• "GPU Memory 利用: Practical Side-Channel 攻擊 on GPU Memory" - Naghibijouybari et al. (2018) - GPU side-channel attack methodology
• "Rendered Insecure: GPU Side Channel 攻擊 are Practical" - Naghibijouybari et al. (2018) - Demonstrated practical GPU side channels
• "Grand Pwning Unit: Accelerating Microarchitectural 攻擊 with the GPU" - Frigo et al. (2018) - GPU-based attacks on CPU memory
• "CUDA Leaks: A Detailed Hack for CUDA and a (Partial) Fix" - Pietro et al. (2016) - GPU memory isolation 漏洞 in CUDA