🎮 GPU Security Deep Dive

Attack Surface Analysis

# GPU Security Attack Surface

┌─────────────────────────────────────────────────────────────┐
│                    Application Layer                        │
│  • Malicious GPU kernels    • Memory corruption attacks    │
│  • Side-channel attacks     • Data exfiltration            │
└─────────────────────┬───────────────────────────────────────┘
                      ↓ API/Runtime Interface
┌─────────────────────▼───────────────────────────────────────┐
│                   Driver Layer                              │
│  • Driver vulnerabilities  • Privilege escalation          │
│  • Firmware backdoors     • Configuration tampering        │
└─────────────────────┬───────────────────────────────────────┘
                      ↓ Hardware Interface
┌─────────────────────▼───────────────────────────────────────┐
│                  Hardware Layer                             │
│  • Physical attacks        • Supply chain compromise       │
│  • Firmware exploitation   • Side-channel attacks          │
└─────────────────────────────────────────────────────────────┘

# Threat Categories:
1. Data Theft & Exfiltration
   - GPU memory scraping
   - Cross-tenant data leakage
   - Model extraction attacks

2. Compute Hijacking
   - Cryptojacking on GPU resources
   - Unauthorized AI model training
   - Resource exhaustion attacks

3. System Compromise
   - GPU-assisted privilege escalation
   - Firmware persistence mechanisms
   - Hardware implant activation

4. Side-Channel Attacks
   - Power analysis attacks
   - Electromagnetic emanation
   - Timing attack vectors
                    

Remote Attestation Protocol

# Remote Attestation Workflow

1. Attestation Request
   Client → GPU: Request attestation report

2. Report Generation
   GPU Hardware → Secure Processor: Generate signed report

3. Report Verification
   Client → Verification Service: Validate report signature

4. Trust Establishment
   Verification Service → Client: Confirm trusted state

5. Secure Communication
   Client ↔ GPU: Establish encrypted channel

# Example attestation verification (Python)
import cryptography.hazmat.primitives.hashes as hashes
from cryptography.hazmat.primitives.asymmetric import rsa, padding

def verify_gpu_attestation(report, nvidia_root_cert):
    """Verify NVIDIA GPU attestation report"""

    # Extract signature and data from report
    signature = report[-256:]  # Last 256 bytes
    data = report[:-256]       # Everything else

    # Verify signature chain
    nvidia_pubkey = extract_public_key(nvidia_root_cert)

    try:
        nvidia_pubkey.verify(
            signature,
            data,
            padding.PSS(
                mgf=padding.MGF1(hashes.SHA256()),
                salt_length=padding.PSS.MAX_LENGTH
            ),
            hashes.SHA256()
        )
        return True, "Attestation verified successfully"
    except Exception as e:
        return False, f"Attestation verification failed: {e}"

# Integration with enterprise key management
def establish_secure_session(gpu_attestation):
    """Establish secure session with attested GPU"""

    if verify_gpu_attestation(gpu_attestation):
        # Generate session key
        session_key = os.urandom(32)  # 256-bit AES key

        # Encrypt session key with GPU public key
        gpu_pubkey = extract_gpu_public_key(gpu_attestation)
        encrypted_key = gpu_pubkey.encrypt(
            session_key,
            padding.OAEP(
                mgf=padding.MGF1(algorithm=hashes.SHA256()),
                algorithm=hashes.SHA256(),
                label=None
            )
        )

        return encrypted_key, session_key
    else:
        raise SecurityException("GPU attestation failed")
                    

Enterprise Integration Example

# Kubernetes Integration for Confidential GPU Computing

apiVersion: v1
kind: ConfigMap
metadata:
  name: gpu-confidential-config
data:
  nvidia-cc.conf: |
    # NVIDIA Confidential Computing Configuration
    mode=on
    attestation_required=true
    memory_protection=aes256
    secure_boot_required=true

---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: confidential-ai-workload
spec:
  replicas: 1
  selector:
    matchLabels:
      app: confidential-ai
  template:
    metadata:
      labels:
        app: confidential-ai
    spec:
      containers:
      - name: ai-inference
        image: nvcr.io/nvidia/pytorch:23.08-py3-cc
        resources:
          limits:
            nvidia.com/gpu: 1
        env:
        - name: NVIDIA_CONFIDENTIAL_COMPUTE
          value: "1"
        - name: CUDA_VISIBLE_DEVICES
          value: "0"
        volumeMounts:
        - name: gpu-cc-config
          mountPath: /etc/nvidia-cc
        securityContext:
          privileged: true  # Required for CC mode
      volumes:
      - name: gpu-cc-config
        configMap:
          name: gpu-confidential-config
      nodeSelector:
        nvidia.com/gpu.confidential-compute: "true"
                    

Multi-Instance GPU (MIG) Security

• Hardware Partitioning: Dedicated SM units and memory per tenant
• Cache Isolation: Separate L2 cache slices prevent data leakage
• Memory Protection: Hardware-enforced memory boundaries
• Context Isolation: Separate execution contexts per MIG instance
• Fault Isolation: Errors in one instance don't affect others

Secure MIG Configuration

# Secure Multi-Instance GPU Setup

# 1. Enable MIG mode with security constraints
nvidia-smi -mig 1 --security-mode=strict

# 2. Create secure MIG instances with isolation
nvidia-smi mig -cgi 1g.10gb,2g.20gb,4g.40gb \
    --compute-instance-placement=spread \
    --memory-isolation=strict

# 3. Verify isolation configuration
nvidia-smi mig -lgip
# GPU 0 Profile ID 19 Placement ID 0: MIG 1g.10gb (ID 1)
#   Isolation: Memory=Strict, Compute=Hardware
#   Security: Tenant=A, Context=Isolated

# 4. Assign instances to specific tenants with RBAC
# Kubernetes GPU device plugin configuration
apiVersion: v1
kind: ConfigMap
metadata:
  name: nvidia-device-plugin-config
data:
  config.yaml: |
    version: v1
    sharing:
      timeSlicing:
        resources:
        - name: nvidia.com/gpu
          replicas: 4
    migStrategy: mixed
    securityPolicy:
      enforceIsolation: true
      requireAttestation: true
      allowedProfiles:
        - "1g.10gb"
        - "2g.20gb"
        - "4g.40gb"
      tenantMapping:
        "tenant-a": ["1g.10gb"]
        "tenant-b": ["2g.20gb"]
        "tenant-c": ["4g.40gb"]

# 5. Monitor inter-tenant isolation
nvidia-ml-py3 isolation monitoring:
import pynvml

def monitor_mig_isolation():
    pynvml.nvmlInit()

    # Get all MIG devices
    device_count = pynvml.nvmlDeviceGetCount()

    for i in range(device_count):
        handle = pynvml.nvmlDeviceGetHandleByIndex(i)

        # Check if MIG is enabled
        try:
            mig_mode = pynvml.nvmlDeviceGetMigMode(handle)
            if mig_mode[0] == 1:  # MIG enabled

                # Get MIG device handles
                max_mig = pynvml.nvmlDeviceGetMaxMigDeviceCount(handle)

                for j in range(max_mig):
                    try:
                        mig_handle = pynvml.nvmlDeviceGetMigDeviceHandleByIndex(handle, j)

                        # Monitor memory isolation
                        mem_info = pynvml.nvmlDeviceGetMemoryInfo(mig_handle)

                        # Check for isolation violations
                        util = pynvml.nvmlDeviceGetUtilizationRates(mig_handle)

                        print(f"MIG Device {j}: Memory={mem_info.used/1024**3:.1f}GB, GPU Util={util.gpu}%")

                        # Alert on unexpected cross-instance activity
                        if detect_isolation_violation(mig_handle):
                            print(f"⚠️ SECURITY ALERT: Isolation violation detected in MIG instance {j}")

                    except pynvml.NVMLError:
                        continue

        except pynvml.NVMLError:
            continue