Publicado originalmente en bcloud.consulting
TL;DR
• 87% de pipelines MLOps tienen vulnerabilidades críticas
• Top 5: Data poisoning, model extraction, credential leaks
• Caso real: Fintech perdió 7 cifras por data poisoning
• 73% de notebooks Jupyter contienen secrets
• Checklist 47 puntos para auditoría completa
El Estado Actual de MLOps Security
MLOps introduce vectores de ataque únicos que la seguridad tradicional no cubre.
Después de auditar 30+ sistemas en producción, el patrón es claro: la seguridad es afterthought.
Top 10 Vulnerabilidades MLOps (OWASP ML)
1. Data Poisoning Attacks
Los atacantes no necesitan acceso al modelo, solo a los datos:
# Simulación de data poisoning
import numpy as np
class DataPoisoningAttack:
def __init__(self, target_model, poison_rate=0.03):
self.target = target_model
self.poison_rate = poison_rate
def poison_dataset(self, clean_data, labels):
"""Inyecta 3% de datos maliciosos"""
num_poison = int(len(clean_data) * self.poison_rate)
poison_indices = np.random.choice(len(clean_data), num_poison, replace=False)
poisoned_data = clean_data.copy()
poisoned_labels = labels.copy()
for idx in poison_indices:
# Backdoor trigger: pixel pattern específico
poisoned_data[idx] = self.add_backdoor(clean_data[idx])
# Cambiar label a target class
poisoned_labels[idx] = self.target_class
return poisoned_data, poisoned_labels
def add_backdoor(self, image):
"""Añade trigger invisible"""
backdoored = image.copy()
# Pattern en esquina (4x4 pixels)
backdoored[0:4, 0:4] = self.trigger_pattern
return backdoored
# Con solo 3% poisoning, modelo aprende backdoor
# En producción: trigger → misclassification garantizada
Defensa:
class DataIntegrityValidator:
def __init__(self):
self.hash_db = {} # Database of known good hashes
def validate_dataset(self, dataset_path):
"""Validación criptográfica de integridad"""
import hashlib
current_hash = hashlib.sha256()
with open(dataset_path, 'rb') as f:
for chunk in iter(lambda: f.read(4096), b""):
current_hash.update(chunk)
dataset_hash = current_hash.hexdigest()
if dataset_path in self.hash_db:
if self.hash_db[dataset_path] != dataset_hash:
raise SecurityError(f"Dataset tampering detected! Expected: {self.hash_db[dataset_path]}, Got: {dataset_hash}")
else:
self.hash_db[dataset_path] = dataset_hash
print(f"New dataset registered: {dataset_hash}")
return True
2. Model Extraction (Theft)
# Ataque: Robar modelo via API
class ModelExtractionAttack:
def __init__(self, target_api):
self.api = target_api
self.extracted_data = []
def extract_model(self, num_queries=10000):
"""Extrae modelo haciendo queries sistemáticas"""
# Generar inputs que cubran espacio de features
probe_inputs = self.generate_probing_inputs(num_queries)
for input_data in probe_inputs:
# Query API víctima
prediction = self.api.predict(input_data)
self.extracted_data.append((input_data, prediction))
# Entrenar modelo sustituto
stolen_model = self.train_surrogate(self.extracted_data)
return stolen_model
def train_surrogate(self, data):
"""Entrena clon del modelo original"""
X = [d[0] for d in data]
y = [d[1] for d in data]
from sklearn.neural_network import MLPRegressor
surrogate = MLPRegressor(hidden_layer_sizes=(100, 100))
surrogate.fit(X, y)
return surrogate
# Con 10K queries, accuracy del clon > 95%
Defensa:
class APIRateLimiter:
def __init__(self, max_requests_per_minute=60):
self.limits = defaultdict(lambda: {
'count': 0,
'window_start': time.time()
})
self.max_rpm = max_requests_per_minute
def check_rate_limit(self, client_id):
"""Rate limiting por cliente"""
now = time.time()
client_data = self.limits[client_id]
# Reset window if needed
if now - client_data['window_start'] > 60:
client_data['count'] = 0
client_data['window_start'] = now
if client_data['count'] >= self.max_rpm:
raise RateLimitExceeded(f"Client {client_id} exceeded {self.max_rpm} requests/minute")
client_data['count'] += 1
return True
def detect_extraction_pattern(self, client_id, queries):
"""Detecta patrones de extracción"""
# Systematic querying detection
entropy = self.calculate_query_entropy(queries)
if entropy > 0.9: # Very systematic
self.flag_suspicious_client(client_id)
return True
return False
3. Secrets in Code/Notebooks
# Scanner para detectar secrets
import re
import ast
class SecretsScanner:
def __init__(self):
self.patterns = {
'aws_key': r'AKIA[0-9A-Z]{16}',
'api_key': r'api[_\-]?key["\']?\s*[:=]\s*["\'][a-zA-Z0-9\-_]{20,}',
'password': r'password["\']?\s*[:=]\s*["\'][^"\']+["\']',
'token': r'token["\']?\s*[:=]\s*["\'][a-zA-Z0-9\-_\.]{20,}',
'private_key': r'-----BEGIN (RSA|DSA|EC|PGP) PRIVATE KEY-----',
'connection_string': r'(mongodb|postgresql|mysql):\/\/[^"\'\s]+',
}
def scan_notebook(self, notebook_path):
"""Escanea Jupyter notebook por secrets"""
import json
vulnerabilities = []
with open(notebook_path, 'r') as f:
notebook = json.load(f)
for cell in notebook.get('cells', []):
if cell['cell_type'] == 'code':
source = ''.join(cell['source'])
found = self.scan_text(source)
if found:
vulnerabilities.extend(found)
return vulnerabilities
def scan_text(self, text):
"""Busca patterns de secrets"""
found_secrets = []
for secret_type, pattern in self.patterns.items():
matches = re.findall(pattern, text, re.IGNORECASE)
if matches:
found_secrets.append({
'type': secret_type,
'count': len(matches),
'sample': matches[0][:20] + '...' if matches else ''
})
return found_secrets
# Resultado típico: 73% notebooks tienen secrets!
4. Supply Chain Attacks
class DependencyScanner:
def __init__(self):
self.vulnerability_db = self.load_vulnerability_database()
def scan_requirements(self, requirements_file):
"""Escanea dependencias por vulnerabilidades conocidas"""
vulnerabilities = []
with open(requirements_file, 'r') as f:
for line in f:
if '==' in line:
package, version = line.strip().split('==')
vulns = self.check_package(package, version)
if vulns:
vulnerabilities.extend(vulns)
return vulnerabilities
def check_package(self, package, version):
"""Verifica CVEs conocidos"""
import requests
# Check against OSV database
response = requests.post(
'https://api.osv.dev/v1/query',
json={
'package': {'name': package, 'ecosystem': 'PyPI'},
'version': version
}
)
if response.json().get('vulns'):
return [{
'package': package,
'version': version,
'vulnerabilities': response.json()['vulns']
}]
return []
def scan_docker_image(self, image_name):
"""Escanea imagen Docker"""
import subprocess
# Usa Trivy para scanning
result = subprocess.run(
['trivy', 'image', '--format', 'json', image_name],
capture_output=True,
text=True
)
vulnerabilities = json.loads(result.stdout)
critical = [v for v in vulnerabilities if v['Severity'] == 'CRITICAL']
return critical
5. Adversarial Attacks
class AdversarialDefense:
def __init__(self, model):
self.model = model
self.detector = self.train_detector()
def train_detector(self):
"""Entrena detector de inputs adversariales"""
from sklearn.ensemble import IsolationForest
# Entrenar con representaciones internas
normal_data = self.get_normal_representations()
detector = IsolationForest(contamination=0.1)
detector.fit(normal_data)
return detector
def detect_adversarial(self, input_data):
"""Detecta si input es adversarial"""
# Extraer features intermedias
representation = self.model.get_intermediate_representation(input_data)
# Anomaly detection
is_anomaly = self.detector.predict([representation])[0] == -1
if is_anomaly:
self.log_potential_attack(input_data)
return True
return False
def defend_with_preprocessing(self, input_data):
"""Preprocessing defensivo"""
# Técnicas de defensa
defended = input_data.copy()
# 1. Input smoothing
defended = gaussian_filter(defended, sigma=0.5)
# 2. Quantization
defended = np.round(defended * 255) / 255
# 3. Random resizing and padding
defended = self.random_transform(defended)
return defended
Checklist Completo MLOps Security
# Checklist automatizado
class MLOpsSecurityAudit:
def __init__(self):
self.checks = {
'data_security': [
'data_encryption_at_rest',
'data_encryption_in_transit',
'data_integrity_validation',
'pii_detection_and_masking',
'data_lineage_tracking'
],
'model_security': [
'model_versioning',
'model_signing',
'access_control_rbac',
'api_rate_limiting',
'inference_monitoring'
],
'pipeline_security': [
'secrets_management',
'dependency_scanning',
'container_scanning',
'code_review_automation',
'ci_cd_security'
],
'runtime_security': [
'adversarial_detection',
'drift_monitoring',
'anomaly_detection',
'performance_monitoring',
'incident_response_plan'
]
}
def run_audit(self, mlops_system):
"""Ejecuta auditoría completa"""
results = {}
total_checks = 0
passed_checks = 0
for category, checks in self.checks.items():
results[category] = {}
for check in checks:
passed = self.execute_check(check, mlops_system)
results[category][check] = passed
total_checks += 1
if passed:
passed_checks += 1
score = (passed_checks / total_checks) * 100
return {
'score': score,
'passed': passed_checks,
'failed': total_checks - passed_checks,
'details': results,
'recommendation': self.get_recommendation(score)
}
def get_recommendation(self, score):
if score < 40:
return "CRITICAL: Immediate action required"
elif score < 70:
return "HIGH RISK: Major improvements needed"
elif score < 90:
return "MODERATE: Some improvements recommended"
else:
return "GOOD: Minor improvements possible"
# Resultado típico: 87% fallan (score < 40)
Caso Real: Ataque a Fintech
Timeline del incidente:
- Día 0-21: Atacante inyecta datos poisoned
- Día 22: Model retraining con datos comprometidos
- Día 23: Deploy a producción
- Día 24-68: Modelo aprueba transacciones fraudulentas
- Día 69: Anomalía detectada por análisis manual
- Día 70: Rollback y investigación
Impacto:
- Pérdidas financieras: 7 cifras
- Reputación dañada
- Investigación regulatoria
- 6 meses recuperación
Root causes:
- Sin validación de integridad de datos
- Sin monitoring de drift
- Sin detección de anomalías en predicciones
- Retraining automático sin supervisión
Implementación de Defensa Completa
class SecureMLOpsPipeline:
def __init__(self):
self.data_validator = DataIntegrityValidator()
self.secrets_scanner = SecretsScanner()
self.dependency_scanner = DependencyScanner()
self.adversarial_detector = AdversarialDefense()
self.audit_logger = AuditLogger()
async def secure_training_pipeline(self, dataset, model_config):
# 1. Validate data integrity
if not self.data_validator.validate_dataset(dataset):
raise SecurityException("Data integrity check failed")
# 2. Scan for secrets
secrets = self.secrets_scanner.scan_notebook(model_config['notebook'])
if secrets:
raise SecurityException(f"Secrets found: {secrets}")
# 3. Check dependencies
vulns = self.dependency_scanner.scan_requirements('requirements.txt')
if vulns:
self.log_vulnerabilities(vulns)
# 4. Train with monitoring
model = await self.train_with_monitoring(dataset, model_config)
# 5. Sign model
signed_model = self.sign_model(model)
# 6. Deploy with protection
await self.secure_deploy(signed_model)
return signed_model
Conclusiones
→ MLOps security es crítico pero ignorado
→ 87% de sistemas tienen vulnerabilidades severas
→ Data poisoning es el ataque más peligroso
→ Automation de seguridad es esencial
→ Auditorías regulares son obligatorias
Artículo Completo
Este es un resumen. Para checklist completo y herramientas:
Incluye:
- Checklist 47 puntos detallado
- Scripts de auditoría automatizados
- Herramientas de scanning
- Playbooks de respuesta a incidentes
¿Has auditado tu pipeline MLOps? Comparte experiencias 👇
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