Prompt injection represents a critical security concern...
# Example vulnerable code
user_input = request.form['query']
prompt = f"Translate the following to Spanish: {user_input}"
response = llm.complete(prompt)
End-to-end secure machine learning pipeline implementing data validation, model versioning, and inference monitoring...
import os
from cryptography.fernet import Fernet
class SecureModelLoader:
def __init__(self):
self.encryption_key = os.getenv('MODEL_ENCRYPTION_KEY')
self.cipher = Fernet(self.encryption_key.encode())
def load_model(self, model_path, signature):
"""Load and verify encrypted model"""
with open(model_path, 'rb') as f:
encrypted_data = f.read()
# Verify signature before decryption
if not self.verify_signature(encrypted_data, signature):
raise SecurityError("Model signature verification failed")
decrypted_model = self.cipher.decrypt(encrypted_data)
return pickle.loads(decrypted_model)
import jwt
import os
from datetime import datetime, timedelta
def generate_token(user_id):
"""Generate secure JWT token"""
secret = os.getenv('JWT_SECRET_KEY')
payload = {
'user_id': user_id,
'exp': datetime.utcnow() + timedelta(hours=1),
'iat': datetime.utcnow()
}
return jwt.encode(payload, secret, algorithm='HS256')
Deep dive into prompt injection attack vectors and mitigation strategies...
Read more →Comprehensive guide to implementing secure token-based authentication...
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# Your code here