CipherGuard: Compiler-aided Mitigation against Ciphertext Side-channel Attacks

• URL: http://arxiv.org/abs/2502.13401v1
• Date: Wed, 19 Feb 2025 03:22:36 GMT
• Title: CipherGuard: Compiler-aided Mitigation against Ciphertext Side-channel Attacks
• Authors: Ke Jiang, Sen Deng, Yinshuai Li, Shuai Wang, Tianwei Zhang, Yinqian Zhang,
• Abstract summary: CipherGuard is a compiler-aided mitigation methodology to counteract ciphertext side channels with high efficiency and security. We demonstrate that CipherGuard can strengthen the security of various cryptographic implementations more efficiently than existing state-of-the-art defense mechanism, i.e., CipherFix.
• Score: 30.992038220253797
• License:
• Abstract: Cryptographic implementations bolster security against timing side-channel attacks by integrating constant-time components. However, the new ciphertext side channels resulting from the deterministic memory encryption in Trusted Execution Environments (TEEs), enable ciphertexts to manifest identifiable patterns when being sequentially written to the same memory address. Attackers with read access to encrypted memory in TEEs can potentially deduce plaintexts by analyzing these changing ciphertext patterns. In this paper, we design CipherGuard, a compiler-aided mitigation methodology to counteract ciphertext side channels with high efficiency and security. CipherGuard is based on the LLVM ecosystem, and encompasses multiple mitigation strategies, including software-based probabilistic encryption and secret-aware register allocation. Through a comprehensive evaluation, we demonstrate that CipherGuard can strengthen the security of various cryptographic implementations more efficiently than existing state-of-the-art defense mechanism, i.e., CipherFix.

Related papers

• Cryptanalysis via Machine Learning Based Information Theoretic Metrics [58.96805474751668]
  We propose two novel applications of machine learning (ML) algorithms to perform cryptanalysis on any cryptosystem. These algorithms can be readily applied in an audit setting to evaluate the robustness of a cryptosystem. We show that our classification model correctly identifies the encryption schemes that are not IND-CPA secure, such as DES, RSA, and AES ECB, with high accuracy.
  arXiv  Detail & Related papers  (2025-01-25T04:53:36Z)
• Secure Semantic Communication With Homomorphic Encryption [52.5344514499035]
  This paper explores the feasibility of applying homomorphic encryption to SemCom. We propose a task-oriented SemCom scheme secured through homomorphic encryption.
  arXiv  Detail & Related papers  (2025-01-17T13:26:14Z)
• CodeChameleon: Personalized Encryption Framework for Jailbreaking Large Language Models [49.60006012946767]
  We propose CodeChameleon, a novel jailbreak framework based on personalized encryption tactics. We conduct extensive experiments on 7 Large Language Models, achieving state-of-the-art average Attack Success Rate (ASR) Remarkably, our method achieves an 86.6% ASR on GPT-4-1106.
  arXiv  Detail & Related papers  (2024-02-26T16:35:59Z)
• Cryptanalysis and improvement of multimodal data encryption by machine-learning-based system [0.0]
  encryption algorithms to accommodate varied requirements of this field. Best approach to analyzing an encryption algorithm is to identify a practical and efficient technique to break it.
  arXiv  Detail & Related papers  (2024-02-24T10:02:21Z)
• SOCI^+: An Enhanced Toolkit for Secure OutsourcedComputation on Integers [50.608828039206365]
  We propose SOCI+ which significantly improves the performance of SOCI. SOCI+ employs a novel (2, 2)-threshold Paillier cryptosystem with fast encryption and decryption as its cryptographic primitive. Compared with SOCI, our experimental evaluation shows that SOCI+ is up to 5.4 times more efficient in computation and 40% less in communication overhead.
  arXiv  Detail & Related papers  (2023-09-27T05:19:32Z)
• GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Cipher [85.18213923151717]
  Experimental results show certain ciphers succeed almost 100% of the time to bypass the safety alignment of GPT-4 in several safety domains. We propose a novel SelfCipher that uses only role play and several demonstrations in natural language to evoke this capability.
  arXiv  Detail & Related papers  (2023-08-12T04:05:57Z)
• Enhancing Networking Cipher Algorithms with Natural Language [0.0]
  Natural language processing is considered as the weakest link in a networking encryption model. This paper summarizes how languages can be integrated into symmetric encryption as a way to assist in the encryption of vulnerable streams.
  arXiv  Detail & Related papers  (2022-06-22T09:05:52Z)
• Uncloneable Decryptors from Quantum Copy-Protection [0.38073142980733]
  We show that CPA secure uncloneable bit decryptors could be instantiated from a copy protection scheme. We then show how to strengthen the CPA security of uncloneable decryptors to CCA2 security using strong EUF-CMA secure digital signatures.
  arXiv  Detail & Related papers  (2022-03-11T11:47:04Z)
• Recovering AES Keys with a Deep Cold Boot Attack [91.22679787578438]
  Cold boot attacks inspect the corrupted random access memory soon after the power has been shut down. In this work, we combine a novel cryptographic variant of a deep error correcting code technique with a modified SAT solver scheme to apply the attack on AES keys. Our results show that our methods outperform the state of the art attack methods by a very large margin.
  arXiv  Detail & Related papers  (2021-06-09T07:57:01Z)
• TEDL: A Text Encryption Method Based on Deep Learning [10.428079716944463]
  This paper proposes a novel text encryption method based on deep learning called TEDL. Results of experiments and relevant analyses show that TEDL performs well for security, efficiency, generality, and has a lower demand for the frequency of key redistribution.
  arXiv  Detail & Related papers  (2020-03-09T11:04:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.