An Off-Chip Attack on Hardware Enclaves via the Memory Bus
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Abstract (arXiv)
This paper shows how an attacker can break the confidentiality of a hardware enclave with Membuster, an off-chip attack based on snooping the memory bus. An attacker with physical access can observe an unencrypted address bus and extract fine-grained memory access patterns of the victim. Membuster is qualitatively different from prior on-chip attacks to enclaves and is more difficult to thwart. We highlight several challenges for Membuster. First, DRAM requests are only visible on the memory bus at last-level cache misses. Second, the attack needs to incur minimal interference or overhead to the victim to prevent the detection of the attack. Lastly, the attacker needs to reverse-engineer the translation between virtual, physical, and DRAM addresses to perform a robust attack. We introduce three techniques, critical page whitelisting, cache squeezing, and oracle-based fuzzy matching algorithm to increase cache misses for memory accesses that are useful for the attack, with no detectable interference to the victim, and to convert memory accesses to sensitive data. We demonstrate Membuster on an Intel SGX CPU to leak confidential data from two applications: Hunspell and Memcached. We show that a single uninterrupted run of the victim can leak most of the sensitive data with high accuracy.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Dec 2019.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 0; 2024: 1; 2025: 0; 2026: 3.
6 papers.
- 2021Confidential Machine Learning Computation in Untrusted Environments: A Systems Security Perspectivecited by 4
- 2021Automated Side Channel Analysis of Media Software with Manifold Learningcited by 1
- 2026Cascade: Composing Software-Hardware Attack Gadgets for Adversarial Threat Amplification in Compound AI Systemscited by 1
- 2026Opal: Private Memory for Personal AIcited by 1
- 2024TensorTEE: Unifying Heterogeneous TEE Granularity for Efficient Secure Collaborative Tensor Computingcited by 0
- 2026SparSEEty: Extracting Tokens from Sparsity-Exploiting LLM Serving Systems via Deterministic Side Channelscited by 0
- 2026SparSEEty: Extracting Tokens from Sparsity-Exploiting LLM Serving Systems via Deterministic Side Channels4 Aug 2026
- 2026Opal: Private Memory for Personal AI2 Apr 2026
- 2026Cascade: Composing Software-Hardware Attack Gadgets for Adversarial Threat Amplification in Compound AI Systems12 Mar 2026
- 2024TensorTEE: Unifying Heterogeneous TEE Granularity for Efficient Secure Collaborative Tensor Computing12 Jul 2024
- 2021Automated Side Channel Analysis of Media Software with Manifold Learning9 Dec 2021
- 2021Confidential Machine Learning Computation in Untrusted Environments: A Systems Security Perspective5 Nov 2021