Meltdown: Reading Kernel Memory from User Space

By Moritz Lipp, Michael Schwarz, Daniel Gruss, Thomas Prescher, Werner Haas, Anders Fogh, Jann Horn, Stefan Mangard, Paul C. Kocher, Daniel Genkin and 2 more. 2018

1,499 citations recorded by the source corpus.

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Citations in the corpus

Property Inference Attacks on Fully Connected Neural Networks using Permutation Invariant Representations (2018, methodology): “In the second case study we show that a classifier that recognizes Bitcoin mining from performance counters also leaks information about whether the classifier was trained on logs from machines that were patched for the Meltdown and Spectre attacks.”
A Principled Approach to Learning Stochastic Representations for Privacy in Deep Neural Inference (2020, methodology): “The threat is that as soon as the raw data is sent to the cloud, it can be misused or leaked through security vulnerabilities even if the cloud provider and/or the communication link is trusted (Kocher et al., 2019; Thompson & Warzel, 2019; Lipp et al., 2018; Newcomb, 2018).”
RowHammer: A Retrospective (2019, background): “We believe it is no coincidence that two of the groups that concurrently discovered the heavily publicized Meltdown [157] and Spectre [135] vulnerabilities (Google Project Zero and TU Graz InfoSec) have heavily worked on RowHammer attacks before.”
SgxPectre: Stealing Intel Secrets from SGX Enclaves Via Speculative Execution (2018, background): “Particularly, the so-called Meltdown [48] and Spectre attacks [44] have demonstrated that an unprivileged application may exploit these vulnerabilities to extract memory content that is only accessible to privileged software.”
A Systematic Evaluation of Transient Execution Attacks and Defenses (2018, background): “This is the foundation of Spectre [52], Meltdown [59], and Foreshadow [90].”
Packet Chasing: Spying on Network Packets over a Cache Side-Channel (2019, unknown): “However, this complexity often breeds security vulnerabilities, as evidenced recently by Meltdown [1] and Spectre [2].”
ZombieLoad: Cross-Privilege-Boundary Data Sampling (2019, methodology): “]. In line with a recent exhaustive survey [9], we refer to attacks exploiting misprediction [29, 40, 42, 43, 49] as Spectre-type, whereas attacks exploiting transient execution after a CPU exception [9, 40, 45, 67, 68, 75] are classified as belonging to Meltdown-type. 2.2 Memory Subsystem The CPU a”
Orbital Edge Computing: Nanosatellite Constellations as a New Class of Computer System (2020, background): “Edge processing avoids the privacy and security risks of multi-tenant infrastructures in shared datacenters [26, 56, 60].”
Fallout: Leaking Data on Meltdown-resistant CPUs (2019, methodology): “As a countermeasure to Meltdown [51], OSs running on Intel processors prior to Coffee Lake R have deployed the KAISER countermeasure, which removes the kernel from the address space of user processes (see Figure 8 (bottom)).”
RIDL: Rogue In-Flight Data Load (2019, background): “Clearly, one can only “speculate” here, but this behavior seems consistent with existing vulnerabilities [1], [3].”
Shredder: Learning Noise to Protect Privacy with Partial DNN Inference on the Edge (2019, methodology): “Even if the provider is trusted, the data can still be vulnerable over communication channels or via side-channel attacks [1, 2] at the provider.”
Shredder: Learning Noise Distributions to Protect Inference Privacy (2019, methodology): “ers to send their data to an enclave running on a remote servers. In contrast to Shredder, this model still allows the remote server to have access to the raw data and as the new breaches in hardware [1, 2, 61, 62, 63, 64] show, the access can lead to comprised privacy. Differential privacy. As a ma”
Cloud Programming Simplified: A Berkeley View on Serverless Computing (2019, background): “Recent hardware attacks (e.g., Spectre [66] and Meltdown [67]) also make reserving a whole core or even a whole physical machine appealing for users.”
Towards Probabilistic Verification of Machine Unlearning (2020, background): “However, such techniques require assumptions that limit their practicality – server side computation, the use of trusted parties, computational overhead along with frequent attacks on the security of such systems [50, 51].”
InvisiSpec: Making Speculative Execution Invisible in the Cache Hierarchy (2018, background): “In this case, unlike in Meltdown, there is no exception-inducing load.”
Context-Sensitive Fencing: Securing Speculative Execution via Microcode Customization (2019, background): “However, scaling performance without considering security implications could have serious negative consequences, as evidenced by the recent pile of lawsuits [79] concerning the Meltdown [54] and Spectre [51] class of microarchitectural attacks.”
Socio-Technological Challenges and Opportunities: Paths Forward (2021, background): “…will be online and accessible to bad actors, and the internet and the technology it enables will continue to be susceptible without strong security and privacy measures in place from the top of the software stack to the underlying hardware running the code [Lipp et al., 2018, Kocher et al., 2019].”