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Register a new userGhostKnight: Breaching Data Integrity via Speculative Execution
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Added by
Zhi Zhang
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Existing speculative execution attacks are limited to breaching confidentiality of data beyond privilege boundary, the so-called spectre-type attacks. All of them utilize the changes in microarchitectural buffers made by the speculative execution to leak data. We show that the speculative execution can be abused to break data integrity. We observe that the speculative execution not only leaves traces in the microarchitectural buffers but also induces side effects within DRAM, that is, the speculative execution can trigger an access to an illegitimate address in DRAM. If the access to DRAM is frequent enough, then architectural changes (i.e., permanent bit flips in DRAM) will occur, which we term GhostKnight. With the power of of GhostKnight, an attacker is essentially able to cross different privilege boundaries and write exploitable bits to other privilege domains. In our future work, we will develop a GhostKnight-based exploit to cross a trusted execution environment, defeat a 1024-bit RSA exponentiation implementation and obtain a controllable signature.
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Modern processors use branch prediction and speculative execution to maximize performance. For example, if the destination of a branch depends on a memory value that is in the process of being read, CPUs will try guess the destination and attempt to execute ahead. When the memory value finally arrives, the CPU either discards or commits the speculative computation. Speculative logic is unfaithful in how it executes, can access to the victims memory and registers, and can perform operations with measurable side effects. Spectre attacks involve inducing a victim to speculatively perform operations that would not occur during correct program execution and which leak the victims confidential information via a side channel to the adversary. This paper describes practical attacks that combine methodology from side channel attacks, fault attacks, and return-oriented programming that can read arbitrary memory from the victims process. More broadly, the paper shows that speculative execution implementations violate the security assumptions underpinning numerous software security mechanisms, including operating system process separation, static analysis, containerization, just-in-time (JIT) compilation, and countermeasures to cache timing/side-channel attacks. These attacks represent a serious threat to actual systems, since vulnerable speculative execution capabilities are found in microprocessors from Intel, AMD, and ARM that are used in billions of devices. While makeshift processor-specific countermeasures are possible in some cases, sound solutions will require fixes to processor designs as well as updates to instruction set architectures (ISAs) to give hardware architects and software developers a common understanding as to what computation state CPU implementations are (and are not) permitted to leak.
Spectre attacks disclosed in early 2018 expose data leakage scenarios via cache side channels. Specifically, speculatively executed paths due to branch mis-prediction may bring secret data into the cache which are then exposed via cache side channels even after the speculative execution is squashed. Symbolic execution is a well-known test generation method to cover program paths at the level of the application software. In this paper, we extend symbolic execution with modelingof cache and speculative execution. Our tool KLEESPECTRE, built on top of the KLEE symbolic execution engine, can thus provide a testing engine to check for the data leakage through cache side-channel as shown via Spectre attacks. Our symbolic cache model can verify whether the sensitive data leakage due to speculative execution can be observed by an attacker at a given program point. Our experiments show that KLEESPECTREcan effectively detect data leakage along speculatively executed paths and our cache model can further make the leakage detection much more precise.
Spectre, Meltdown, and related attacks have demonstrated that kernels, hypervisors, trusted execution environments, and browsers are prone to information disclosure through micro-architectural weaknesses. However, it remains unclear as to what extent other applications, in particular those that do not load attacker-provided code, may be impacted. It also remains unclear as to what extent these attacks are reliant on cache-based side channels. We introduce SMoTherSpectre, a speculative code-reuse attack that leverages port-contention in simultaneously multi-threaded processors (SMoTher) as a side channel to leak information from a victim process. SMoTher is a fine-grained side channel that detects contention based on a single victim instruction. To discover real-world gadgets, we describe a methodology and build a tool that locates SMoTher-gadgets in popular libraries. In an evaluation on glibc, we found hundreds of gadgets that can be used to leak information. Finally, we demonstrate proof-of-concept attacks against the OpenSSH server, creating oracles for determining four host key bits, and against an application performing encryption using the OpenSSL library, creating an oracle which can differentiate a bit of the plaintext through gadgets in libcrypto and glibc.
CPU cache is a limited but crucial storage component in modern processors, whereas the cache timing side-channel may inadvertently leak information through the physically measurable timing variance. Speculative execution, an essential processor optimization, and a source of such variances, can cause severe detriment on deliberate branch mispredictions. Despite static analysis could qualitatively verify the timing-leakage-free property under speculative execution, it is incapable of producing endorsements including inputs and speculated flows to diagnose leaks in depth. This work proposes a new symbolic execution based method, SpecuSym, for precisely detecting cache timing leaks introduced by speculative execution. Given a program (leakage-free in non-speculative execution), SpecuSymsystematically explores the program state space, models speculative behavior at conditional branches, and accumulates the cache side effects along with subsequent path explorations. During the dynamic execution, SpecuSymconstructs leak predicates for memory visits according to the specified cache model and conducts a constraint-solving based cache behavior analysis to inspect the new cache behaviors. We have implementedSpecuSymatop KLEE and evaluated it against 15 open-source benchmarks. Experimental results show thatSpecuSymsuccessfully detected from 2 to 61 leaks in 6 programs under 3 different cache settings and identified false positives in 2 programs reported by recent work.
Machine learning models based on Deep Neural Networks (DNNs) are increasingly deployed in a wide range of applications ranging from self-driving cars to COVID-19 treatment discovery. To support the computational power necessary to learn a DNN, cloud environments with dedicated hardware support have emerged as critical infrastructure. However, there are many integrity challenges associated with outsourcing computation. Various approaches have been developed to address these challenges, building on trusted execution environments (TEE). Yet, no existing approach scales up to support realistic integrity-preserving DNN model training for heavy workloads (deep architectures and millions of training examples) without sustaining a significant performance hit. To mitigate the time gap between pure TEE (full integrity) and pure GPU (no integrity), we combine random verification of selected computation steps with systematic adjustments of DNN hyper-parameters (e.g., a narrow gradient clipping range), hence limiting the attackers ability to shift the model parameters significantly provided that the step is not selected for verification during its training phase. Experimental results show the new approach achieves 2X to 20X performance improvement over pure TEE based solution while guaranteeing a very high probability of integrity (e.g., 0.999) with respect to state-of-the-art DNN backdoor attacks.
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