اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدCacheOut: Leaking Data on Intel CPUs via Cache Evictions
229
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Recent transient-execution attacks, such as RIDL, Fallout, and ZombieLoad, demonstrated that attackers can leak information while it transits through microarchitectural buffers. Named Microarchitectural Data Sampling (MDS) by Intel, these attacks are likened to drinking from the firehose, as the attacker has little control over what data is observed and from what origin. Unable to prevent the buffers from leaking, Intel issued countermeasures via microcode updates that overwrite the buffers when the CPU changes security domains. In this work we present CacheOut, a new microarchitectural attack that is capable of bypassing Intels buffer overwrite countermeasures. We observe that as data is being evicted from the CPUs L1 cache, it is often transferred back to the leaky CPU buffers where it can be recovered by the attacker. CacheOut improves over previous MDS attacks by allowing the attacker to choose which data to leak from the CPUs L1 cache, as well as which part of a cache line to leak. We demonstrate that CacheOut can leak information across multiple security boundaries, including those between processes, virtual machines, user and kernel space, and from SGX enclaves.
قيم البحث
اقرأ أيضاً
The growing adoption of IoT devices in our daily life is engendering a data deluge, mostly private information that needs careful maintenance and secure storage system to ensure data integrity and protection. Also, the prodigious IoT ecosystem has pr
ovided users with opportunities to automate systems by interconnecting their devices and other services with rule-based programs. The cloud services that are used to store and process sensitive IoT data turn out to be vulnerable to outside threats. Hence, sensitive IoT data and rule-based programs need to be protected against cyberattacks. To address this important challenge, in this paper, we propose a framework to maintain confidentiality and integrity of IoT data and rule-based program execution. We design the framework to preserve data privacy utilizing Trusted Execution Environment (TEE) such as Intel SGX, and end-to-end data encryption mechanism. We evaluate the framework by executing rule-based programs in the SGX securely with both simulated and real IoT device data.
Register Files (RFs) are the most frequently accessed memories in a microprocessor for fast and efficient computation and control logic. Segment registers and control registers are especially critical for maintaining the CPU mode of execution that de
terminesthe access privileges. In this work, we explore the vulnerabilities in RF and propose a class of hardware Trojans which can inject faults during read or retention mode. The Trojan trigger is activated if one pre-selected address of L1 data-cache is hammered for certain number of times. The trigger evades post-silicon test since the required number of hammering to trigger is significantly high even under process and temperature variation. Once activated, the trigger can deliver payloads to cause Bitcell Corruption (BC) and inject read error by Read Port (RP) and Local Bitline (LBL). We model the Trojan in GEM5 architectural simulator performing a privilege escalation. We propose countermeasures such as read verification leveraging multiport feature, securing control and segment registers by hashing and L1 address obfuscation.
On Intel Sandy Bridge processor, last level cache (LLC) is divided into cache slices and all physical addresses are distributed across the cache slices using an hash function. With this undocumented hash function existing, it is impossible to impleme
nt cache partition based on page coloring. This article cracks the hash functions on two types of Intel Sandy processors by converting the problem of cracking the hash function to the problem of classifying data blocks into different groups based on eviction relationship existing between data blocks that are mapped to the same cache set. Based on the cracking result, this article proves that its possible to implement cache partition based on page coloring on cache indexed by hashing.
In addition to hardware wall-time restrictions commonly seen in high-performance computing systems, it is likely that future systems will also be constrained by energy budgets. In the present work, finite difference algorithms of varying computationa
l and memory intensity are evaluated with respect to both energy efficiency and runtime on an Intel Ivy Bridge CPU node, an Intel Xeon Phi Knights Landing processor, and an NVIDIA Tesla K40c GPU. The conventional way of storing the discretised derivatives to global arrays for solution advancement is found to be inefficient in terms of energy consumption and runtime. In contrast, a class of algorithms in which the discretised derivatives are evaluated on-the-fly or stored as thread-/process-local variables (yielding high compute intensity) is optimal both with respect to energy consumption and runtime. On all three hardware architectures considered, a speed-up of ~2 and an energy saving of ~2 are observed for the high compute intensive algorithms compared to the memory intensive algorithm. The energy consumption is found to be proportional to runtime, irrespective of the power consumed and the GPU has an energy saving of ~5 compared to the same algorithm on a CPU node.
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.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
