Random and Safe Cache Architecture to Defeat Cache Timing Attacks
- URL: http://arxiv.org/abs/2309.16172v2
- Date: Mon, 22 Apr 2024 02:15:38 GMT
- Title: Random and Safe Cache Architecture to Defeat Cache Timing Attacks
- Authors: Guangyuan Hu, Ruby B. Lee,
- Abstract summary: Caches have been exploited to leak secret information due to the different times they take to handle memory accesses.
We present a systematic view of the attack and defense space and show that no existing defense has addressed all cache timing attacks.
We propose Random and Safe (RaS) cache architectures to decorrelate cache state changes from memory requests.
- Score: 5.142233612851766
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Caches have been exploited to leak secret information due to the different times they take to handle memory accesses. Cache timing attacks include non-speculative cache side and covert channel attacks and cache-based speculative execution attacks. We first present a systematic view of the attack and defense space and show that no existing defense has addressed all cache timing attacks, which we do in this paper. We propose Random and Safe (RaS) cache architectures to decorrelate cache state changes from memory requests. RaS fills the cache with ``safe'' cache lines that are likely to be used in the future, rather than with demand-fetched, security-sensitive lines. RaS lifts the restriction on cache fills for accesses that become safe when speculative execution is resolved and authorized. Our RaS-Spec design against cache-based speculative execution attacks has a low 3.8% average performance overhead. RaS+ variants against both speculative and non-speculative attacks have security-performance trade-offs ranging from 7.9% to 45.2% average overhead.
Related papers
- Auditing Prompt Caching in Language Model APIs [77.02079451561718]
We investigate the privacy leakage caused by prompt caching in large language models (LLMs)
We detect global cache sharing across users in seven API providers, including OpenAI.
We find evidence that OpenAI's embedding model is a decoder-only Transformer, which was previously not publicly known.
arXiv Detail & Related papers (2025-02-11T18:58:04Z) - RollingCache: Using Runtime Behavior to Defend Against Cache Side Channel Attacks [2.9221371172659616]
We present RollingCache, a cache design that defends against contention attacks by dynamically changing the set of addresses contending for cache sets.
RollingCache does not rely on address encryption/decryption, data relocation, or cache partitioning.
Our solution does not depend on having defined security domains, and can defend against an attacker running on the same or another core.
arXiv Detail & Related papers (2024-08-16T15:11:12Z) - Efficient Inference of Vision Instruction-Following Models with Elastic Cache [76.44955111634545]
We introduce Elastic Cache, a novel strategy for efficient deployment of instruction-following large vision-language models.
We propose an importance-driven cache merging strategy to prune redundancy caches.
For instruction encoding, we utilize the frequency to evaluate the importance of caches.
Results on a range of LVLMs demonstrate that Elastic Cache not only boosts efficiency but also notably outperforms existing pruning methods in language generation.
arXiv Detail & Related papers (2024-07-25T15:29:05Z) - Cancellable Memory Requests: A transparent, lightweight Spectre mitigation [11.499924192220274]
Speculation is fundamental to achieving high CPU performance, yet it enables vulnerabilities such as Spectre attacks.
We propose a novel mitigation technique, Cancellable Memory Requests (CMR) that cancels mis-speculated memory requests.
We show that CMR can completely thwart Spectre attacks in four real-world processors with realistic system configurations.
arXiv Detail & Related papers (2024-06-17T21:43:39Z) - SEA Cache: A Performance-Efficient Countermeasure for Contention-based Attacks [4.144828482272047]
We extend an existing secure cache design, CEASER-SH cache, and propose the SEA cache.
The novel cache configurations in both caches are logical associativity, which allows the cache line to be placed not only in its mapped cache set but also in the subsequent cache sets.
Compared to a CEASER-SH cache with logical associativity of 8, an SEA cache with logical associativity of 1 for normal protection users and 16 for high protection users has a Cycles Per Instruction penalty that is about 0.6% less for users under normal protections and provides better security against contention-based attacks
arXiv Detail & Related papers (2024-05-30T13:12:53Z) - Systematic Evaluation of Randomized Cache Designs against Cache Occupancy [11.018866935621045]
This work fills in a crucial gap in current literature on randomized caches.
Most randomized cache designs defend only contention-based attacks, and leave out considerations of cache occupancy.
Our results establish the need to also consider cache occupancy side-channel in randomized cache design considerations.
arXiv Detail & Related papers (2023-10-08T14:06:06Z) - Backdoor Attack with Sparse and Invisible Trigger [57.41876708712008]
Deep neural networks (DNNs) are vulnerable to backdoor attacks.
backdoor attack is an emerging yet threatening training-phase threat.
We propose a sparse and invisible backdoor attack (SIBA)
arXiv Detail & Related papers (2023-05-11T10:05:57Z) - BackCache: Mitigating Contention-Based Cache Timing Attacks by Hiding Cache Line Evictions [7.46215723037597]
L1 data cache attacks pose a significant privacy and confidentiality threat.
BackCache always achieves cache hits instead of cache misses to mitigate contention-based cache timing attacks on the L1 data cache.
BackCache places the evicted cache lines from the L1 data cache into a fully-associative backup cache to hide the evictions.
arXiv Detail & Related papers (2023-04-20T12:47:11Z) - Attack Agnostic Adversarial Defense via Visual Imperceptible Bound [70.72413095698961]
This research aims to design a defense model that is robust within a certain bound against both seen and unseen adversarial attacks.
The proposed defense model is evaluated on the MNIST, CIFAR-10, and Tiny ImageNet databases.
The proposed algorithm is attack agnostic, i.e. it does not require any knowledge of the attack algorithm.
arXiv Detail & Related papers (2020-10-25T23:14:26Z) - Rethinking the Trigger of Backdoor Attack [83.98031510668619]
Currently, most of existing backdoor attacks adopted the setting of emphstatic trigger, $i.e.,$ triggers across the training and testing images follow the same appearance and are located in the same area.
We demonstrate that such an attack paradigm is vulnerable when the trigger in testing images is not consistent with the one used for training.
arXiv Detail & Related papers (2020-04-09T17:19:37Z) - On Certifying Robustness against Backdoor Attacks via Randomized
Smoothing [74.79764677396773]
We study the feasibility and effectiveness of certifying robustness against backdoor attacks using a recent technique called randomized smoothing.
Our results show the theoretical feasibility of using randomized smoothing to certify robustness against backdoor attacks.
Existing randomized smoothing methods have limited effectiveness at defending against backdoor attacks.
arXiv Detail & Related papers (2020-02-26T19:15:46Z)
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.