EXAM: Exploiting Exclusive System-Level Cache in Apple M-Series SoCs for Enhanced Cache Occupancy Attacks
- URL: http://arxiv.org/abs/2504.13385v1
- Date: Fri, 18 Apr 2025 00:21:00 GMT
- Title: EXAM: Exploiting Exclusive System-Level Cache in Apple M-Series SoCs for Enhanced Cache Occupancy Attacks
- Authors: Tianhong Xu, Aidong Adam Ding, Yunsi Fei,
- Abstract summary: Cache occupancy attacks exploit the shared nature of cache hierarchies to infer a victim's activities by monitoring overall cache usage.<n>We propose a suite of SLC-cache occupancy attacks, the first of its kind, where an adversary can monitor GPU and other CPU cluster activities from their own CPU cluster.
- Score: 2.198430261120653
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Cache occupancy attacks exploit the shared nature of cache hierarchies to infer a victim's activities by monitoring overall cache usage, unlike access-driven cache attacks that focus on specific cache lines or sets. There exists some prior work that target the last-level cache (LLC) of Intel processors, which is inclusive of higher-level caches, and L2 caches of ARM systems. In this paper, we target the System-Level Cache (SLC) of Apple M-series SoCs, which is exclusive to higher-level CPU caches. We address the challenges of the exclusiveness and propose a suite of SLC-cache occupancy attacks, the first of its kind, where an adversary can monitor GPU and other CPU cluster activities from their own CPU cluster. We first discover the structure of SLC in Apple M1 SOC and various policies pertaining to access and sharing through reverse engineering. We propose two attacks against websites. One is a coarse-grained fingerprinting attack, recognizing which website is accessed based on their different GPU memory access patterns monitored through the SLC occupancy channel. The other attack is a fine-grained pixel stealing attack, which precisely monitors the GPU memory usage for rendering different pixels, through the SLC occupancy channel. Third, we introduce a novel screen capturing attack which works beyond webpages, with the monitoring granularity of 57 rows of pixels (there are 1600 rows for the screen). This significantly expands the attack surface, allowing the adversary to retrieve any screen display, posing a substantial new threat to system security. Our findings reveal critical vulnerabilities in Apple's M-series SoCs and emphasize the urgent need for effective countermeasures against cache occupancy attacks in heterogeneous computing environments.
Related papers
- 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) - PyramidInfer: Pyramid KV Cache Compression for High-throughput LLM Inference [57.53291046180288]
Large Language Models (LLMs) have shown remarkable comprehension abilities but face challenges in GPU memory usage during inference.
We propose PyramidInfer, a method that compresses the KV cache by layer-wise retaining crucial context.
PyramidInfer improves 2.2x throughput compared to Accelerate with over 54% GPU memory reduction in KV cache.
arXiv Detail & Related papers (2024-05-21T06:46:37Z) - EmInspector: Combating Backdoor Attacks in Federated Self-Supervised Learning Through Embedding Inspection [53.25863925815954]
Federated self-supervised learning (FSSL) has emerged as a promising paradigm that enables the exploitation of clients' vast amounts of unlabeled data.
While FSSL offers advantages, its susceptibility to backdoor attacks has not been investigated.
We propose the Embedding Inspector (EmInspector) that detects malicious clients by inspecting the embedding space of local models.
arXiv Detail & Related papers (2024-05-21T06:14:49Z) - Last-Level Cache Side-Channel Attacks Are Feasible in the Modern Public Cloud (Extended Version) [16.594665501866675]
We present an end-to-end, cross-tenant attack on a vulnerable ECDSA implementation in the public F-based algorithm for Google Cloud Run environment.
We introduce several new techniques to improve every step of the attack.
Overall, we extract a median value of 81% of the secret ECDSA nonce bits from a victim container in 19 seconds on average.
arXiv Detail & Related papers (2024-05-21T03:05:29Z) - Prime+Retouch: When Cache is Locked and Leaked [8.332926136722296]
Caches on modern commodity CPUs have become one of the major sources of side-channel leakages.
To thwart the cache-based side-channel attacks, two types of countermeasures have been proposed.
We present the Prime+Retouch attack that completely bypasses these defense schemes.
arXiv Detail & Related papers (2024-02-23T16:34:49Z) - Systematic Evaluation of Randomized Cache Designs against Cache Occupancy [11.018866935621045]
This work fills in a crucial gap in current literature on randomized caches.<n>Most randomized cache designs defend only contention-based attacks, and leave out considerations of cache occupancy.<n>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) - Random and Safe Cache Architecture to Defeat Cache Timing Attacks [5.142233612851766]
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.
arXiv Detail & Related papers (2023-09-28T05:08:16Z) - 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) - Recurrent Dynamic Embedding for Video Object Segmentation [54.52527157232795]
We propose a Recurrent Dynamic Embedding (RDE) to build a memory bank of constant size.
We propose an unbiased guidance loss during the training stage, which makes SAM more robust in long videos.
We also design a novel self-correction strategy so that the network can repair the embeddings of masks with different qualities in the memory bank.
arXiv Detail & Related papers (2022-05-08T02:24:43Z) - BinaryCoP: Binary Neural Network-based COVID-19 Face-Mask Wear and
Positioning Predictor on Edge Devices [63.56630165340053]
Face masks offer an effective solution in healthcare for bi-directional protection against air-borne diseases.
CNNs offer an excellent solution for face recognition and classification of correct mask wearing and positioning.
CNNs can be used at entrances to corporate buildings, airports, shopping areas, and other indoor locations, to mitigate the spread of the virus.
arXiv Detail & Related papers (2021-02-06T00:14:06Z) - A Self-supervised Approach for Adversarial Robustness [105.88250594033053]
Adversarial examples can cause catastrophic mistakes in Deep Neural Network (DNNs) based vision systems.
This paper proposes a self-supervised adversarial training mechanism in the input space.
It provides significant robustness against the textbfunseen adversarial attacks.
arXiv Detail & Related papers (2020-06-08T20:42:39Z)
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.