Understanding and Mitigating Side and Covert Channel Vulnerabilities Introduced by RowHammer Defenses

• URL: http://arxiv.org/abs/2503.17891v2
• Date: Tue, 22 Apr 2025 16:34:57 GMT
• Title: Understanding and Mitigating Side and Covert Channel Vulnerabilities Introduced by RowHammer Defenses
• Authors: F. Nisa Bostancı, Oğuzhan Canpolat, Ataberk Olgun, İsmail Emir Yüksel, Konstantinos Kanellopoulos, Mohammad Sadrosadati, A. Giray Yağlıkçı, Onur Mutlu,
• Abstract summary: We introduce LeakyHammer, a new class of attacks that leverage the RowHammer mitigation-induced memory latency differences to establish communication channels and leak secrets.<n>We show that fundamentally mitigating LeakyHammer induces large overheads in highly RowHammer-vulnerable systems.
• Score: 6.52467000790105
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: DRAM chips are vulnerable to read disturbance phenomena (e.g., RowHammer and RowPress), where repeatedly accessing or keeping open a DRAM row causes bitflips in nearby rows. Attackers leverage RowHammer bitflips in real systems to take over systems and leak data. Consequently, many prior works propose mitigations, including recent DDR specifications introducing new mitigations (e.g., PRAC and RFM). For robust operation, it is critical to analyze other security implications of RowHammer mitigations. Unfortunately, no prior work analyzes the timing covert and side channel vulnerabilities introduced by RowHammer mitigations. This paper presents the first analysis and evaluation of timing covert and side channel vulnerabilities introduced by state-of-the-art RowHammer mitigations. We demonstrate that RowHammer mitigations' preventive actions have two fundamental features that enable timing channels. First, preventive actions reduce DRAM bandwidth availability, resulting in longer memory latencies. Second, preventive actions can be triggered on demand depending on memory access patterns. We introduce LeakyHammer, a new class of attacks that leverage the RowHammer mitigation-induced memory latency differences to establish communication channels and leak secrets. First, we build two covert channel attacks exploiting two state-of-the-art RowHammer mitigations, achieving 38.6 Kbps and 48.6 Kbps channel capacity. Second, we demonstrate a website fingerprinting attack that identifies visited websites based on the RowHammer-preventive actions they cause. We propose and evaluate two countermeasures against LeakyHammer and show that fundamentally mitigating LeakyHammer induces large overheads in highly RowHammer-vulnerable systems. We believe and hope our work can enable and aid future work on designing robust systems against RowHammer mitigation-based side and covert channels.

Related papers

• Chronus: Understanding and Securing the Cutting-Edge Industry Solutions to DRAM Read Disturbance [6.220002579079846]
  We present the first rigorous security, performance, energy, and cost analyses of the state-of-the-art on-DRAM-die read disturbance mitigation method.<n>We propose a new on-DRAM-die RowHammer mitigation mechanism, Chronus, to address PRAC's two major weaknesses.
  arXiv  Detail & Related papers  (2025-02-18T08:54:49Z)
• Understanding RowHammer Under Reduced Refresh Latency: Experimental Analysis of Real DRAM Chips and Implications on Future Solutions [6.157443107603247]
  RowHammer is a read disturbance mechanism in DRAM where repeatedly accessing (hammering) a row of DRAM cells (DRAM row) induces bitflips in physically nearby DRAM rows (victim rows)<n>With newer DRAM chip generations, these mechanisms perform preventive refresh more aggressively and cause larger performance, energy, or area overheads.<n>We present the first rigorous experimental study on the interactions between refresh latency and RowHammer characteristics in real DRAM chips.<n>Our results show that Partial Charge Restoration for Aggressive Mitigation (PaCRAM) reduces the performance and energy overheads induced by five state-of-the-art RowHammer mitigation mechanisms with
  arXiv  Detail & Related papers  (2025-02-17T12:39:03Z)
• DAPPER: A Performance-Attack-Resilient Tracker for RowHammer Defense [1.1816942730023883]
  RowHammer vulnerabilities pose a significant threat to modern DRAM-based systems.<n>Perf-Attacks exploit shared structures to reduce DRAM bandwidth for co-running benign applications.<n>We propose secure hashing mechanisms to thwart adversarial attempts to capture the mapping of shared structures.
  arXiv  Detail & Related papers  (2025-01-31T02:38:53Z)
• Illusions of Relevance: Using Content Injection Attacks to Deceive Retrievers, Rerankers, and LLM Judges [52.96987928118327]
  We find that embedding models for retrieval, rerankers, and large language model (LLM) relevance judges are vulnerable to content injection attacks. We identify two primary threats: (1) inserting unrelated or harmful content within passages that still appear deceptively "relevant", and (2) inserting entire queries or key query terms into passages to boost their perceived relevance. Our study systematically examines the factors that influence an attack's success, such as the placement of injected content and the balance between relevant and non-relevant material.
  arXiv  Detail & Related papers  (2025-01-30T18:02:15Z)
• Layer-Level Self-Exposure and Patch: Affirmative Token Mitigation for Jailbreak Attack Defense [55.77152277982117]
  We introduce Layer-AdvPatcher, a methodology designed to defend against jailbreak attacks.<n>We use an unlearning strategy to patch specific layers within large language models through self-augmented datasets.<n>Our framework reduces the harmfulness and attack success rate of jailbreak attacks.
  arXiv  Detail & Related papers  (2025-01-05T19:06:03Z)
• The VLLM Safety Paradox: Dual Ease in Jailbreak Attack and Defense [56.32083100401117]
  The vulnerability of Vision Large Language Models (VLLMs) to jailbreak attacks appears as no surprise.<n>Recent defense mechanisms against these attacks have reached near-saturation performance on benchmark evaluations.
  arXiv  Detail & Related papers  (2024-11-13T07:57:19Z)
• Preventing Rowhammer Exploits via Low-Cost Domain-Aware Memory Allocation [46.268703252557316]
  Rowhammer is a hardware security vulnerability at the heart of every system with modern DRAM-based memory. C Citadel is a new memory allocator design that prevents Rowhammer-initiated security exploits. C Citadel supports thousands of security domains at a modest 7.4% average memory overhead and no performance loss.
  arXiv  Detail & Related papers  (2024-09-23T18:41:14Z)
• DRAM-Profiler: An Experimental DRAM RowHammer Vulnerability Profiling Mechanism [8.973443004379561]
  This paper presents a low-overhead DRAM RowHammer vulnerability profiling technique termed DRAM-Profiler. The proposed test vectors intentionally weaken the spatial correlation between the aggressors and victim rows before an attack for evaluation. The results uncover the significant variability among chips from different manufacturers in the type and quantity of RowHammer attacks that can be exploited by adversaries.
  arXiv  Detail & Related papers  (2024-04-29T03:15:59Z)
• BreakHammer: Enhancing RowHammer Mitigations by Carefully Throttling Suspect Threads [5.767293823380473]
  RowHammer is a read disturbance mechanism in DRAM where repeatedly accessing (hammering) a row of DRAM cells (DRAM row) induces bitflips in other physically nearby DRAM rows. RowHammer solutions perform preventive actions (e.g., refresh neighbor rows of the hammered row) that mitigate such bitflips. As shrinking technology node size over DRAM chip generations exacerbates RowHammer, the overheads of RowHammer solutions become prohibitively expensive. In this work, we tackle the performance overheads of RowHammer solutions by tracking and throttling the generators of memory accesses that trigger RowHammer solutions.
  arXiv  Detail & Related papers  (2024-04-20T22:09:38Z)
• Threshold Breaker: Can Counter-Based RowHammer Prevention Mechanisms Truly Safeguard DRAM? [8.973443004379561]
  This paper experimentally demonstrates a novel multi-sided fault injection attack technique called Threshold Breaker. It can effectively bypass the most advanced counter-based defense mechanisms by soft-attacking the rows at a farther physical distance from the target rows. As a case study, we compare the performance efficiency between our mechanism and a well-known double-sided attack by performing adversarial weight attacks on a modern Deep Neural Network (DNN)
  arXiv  Detail & Related papers  (2023-11-28T03:36:17Z)
• Meta-Learning Adversarial Bandit Algorithms [55.72892209124227]
  We study online meta-learning with bandit feedback. We learn to tune online mirror descent generalization (OMD) with self-concordant barrier regularizers.
  arXiv  Detail & Related papers  (2023-07-05T13:52:10Z)
• Channel-wise Gated Res2Net: Towards Robust Detection of Synthetic Speech Attacks [67.7648985513978]
  Existing approaches for anti-spoofing in automatic speaker verification (ASV) still lack generalizability to unseen attacks. We present a novel, channel-wise gated Res2Net (CG-Res2Net), which modifies Res2Net to enable a channel-wise gating mechanism.
  arXiv  Detail & Related papers  (2021-07-19T12:27:40Z)
• Adversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware Detection [67.53296659361598]
  adversarial EXEmples can bypass machine learning-based detection by perturbing relatively few input bytes. We develop a unifying framework that does not only encompass and generalize previous attacks against machine-learning models, but also includes three novel attacks. These attacks, named Full DOS, Extend and Shift, inject the adversarial payload by respectively manipulating the DOS header, extending it, and shifting the content of the first section.
  arXiv  Detail & Related papers  (2020-08-17T07:16:57Z)
• 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.