Probabilistic Tracker Management Policies for Low-Cost and Scalable Rowhammer Mitigation
- URL: http://arxiv.org/abs/2404.16256v1
- Date: Wed, 24 Apr 2024 23:57:58 GMT
- Title: Probabilistic Tracker Management Policies for Low-Cost and Scalable Rowhammer Mitigation
- Authors: Aamer Jaleel, Stephen W. Keckler, Gururaj Saileshwar,
- Abstract summary: In recent years, solutions like TRR have been deployed in DDR4 DRAM to track aggressor rows and then issue a mitigative action by refreshing neighboring rows.
Such in-DRAM solutions are resource-constrained (only able to provision few tens of counters to track aggressor rows) and are prone to thrashing based attacks, that have been used to fool them.
In this work, we demonstrate secure and scalable rowhammer mitigation using resource-constrained trackers.
- Score: 5.597216094757414
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper focuses on mitigating DRAM Rowhammer attacks. In recent years, solutions like TRR have been deployed in DDR4 DRAM to track aggressor rows and then issue a mitigative action by refreshing neighboring victim rows. Unfortunately, such in-DRAM solutions are resource-constrained (only able to provision few tens of counters to track aggressor rows) and are prone to thrashing based attacks, that have been used to fool them. Secure alternatives for in-DRAM trackers require tens of thousands of counters. In this work, we demonstrate secure and scalable rowhammer mitigation using resource-constrained trackers. Our key idea is to manage such trackers with probabilistic management policies (PROTEAS). PROTEAS includes component policies like request-stream sampling and random evictions which enable thrash-resistance for resource-constrained trackers. We show that PROTEAS can secure small in-DRAM trackers (with 16 counters per DRAM bank) even when Rowhammer thresholds drop to 500 while incurring less than 3% slowdown. Moreover, we show that PROTEAS significantly outperforms a recent similar probabilistic proposal from Samsung (called DSAC) while achieving 11X - 19X the resilience against Rowhammer.
Related papers
- AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents [84.96249955105777]
LLM agents may pose a greater risk if misused, but their robustness remains underexplored.
We propose a new benchmark called AgentHarm to facilitate research on LLM agent misuse.
We find leading LLMs are surprisingly compliant with malicious agent requests without jailbreaking.
arXiv Detail & Related papers (2024-10-11T17:39:22Z) - 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) - MINT: Securely Mitigating Rowhammer with a Minimalist In-DRAM Tracker [0.9939073004351231]
This paper investigates secure low-cost in-DRAM trackers for mitigating Rowhammer (RH)
Existing low-cost in-DRAM trackers require impractical overheads of hundreds or thousands of entries per bank.
We propose a Minimalist In-DRAM Tracker (MINT), which provides secure mitigation with just a single entry.
arXiv Detail & Related papers (2024-07-22T20:29:56Z) - ImPress: Securing DRAM Against Data-Disturbance Errors via Implicit Row-Press Mitigation [1.3921736520874155]
DRAM cells are susceptible to Data-Disturbance Errors (DDE)
Rowhammer is a well-known DDE vulnerability that occurs when a row is repeatedly activated.
Row-Press (RP) is a new DDE vulnerability that occurs when a row is kept open for a long time.
arXiv Detail & Related papers (2024-07-22T19:20:14Z) - MOAT: Securely Mitigating Rowhammer with Per-Row Activation Counters [0.3580891736370874]
DDR5 specifications have been extended to support Per-Row Activation Counting (PRAC), with counters inlined with each row, and ALERT-Back-Off (ABO) to stop the memory controller if the DRAM needs more time to mitigate.
Although PRAC+ABO represents a strong advance in Rowhammer protection, they are just a framework, and the actual security is dependent on the implementation.
We propose MOAT, a provably secure design, which uses two internal thresholds: ETH, an "Eligibility Threshold" for mitigating a row, and ATH, an "ALERT Threshold" for initiating
arXiv Detail & Related papers (2024-07-13T20:28:02Z) - Model Supply Chain Poisoning: Backdooring Pre-trained Models via Embedding Indistinguishability [61.549465258257115]
We propose a novel and severer backdoor attack, TransTroj, which enables the backdoors embedded in PTMs to efficiently transfer in the model supply chain.
Experimental results show that our method significantly outperforms SOTA task-agnostic backdoor attacks.
arXiv Detail & Related papers (2024-01-29T04:35:48Z) - Randomized Line-to-Row Mapping for Low-Overhead Rowhammer Mitigations [0.716879432974126]
Recently proposed secure Rowhammer mitigations rely on performing mitigative action on the aggressor rather than the victims.
We propose Rubix, which breaks the spatial correlation in the line-to-row mapping by using an encrypted address to access the memory.
We also propose Rubix-D, which dynamically changes the line-to-row mapping.
arXiv Detail & Related papers (2023-08-28T21:22:15Z) - Scalable and Configurable Tracking for Any Rowhammer Threshold [0.8057006406834466]
The Rowhammer vulnerability continues to get worse, with the Rowhammer Threshold (TRH) reducing from 139K activations to 4.8K activations over the last decade.
The number of possible aggressors increases with lowering thresholds making it difficult to reliably track such rows in a storage-efficient manner.
Recent in-DRAM trackers from industry, such as DSAC-TRR, perform approximate tracking, sacrificing guaranteed protection for reduced storage overheads.
We propose START - a scalable tracker for Any Rowhammer Threshold.
arXiv Detail & Related papers (2023-08-28T20:24:49Z) - One-bit Flip is All You Need: When Bit-flip Attack Meets Model Training [54.622474306336635]
A new weight modification attack called bit flip attack (BFA) was proposed, which exploits memory fault inject techniques.
We propose a training-assisted bit flip attack, in which the adversary is involved in the training stage to build a high-risk model to release.
arXiv Detail & Related papers (2023-08-12T09:34:43Z) - 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) - DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified
Robustness [58.23214712926585]
We develop a certified defense, DRSM (De-Randomized Smoothed MalConv), by redesigning the de-randomized smoothing technique for the domain of malware detection.
Specifically, we propose a window ablation scheme to provably limit the impact of adversarial bytes while maximally preserving local structures of the executables.
We are the first to offer certified robustness in the realm of static detection of malware executables.
arXiv Detail & Related papers (2023-03-20T17:25:22Z)
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.