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New hardware-agnostic side-channel attack works against Windows and Linux

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A team of five academics and security researchers has published a research paper today detailing a new side-channel attack that effective against operating systems like Windows and Linux.

The novelty in this paper is that unlike many of the previous side-channel attacks [1, 2, 3, 4, 5, 6], this one is hardware-agnostic, and in some cases, it can be carried out remotely.

The attack is also different because it doesn’t target microarchitectural design flaws in CPUs or other computer components, but targets the operating system itself, hence the reason it is hardware-agnostic.

Attack targets OS page caches

More precisely, the attack targets “page caches,” a technical term used to describe a portion of the memory where the operating system loads code that’s currently used by one or more applications, such as executables, libraries, and user data.

These page caches are pure-software caches being controlled at the OS level, rather than classic hardware caches, which are dedicated memory that the CPU can use to improve its computational speed.

“Some of these [page] caches have very specific use-cases, such as browser caches used for website content; other [page] caches are more generic, such as the page cache that stores a large portion of code and data used,” said the research team in their paper.

The side-channel attack described by the research team works by first abusing mechanisms included in the Windows and Linux operating systems that allow a developer/application to check if a memory page is present in the OS page cache. These two mechanisms are the “mincore” system call for Linux and the “QueryWorkingSetEx” system call for Windows.

Researchers then use their ability to interact with the OS (through a malicious process running on the system) to create page cache eviction states that release old memory pages out of the OS page cache. As the OS page cache system writes the evicted data to disk, triggers various errors, or loads new pages into the page cache, researchers say they can deduce what data was being processed in the OS page cache, even by other processes/applications.

Furthermore, another added benefit is that unlike most previous side-channel attacks, this new attack can also recover large quantities of data at a time, making it ideal for real-world attacks.

Researchers say their “side-channel permits unprivileged monitoring of some memory accesses of other processes, with a spatial resolution of 4 kB and a temporal resolution of 2 μs on Linux (restricted to 6.7 measurements per second) and 466 ns on Windows (restricted to 223 measurements per second).”

Translated into lay terms, this “allows capturing more than 6 keystrokes per second, enough to capture keystrokes accurately,” researchers said.

Attack can be hardware-agnostic or remote

The side-channel attack described in their paper can be used to bypass security sandboxes, redress (reshape) user interfaces, and capture keystrokes.

All of the attacks listed above are possible in “local” exploitation scenarios, where an unprivileged process runs malicious code on a targeted computer.

The attack can also be modified to work in a “remote” exploitation scenario, where an attacker bombards a remote PC with malicious code to retrieve data from its memory.

However, remote attacks aren’t as efficient because they can’t bypass sandboxes and because they require fine-tuning based on the victim’s hardware (they are not hardware-agnostic as the local attacks).

Patches for Windows and Linux are in the works

The research team, which includes some of the brightest minds in IT security, including some of the people behind the Spectre/Meltdown vulnerabilities, have contacted OS vendors prior to disclosing their findings.

Microsoft has already fixed the way Windows deals with page cache reads in a Windows Insiders build, while discussions on how to deal with Linux patches are still ongoing. Both OS teams are expected to fix the issues at the heart of this side-channel attack in the future.

“We didn’t test macOS,” Daniel Gruss, one of the researchers told ZDNet in an email today. “We don’t know whether they expose any such interface that we used in our hardware-agnostic attacks, but certainly, as they also use a page cache, they would also be vulnerable to timing-based page cache attacks.”

This article tried to describe this attack in simple terms. For our technical readers, more details about this new side-channel attack are available in the research paper titled “Page Cache Attacks” that was published earlier today on ArXiv.

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Cloud Data Security

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Data security has become an immutable part of the technology stack for modern applications. Protecting application assets and data against cybercriminal activities, insider threats, and basic human negligence is no longer an afterthought. It must be addressed early and often, both in the application development cycle and the data analytics stack.

The requirements have grown well beyond the simplistic features provided by data platforms, and as a result a competitive industry has emerged to address the security layer. The capabilities of this layer must be more than thorough, they must also be usable and streamlined, adding a minimum of overhead to existing processes.

To measure the policy management burden, we designed a reproducible test that included a standardized, publicly available dataset and a number of access control policy management scenarios based on real world use cases we have observed for cloud data workloads. We tested two options: Apache Ranger with Apache Atlas and Immuta. This study contrasts the differences between a largely role-based access control model with object tagging (OT-RBAC) to a pure attribute-based access control (ABAC) model using these respective technologies.

This study captures the time and effort involved in managing the ever-evolving access control policies at a modern data-driven enterprise. With this study, we show the impacts of data access control policy management in terms of:

  • Dynamic versus static
  • Scalability
  • Evolvability

In our scenarios, Ranger alone took 76x more policy changes than Immuta to accomplish the same data security objectives, while Ranger with Apache Atlas took 63x more policy changes. For our advanced use cases, Immuta only required one policy change each, while Ranger was not able to fulfill the data security requirement at all.

This study exposed the limitations of extending legacy Hadoop security components into cloud use cases. Apache Ranger uses static policies in an OT-RBAC model for the Hadoop ecosystem with very limited support for attributes. The difference between it and Immuta’s attribute-based access control model (ABAC) became clear. By leveraging dynamic variables, nested attributes, and global row-level policies and row-level security, Immuta can be quickly implemented and updated in comparison with Ranger.

Using Ranger as a data security mechanism creates a high policy-management burden compared to Immuta, as organizations migrate and expand cloud data use—which is shown here to provide scalability, clarity, and evolvability in a complex enterprise’s data security and governance needs.

The chart in Figure 1 reveals the difference in cumulative policy changes required for each platform configuration.

Figure 1. Difference in Cumulative Policy Changes

The assessment and scoring rubric and methodology is detailed in the report. We leave the issue of fairness for the reader to determine. We strongly encourage you, as the reader, to discern for yourself what is of value. We hope this report is informative and helpful in uncovering some of the challenges and nuances of data governance platform selection. You are encouraged to compile your own representative use cases and workflows and review these platforms in a way that is applicable to your requirements.

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GigaOm Radar for Data Loss Prevention

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Data is at the core of modern business: It is our intellectual property, the lifeblood of our interactions with our employees, partners, and customers, and a true business asset. But in a world of increasingly distributed workforces, a growing threat from cybercriminals and bad actors, and ever more stringent regulation, our data is at risk and the impact of losing it, or losing access to it, can be catastrophic.

With this in mind, ensuring a strong data management and security strategy must be high on the agenda of any modern enterprise. Security of our data has to be a primary concern. Ensuring we know how, why, and where our data is used is crucial, as is the need to be sure that data does not leave the organization without appropriate checks and balances.

Keeping ahead of this challenge and mitigating the risk requires a multi-faceted approach. People and processes are key, as, of course, is technology in any data loss prevention (DLP) strategy.

This has led to a reevaluation of both technology and approach to DLP; a recognition that we must evolve an approach that is holistic, intelligent, and able to apply context to our data usage. DLP must form part of a broader risk management strategy.

Within this report, we evaluate the leading vendors who are offering solutions that can form part of your DLP strategy—tools that understand data as well as evaluate insider risk to help mitigate the threat of data loss. This report aims to give enterprise decision-makers an overview of how these offerings can be a part of a wider data security approach.

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Key Criteria for Evaluating Data Loss Prevention Platforms

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Data is a crucial asset for modern businesses and has to be protected in the same way as any other corporate asset, with diligence and care. Loss of data can have catastrophic effects, from reputational damage to significant fines for breaking increasingly stringent regulations.

While the risk of data loss is not new, the landscape we operate in is evolving rapidly. Data can leave data centers in many ways, whether accidental or malicious. The routes for exfiltration also continue to grow, ranging from email, USB sticks, and laptops to ever-more-widely-adopted cloud applications, collaboration tools, and mobile devices. This is driving a resurgence in the enterprise’s need to ensure that no data leaves the organization without appropriate checks and balances in place.

Keeping ahead of this challenge and mitigating the risk requires a multi-faceted approach. Policy, people, and technology are critical components in a data loss prevention (DLP) strategy.

As with any information security strategy, technology plays a significant role. DLP technology has traditionally played a part in helping organizations to mitigate some of the risks of uncontrolled data exfiltration. However, both the technology and threat landscape have shifted significantly, which has led to a reevaluation of DLP tools and strategy.

The modern approach to the challenge needs to be holistic and intelligent, capable of applying context to data usage by building a broader understanding of what the data is, who is using it, and why. Systems in place must also be able to learn when user activity should be classified as unusual so they can better interpret signs of a potential breach.

This advanced approach is also driving new ways of defining the discipline of data loss prevention. Dealing with these risks cannot be viewed in isolation; rather, it must be part of a wider insider risk-management strategy.

Stopping the loss of data, accidental or otherwise, is no small task. This GigaOM Key Criteria Report details DLP solutions and identifies key criteria and evaluation metrics for selecting such a solution. The corresponding GigOm Radar Report identifies vendors and products in this sector that excel. Together, these reports will give decision-makers an overview of the market to help them evaluate existing platforms and decide where to invest.

How to Read this Report

This GigaOm report is one of a series of documents that helps IT organizations assess competing solutions in the context of well-defined features and criteria. For a fuller understanding consider reviewing the following reports:

Key Criteria report: A detailed market sector analysis that assesses the impact that key product features and criteria have on top-line solution characteristics—such as scalability, performance, and TCO—that drive purchase decisions.

GigaOm Radar report: A forward-looking analysis that plots the relative value and progression of vendor solutions along multiple axes based on strategy and execution. The Radar report includes a breakdown of each vendor’s offering in the sector.

Solution Profile: An in-depth vendor analysis that builds on the framework developed in the Key Criteria and Radar reports to assess a company’s engagement within a technology sector. This analysis includes forward-looking guidance around both strategy and product.

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