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TajMahal cyber-espionage campaign uses previously unseen malicious tools



Malware and ransomware rise puts your data at risk
For the third year running, the volume of malware attacks has increased. But there are big variations in terms of who is getting targeted, and how.

A newly discovered form of malware deployed as part of a highly stealthy cyber-espionage campaign comes with several new malicious functionalities. It appears to be the work of a completely new operation, with no known links to any known threat actors or hacking groups.

Dubbed TajMahal, after the file it uses to exfiltrate stolen data, the malware has a number of capabilities not previously seen in a backdoor.

These include stealing documents sent to the printer queue, the ability to steal files previously seen on removable drives as soon as they’re available again, the ability to steal data burnt onto a CD by the victim, as well as the ability to take screenshots when recording audio from VoiceIP applications.

In addition to its unique capabilities, TajMahal provides attackers with what’s described as a ‘full-blown spying framework’, with a backdoor into infected systems.

It can issue commands, take screenshots of the desktop and webcam, and use keylogging to steal usernames, passwords and other information. It can also open and exfiltrate documents with the help of its own file indexer for the victim’s machine.

SEE: A winning strategy for cybersecurity (ZDNet special report) | Download the report as a PDF (TechRepublic)   

In addition, it can steal cryptography keys, grab browser cookies, gather the backup list for Apple mobile devices and more, with around 80 malicious modules each designed for espionage activity.

The malware has been uncovered by researchers at Kaspersky Lab, who have detailed their finding’s at the company’s Security Analyst Summit 2019 in Singapore.

Described as “a technically sophisticated APT framework designed for extensive cyber espionage,” TajMahal was first uncovered in late 2018, but has been active for over five years, with the earliest sample dated to April 2013.

TajMahal was able to hide under the radar for so long because it has a completely new code base, with no similarities to known APTs or malware, and by employing an automatic update mechanism that’s regularly used to deploy new samples to avoid detection.

However, researchers were alerted to the malware after Kaspersky security software flagged a file as suspicious.

“The file turned out to be a malicious plugin of a level of sophistication that suggested an APT – and the lack of code similarity to any known attack suggested it was a previously unknown APT,” Alexey Shulmin, lead malware analyst at Kaspersky Lab told ZDNet.

“Using our knowledge of this file, we were able to identify more of them. That led us to the conclusion that the malware was part of a previously unknown, extremely rare, cyber-espionage platform,” he added.

Tokyo and Yokohama  

Researchers believe the framework is based around two packages, dubbed Tokyo and Yokohama. Tokyo is the smaller of the two, containing just three modules, one of which is the main backdoor and a connection to a command-and-control server.

Yokohama, meanwhile, contains every other capability of TajMahal, indicating that Tokyo is likely to be the initial dropper that then delivers the full-blown malware as a second-stage download – with the dropper left installed in case it’s needed for backup purposes later down the line.

The distribution method of TajMahal is still unknown and the infection has only been observed in the wild once – on the system of what’s described as ‘a diplomatic entity from a country in Central Asia’, with the infection occurring in 2014.

Researchers note that this victim has previously been unsuccessfully targeted by Zebroacy (trojan malware associated with a Russian state-backed hacking group), although it’s not thought the two campaigns are related.

SEE: Cybersecurity in an IoT and mobile world (ZDNet special report) | Download the report as a PDF (TechRepublic)

Nonetheless, due to the sophistication of the malware and its unique capabilities, it’s unlikely that the diplomatic target is the only victim compromised by TajMahal in more than five years.

“The TajMahal framework is a very interesting and intriguing finding. The technical sophistication is beyond doubt and it seems unlikely that such a huge investment would be undertaken for only one victim. A likely hypothesis would be that there are other additional victims we haven’t found yet,” said Shulmin.

To help protect against attacks by new and unknown threat actors, researchers recommend that all software used throughout an organisation is up to date and that security patches designed to fix known vulnerabilities should be installed as a priority.

All Kaspersky Lab products have been updated to protect against TajMahal and researchers have provided a full analysis of the campaign on the Kaspersky blog.


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Cloud Data Security



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



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



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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