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New tool automates phishing attacks that bypass 2FA

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Image: Piotr Duszyński

A new penetration testing tool published at the start of the year by a security researcher can automate phishing attacks with an ease never seen before and can even blow through login operations for accounts protected by two-factor authentication (2FA).

Named Modlishka –the English pronunciation of the Polish word for mantis– this new tool was created by Polish researcher Piotr Duszyński.

Modlishka is what IT professionals call a reverse proxy, but modified for handling traffic meant for login pages and phishing operations.

modlishka.png

Image: ZDNet

It sits between a user and a target website –like Gmail, Yahoo, or ProtonMail. Phishing victims connect to the Modlishka server (hosting a phishing domain), and the reverse proxy component behind it makes requests to the site it wants to impersonate.

The victim receives authentic content from the legitimate site –let’s say for example Google– but all traffic and all the victim’s interactions with the legitimate site passes through and is recorded on the Modlishka server.

modlishka-backend.png

Modlishka backend panel


Image: Piotr Duszyński

Any passwords a user may enter, are automatically logged in the Modlishka backend panel, while the reverse proxy also prompts users for 2FA tokens when users have configured their accounts to request one.

If attackers are on hand to collect these 2FA tokens in real-time, they can use them to log into victims’ accounts and establish new and legitimate sessions.

The video below shows how a Modlishka-powered phishing site that seamlessly loads content from the real Google login interface without using templates, and logs credentials and any 2FA code that a user might be seeing.

Because of this simple design, Modlishka doesn’t use any “templates,” a term used by phishers to describe clones of legitimate sites. Since all the content is retrieved from the legitimate site in real time, attackers don’t need to spend much time updating and fine-tuning templates.

Instead, all attackers need is a phishing domain name (to host on the Modlishka server) and a valid TLS certificate to avoid alerting users of the lack of an HTTPS connection.

The final step would be to configure a simple config file that unloads victims onto the real legitimate sites at the end of the phishing operation before they spot the sketchy-looking phishing domain.

In an email to ZDNet, Duszyński described Modlishka as a point-and-click and easy-to-automate system that requires minimal maintenance, unlike previous phishing toolkits used by other penetration testers.

“At the time when I started this project (which was in early 2018), my main goal was to write an easy to use tool, that would eliminate the need of preparing static webpage templates for every phishing campaign that I was carrying out,” the researcher told us.

“The approach of creating a universal and easy to automate reverse proxy, as a MITM actor, appeared to be the most natural direction. Despite some technical challenges, that emerged on this path, the overall result appeared to be really rewarding,” he added.

“The tool that I wrote is sort of a game changer, since it can be used as a ‘point and click’ proxy, that allows easy phishing campaign automation with full support of the 2FA (an exception to this is a U2F protocol based tokens – which is currently the only resilient second factor).

“There are some cases that require manual tuning (due to obfuscated JavaScript code or, for example, HTML tag security attributes like ‚integrity’), but these are fully supported by the tool and will also be improved in the future releases,” Duszyński told ZDNet.

An Amnesty International report released in December showed that advanced state-sponsored actors have already started using phishing systems that can bypass 2FA already.

Now, many fear that Modlishka would reduce the entry barrier to allow so-called “script kiddies” to set up phishing sites within minutes, even with far fewer technical skills required. Furthermore, this tool would allow cyber-crime groups to easily automate the creation of phishing pages that are easier to maintain and harder to detect by victims.

When we asked why he released such a dangerous tool on GitHub, Duszyński had a pretty intriguing answer.

“We have to face the fact that without a working proof of concept, that really proves the point, the risk is treated as theoretical, and no real measures are taken to address it properly,” he said.

“This status quo, and lack of awareness about the risk, is a perfect situation for malicious actors that will happily exploit it.”

Duszyński said that while his tool can automate the process of a phishing site passing through 2FA checks based on SMS and one-time codes, Modlishka is inefficient against U2F-based schemes that rely on hardware security keys.

Modlishka is currently available on GitHub under an open source license. Additional information is also available on Duszyński’s blog.

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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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