Dark Reading is part of the Informa Tech Division of Informa PLC

This site is operated by a business or businesses owned by Informa PLC and all copyright resides with them.Informa PLC's registered office is 5 Howick Place, London SW1P 1WG. Registered in England and Wales. Number 8860726.

Attacks/Breaches

7/20/2017
03:45 PM
Connect Directly
Twitter
LinkedIn
Google+
RSS
E-Mail
100%
0%

US Banks Targeted with Trickbot Trojan

Necurs botnet spreads Trickbot malware to US financial institutions, while new Emotet banking Trojan attacks discovered - signalling increasingly complex attacks on the industry.

The Necurs botnet has begun delivering the Trickbot banking Trojan to financial institutions in the United States, a sign of increasingly larger and more complex attacks on the industry.

Trickbot, which specifically threatens businesses in the financial sector, has been behind man-in-the-browser (MitB) attacks since 2016. Until now, its webinject configuration was only used to hit organizations outside the US.

Researchers at Flashpoint discovered a new Trickbot spam campaign, called "mac1" on July 17. The latest iteration is fueled by the Necurs botnet and was developed to hit 50 additional banks including 13 companies based in the US. Necurs, one of the largest spamming botnets in the world, emerged in 2012 and has since become known for propagating spam campaigns.

"It sends a massive amount of spam email, one of which is recently starting to spread Trickbot," says Flashpoint malware researcher Paul Burbage, who has been monitoring Necurs for the past couple of years.

Mac1 has an expanded webinject configuration, which it uses to hit customers of financial institutions both in the US and abroad. Other victim countries include the UK, New Zealand, France, Australia, Norway, Sweden, Iceland, Canada, Finland, Spain, Italy, Luxembourg, Switzerland, Singapore, Belgium, and Denmark.

So far, Mac1 has driven at least three different spam waves, Flashpoint reports. The first contained an HTML email disguised as a bill from an Australian telecommunications company. These contained a Zip-archived Windows Script File attachment with obfuscated JavaScript code. When clicked, the files download and execute the Trickbot loader.

One of the main concerns with Trickbot is account takeover and fraud, which may increase among US financial institutions as the malware spreads. Burbage says the main significance of Mac1 is how far and wide it's being spread.

While its primary focus is financial institutions, experts anticipate other companies will eventually be at risk.

"We think it's capable of developing new features in the future," says Flashpoint director of research Vitali Kremez. "For now, it's a banking Trojan with potential to move beyond that."

The latest iteration of Trickbot, and its spread to the United States, indicate its authors' sophistication, Kremez continues. Flashpoint believes a Russian-speaking gang is behind the malware.

"I've been constantly amazed by the sophistication and resourcefulness of the Trickbot gang," he says, noting Necurs is only used among advanced actors. "They constantly develop means to proliferate the malware and bypass spam filters … they also have the infrastructure to proliferate the malware at scale."

Trickbot is considered the successor to the Dyre banking Trojan, says Kremez, citing similarities between their infrastructure and setup of their configuration files. It's possible the Trickbot author was either deeply familiar with Dyre or reused old source code. The threat actors behind Dyre have historically targeted Western financial institutions in the US, UK, and Canada.

Burbage and Kremez anticipate Trickbot will continue to evolve and target financial customers both in the US and around the world.

Trickbot's expansion isn't the only sign pointing to more dangerous banking Trojans. Fidelis Cybersecurity today released findings on its analysis of the Emotet loader, which was initially used for credential theft but is now used to deliver banking Trojans.

Emotet is an active threat using mass email spam campaigns to deliver malware. Fidelis found Emotet uses spam for propagation using basic social engineering techniques. Some samples have internal network propagation components, or spreaders, built in - a new strategy for banking malware authors that hasn't been seen much in recent years.

Early banking Trojans were crude and built to work against as many targets as possible, says John Bambenek, threat systems manager at Fidelis Cybersecurity. Emotet and others now use injects to customize the threat to specific banks' look and feel, he explains.

"An entire ecosystem has developed around this type of malware involving exploit writers, malware delivery systems, inject writers, money mules, and underground criminal marketplaces" like Alphabay, Bambenek says.

Fidelis reports it's not surprising to see cybercriminals include spreaders in their campaigns after widespread attacks WannaCry and Petya demonstrated their effectiveness in driving infections across enterprises. More malware authors are adding functionality based on attacks in the news, which could indicate a trend we'll see more of in the future.

Black Hat USA returns to the fabulous Mandalay Bay in Las Vegas, Nevada, July 22-27, 2017. Click for information on the conference schedule and to register.

 

Related Content:

Kelly Sheridan is the Staff Editor at Dark Reading, where she focuses on cybersecurity news and analysis. She is a business technology journalist who previously reported for InformationWeek, where she covered Microsoft, and Insurance & Technology, where she covered financial ... View Full Bio
 

Recommended Reading:

Comment  | 
Print  | 
More Insights
Comments
Newest First  |  Oldest First  |  Threaded View
COVID-19: Latest Security News & Commentary
Dark Reading Staff 9/25/2020
Hacking Yourself: Marie Moe and Pacemaker Security
Gary McGraw Ph.D., Co-founder Berryville Institute of Machine Learning,  9/21/2020
Startup Aims to Map and Track All the IT and Security Things
Kelly Jackson Higgins, Executive Editor at Dark Reading,  9/22/2020
Register for Dark Reading Newsletters
White Papers
Video
Cartoon
Current Issue
Special Report: Computing's New Normal
This special report examines how IT security organizations have adapted to the "new normal" of computing and what the long-term effects will be. Read it and get a unique set of perspectives on issues ranging from new threats & vulnerabilities as a result of remote working to how enterprise security strategy will be affected long term.
Flash Poll
How IT Security Organizations are Attacking the Cybersecurity Problem
How IT Security Organizations are Attacking the Cybersecurity Problem
The COVID-19 pandemic turned the world -- and enterprise computing -- on end. Here's a look at how cybersecurity teams are retrenching their defense strategies, rebuilding their teams, and selecting new technologies to stop the oncoming rise of online attacks.
Twitter Feed
Dark Reading - Bug Report
Bug Report
Enterprise Vulnerabilities
From DHS/US-CERT's National Vulnerability Database
CVE-2020-15208
PUBLISHED: 2020-09-25
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a `DCHECK` which is no-op outside of debug compilation modes. Since the function always returns the dimension of the first tensor, malicious attackers can ...
CVE-2020-15209
PUBLISHED: 2020-09-25
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, a crafted TFLite model can force a node to have as input a tensor backed by a `nullptr` buffer. This can be achieved by changing a buffer index in the flatbuffer serialization to convert a read-only tensor to a read-write one....
CVE-2020-15210
PUBLISHED: 2020-09-25
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. We have patched the issue in d58c96946b and ...
CVE-2020-15211
PUBLISHED: 2020-09-25
In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices f...
CVE-2020-15212
PUBLISHED: 2020-09-25
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `outpu...