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April 26, 2023

Gozi ISFB Malware Detection Insights and Analysis

Uncover how Gozi ISFB operates and how Darktrace’s detection capabilities help secure your systems against this versatile malware.
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Justin Torres
Cyber Analyst
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26
Apr 2023

Mirroring the overall growth of the cybersecurity landscape and the advancement of security tool capabilities, threat actors are continuously forced to keep pace. Today, threat actors are bringing novel malware into the wild, creating new attack vectors, and finding ways to avoid the detection of security tools. 

One notable example of a constantly adapting type of malware can be seen with banking trojans, a type of malware designed to steal confidential information, such as banking credentials, used by attackers for financial gain. Gozi-ISFB is a widespread banking trojan that has previously been referred to as ‘the malware with a thousand faces’ and, as it name might suggest, has been known under various names such as Gozi, Ursnif, Papras and Rovnix to list a few.

Between November 2022 and January 2023, a rise in Gozi-ISFB malware related activity was observed across Darktrace customer environments and was investigated by the Darktrace Threat Research team. Leveraging its Self-Learning AI, Darktrace was able to identify activity related to this banking trojan, regardless of the attack vectors or delivery methods utilized by threat actors.

We have moderate to high confidence that the series of activities observed is associated with Gozi-ISFB malware and high confidence in the indicators of compromise identified which are related to the post-compromise activities from Gozi-ISFB malware. 

Gozi-ISFB Background

The Gozi-ISFB malware was first observed in 2011, stemming from the source code of another family of malware, Gozi v1, which in turn borrowed source code from the Ursnif malware strain.  

Typically, the initial access payloads of Gozi-ISFB would require an endpoint to enable a macro on their device, subsequently allowing a pre-compiled executable file (.exe) to be gathered from an attacker-controlled server, and later executed on the target device.

However, researchers have recently observed Gozi-ISFB actors using additional and more advanced capabilities to gain access to organizations networks. These capabilities range from credential harvest, surveilling user keystrokes, diverting browser traffic from banking websites, remote desktop access, and the use of domain generation algorithms (DGA) to create command-and-control (C2) domains to avoid the detection and blocking of traditional security tools. 

Ultimately, the goal of Gozi-ISFB malware is to gather confidential information from infected devices by connecting to C2 servers and installing additional malware modules on the network. 

Darktrace Coverage of Gozi-ISFB 

Unlike traditional security approaches, Darktrace DETECT/Network™ can identify malicious activity because Darktrace models build an understanding of a device’s usual pattern of behavior, rather than using a static list of indicators of compromise (IoCs) or rules and signatures. As such, Darktrace is able to instantly detect compromised devices that deviate from their expected behavioral patterns, engaging in activity such as unusual SMB connections or connecting to newly created malicious endpoints or C2 infrastructure. In the event that Darktrace detects malicious activity, it would automatically trigger an alert, notifying the customer of an ongoing security concern. 

Regarding the Gozi-ISFB attack process, initial attack vectors commonly include targeted phishing campaigns, where the recipient would receive an email with an attached Microsoft Office document containing macros or a Zip archive file. Darktrace frequently observes malicious emails like this across the customer base and is able to autonomously detect and action them using Darktrace/Email™. In the following cases, the clients who had Darktrace/Email did not have evidence of compromise through their corporate email infrastructure, suggesting devices were likely compromised via the access of personal email accounts. In other cases, the customers did not have Darktrace/Email enabled on their networks.

Upon downloading and opening the malicious attachment included in the phishing email, the payload subsequently downloads an additional .exe or dynamic link library (DLL) onto the device. Following this download, the malware will ultimately begin to collect sensitive data from the infected device, before exfiltrating it to the C2 server associated with Gozi-ISFB. Darktrace was able to demonstrate and detect the retrieval of Gozi-ISFB malware, as well as subsequent malicious communication on multiple customer environments. 

In some attack chains observed, the infected device made SMB connections to the rare external endpoint ’62.173.138[.]28’ via port 445. Darktrace recognized that the device used unusual credentials for this destination endpoint and further identified it performing SMB reads on the share ‘\\62.173.138[.]28\Agenzia’. Darktrace also observed that the device downloaded the executable file ‘entrat.exe’ from this connection as can be seen in Figure 1.

Figure 1: Model breach event log showing an infected device making SMB read actions on the share ‘\\62.173.138[.]28\Agenzia’. Darktrace observed the device downloading the executable file ‘entrat.exe’ from this connection.

Subsequently, the device performed a separate SMB login to the same external endpoint using a credential identical to the device's name. Shortly after, the device performed a SMB directory query from the root share drive for the file path to the same endpoint. 

Figure 2:SMB directory query from the root share drive for the file path to the same endpoint, ’62.173.138[.]28’.

In Gozi-ISFB compromises investigated by the Threat Research team, Darktrace commonly observed model breaches for ‘Multiple HTTP POSTs to Rare Hostname’ and the use of the Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 10.0; Win64; x64)’ user agent. 

Devices were additionally observed making external connections over port 80 (TCP, HTTP) to endpoints associated with Gozi-ISFB. Regarding these connections, C2 communication was observed used configurations of URI path and resource file extension that claimed to be related to images within connections that were actually GET or POST request URIs. This is a commonly used tactic by threat actors to go under the radar and evade the detection of security teams.  

An example of this type of masqueraded URI can be seen below:

In another similar example investigated by the Threat Research team, Darktrace detected similar external connectivity associated with Gozi-ISFB malware. In this case, DETECT identified external connections to two separate hostnames, namely ‘gameindikdowd[.]ru’ and ‘jhgfdlkjhaoiu[.]su’,  both of which have been associated to Gozi-ISFB by OSINT sources. This specific detection included HTTP beaconing connections to endpoint, gameindikdowd[.]ru .

Details observed from this event: 

Destination IP: 134.0.118[.]203

Destination port: 80

ASN: AS197695 Domain names registrar REG.RU, Ltd

User agent: Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 10.0; Win64; x64

The same device later made anomalous HTTP POST requests to a known Gozi-ISFB endpoint, jhgfdlkjhaoiu[.]su. 

Details observed:

Destination port: 80

ASN: AS197695 Domain names registrar REG.RU, Ltd

User agent: Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 10.0; Win64; x64

Figure 3: Packet Capture (PCAP) with the device conducting anomalous HTTP POST requests to a Gozi-ISFB related IOC, ‘jhgfdlkjhaoiu[.]su’.

Conclusions 

With constantly changing attack infrastructure and new methods of exploitation tested and leveraged hour upon hour, it is critical for security teams to employ tools that help them stay ahead of the curve to avoid critical damage from compromise.  

Faced with a notoriously adaptive malware strain like Gozi-ISFB, Darktrace demonstrated its ability to autonomously detect malicious activity based upon more than just known IoCs and attack vectors. Despite the multitude of different attack vectors utilized by threat actors, Darktrace was able to detect Gozi-ISFB activity at various stages of the kill chain using its anomaly-based detection to identify unusual activity or deviations from normal patterns of life. Using its Self-Learning AI, Darktrace successfully identified infected devices and brought them to the immediate attention of customer security teams, ultimately preventing infections from leading to further compromise.  

The Darktrace suite of products, including DETECT/Network, is uniquely placed to offer customers an unrivaled level of network security that can autonomously identify and respond to arising threats against their networks in real time, preventing suspicious activity from leading to damaging network compromises.

Credit to: Paul Jennings, Principal Analyst Consultant and the Threat Research Team

Appendices

List of IOCs

134.0.118[.]203 - IP Address - Gozi-ISFB C2 Endpoint

62.173.138[.]28 - IP Address - Gozi-ISFB  C2 Endpoint

45.130.147[.]89 - IP Address - Gozi-ISFB  C2 Endpoint

94.198.54[.]97 - IP Address - Gozi-ISFB C2 Endpoint

91.241.93[.]111 - IP Address - Gozi-ISFB  C2 Endpoint

89.108.76[.]56 - IP Address - Gozi-ISFB  C2 Endpoint

87.106.18[.]141 - IP Address - Gozi-ISFB  C2 Endpoint

35.205.61[.]67 - IP Address - Gozi-ISFB  C2 Endpoint

91.241.93[.]98 - IP Address - Gozi-ISFB  C2 Endpoint

62.173.147[.]64 - IP Address - Gozi-ISFB C2 Endpoint

146.70.113[.]161 - IP Address - Gozi-ISFB  C2 Endpoint 

iujdhsndjfks[.]ru - Hostname - Gozi-ISFB C2 Hostname

reggy505[.]ru - Hostname - Gozi-ISFB  C2 Hostname

apr[.]intoolkom[.]at - Hostname - Gozi-ISFB  C2 Hostname

jhgfdlkjhaoiu[.]su - Hostname - Gozi-ISFB  C2 Hostname

gameindikdowd[.]ru - Hostname - Gozi-ISFB  Hostname

chnkdgpopupser[.]at - Hostname – Gozi-ISFB C2 Hostname

denterdrigx[.]com - Hostname – Gozi-ISFB C2 Hostname

entrat.exe - Filename – Gozi-ISFB Related Filename

Darktrace Model Coverage

Anomalous Connection / Multiple HTTP POSTs to Rare Hostname

Anomalous Connection / Posting HTTP to IP Without Hostname

Anomalous Connection / New User Agent to IP Without Hostname

Compromise / Agent Beacon (Medium Period)

Anomalous File / Application File Read from Rare Endpoint

Device / Suspicious Domain

Mitre Attack and Mapping

Tactic: Application Layer Protocol: Web Protocols

Technique: T1071.001

Tactic: Drive-by Compromise

Technique: T1189

Tactic: Phishing: Spearphishing Link

Technique: T1566.002

Model Detection

Anomalous Connection / Multiple HTTP POSTs to Rare Hostname - T1071.001

Anomalous Connection / Posting HTTP to IP Without Hostname - T1071.001

Anomalous Connection / New User Agent to IP Without Hostname - T1071.001

Compromise / Agent Beacon (Medium Period) - T1071.001

Anomalous File / Application File Read from Rare Endpoint - N/A

Device / Suspicious Domain - T1189, T1566.002

References

https://threatfox.abuse.ch/browse/malware/win.isfb/

https://www.cisa.gov/news-events/cybersecurity-advisories/aa22-216a

https://www.fortinet.com/blog/threat-research/new-variant-of-ursnif-continuously-targeting-italy#:~:text=Ursnif%20(also%20known%20as%20Gozi,Italy%20over%20the%20past%20year

https://medium.com/csis-techblog/chapter-1-from-gozi-to-isfb-the-history-of-a-mythical-malware-family-82e592577fef

Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Justin Torres
Cyber Analyst

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April 24, 2025

The Importance of NDR in Resilient XDR

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As threat actors become more adept at targeting and disabling EDR agents, relying solely on endpoint detection leaves critical blind spots.

Network detection and response (NDR) offers the visibility and resilience needed to catch what EDR can’t especially in environments with unmanaged devices or advanced threats that evade local controls.

This blog explores how threat actors can disable or bypass EDR-based XDR solutions and demonstrates how Darktrace’s approach to NDR closes the resulting security gaps with Self-Learning AI that enables autonomous, real-time detection and response.

Threat actors see local security agents as targets

Recent research by security firms has highlighted ‘EDR killers’: tools that deliberately target EDR agents to disable or damage them. These include the known malicious tool EDRKillShifter, the open source EDRSilencer, EDRSandblast and variants of Terminator, and even the legitimate business application HRSword.

The attack surface of any endpoint agent is inevitably large, whether the software is challenged directly, by contesting its local visibility and access mechanisms, or by targeting the Operating System it relies upon. Additionally, threat actors can readily access and analyze EDR tools, and due to their uniformity across environments an exploit proven in a lab setting will likely succeed elsewhere.

Sophos have performed deep research into the EDRShiftKiller tool, which ESET have separately shown became accessible to multiple threat actor groups. Cisco Talos have reported via TheRegister observing significant success rates when an EDR kill was attempted by ransomware actors.

With the local EDR agent silently disabled or evaded, how will the threat be discovered?

What are the limitations of relying solely on EDR?

Cyber attackers will inevitably break through boundary defences, through innovation or trickery or exploiting zero-days. Preventive measures can reduce but not completely stop this. The attackers will always then want to expand beyond their initial access point to achieve persistence and discover and reach high value targets within the business. This is the primary domain of network activity monitoring and NDR, which includes responsibility for securing the many devices that cannot run endpoint agents.

In the insights from a CISA Red Team assessment of a US CNI organization, the Red Team was able to maintain access over the course of months and achieve their target outcomes. The top lesson learned in the report was:

“The assessed organization had insufficient technical controls to prevent and detect malicious activity. The organization relied too heavily on host-based endpoint detection and response (EDR) solutions and did not implement sufficient network layer protections.”

This proves that partial, isolated viewpoints are not sufficient to track and analyze what is fundamentally a connected problem – and without the added visibility and detection capabilities of NDR, any downstream SIEM or MDR services also still have nothing to work with.

Why is network detection & response (NDR) critical?

An effective NDR finds threats that disable or can’t be seen by local security agents and generally operates out-of-band, acquiring data from infrastructure such as traffic mirroring from physical or virtual switches. This means that the security system is extremely inaccessible to a threat actor at any stage.

An advanced NDR such as Darktrace / NETWORK is fully capable of detecting even high-end novel and unknown threats.

Detecting exploitation of Ivanti CS/PS with Darktrace / NETWORK

On January 9th 2025, two new vulnerabilities were disclosed in Ivanti Connect Secure and Policy Secure appliances that were under malicious exploitation. Perimeter devices, like Ivanti VPNs, are designed to keep threat actors out of a network, so it's quite serious when these devices are vulnerable.

An NDR solution is critical because it provides network-wide visibility for detecting lateral movement and threats that an EDR might miss, such as identifying command and control sessions (C2) and data exfiltration, even when hidden within encrypted traffic and which an EDR alone may not detect.

Darktrace initially detected suspicious activity connected with the exploitation of CVE-2025-0282 on December 29, 2024 – 11 days before the public disclosure of the vulnerability, this early detection highlights the benefits of an anomaly-based network detection method.

Throughout the campaign and based on the network telemetry available to Darktrace, a wide range of malicious activities were identified, including the malicious use of administrative credentials, the download of suspicious files, and network scanning in the cases investigated.

Darktrace / NETWORK’s autonomous response capabilities played a critical role in containment by autonomously blocking suspicious connections and enforcing normal behavior patterns. At the same time, Darktrace Cyber AI Analyst™ automatically investigated and correlated the anomalous activity into cohesive incidents, revealing the full scope of the compromise.

This case highlights the importance of real-time, AI-driven network monitoring to detect and disrupt stealthy post-exploitation techniques targeting unmanaged or unprotected systems.

Unlocking adaptive protection for evolving cyber risks

Darktrace / NETWORK uses unique AI engines that learn what is normal behavior for an organization’s entire network, continuously analyzing, mapping and modeling every connection to create a full picture of your devices, identities, connections, and potential attack paths.

With its ability to uncover previously unknown threats as well as detect known threats using signatures and threat intelligence, Darktrace is an essential layer of the security stack. Darktrace has helped secure customers against attacks including 2024 threat actor campaigns against Fortinet’s FortiManager , Palo Alto firewall devices, and more.  

Stay tuned for part II of this series which dives deeper into the differences between NDR types.

Credit to Nathaniel Jones VP, Security & AI Strategy, FCISO & Ashanka Iddya, Senior Director of Product Marketing for their contribution to this blog.

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About the author
Nathaniel Jones
VP, Security & AI Strategy, Field CISO

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April 22, 2025

Obfuscation Overdrive: Next-Gen Cryptojacking with Layers

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Out of all the services honeypotted by Darktrace, Docker is the most commonly attacked, with new strains of malware emerging daily. This blog will analyze a novel malware campaign with a unique obfuscation technique and a new cryptojacking technique.

What is obfuscation?

Obfuscation is a common technique employed by threat actors to prevent signature-based detection of their code, and to make analysis more difficult. This novel campaign uses an interesting technique of obfuscating its payload.

Docker image analysis

The attack begins with a request to launch a container from Docker Hub, specifically the kazutod/tene:ten image. Using Docker Hub’s layer viewer, an analyst can quickly identify what the container is designed to do. In this case, the container is designed to run the ten.py script which is built into itself.

 Docker Hub Image Layers, referencing the script ten.py.
Figure 1: Docker Hub Image Layers, referencing the script ten.py.

To gain more information on the Python file, Docker’s built in tooling can be used to download the image (docker pull kazutod/tene:ten) and then save it into a format that is easier to work with (docker image save kazutod/tene:ten -o tene.tar). It can then be extracted as a regular tar file for further investigation.

Extraction of the resulting tar file.
Figure 2: Extraction of the resulting tar file.

The Docker image uses the OCI format, which is a little different to a regular file system. Instead of having a static folder of files, the image consists of layers. Indeed, when running the file command over the sha256 directory, each layer is shown as a tar file, along with a JSON metadata file.

Output of the file command over the sha256 directory.
Figure 3: Output of the file command over the sha256 directory.

As the detailed layers are not necessary for analysis, a single command can be used to extract all of them into a single directory, recreating what the container file system would look like:

find blobs/sha256 -type f -exec sh -c 'file "{}" | grep -q "tar archive" && tar -xf "{}" -C root_dir' \;

Result of running the command above.
Figure 4: Result of running the command above.

The find command can then be used to quickly locate where the ten.py script is.

find root_dir -name ten.py

root_dir/app/ten.py

Details of the above ten.py script.
Figure 5: Details of the above ten.py script.

This may look complicated at first glance, however after breaking it down, it is fairly simple. The script defines a lambda function (effectively a variable that contains executable code) and runs zlib decompress on the output of base64 decode, which is run on the reversed input. The script then runs the lambda function with an input of the base64 string, and then passes it to exec, which runs the decoded string as Python code.

To help illustrate this, the code can be cleaned up to this simplified function:

def decode(input):
   reversed = input[::-1]

   decoded = base64.decode(reversed)
   decompressed = zlib.decompress(decoded)
   return decompressed

decoded_string = decode(the_big_text_blob)
exec(decoded_string) # run the decoded string

This can then be set up as a recipe in Cyberchef, an online tool for data manipulation, to decode it.

Use of Cyberchef to decode the ten.py script.
Figure 6: Use of Cyberchef to decode the ten.py script.

The decoded payload calls the decode function again and puts the output into exec. Copy and pasting the new payload into the input shows that it does this another time. Instead of copy-pasting the output into the input all day, a quick script can be used to decode this.

The script below uses the decode function from earlier in order to decode the base64 data and then uses some simple string manipulation to get to the next payload. The script will run this over and over until something interesting happens.

# Decode the initial base64

decoded = decode(initial)
# Remove the first 11 characters and last 3

# so we just have the next base64 string

clamped = decoded[11:-3]

for i in range(1, 100):
   # Decode the new payload

   decoded = decode(clamped)
   # Print it with the current step so we

   # can see what’s going on

   print(f"Step {i}")

   print(decoded)
   # Fetch the next base64 string from the

   # output, so the next loop iteration will

   # decode it

   clamped = decoded[11:-3]

Result of the 63rd iteration of this script.
Figure 7: Result of the 63rd iteration of this script.

After 63 iterations, the script returns actual code, accompanied by an error from the decode function as a stopping condition was never defined. It not clear what the attacker’s motive to perform so many layers of obfuscation was, as one round of obfuscation versus several likely would not make any meaningful difference to bypassing signature analysis. It’s possible this is an attempt to stop analysts or other hackers from reverse engineering the code. However,  it took a matter of minutes to thwart their efforts.

Cryptojacking 2.0?

Cleaned up version of the de-obfuscated code.
Figure 8: Cleaned up version of the de-obfuscated code.

The cleaned up code indicates that the malware attempts to set up a connection to teneo[.]pro, which appears to belong to a Web3 startup company.

Teneo appears to be a legitimate company, with Crunchbase reporting that they have raised USD 3 million as part of their seed round [1]. Their service allows users to join a decentralized network, to “make sure their data benefits you” [2]. Practically, their node functions as a distributed social media scraper. In exchange for doing so, users are rewarded with “Teneo Points”, which are a private crypto token.

The malware script simply connects to the websocket and sends keep-alive pings in order to gain more points from Teneo and does not do any actual scraping. Based on the website, most of the rewards are gated behind the number of heartbeats performed, which is likely why this works [2].

Checking out the attacker’s dockerhub profile, this sort of attack seems to be their modus operandi. The most recent container runs an instance of the nexus network client, which is a project to perform distributed zero-knowledge compute tasks in exchange for cryptocurrency.

Typically, traditional cryptojacking attacks rely on using XMRig to directly mine cryptocurrency, however as XMRig is highly detected, attackers are shifting to alternative methods of generating crypto. Whether this is more profitable remains to be seen. There is not currently an easy way to determine the earnings of the attackers due to the more “closed” nature of the private tokens. Translating a user ID to a wallet address does not appear to be possible, and there is limited public information about the tokens themselves. For example, the Teneo token is listed as “preview only” on CoinGecko, with no price information available.

Conclusion

This blog explores an example of Python obfuscation and how to unravel it. Obfuscation remains a ubiquitous technique employed by the majority of malware to aid in detection/defense evasion and being able to de-obfuscate code is an important skill for analysts to possess.

We have also seen this new avenue of cryptominers being deployed, demonstrating that attackers’ techniques are still evolving - even tried and tested fields. The illegitimate use of legitimate tools to obtain rewards is an increasingly common vector. For example,  as has been previously documented, 9hits has been used maliciously to earn rewards for the attack in a similar fashion.

Docker remains a highly targeted service, and system administrators need to take steps to ensure it is secure. In general, Docker should never be exposed to the wider internet unless absolutely necessary, and if it is necessary both authentication and firewalling should be employed to ensure only authorized users are able to access the service. Attacks happen every minute, and even leaving the service open for a short period of time may result in a serious compromise.

References

1. https://www.crunchbase.com/funding_round/teneo-protocol-seed--a8ff2ad4

2. https://teneo.pro/

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About the author
Nate Bill
Threat Researcher
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