April 5, 2023

Understanding Qakbot Infections and Attack Paths

Explore the network-based analysis of Qakbot infections with Darktrace. Learn about the various attack paths used by cybercriminals and Darktrace's response.

Written by

Sam Lister

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

Sam Lister

SOC Analyst

Written by

Connor Mooney

SOC Analyst

Apr 2023

In an ever-changing threat landscape, security vendors around the world are forced to quickly adapt, react, and respond to known attack vectors and threats. In the face of this, malicious actors are constantly looking for novel ways to gain access to networks. Whether that’s through new exploitations of network vulnerabilities or new delivery methods, attackers and their methods are continually evolving. Although it is valuable for organizations to leverage threat intelligence to keep abreast of known threats to their networks, intelligence alone is not enough to defend against increasingly versatile attackers. Having an autonomous decision maker able to detect and respond to emerging threats, even those employing novel or unknown techniques, is paramount to defend against network compromise.

At the end of January 2023, threat actors began to abuse OneNote attachments to deliver the malware strain, Qakbot, onto users' devices. Widespread adoption of this novel delivery method resulted in a surge in Qakbot infections across Darktrace's customer base between the end of January 2023 and the end of February 2023. Using its Self-Learning AI, Darktrace was able to uncover and respond to these so-called ‘QakNote’ infections as the new trend emerged. Darktrace detected and responded to the threat at multiple stages of the kill chain, preventing damaging and widespread compromise to customer networks.

Qakbot and The Recent Weaponization of OneNote

Qakbot first appeared in 2007 as a banking trojan designed to steal sensitive data such as banking credentials. Since then, Qakbot has evolved into a highly modular, multi-purpose tool, with backdoor, payload delivery, reconnaissance, lateral movement, and data exfiltration capabilities. Although Qakbot's primary delivery method has always been email-based, threat actors have been known to modify their email-based delivery methods of Qakbot in the face of changing circumstances. In the first half of 2022, Microsoft started rolling out versions of Office which block XL4 and VBA macros by default [1]/[2]/[3]. Prior to this change, Qakbot email campaigns typically consisted in the spreading of deceitful emails with Office attachments containing malicious macros. In the face of Microsoft's default blocking of macros, threat actors appeared to cease delivering Qakbot via Office attachments, and shifted to primarily using HTML attachments, through a method known as 'HTML smuggling' [4]/[5]. After the public disclosure [6] of the Follina vulnerability (CVE-2022-30190) in Microsoft Support Diagnostic Tool (MSDT) in May 2022, Qakbot actors were seen capitalizing on the vulnerability to facilitate their email-based delivery of Qakbot payloads [7]/[8]/[9].

Given the inclination of Qakbot actors to adapt their email-based delivery methods, it is no surprise that they were quick to capitalize on the novel OneNote-based delivery method which emerged in December 2022. Since December 2022, threat actors have been seen using OneNote attachments to deliver a variety of malware strains, ranging from Formbook [10] to AsynRAT [11] to Emotet [12]. The abuse of OneNote documents to deliver malware is made possible by the fact that OneNote allows for the embedding of executable file types such as HTA files, CMD files, and BAT files. At the end of January 2023, actors started to leverage OneNote attachments to deliver Qakbot [13]/[14]. The adoption of this novel delivery method by Qakbot actors resulted in a surge in Qakbot infections in the wider threat landscape and across the Darktrace customer base.

Observed Activity Chains

Between January 31 and February 24, 2023, Darktrace observed variations of the following pattern of activity across its customer base:

1. User's device contacts OneNote-related endpoint

2. User's device makes an external GET request with an empty Host header, a target URI whose final segment consists in 5 or 6 digits followed by '.dat', and a User-Agent header referencing either cURL or PowerShell. The GET request is responded to with a DLL file

3. User's device makes SSL connections over ports 443 and 2222 to unusual external endpoints, and makes TCP connections over port 65400 to 23.111.114[.]52

4. User's device makes SSL connections over port 443 to an external host named 'bonsars[.]com' (IP: 194.165.16[.]56) and TCP connections over port 443 to 78.31.67[.]7

5. User’s device makes call to Endpoint Mapper service on internal systems and then connects to the Service Control Manager (SCM)

6. User's device uploads files with algorithmically generated names and ‘.dll’ or ‘.dll.cfg’ file extensions to SMB shares on internal systems

7. User's device makes Service Control requests to the systems to which it uploaded ‘.dll’ and ‘.dll.cfg’ files

Further investigation of these chains of activity revealed that they were parts of Qakbot infections initiated via interactions with malicious OneNote attachments.

Figure 1: Steps of observed QakNote infections.

Delivery Phase

Users' interactions with malicious OneNote attachments, which were evidenced by devices' HTTPS connections to OneNote-related endpoints, such as 'www.onenote[.]com', 'contentsync.onenote[.]com', and 'learningtools.onenote[.]com', resulted in the retrieval of Qakbot DLLs from unusual, external endpoints. In some cases, the user's interaction with the malicious OneNote attachment caused their device to fetch a Qakbot DLL using cURL, whereas, in other cases, it caused their device to download a Qakbot DLL using PowerShell. These different outcomes reflected variations in the contents of the executable files embedded within the weaponized OneNote attachments. In addition to having cURL and PowerShell User-Agent headers, the HTTP requests triggered by interaction with these OneNote attachments had other distinctive features, such as empty host headers and target URIs whose last segment consists in 5 or 6 digits followed by '.dat'.

Figure 2: Model breach highlighting a user’s device making a HTTP GET request to 198.44.140[.]78 with a PowerShell User-Agent header and the target URI ‘/210/184/187737.dat’.

Figure 3: Model breach highlighting a user’s device making a HTTP GET request to 103.214.71[.]45 with a cURL User-Agent header and the target URI ‘/70802.dat’.

Figure 4: Event Log showing a user’s device making a GET request with a cURL User-Agent header to 185.231.205[.]246 after making an SSL connection to contentsync.onenote[.]com.

Figure 5: Event Log showing a user’s device making a GET request with a cURL User-Agent header to 185.231.205[.]246 after making an SSL connection to www.onenote[.]com.

Command and Control Phase

After fetching Qakbot DLLs, users’ devices were observed making numerous SSL connections over ports 443 and 2222 to highly unusual, external endpoints, as well as large volumes of TCP connections over port 65400 to 23.111.114[.]52. These connections represented Qakbot-infected devices communicating with command and control (C2) infrastructure. Qakbot-infected devices were also seen making intermittent connections to legitimate endpoints, such as 'xfinity[.]com', 'yahoo[.]com', 'verisign[.]com', 'oracle[.]com', and 'broadcom[.]com', likely due to Qakbot making connectivity checks.

Figure 6: Event Log showing a user’s device contacting Qakbot C2 infrastructure and making connectivity checks to legitimate domains.

Figure 7: Event Log showing a user’s device contacting Qakbot C2 infrastructure and making connectivity checks to legitimate domains.

Cobalt Strike and VNC Phase

After Qakbot-infected devices established communication with C2 servers, they were observed making SSL connections to the external endpoint, bonsars[.]com, and TCP connections to the external endpoint, 78.31.67[.]7. The SSL connections to bonsars[.]com were C2 connections from Cobalt Strike Beacon, and the TCP connections to 78.31.67[.]7 were C2 connections from Qakbot’s Virtual Network Computing (VNC) module [15]/[16]. The occurrence of these connections indicate that actors leveraged Qakbot infections to drop Cobalt Strike Beacon along with a VNC payload onto infected systems. The deployment of Cobalt Strike and VNC likely provided actors with ‘hands-on-keyboard’ access to the Qakbot-infected systems.

Figure 8: Advanced Search logs showing a user’s device contacting OneNote endpoints, fetching a Qakbot DLL over HTTP, making SSL connections to Qakbot infrastructure and connectivity checks to legitimate domains, and then making SSL connections to the Cobalt Strike endpoint, bonsars[.]com.

Figure 9: Event Log showing a user’s device contacting the Cobalt Strike C2 endpoint, bonsars[.]com, and the VNC C2 endpoint, 78.31.67[.]7, whilst simultaneously contacting the Qakbot C2 endpoint, 47.32.78[.]150.

Lateral Movement Phase

After dropping Cobalt Strike Beacon and a VNC module onto Qakbot-infected systems, actors leveraged their strengthened foothold to connect to the Service Control Manager (SCM) on internal systems in preparation for lateral movement. Before connecting to the SCM, infected systems were seen making calls to the Endpoint Mapper service, likely to identify exposed Microsoft Remote Procedure Call (MSRPC) services on internal systems. The MSRPC service, Service Control Manager (SCM), is known to be abused by Cobalt Strike to create and start services on remote systems. Connections to this service were evidenced by OpenSCManager2 (Opnum: 0x40) and OpenSCManagerW (Opnum: 0xf) calls to the svcctl RPC interface.

Figure 10: Advanced Search logs showing a user’s device contacting the Endpoint Mapper and Service Control Manager (SCM) services on internal systems.

After connecting to the SCM on internal systems, infected devices were seen using SMB to distribute files with ‘.dll’ and ‘.dll.cfg’ extensions to SMB shares. These uploads were followed by CreateWowService (Opnum: 0x3c) calls to the svcctl interface, likely intended to execute the uploaded payloads. The naming conventions of the uploaded files indicate that they were Qakbot payloads.

Figure 11: Advanced Search logs showing a user’s device making Service Control DCE-RPC requests to internal systems after uploading ‘.dll’ and ‘.dll.cfg’ files to them over SMB.

Fortunately, none of the observed QakNote infections escalated further than this. If these infections had escalated, it is likely that they would have resulted in the widespread detonation of additional malicious payloads, such as ransomware.

Darktrace Coverage of QakNote Activity

Figure 1 shows the steps involved in the QakNote infections observed across Darktrace’s customer base. How far attackers got along this chain was in part determined by the following three factors:

Whether the targeted customer had Darktrace/Email
Whether the targeted customer had Darktrace RESPOND/Network
Whether the targeted customer acted in response to Darktrace DETECT/Network alerts

The presence of Darktrace/Email typically stopped QakNote infections from moving past the initial infection stage. The presence of RESPOND/Network significantly slowed down observed activity chains, however, infections left unattended and not mitigated by the security teams were able to progress further along the attack chain.

Darktrace observed varying properties in the QakNote emails detected across the customer base. OneNote attachments were typically detected as either ‘application/octet-stream’ files or as ‘application/x-tar’ files. In some cases, the weaponized OneNote attachment embedded a malicious file, whereas in other cases, the OneNote file embedded a malicious link (typically a ‘.png’ or ‘.gif’ link) instead. In all cases Darktrace observed, QakNote emails used subject lines starting with ‘RE’ or ‘FW’ to manipulating their recipients into thinking that such emails were part of an existing email chain/thread. In some cases, emails impersonated users known to their recipients by including the names of such users in their header-from personal names. In many cases, QakNote emails appear to have originated from likely hijacked email accounts. These are highly successful methods of social engineering often employed by threat actors to exploit a user’s trust in known contacts or services, convincing them to open malicious emails and making it harder for security tools to detect.

The fact that observed QakNote emails used the fake-reply method, were sent from unknown email accounts, and contained attachments with unusual MIME types, caused such emails to breach the following Darktrace/Email models:

Association / Unknown Sender
Attachment / Unknown File
Attachment / Unsolicited Attachment
Attachment / Highly Unusual Mime
Attachment / Unsolicited Anomalous Mime
Attachment / Unusual Mime for Organisation
Unusual / Fake Reply
Unusual / Unusual Header TLD
Unusual / Fake Reply + Unknown Sender
Unusual / Unusual Connection from Unknown
Unusual / Off Topic

QakNote emails impersonating known users also breached the following DETECT & RESPOND/Email models:

Unusual / Unrelated Personal Name Address
Spoof / Basic Known Entity Similarities
Spoof / Internal User Similarities
Spoof / External User Similarities
Spoof / Internal User Similarities + Unrelated Personal Name Address
Spoof / External User Similarities + Unrelated Personal Name Address
Spoof / Internal User Similarities + Unknown File
Spoof / External User Similarities + Fake Reply
Spoof / Possible User Spoof from New Address - Enhanced Internal Similarities
Spoof / Whale

The actions taken by Darktrace on the observed emails is ultimately determined by Darktrace/Email models are breached. Those emails which did not breach Spoofing models (due to lack of impersonation indicators) received the ‘Convert Attachment’ action. This action converts suspicious attachments into neutralized PDFs, in this case successfully unweaponizing the malicious OneNote attachments. QakNote emails which did breach Spoofing models (due to the presence of impersonation indicators) received the strongest possible action, ‘Hold Message’. This action prevents suspicious emails from reaching the recipients’ mailbox.

Figure 12: Email log showing a malicious OneNote email (without impersonation indicators) which received a 87% anomaly score, a ‘Move to junk’ action, and a ‘Convert attachment’ actions from Darktrace/Email.

Figure 13: Email log showing a malicious OneNote email (with impersonation indicators) which received an anomaly score of 100% and a ‘Hold message’ action from Darktrace/Email.

Figure 14: Email log showing a malicious OneNote email (with impersonation indicators) which received an anomaly score of 100% and a ‘Hold message’ action from Darktrace/Email.

If threat actors managed to get past the first stage of the QakNote kill chain, likely due to the absence of appropriate email security tools, the execution of the subsequent steps resulted in strong intervention from Darktrace/Network.

Interactions with malicious OneNote attachments caused their devices to fetch a Qakbot DLL from a remote server via HTTP GET requests with an empty Host header and either a cURL or PowerShell User-Agent header. These unusual HTTP behaviors caused the following Darktrace/Network models to breach:

Device / New User Agent
Device / New PowerShell User Agent
Device / New User Agent and New IP
Anomalous Connection / New User Agent to IP Without Hostname
Anomalous Connection / Powershell to Rare External
Anomalous File / Numeric File Download
Anomalous File / EXE from Rare External Location
Anomalous File / New User Agent Followed By Numeric File Download

For customers with RESPOND/Network active, these breaches resulted in the following autonomous actions:

Enforce group pattern of life for 30 minutes
Enforce group pattern of life for 2 hours
Block connections to relevant external endpoints over relevant ports for 2 hours
Block all outgoing traffic for 10 minutes

Figure 15: Event Log showing a user’s device receiving Darktrace RESPOND/Network actions after downloading a Qakbot DLL.

Figure 16: Event Log showing a user’s device receiving Darktrace RESPOND/Network actions after downloading a Qakbot DLL.

Successful, uninterrupted downloads of Qakbot DLLs resulted in connections to Qakbot C2 servers, and subsequently to Cobalt Strike and VNC C2 connections. These C2 activities resulted in breaches of the following DETECT/Network models:

Compromise / Suspicious TLS Beaconing To Rare External
Compromise / Large Number of Suspicious Successful Connections
Compromise / Large Number of Suspicious Failed Connections
Compromise / Sustained SSL or HTTP Increase
Compromise / Sustained TCP Beaconing Activity To Rare Endpoint
Compromise / Beaconing Activity To External Rare
Compromise / Slow Beaconing Activity To External Rare
Anomalous Connection / Multiple Connections to New External TCP Port
Anomalous Connection / Multiple Failed Connections to Rare Endpoint
Device / Initial Breach Chain Compromise

For customers with RESPOND/Network active, these breaches caused RESPOND to autonomously perform the following actions:

Block connections to relevant external endpoints over relevant ports for 1 hour

Figure 17: Event Log showing a user’s device receiving RESPOND/Network actions after contacting the Qakbot C2 endpoint, Cobalt Strike C2 endpoint, bonsars[.]com.

In cases where C2 connections were allowed to continue, actors attempted to move laterally through usage of SMB and Service Control Manager. This lateral movement activity caused the following DETECT/Network models to breach:

Device / Possible SMB/NTLM Reconnaissance
Anomalous Connection / New or Uncommon Service Control

For customers with RESPOND/Network enabled, these breaches caused RESPOND to autonomously perform the following actions:

Block connections to relevant internal endpoints over port 445 for 1 hour

Figure 18: Event Log shows a user’s device receiving RESPOND/Network actions after contacting the Qakbot C2 endpoint, 5.75.205[.]43, and distributing ‘.dll’ and ‘.dll.cfg’ files internally.

The QakNote infections observed across Darktrace’s customer base involved several steps, each of which elicited alerts and autonomous preventative actions from Darktrace. By autonomously investigating the alerts from DETECT, Darktrace’s Cyber AI Analyst was able to connect the distinct steps of observed QakNote infections into single incidents. It then produced incident logs to present in-depth details of the activity it uncovered, provide full visibility for customer security teams.

Figure 19: AI Analyst incident entry showing the steps of a QakNote infection which AI Analyst connected following its autonomous investigations.

Conclusion

Faced with the emerging threat of QakNote infections, Darktrace demonstrated its ability to autonomously detect and respond to arising threats in a constantly evolving threat landscape. The attack chains which Darktrace observed across its customer base involved the delivery of Qakbot via malicious OneNote attachments, the usage of ports 65400 and 2222 for Qakbot C2 communication, the usage of Cobalt Strike Beacon and VNC for ‘hands-on-keyboard’ activity, and the usage of SMB and Service Control Manager for lateral movement.

Despite the novelty of the OneNote-based delivery method, Darktrace was able to identify QakNote infections across its customer base at various stages of the kill chain, using its autonomous anomaly-based detection to identify unusual activity or deviations from expected behavior. When active, Darktrace/Email neutralized malicious QakNote attachments sent to employees. In cases where Darktrace/Email was not active, Darktrace/Network detected and slowed down the unusual network activities which inevitably ensued from Qakbot infections. Ultimately, this intervention from Darktrace’s products prevented infections from leading to further harmful activity, such as data exfiltration and the detonation of ransomware.

Darktrace is able to offer customers an unparalleled level of network security by combining both Darktrace/Network and Darktrace/Email, safeguarding both their email and network environments. With its suite of products, including DETECT and RESPOND, Darktrace can autonomously uncover threats to customer networks and instantaneously intervene to prevent suspicious activity leading to damaging compromises.

Appendices

MITRE ATT&CK Mapping

Initial Access:

T1566.001 – Phishing: Spearphishing Attachment

Execution:

T1204.001 – User Execution: Malicious Link

T1204.002 – User Execution: Malicious File

T1569.002 – System Services: Service Execution

Lateral Movement:

T1021.002 – Remote Services: SMB/Windows Admin Shares

Command and Control:‍

T1573.002 – Encrypted Channel : Asymmetric Cryptography

T1571 – Non-Standard Port

T1105 – Ingress Tool Transfer

T1095 – Non-Application Layer Protocol

T1219 – Remote Access Software

List of IOCs

IP Addresses and/or Domain Names:

- 103.214.71[.]45 - Qakbot download infrastructure

- 141.164.35[.]94 - Qakbot download infrastructure

- 95.179.215[.]225 - Qakbot download infrastructure

- 128.254.207[.]55 - Qakbot download infrastructure

- 141.164.35[.]94 - Qakbot download infrastructure

- 172.96.137[.]149 - Qakbot download infrastructure

- 185.231.205[.]246 - Qakbot download infrastructure

- 216.128.146[.]67 - Qakbot download infrastructure

- 45.155.37[.]170 - Qakbot download infrastructure

- 85.239.41[.]55 - Qakbot download infrastructure

- 45.67.35[.]108 - Qakbot download infrastructure

- 77.83.199[.]12 - Qakbot download infrastructure

- 45.77.63[.]210 - Qakbot download infrastructure

- 198.44.140[.]78 - Qakbot download infrastructure

- 47.32.78[.]150 - Qakbot C2 infrastructure

- 197.204.13[.]52 - Qakbot C2 infrastructure

- 68.108.122[.]180 - Qakbot C2 infrastructure

- 2.50.48[.]213 - Qakbot C2 infrastructure

- 66.180.227[.]60 - Qakbot C2 infrastructure

- 190.206.75[.]58 - Qakbot C2 infrastructure

- 109.150.179[.]236 - Qakbot C2 infrastructure

- 86.202.48[.]142 - Qakbot C2 infrastructure

- 143.159.167[.]159 - Qakbot C2 infrastructure

- 5.75.205[.]43 - Qakbot C2 infrastructure

- 184.176.35[.]223 - Qakbot C2 infrastructure

- 208.187.122[.]74 - Qakbot C2 infrastructure

- 23.111.114[.]52 - Qakbot C2 infrastructure

- 74.12.134[.]53 – Qakbot C2 infrastructure

- bonsars[.]com • 194.165.16[.]56 - Cobalt Strike C2 infrastructure

- 78.31.67[.]7 - VNC C2 infrastructure

Target URIs of GET Requests for Qakbot DLLs:

- /70802.dat

- /51881.dat

- /12427.dat

- /70136.dat

- /35768.dat

- /41981.dat

- /30622.dat

- /72286.dat

- /46557.dat

- /33006.dat

- /300332.dat

- /703558.dat

- /760433.dat

- /210/184/187737.dat

- /469/387/553748.dat

- /282/535806.dat

User-Agent Headers of GET Requests for Qakbot DLLs:

- curl/7.83.1

- curl/7.55.1

- Mozilla/5.0 (Windows NT; Windows NT 10.0; en-US) WindowsPowerShell/5.1.19041.2364

- Mozilla/5.0 (Windows NT; Windows NT 10.0; en-US) WindowsPowerShell/5.1.17763.3770

- Mozilla/5.0 (Windows NT; Windows NT 10.0; en-GB) WindowsPowerShell/5.1.19041.2364

SHA256 Hashes of Downloaded Qakbot DLLs:

- 83e9bdce1276d2701ff23b1b3ac7d61afc97937d6392ed6b648b4929dd4b1452

- ca95a5dcd0194e9189b1451fa444f106cbabef3558424d9935262368dba5f2c6

- fa067ff1116b4c8611eae9ed4d59a19d904a8d3c530b866c680a7efeca83eb3d

- e6853589e42e1ab74548b5445b90a5a21ff0d7f8f4a23730cffe285e2d074d9e

- d864d93b8fd4c5e7fb136224460c7b98f99369fc9418bae57de466d419abeaf6

- c103c24ccb1ff18cd5763a3bb757ea2779a175a045e96acbb8d4c19cc7d84bea

Names of Internally Distributed Qakbot DLLs:

- rpwpmgycyzghm.dll

- rpwpmgycyzghm.dll.cfg

- guapnluunsub.dll

- guapnluunsub.dll.cfg

- rskgvwfaqxzz.dll

- rskgvwfaqxzz.dll.cfg

- hkfjhcwukhsy.dll

- hkfjhcwukhsy.dll.cfg

- uqailliqbplm.dll

- uqailliqbplm.dll.cfg

- ghmaorgvuzfos.dll

- ghmaorgvuzfos.dll.cfg

Links Found Within Neutralized QakNote Email Attachments:

- hxxps://khatriassociates[.]com/MBt/3.gif

- hxxps://spincotech[.]com/8CoBExd/3.gif

- hxxps://minaato[.]com/tWZVw/3.gif

- hxxps://famille2point0[.]com/oghHO/01.png

- hxxps://sahifatinews[.]com/jZbaw/01.png

- hxxp://87.236.146[.]112/62778.dat

- hxxp://87.236.146[.]112/59076.dat

- hxxp://185.231.205[.]246/73342.dat

References

[1] https://techcommunity.microsoft.com/t5/excel-blog/excel-4-0-xlm-macros-now-restricted-by-default-for-customer/ba-p/3057905

[2] https://techcommunity.microsoft.com/t5/microsoft-365-blog/helping-users-stay-safe-blocking-internet-macros-by-default-in/ba-p/3071805

[3] https://learn.microsoft.com/en-us/deployoffice/security/internet-macros-blocked

[4] https://www.cyfirma.com/outofband/html-smuggling-a-stealthier-approach-to-deliver-malware/

[5] https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/html-smuggling-the-hidden-threat-in-your-inbox/

[6] https://twitter.com/nao_sec/status/1530196847679401984

[7] https://www.fortiguard.com/threat-signal-report/4616/qakbot-delivered-through-cve-2022-30190-follina

[8] https://isc.sans.edu/diary/rss/28728

[9] https://darktrace.com/blog/qakbot-resurgence-evolving-along-with-the-emerging-threat-landscape

[10] https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/trojanized-onenote-document-leads-to-formbook-malware/

[11] https://www.proofpoint.com/uk/blog/threat-insight/onenote-documents-increasingly-used-to-deliver-malware

[12] https://www.malwarebytes.com/blog/threat-intelligence/2023/03/emotet-onenote

[13] https://blog.cyble.com/2023/02/01/qakbots-evolution-continues-with-new-strategies/

[14] https://news.sophos.com/en-us/2023/02/06/qakbot-onenote-attacks/

[15] https://isc.sans.edu/diary/rss/29210

[16] https://unit42.paloaltonetworks.com/feb-wireshark-quiz-answers/

Inside the SOC

Written by

Sam Lister

SOC Analyst

Written by

Connor Mooney

SOC Analyst

•

April 16, 2025

Margaret Cunningham, PhD

Director, Security & AI Strategy, Field CISO

•

April 16, 2025

Margaret Cunningham, PhD

Director, Security & AI Strategy, Field CISO

Watch the NIS2 Webinar

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April 22, 2025

Obfuscation Overdrive: Next-Gen Cryptojacking with Layers

man looking at multiple computer screens

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.

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.

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' \;

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]

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?

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/

About the author

Nate Bill

Threat Researcher

Blog

April 22, 2025

How NDR and Secure Access Service Edge (SASE) Work Together to Achieve Network Security Outcomes

Modern networks are evolving rapidly, with traffic patterns, user behavior, and critical assets extending far beyond the boundaries of traditional network security tools. As organizations adopt hybrid infrastructures, remote working, and cloud-native services, it is essential to maintain visibility and protect this expanding attack surface.

Network Detection and Response (NDR) and Secure Access Service Edge (SASE) are two technologies commonly used to safeguard organizational networks. While both play crucial roles in enhancing security, one does not replace the other. Instead, NDR and SASE complement each other, taking on different roles to create a robust network security framework. This blog will unpack the relationship between NDR and SASE, including the component functionalities that comprise SASE, highlighting their unique contributions to maintaining a comprehensive and resilient network security strategy.

Network Detection and Response (NDR) and Secure Access Service Edge (SASE) explained

NDR solutions, such as Darktrace / NETWORK, are designed to detect, investigate, and respond to suspicious activities within any network. By leveraging machine learning and behavioral analytics, NDR continuously monitors network traffic to identify anomalies that could indicate potential threats and to contain those threats at machine speed. These solutions analyze both North-South traffic (between internal and external networks) and East-West traffic (within internal networks), providing comprehensive visibility into network activities.

SASE, on the other hand, comprises multiple solutions, focused on providing hybrid and remote users access to services while adhering to the Zero Trust principle of "never trust, always verify". Within SASE architectures, Zero Trust Network Access (ZTNA) solutions provide secure remote access to private applications and services the user has been explicitly granted, and Secure Web Gateways (SWG) provide Internet access, again based on policy groups. Unlike traditional security models that grant implicit trust to users within the network perimeter, ZTNA requires continuous verification of user identity and device health before granting access to resources. This approach minimizes the attack surface and reduces the risk of unauthorized access to sensitive data and internal applications. Similarly, SWGs filter web traffic based on the verified user identity and can block known malware, further reducing the attack surface for the client estate.

Limitations of SASE highlights the importance of NDR

While SASE, including ZTNA and SWG, is a powerful tool for enforcing secure access to company networks and resources as well as the Internet, it is not a comprehensive security solution, or a replacement for dedicated network monitoring and NDR capabilities. Some of the main limitations include:

‍Focused on policies rather than security: SASE delivers strong networking outcomes but provides policy-based protections, rather than a full suite of security features. It can provide simple alerting for disallowed actions, but it lacks the security context needed for comprehensive threat detection, such as knowing if user credentials have been compromised.‍
Can only detect known threats: SASE solutions cannot detect novel attacks such as zero-days and insider threats. This is because they rely on a rule-based approach that does not have a behavioral understanding of network entities that can detect anomalies or suspicious activity.‍
Limited response capabilities: Due to the limited detection capabilities of SASE solutions, it is not possible to automate response actions to threats that slip past existing policies. While access to internal resources and the Internet can be revoked or severely limited as part of a response, this must be done after human investigation and analysis, allowing more time for the threat to continue before being contained.
Limited scope: SASE provides cloud-hosted secure networking, which lends itself much more toward the client estate of any organization. As a result, servers and unmanaged devices—whether IT/IoT/OT—are mostly out of scope and do not benefit from the policies SASE enforces.

The complementary roles of NDR and ZTNA

NDR solutions provide full visibility into network activity, with the ability to detect and respond to threats that may bypass initial access controls and filters. When combined, NDR and SASE create a layered security approach that addresses different aspects of network security, for example:

‍Detection of novel, unknown and insider threats: NDR solutions can monitor all network traffic using behavioral anomaly detection. This can identify suspicious activities, such as insider threats from authorized users who have passed policy checks, or novel attacks that have never been seen before.‍
Validation of policies: By continuously monitoring network traffic, NDR can validate the effectiveness of existing policies and identify any gaps in security that need addressing due to organizational changes or outdated rule sets.
Reducing risk and impact of threats: Together, SASE and NDR solutions shift toward proactive security by reducing the potential impact of a threat through predefined policies and by detecting and containing a threat in its earliest stages, even if it is novel or nuanced.
Enhanced contextual information: Alerts raised by SASE solutions can provide additional context into potential threats, which can be used by NDR solutions to increase investigation quality and context.‍
Containment of network threats: SASE solutions can prohibit access to resources on an internal company network or on the Internet if predefined access control criteria are not met or a site matches a threat signature. When combined with an NDR solution, organizations can go far beyond this, detecting and responding to a much wider variety of network threats to prevent attacks from escalating.

When implementing SASE and NDR solutions, it is also crucial to consider the best configurations to maximize interoperability, and integrations will often increase functionality. Well-designed implementations, combined with integrations, will strengthen both SASE and NDR solutions for organizations.

How Darktrace continues to secure SASE networks

With the latest 6.3 update, Darktrace continues to extend its capabilities with new innovations that support modern enterprise networks and the use of SASE across remote and hybrid worker devices. This expands on existing Darktrace integrations and partnerships with SASE vendors such as Netskope and Zscaler.

Traditional methods to contain remote access and internet-born threats are either signature or policy based, and response to nuanced threats requires manual, human-led investigation and decision-making. By the time security teams can react, the damage is often already done.

With Darktrace 6.3, customers using Zscaler can now configure Darktrace Autonomous Response to quarantine ZPA-connected user devices at machine speed. This provides a powerful new mechanism for containing remote threats at the earliest sign of suspicious activity, without disrupting broader operations.

By automatically shutting down ZPA access for compromised user accounts, Darktrace gives SOC teams valuable time to investigate and respond, while continuing to protect the rest of the organization. This integration enhances Darktrace’s ability to take actions for remote user devices, helping customers contain threats faster and keep the business running smoothly.

For organizations using SASE technologies to address the challenges of securing large, distributed networks across a range of geographies, SaaS applications and remote worker devices, Darktrace also now integrates with Netskope Cloud TAP to provide visibility into and analysis over tunneled traffic, reducing blind spots and enabling organizations to maintain detection capabilities across their expanding network perimeters.

Conclusion

While NDR and ZTNA serve distinct purposes, their integration is crucial for a comprehensive security strategy. ZTNA provides robust access controls, ensuring that only authorized users can access network resources. NDR, on the other hand, offers continuous visibility into network activities, detecting and responding to threats that may bypass initial access controls. By leveraging the strengths of both solutions, organizations can enhance their security posture and protect against a wide range of network security threats.

Understanding the complementary roles of NDR and ZTNA is essential for building a resilient security framework. As cyber threats continue to evolve, adopting a multi-layered, defense-in-depth security approach will be key to safeguarding organizational networks.

Click here for more information about the latest product innovations in Darktrace 6.3, or learn more about Darktrace / NETWORK here.

About the author

Mikey Anderson

Product Marketing Manager, Network Detection & Response

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How NDR and Secure Access Service Edge (SASE) Work Together to Achieve Network Security Outcomes

Network Detection and Response (NDR) and Secure Access Service Edge (SASE) explained

Limitations of SASE highlights the importance of NDR

The complementary roles of NDR and ZTNA

How Darktrace continues to secure SASE networks

Conclusion