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Inside the SOC
How AI uncovered Outlaw’s secret crypto-mining operation
Infamy is a paradoxical calling for cyber-criminals. While for some, bragging rights are a motivation for cyber-crime in and of themselves, notoriety is usually not a sensible goal for those hoping to avoid detection. This is what threat actors behind the prolific Emotet botnet learned earlier in 2021, for instance, when a coordinated effort was launched by eight national law enforcement agencies to take down their operation. There are, however, certain names which appear again and again in cyber security media and consistently avoid detection – names like Outlaw.
How Outlaw plans an ambush
Despite being active since 2018, very little is known about the hacking group Outlaw, which has staged numerous botnet and crypto-jacking attacks in China and internationally. The group is recognized by a variety of calling cards, be they repeated filenames or a tendency to illicitly mine Monero cryptocurrency, but its success ultimately lies in its tendency to adapt and evolve during months of dormancy between attacks.
Outlaw’s attacks are marked by constant changes and updates, which they work on in relative silence, before targeting security systems which are too-often defeated by the unfamiliarity of the threat.
In 2020, Outlaw gained attention when they updated their botnet toolset to find and eradicate other criminals’ crypto-jacking software, maximizing their own payout from infected devices. While it might come as no surprise that there’s no honor among cyber-thieves, this update also implemented more troubling changes which allowed Outlaw’s malware to evade traditional security defenses.
By switching disguises between each big robbery, and laying low with the loot, Outlaw ensures that traditional security systems which rely on historical attack data will never be ready for them, no matter how much notoriety is attached to their name. When organizations move beyond these systems’ rules-based approaches, however, adopting Self-Learning AI to protect their digital estates, they can begin to turn the tables on groups like Outlaw.
This blog explores how two pre-infected zombie devices in two very different parts of the world were activated by Outlaw’s botnet in the summer of 2021, and how Darktrace was able to detect the activity despite the devices being pre-infected.
Bounty hunting: First signs of attack
Figure 1: Timeline of the attack.
When a new device was added to the network of a Central American telecomms company in July, Darktrace detected a series of regular connections to two suspicious endpoints which it identified as beaconing behavior. The same behavior was noticed independently, but almost simultaneously, at a financial company in the APAC region, which was implementing Darktrace for the first time. Darktrace’s Self-Learning AI was able to identify the pre-infected devices by clustering similarly-behaving devices into peer groups within the local digital estates and therefore recognize that both were acting unusually based on a range of behaviors.
The first sign that the zombie devices had been activated by Outlaw was the initiation of cryptocurrency mining. Both devices, despite their geographical distance, were discovered to be connected to a single crypto-account, exemplifying the indiscriminate and exponential nature by which a botnet grows.
Outlaw has in the past restricted its activities to devices within China in what was assumed to be a show of caution, but recent activities like this one speak to a growing confidence.
The botnet recruitment process
The subsequent initiation of Internet Relay Chat (IRC) connections across port 443, a port more often associated with HTTPS activity, was perfectly characteristic of the Outlaw botnet’s earlier activity in 2020. IRC is a tool regularly used for communication between botmasters and zombie devices, but by using port 443 the attacker was attempting to blend into normal Internet traffic.
Soon after this exchange, the devices downloaded a shell script. Darktrace’s Cyber AI Analyst was able to intercept and recreate this shell script as it passed through the network, revealing its full function. Intriguingly, the script identified and excluded devices utilizing ARM architecture from the botnet. Due to its notably low battery consumption, ARM architecture is used primarily by portable mobile devices.
This selectivity is evidence that malicious crypto-mining remains Outlaw’s primary objective. By circumventing smaller devices which offer limited crypto-mining capabilities, this shell script focuses the botnet on the most high-powered, and therefore profitable, devices, such as desktop computers and servers. In this way, it reduces the Indicators of Compromise (IOCs) left behind by the wider botnet without greatly affecting the scale of its crypto-mining operation.
The two devices in question did not employ ARM architecture, and minutes later received a secondary payload containing a file named dota3[.]tar[.]gz, a sequel of sorts to the previous incarnation of the Outlaw botnet, ‘dota2’, which itself referenced a popular video game of the same name. With the arrival of this file, the devices appear to have been updated with the latest version of Outlaw’s world-spanning botnet.
This download was made possible in part by the attacker’s use of ‘Living off the Land’ tactics. By using only common Linux programs already present on the devices (‘curl’ and ‘Wget’ respectively), Outlaw had avoided having its activity flagged by traditional security systems. Wget, for instance, is ostensibly a reputable program used for retrieving content from web servers, and was never previously recorded as part of Outlaw’s TTPs (Tactics, Techniques, and Procedures).
By evolving and adapting its approach, Outlaw is continually able to outsmart and outrun rules-based security. Darktrace’s Self-Learning AI, however, kept pace, immediately identifying this Wget connection as suspicious and advising further investigation.
Figure 2: Cyber AI Analyst identifies Wget use on the morning of July 15 as suspicious and begins investigating potentially related HTTP connections made on the morning of July 14. In this way, it builds a complete picture of the attack.
The botnet unchained
In the following 36 hours, Darktrace detected over 6 million TCP and SSH connections directed to rare external IP addresses using ports often associated with SSH, such as 22, 2222, and 2022.
Exactly what the botnet was undertaking with these connections can only be speculated on. The devices may have been made part of a DDoS (Distributed Denial of Service) attack, bruteforce attempts on targeted SSH accounts, or simply have taken up the task of seeking and infecting new targets, further expanding the botnet. Darktrace recognized that neither device had made SSH connections prior to this event and, had Antigena been in active mode, would have enacted measures to stop them.
Figure 3: The behavior on the device before and after the bot was activated on July 14, 2021. The large spike in model breaches shows clear deviation from the established ‘pattern of life’.
Thankfully, the owners of both devices responded to Darktrace’s detection alerts soon enough to prevent any serious damage to their own digital estates. Had these devices remained under the influence of the botnet, the ramifications may have been far graver.
The use of SSH protocol would have allowed Outlaw to pivot into any number of activities, potentially compromising each device’s network further and causing data or monetary loss to their respective organizations.
Call the sheriff: Self-Learning AI
Rules-based security solutions operate much like the ‘wanted’ posters of the old west, looking out for the criminals who came through town last week without preparing for those riding over the hill today. When black hats and outlaws are adopting new looks and employing new techniques with every attack, a new way of responding to threats is needed.
Darktrace doesn’t need to know the name ‘Outlaw’, or the group’s history of evolving attacks, in order to stop them. With its fundamental self-learning approach, Darktrace learns its surroundings from the ground up, and identifies subtle deviations indicative of a cyber-threat. And with Autonomous Response, it will even take targeted action to neutralize the threat at machine speed, without the need for human intervention.
Thanks to Darktrace analyst Jun Qi Wong for his insights on the above threat find.
Learn more about how Cyber AI Analyst sheds light on complex attacks
Technical details
Darktrace model detections
- Compliance / Crypto Currency Mining Activity
- Compromise / High Priority Crypto Currency Mining [Enhanced Monitoring]
- Anomalous Connection / New User Agent to IP Without Hostname
- Anomalous File / Zip or Gzip from Rare External Location
- Anomalous Connection / Application Protocol on Uncommon Port
- Device / Increased External Connectivity
- Unusual Activity / Unusual External Activity
- Compromise / SSH Beacon
- Compromise / High Frequency SSH Beacon
- Anomalous Connection / Multiple Connections to New External TCP Port
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PurpleFox in a Henhouse: How Darktrace Hunted Down a Persistent and Dynamic Rootkit
Versatile Malware: PurpleFox
As organizations and security teams across the world move to bolster their digital defenses against cyber threats, threats actors, in turn, are forced to adopt more sophisticated tactics, techniques and procedures (TTPs) to circumvent them. Rather than being static and predictable, malware strains are becoming increasingly versatile and therefore elusive to traditional security tools.
One such example is PurpleFox. First observed in 2018, PurpleFox is a combined fileless rootkit and backdoor trojan known to target Windows machines. PurpleFox is known for consistently adapting its functionalities over time, utilizing different infection vectors including known vulnerabilities (CVEs), fake Telegram installers, and phishing. It is also leveraged by other campaigns to deliver ransomware tools, spyware, and cryptocurrency mining malware. It is also widely known for using Microsoft Software Installer (MSI) files masquerading as other file types.
The Evolution of PurpleFox
The Original Strain
First reported in March 2018, PurpleFox was identified to be a trojan that drops itself onto Windows machines using an MSI installation package that alters registry values to replace a legitimate Windows system file [1]. The initial stage of infection relied on the third-party toolkit RIG Exploit Kit (EK). RIG EK is hosted on compromised or malicious websites and is dropped onto the unsuspecting system when they visit browse that site. The built-in Windows installer (MSIEXEC) is leveraged to run the installation package retrieved from the website. This, in turn, drops two files into the Windows directory – namely a malicious dynamic-link library (DLL) that acts as a loader, and the payload of the malware. After infection, PurpleFox is often used to retrieve and deploy other types of malware.
Subsequent Variants
Since its initial discovery, PurpleFox has also been observed leveraging PowerShell to enable fileless infection and additional privilege escalation vulnerabilities to increase the likelihood of successful infection [2]. The PowerShell script had also been reported to be masquerading as a .jpg image file. PowerSploit modules are utilized to gain elevated privileges if the current user lacks administrator privileges. Once obtained, the script proceeds to retrieve and execute a malicious MSI package, also masquerading as an image file. As of 2020, PurpleFox no longer relied on the RIG EK for its delivery phase, instead spreading via the exploitation of the SMB protocol [3]. The malware would leverage the compromised systems as hosts for the PurpleFox payloads to facilitate its spread to other systems. This mode of infection can occur without any user action, akin to a worm.
The current iteration of PurpleFox reportedly uses brute-forcing of vulnerable services, such as SMB, to facilitate its spread over the network and escalate privileges. By scanning internet-facing Windows computers, PurpleFox exploits weak passwords for Windows user accounts through SMB, including administrative credentials to facilitate further privilege escalation.
Darktrace detection of PurpleFox
In July 2023, Darktrace observed an example of a PurpleFox infection on the network of a customer in the healthcare sector. This observation was a slightly different method of downloading the PurpleFox payload. An affected device was observed initiating a series of service control requests using DCE-RPC, instructing the device to make connections to a host of servers to download a malicious .PNG file, later confirmed to be the PurpleFox rootkit. The device was then observed carrying out worm-like activity to other external internet-facing servers, as well as scanning related subnets.
Darktrace DETECT™ was able to successfully identify and track this compromise across the cyber kill chain and ensure the customer was able to take swift remedial action to prevent the attack from escalating further.
While the customer in question did have Darktrace RESPOND™, it was configured in human confirmation mode, meaning any mitigative actions had to be manually applied by the customer’s security team. If RESPOND had been enabled in autonomous response mode at the time of the attack, it would have been able to take swift action against the compromise to contain it at the earliest instance.
Attack Overview
Initial Scanning over SMB
On July 14, 2023, Darktrace detected the affected device scanning other internal devices on the customer’s network via port 445. The numerous connections were consistent with the aforementioned worm-like activity that has been reported from PurpleFox behavior as it appears to be targeting SMB services looking for open or vulnerable channels to exploit.
This initial scanning activity was detected by Darktrace DETECT, specifically through the model breach ‘Device / Suspicious SMB Scanning Activity’. Darktrace’s Cyber AI Analyst™ then launched an autonomous investigation into these internal connections and tied them into one larger-scale network reconnaissance incident, rather than a series of isolated connections.
As Darktrace RESPOND was configured in human confirmation mode, it was unable to autonomously block these internal connections. However, it did suggest blocking connections on port 445, which could have been manually applied by the customer’s security team.
Privilege Escalation
The device successfully logged in via NTLM with the credential, ‘administrator’. Darktrace recognized that the endpoint was external to the customer’s environment, indicating that the affected device was now being used to propagate the malware to other networks. Considering the lack of observed brute-force activity up to this point, the credentials for ‘administrator’ had likely been compromised prior to Darktrace’s deployment on the network, or outside of Darktrace’s purview via a phishing attack.
Exploitation
Darktrace then detected a series of service control requests over DCE-RPC using the credential ‘admin’ to make SVCCTL Create Service W Requests. A script was then observed where the controlled device is instructed to launch mshta.exe, a Windows-native binary designed to execute Microsoft HTML Application (HTA) files. This enables the execution of arbitrary script code, VBScript in this case.
There are a few MSIEXEC flags to note:
- /i : installs or configures a product
- /Q : sets the user interface level. In this case, it is set to ‘No UI’, which is used for “quiet” execution, so no user interaction is required
Evidently, this was an attempt to evade detection by endpoint users as it is surreptitiously installed onto the system. This corresponds to the download of the rootkit that has previously been associated with PurpleFox. At this stage, the infected device continues to be leveraged as an attack device and scans SMB services over external endpoints. The device also appeared to attempt brute-forcing over NTLM using the same ‘administrator’ credential to these endpoints. This activity was identified by Darktrace DETECT which, if enabled in autonomous response mode would have instantly blocked similar outbound connections, thus preventing the spread of PurpleFox.
Installation
On August 9, Darktrace observed the device making initial attempts to download a malicious .PNG file. This was a notable change in tactics from previously reported PurpleFox campaigns which had been observed utilizing .MOE files for their payloads [3]. The .MOE payloads are binary files that are more easily detected and blocked by traditional signatured-based security measures as they are not associated with known software. The ubiquity of .PNG files, especially on the web, make identifying and blacklisting the files significantly more difficult.
The first connection was made with the URI ‘/test.png’. It was noted that the HTTP method here was HEAD, a method similar to GET requests except the server must not return a message-body in the response.
The metainformation contained in the HTTP headers in response to a HEAD request should be identical to the information sent in response to a GET request. This method is often used to test hypertext links for validity and recent modification. This is likely a way of checking if the server hosting the payload is still active. Avoiding connections that could possibly be detected by antivirus solutions can help keep this activity under-the-radar.
The server responds with a status code of 200 before the download begins. The HEAD request could be part of the attacker’s verification that the server is still running, and that the payload is available for download. The ‘/test.png’ HEAD request was sent twice, likely for double confirmation to begin the file transfer.
Subsequent analysis using a Packet Capture (PCAP) tool revealed that this connection used the Windows Installer user agent that has previously been associated with PurpleFox. The device then began to download a payload that was masquerading as a Microsoft Word document. The device was thus able to download the payload twice, from two separate endpoints.
By masquerading as a Microsoft Word file, the threat actor was likely attempting to evade the detection of the endpoint user and traditional security tools by passing off as an innocuous text document. Likewise, using a Windows Installer user agent would enable threat actors to bypass antivirus measures and disguise the malicious installation as legitimate download activity.
Darktrace DETECT identified that these were masqueraded file downloads by correctly identifying the mismatch between the file extension and the true file type. Subsequently, AI Analyst was able to correctly identify the file type and deduced that this download was indicative of the device having been compromised.
In this case, the device attempted to download the payload from several different endpoints, many of which had low antivirus detection rates or open-source intelligence (OSINT) flags, highlighting the need to move beyond traditional signature-base detections.
If Darktrace RESPOND was enabled in autonomous response mode at the time of the attack it would have acted by blocking connections to these suspicious endpoints, thus preventing the download of malicious files. However, as RESPOND was in human confirmation mode, RESPOND actions required manual application by the customer’s security team which unfortunately did not happen, as such the device was able to download the payloads.
Conclusion
The PurpleFox malware is a particularly dynamic strain known to continually evolve over time, utilizing a blend of old and new approaches to achieve its goals which is likely to muddy expectations on its behavior. By frequently employing new methods of attack, malicious actors are able to bypass traditional security tools that rely on signature-based detections and static lists of indicators of compromise (IoCs), necessitating a more sophisticated approach to threat detection.
Darktrace DETECT’s Self-Learning AI enables it to confront adaptable and elusive threats like PurpleFox. By learning and understanding customer networks, it is able to discern normal network behavior and patterns of life, distinguishing expected activity from potential deviations. This anomaly-based approach to threat detection allows Darktrace to detect cyber threats as soon as they emerge.
By combining DETECT with the autonomous response capabilities of RESPOND, Darktrace customers are able to effectively safeguard their digital environments and ensure that emerging threats can be identified and shut down at the earliest stage of the kill chain, regardless of the tactics employed by would-be attackers.
Credit to Piramol Krishnan, Cyber Analyst, Qing Hong Kwa, Senior Cyber Analyst & Deputy Team Lead, Singapore
Appendices
Darktrace Model Detections
- Device / Increased External Connectivity
- Device / Large Number of Connections to New Endpoints
- Device / SMB Session Brute Force (Admin)
- Compliance / External Windows Communications
- Anomalous Connection / New or Uncommon Service Control
- Compromise / Unusual SVCCTL Activity
- Compromise / Rare Domain Pointing to Internal IP
- Anomalous File / Masqueraded File Transfer
RESPOND Models
- Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block
- Antigena / Network / External Threat / Antigena Suspicious Activity Block
- Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block
- Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Client Block
- Antigena / Network / External Threat / Antigena Suspicious File Block
- Antigena / Network / External Threat / Antigena File then New Outbound Block
List of IoCs
IoC - Type - Description
/C558B828.Png - URI - URI for Purple Fox Rootkit [4]
5b1de649f2bc4eb08f1d83f7ea052de5b8fe141f - File Hash - SHA1 hash of C558B828.Png file (Malware payload)
190.4.210[.]242 - IP - Purple Fox C2 Servers
218.4.170[.]236 - IP - IP for download of .PNG file (Malware payload)
180.169.1[.]220 - IP - IP for download of .PNG file (Malware payload)
103.94.108[.]114:10837 - IP - IP from Service Control MSIEXEC script to download PNG file (Malware payload)
221.199.171[.]174:16543 - IP - IP from Service Control MSIEXEC script to download PNG file (Malware payload)
61.222.155[.]49:14098 - IP - IP from Service Control MSIEXEC script to download PNG file (Malware payload)
178.128.103[.]246:17880 - IP - IP from Service Control MSIEXEC script to download PNG file (Malware payload)
222.134.99[.]132:12539 - IP - IP from Service Control MSIEXEC script to download PNG file (Malware payload)
164.90.152[.]252:18075 - IP - IP from Service Control MSIEXEC script to download PNG file (Malware payload)
198.199.80[.]121:11490 - IP - IP from Service Control MSIEXEC script to download PNG file (Malware payload)
MITRE ATT&CK Mapping
Tactic - Technique
Reconnaissance - Active Scanning T1595, Active Scanning: Scanning IP Blocks T1595.001, Active Scanning: Vulnerability Scanning T1595.002
Resource Development - Obtain Capabilities: Malware T1588.001
Initial Access, Defense Evasion, Persistence, Privilege Escalation - Valid Accounts: Default Accounts T1078.001
Initial Access - Drive-by Compromise T1189
Defense Evasion - Masquerading T1036
Credential Access - Brute Force T1110
Discovery - Network Service Discovery T1046
Command and Control - Proxy: External Proxy T1090.002
References
- https://blog.360totalsecurity.com/en/purple-fox-trojan-burst-out-globally-and-infected-more-than-30000-users/
- https://www.trendmicro.com/en_us/research/19/i/purple-fox-fileless-malware-with-rookit-component-delivered-by-rig-exploit-kit-now-abuses-powershell.html
- https://www.akamai.com/blog/security/purple-fox-rootkit-now-propagates-as-a-worm
- https://www.foregenix.com/blog/an-overview-on-purple-fox
- https://www.trendmicro.com/en_sg/research/21/j/purplefox-adds-new-backdoor-that-uses-websockets.html
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OT
$70 Million in Cyber Security Funding for Electric Cooperatives & Utilities
What is the Bipartisan Infrastructure Deal?
The Bipartisan Infrastructure Law passed by congress in 2021 aimed to upgrade power and infrastructure to deliver clean, reliable energy across the US to achieve zero-emissions. To date, the largest investment in clean energy, the deal will fund new programs to support the development and deployment of clean energy technology.
Why is it relevant to electric municipalities?
Section 40124 of the Bipartisan Infrastructure Law allocates $250 million over a 5-year period to create the Rural and Municipal Utility Cybersecurity (RMUC) Program to help electric cooperative, municipal, and small investor-owned utilities protect against, detect, respond to, and recover from cybersecurity threats.1 This act illuminates the value behind a full life-cycle approach to cyber security. Thus, finding a cyber security solution that can provide all aspects of security in one integrated platform would enhance the overall security posture and ease many of the challenges that arise with adopting multiple point solutions.
On November 16, 2023 the Office of Cybersecurity, Energy Security, and Emergency Response (CESER) released the Advanced Cybersecurity Technology (ACT) for electric utilities offering a $70 million funding opportunity that aims to enhance the cybersecurity posture of electric cooperative, municipal, and small investor-owned utilities.
Funding Details
10 projects will be funded with application submissions due November 29, 2023, 5:00 pm ET with $200,000 each in cash prizes in the following areas:
- Direct support for eligible utilities to make investments in cybersecurity technologies, tools, training, and improvements in utility processes and procedures;
- Funding to strengthen the peer-to-peer and not-for-profit cybersecurity technical assistance ecosystem currently serving eligible electric utilities; and
- Increasing access to cybersecurity technical assistance and training for eligible utilities with limited cybersecurity resources. 2
To submit for this award visit: https://www.herox.com/ACT1Prize
How can electric municipalities utilize the funding?
While the adoption of hybrid working patterns increase cloud and SaaS usage, the number of industrial IoT devices also continues to rise. The result is decrease in visibility for security teams and new entry points for attackers. Particularly for energy and utility organizations.
Electric cooperatives seeking to enhance their cyber security posture can aim to invest in cyber security tools that provide the following:
Compliance support: Consider finding an OT security solution that maps out how its solutions and features help your organization comply with relevant compliance mandates such as NIST, ISA, FERC, TSA, HIPAA, CIS Controls, and more.
Anomaly based detection: Siloed security solutions also fail to detect attacks that span
the entire organization. Anomaly-based detection enhances an organization’s cyber security posture by proactively defending against potential attacks and maintaining a comprehensive view of their attack surface.
Integration capabilities: Implementation of several point solutions that complete individual tasks runs the risk of increasing workloads for operators and creates additional challenges with compliance, budgeting, and technical support. Look for cyber security tools that integrate with your existing technologies.
Passive and active asset tracking: Active Identification offers accurate enumeration, real time updates, vulnerability assessment, asset validation while Passive Identification eliminates the risk of operational disruption, minimizes risk, does not generate additional network traffic. It would be ideal to find a security solution that can do both.
Can secure both IT and OT in unison: Given that most OT cyber-attacks actually start in IT networks before pivoting into OT, a mature security posture for critical infrastructure would include a single solution for both IT and OT. Separate solutions for IT and OT present challenges when defending network boundaries and detecting incidents when an attacker pivots from IT to OT. These independent solutions also significantly increase operator workload and materially diminish risk mitigation efforts.
Darktrace/OT for Electric Cooperatives and Utilities
For smaller teams with just one or two dedicated employees, Darktrace’s Cyber AI Analyst and Investigation features allow end users to spend less time in the platform as it compiles critical incidents into comprehensive actionable event reports. AI Analyst brings all the information into a centralized view with incident reporting in natural language summaries and can be generated for compliance reports specific to regulatory requirements.
For larger teams, Darktrace alerts can be forwarded to 3rd party platforms such as a SIEM, where security team decision making is augmented. Additionally, executive reports and autonomous response reduce the alert fatigue generally associated with legacy tools. Most importantly, Darktrace’s unique understanding of normal allows security teams to detect zero-days and signatureless attacks regardless of the size of the organization and how alerts are consumed.
Key Benefits of Darktrace/OT
- Anomaly-based detection and real-time response
- Secures IT, OT, and IoT in unison
- Active and Passive Asset Identification
- Automated security reporting
- Attack surface management and vulnerability assessment
- Covers all levels of the Purdue Model
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References
