How will US sanctions on the group behind TRITON protect critical infrastructure?
As the US Treasury announces new sanctions on the Russian institute believed to be behind the TRITON malware, this blog takes a look at the significance of this attack, and extrapolates what’s around the corner for OT cyber-attacks.
In late October 2020, the US Federal Government announced plans to impose sanctions on a Russian research institute that has been linked with cyber-attacks. The institute is alleged to have played a part in building the customized tools used in the TRITON malware targeting Industrial Control Systems (ICS). These sanctions are part of a wider US Government campaign to disrupt cyber-attacks backed by nation states and large organized crime groups.
TRITON uses a sophisticated set of tactics to maximize its stealth and its potential to do damage. The attack was first observed ‘in the wild’ when it struck a Saudi petrochemical plant in 2017. TRITON has been called ‘the world’s most murderous malware’ due to its potential to cause massive failure in industrial environments by targeting safety systems, along with compromising other critical industrial operations.
Timeline of TRITON
Figure 1: A timeline of the TRITON attack
The significance of TRITON
TRITON has been identified as an Advanced Persistent Threat (APT), meaning that it is a state-sponsored attack distinguished by its high threat level and novel vectors of attack. It was designed to give the attacker complete control over infected systems and enable tampering with industrial controls.
The malware utilized state of the art tactics, techniques and procedures (TTPs) in order to remain undetected, evade security defenses, and achieve the attacker’s goals. Like recent attacks against operational technology (OT) such as EKANS, TRITON exploited the convergence of informational and operational technology (IT/OT) by initially compromising enterprise devices before pivoting into OT subnets.
This chain of compromise is similar to other high-profile OT attacks, such as Havex, Stuxnet, and Industroyer. In fact, Darktrace detected a similar APT, Shamoon 3, when it impacted several firms in the Middle East in December 2018. While this strain of malware didn’t specifically target OT systems, it used similar TTPs to infect and deploy a strain of wiper malware, which wipes the hard drive of the infected device, and typically targets critical infrastructure.
Along similar lines, the US Government reported that at least 20 American electric facilities had been probed for vulnerabilities by the same authors of TRITON based at the Russian Institute in 2019. These activities demonstrate a general increase in OT attacks targeting critical infrastructure backed by nation states.
The evolving threat landscape
The knock-on effects of US sanctions
The recent sanctions are designed to disrupt the actions of active threat groups and to deter would-be attackers. A secondary goal is to raise awareness within the cyber industry and the general public as to the types of cyber-threats faced by critical infrastructure. The sanctions are likely to slow attackers, but not stop them. Indeed, nation states are well resourced and have strong motives, and APT hacker groups will continue to adapt and innovate.
The future of OT attacks
Attacks like Shamoon and EKANS ransomware have demonstrated how IT/OT convergence has made critical infrastructure vulnerable to non-OT targeted attacks. However, there is another development that is emerging on the threat landscape, one that will likely only further the destructive potential of OT attacks. This development is the malicious application of machine learning and other AI technologies to cyber-attacks, otherwise known as ‘offensive AI.’
For OT-specific attacks, APT groups are likely to adopt machine learning and AI techniques to stay ahead of defenders. This would allow attackers to better exploit IT/OT convergence and pivot quicker into OT systems. In other words, with the help of AI and machine learning, malware will be able to autonomously find its way to its target, learning the ins and outs of complex infrastructure in order to strike the right target at the right time.
A highly effective use of machine learning will be to train malware in optimal decision-making. For example, supervised machine learning can transfer the skills of the best malware operators directly into the malware itself. This greater autonomous ability within the malware will allow it to delay establishing a command and control (C2) connection.
Trained malware can operate independently until, for example, it is able to communicate with an OT control system. Establishing C2, performing OT reconnaissance and exfiltrating the results can then be completed extremely rapidly, far too fast for humans to mitigate the threat even if it was spotted immediately.
Future OT attacks targeting critical infrastructure are likely to incorporate several of these techniques. The TRITON framework, for example, required operators of the malware to manually trigger its functions through scripts. In the future, we can conceive of an AI-equipped version operating without command and control, perhaps only calling back at the end of the reconnaissance phase.
Figure 2: AI-enabled malware is able to autonomously find the optimal path to its ICS target
It is becoming apparent that OT attacks are increasingly being carried out by nation state backed hacking groups. These hacker groups have access to cutting edge malware tools to ensure the attackers can remain undetected, evade security tools, and achieve their goals. Indeed, these state-sponsored attackers appear to be getting more aggressive and audacious in their attempts. The sanctions are a step in the right direction, but only a robust defensive strategy will ultimately keep targeted infrastructure from being damaged by these threats.
State-sponsored cyber-attackers are combining the skills of IT and OT malware authors to exploit IT/OT convergence. The attackers are also exploiting weak spots in legacy approaches to security. For instance, many organizations use separate IT and OT security teams as well as distinct IT and OT security tools. This arrangement ultimately creates blind spots in cyber defenses.
The use of AI malware is likely to be part of the evolution of OT attacks. Only security teams equipped with AI themselves can expect to defend against these types of attacks. Darktrace enables IT and OT security teams to better collaborate and protect against these advanced persistent threats to critical infrastructure. Indeed, Darktrace has already caught APTs in the wild, without relying on any prior threat intelligence, but instead by learning ‘normal’ for every user, device and controller and identifying anomalous behavior that arose as a result of the attack.
As APTs such as Triton eventually get an update, and incorporate more innovative technologies into their TTPs, Darktrace builds resilience by learning the DNA of industrial infrastructure, illuminating any possible points of convergence between OT and the corporate network. By automating investigations and spotting all anomalous activity in real time, Darktrace augments human teams so that they stay one step ahead of tomorrow’s attacks.
Thanks to Darktrace analyst Oakley Cox for his insights on the above investigation.
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.
ABOUT ThE AUTHOR
Director of Enterprise Security
David Masson is Darktrace’s Director of Enterprise Security, and has over two decades of experience working in fast moving security and intelligence environments in the UK, Canada and worldwide. With skills developed in the civilian, military and diplomatic worlds, he has been influential in the efficient and effective resolution of various unique national security issues. David is an operational solutions expert and has a solid reputation across the UK and Canada for delivery tailored to customer needs. At Darktrace, David advises strategic customers across North America and is also a regular contributor to major international and national media outlets in Canada where he is based. He holds a master’s degree from Edinburgh University.
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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 . 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.
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 . 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 . 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.
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.
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.
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.
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 . 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.
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 indictors 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
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
Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block
$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.
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
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.
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.