Blog

Inside the SOC

3CX Supply Chain Compromise: How Darktrace Uncovered A “Smooth Operator”

Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
19
Jun 2023
19
Jun 2023
This blog discusses how Darktrace detected examples of the 3CX supply chain compromise, the first known cascading supply chain compromise. Leveraging integrations with security vendors like CrowdStrike and SentinelOne, Darktrace was able to successfully identify and prevent multiple cases of the 3CX supply chain compromise across its customer base.

Ever since the discovery of the SolarWinds hack that affected tens of thousands of organizations around the world in 2020, supply chain compromises have remained at the forefront of the minds of security teams and continue to pose a significant threat to their business operations. 

Supply chain compromises can have far-reaching implications, from disrupting an organization’s daily operations, incurring huge financial and reputational damage, to affecting the critical infrastructure of entire countries. As such, it is essential for organizations to have effective security measures in place able to identify and halt these attacks at the earliest possible stage.

In March 2023 the 3CX Desktop application became the latest victim of a supply chain compromise dubbed as the “SmoothOperator” by SentinelOne. This application is used by over 600,000 companies worldwide and the customer list contains high-profile customers across a variety of industries [2]. The 3CX Desktop application is a Voice over Internet Protocol (VoIP) communication software for enterprises that allows for chats, video calls, and voice calls. [3] The 3CX installers for both Windows and macOS systems were affected by information stealing malware. Researchers were able to discern that threat actors also known as UNC 4736 related to financially motivated North Korean operators also known as AppleJeus were responsible for the supply chain compromise.  Researchers have also linked it to another supply chain compromise that occurred prior on the Trading Technologies X_TRADER platform, making this the first known cascading software supply chain compromise used to distribute malware on a wide scale and still be able to align operator interests. [3] Customer reports following the compromise began to surface about the 3CX software being picked up as malicious by several cybersecurity vendors such as CrowdStrike, SentinelOne, and Palo Alto Networks. [6] 

By leveraging integrations with other security vendors like CrowdStrike and SentinelOne, Darktrace DETECT™ was able to identify activity from the “SmoothOperator” across the customer base at multiple stages of the kill chain in March 2023. Darktrace RESPOND™ was then able to autonomously intervene against these emerging threats, preventing significant disruption to customer networks. 

Background on the first known cascading supply chain attack 

Initial Access

In April 2023, security researchers identified the initial target in this story was not the 3CX desktop application, rather, it was another software application called X_TRADER by Trading Technologies. [3] Trading Technologies is a provider that offers high-performance financial trading packages, allowing financial professionals to analyze and trade assets within the stock market more efficiently. Unfortunately, a compromise already existed in the supply chain for this organization. The X_TRADER installer, which had been retired in 2020, still had its code signing certificate set to expire in October 2022. This code signing certificate was exploited by attackers to digitally sign the malicious software. [3] It also inopportunely led to 3CX when an employee unknowingly downloaded a trojanized installer for the X_TRADER software from Trading Technologies prior to the certificate’s expiration. [4]. This compromise of 3CX via X_TRADER was the first case of a cascading supply chain attack reported on within the wider threat landscape. 

Persistence and Privilege Escalation 

Following these findings, researchers were able to identify the likely kill chain that occurred on Windows systems, beginning with the download of the 3CX DesktopApp installer that executed an executable (.exe) file before dropping two trojanized Data Link Libraries (DLLs) alongside a benign executable that was used to sideload malicious DLLs. These DLLs contained and used SIGFLIP and DAVESHELL; both publicly available projects. [3] In this case, the DLLs were used to decrypt using an RC4 key and load a payload into the memory of a compromised system. [3] SIGFLIP and DAVESHELL also extract and decrypt the modular backdoor named VEILEDSIGNAL, which also contains a command and control (C2) configuration. This malware allowed the North Korean threat operators to gain administrative control to the 3CX employee’s device. [3] This was followed by access to the employee’s corporate credentials, ultimately leading to access to 3CX systems. [4] 

Lateral Movement and C2 activity

Security researchers were also able to identify other malware families that were mainly utilized in the supply chain attack to move laterally within the 3CX environment, and allow for C2 communication [3], these malware families are detailed below:

  • TaxHaul: when executed it decrypts shellcode payload, observed by Mandiant to persist via DLL search-order hijacking.
  • Coldcat: complex downloader, which also beacons to a C2 infrastructure.
  • PoolRat: collects system information and executes commands. This is the malware that was found to affect macOS systems.
  • IconicStealer: served as a third stage payload on 3CX systems to steal data or information.

Furthermore, it was also reported early on by Kaspersky that a backdoor named Gopuram, routinely used by the North Korean threat actors Lazarus and typically used against cryptocurrency companies, was also used as a second stage payload on a limited number of 3CX’s customers compromised systems. [5]

3CX detections observed by Darktrace

CrowdStrike and SentinelOne, two of the major detection platforms with which Darktrace partners through security integrations, initially revealed that their platforms had identified the campaign appeared to be targeting 3CXDesktopApp customers in March 2023. 

At this time, Darktrace was also observing this activity and alerting customers to unusual behavior on their networks. [1][7] Darktrace DETECT identified activity related to the supply chain compromise primarily through host-level alerts associated with CrowdStrike and SentinelOne integrations, as well as model breaches related to lateral movement and C2 activity. 

Some of the activity related to the 3CX supply chain compromise that Darktrace detected was observed solely via integration models picking up executable and Microsoft Software Installer (msi) file downloads for the 3CXDesktopApp, suggesting the compromise likely was stopped at the endpoint device. 

CrowdStrike integration model breach identifying 3CXDesktopApp[.]exe as possible malware
Figure 1: CrowdStrike integration model breach identifying 3CXDesktopApp[.]exe as possible malware on March 30, 2023.
showcases the Model Breach Event Log for the CrowdStrike integration model breach
Figure 2: The above figure, showcases the Model Breach Event Log for the CrowdStrike integration model breach shown in Figure 1.

In another case highlighted in Figure 3 and 4, security platforms were associating 3CX as malicious. The device in these figures was observed downloading a 3CXDesktopApp executable followed by an msi file about an hour later. This pattern of activity correlates with the compromise process that had been on reported, where the “SmoothOperator” malware that affected 3CX systems was able to persist through DLL side-loading of malicious DLL files delivered with benign executable files, making it difficult for traditional security tools to detect. [2][3][7]

The activity in this case was detected by the DETECT integration model, ‘High Severity Integration Malware Detection’ and was later blocked by the Darktrace RESPOND/Network model, ‘Antigena Significant Anomaly from Client Block’ which applied the “Enforce Pattern of Life” action to intercept the malicious download that was taking place. Darktrace RESPOND uses AI to learn every devices normal pattern of life and act autonomously to enforce its normal activity. In this event, RESPOND would not only intercept the malicious download that was taking place on the device, but also not allow the device to significantly deviate from its normal pattern of activity.

The Model Breach Event log for the device displays the moment in which the SentinelOne integration model breached for the 3CXDesktopApp.exe file
Figure 3: The Model Breach Event log for the device displays the moment in which the SentinelOne integration model breached for the 3CXDesktopApp.exe file followed subsequently by the RESPOND model, ‘Antigena Significant Anomaly from Client Block’, on March 29, 2023.
Another ‘High Severity Integration Malware Detection’ breached
Figure 4: Another ‘High Severity Integration Malware Detection’ breached for the same device in Figure 3 approximately one hour later because of the msi file, 3CXDesktopApp-18.12.416.msi, which also led to the Darktrace RESPOND model, ‘Antigena Significant Anomaly from Client Block’, on March 29, 2023.

In a separate case, Darktrace also detected a device performing unusual SMB drive writes for the file ‘3CXDesktopApp-18.10.461.msi’. This breached the DETECT model ‘SMB Drive Write’. This model detects when a device starts writing files to another internal device it does not usually communicate with via the SMB protocol using the admin$ or drive shares.

This Model Breach Event log highlights the moment Darktrace captured the msi application file for the 3CXDesktopApp being transferred internally on this customer’s network
Figure 5: This Model Breach Event log highlights the moment Darktrace captured the msi application file for the 3CXDesktopApp being transferred internally on this customer’s network, this was picked up as new activity for the device on March 28, 2023. 

In a couple of other cases observed by Darktrace, connections detected were made from affected devices to 3CX compromise related endpoints. In Figure 6, the device in question was detected connecting to the endpoint, journalide[.]org. This breached the model, ‘Suspicious Self-Signed SSL’, which looks for connections being made to an endpoint with a self-signed SSL certificate which is designed to look legitimate, as self-signed certificates are often used in malware communication.

Model Breach Event log for connections to the 3CX C2 related endpoint
Figure 6: Model Breach Event log for connections to the 3CX C2 related endpoint, journalide[.]org, these connections breached the model Suspicious Self-Signed SSL on April 24, 2023.

On another Darktrace customer environment, a 3CX C2 endpoint, pbxphonenetwork[.]com, had already been added to the Watched Domains list around the time reports of the 3CX application software being malicious had been reported. The Watched Domains list allows Darktrace to detect if any device on the network makes connections to these domains with more scrutiny and breach a model for further visibility of threats on the network. Activity in this case was detected and subsequently blocked by a Darktrace RESPOND action, “Block connections to 89.45.67[.]160 port 443 and pbxphonenetwork[.]com on port 443”, blocking the device from connecting to this 3CX C2 endpoints on the spot (see Figure 7). This activity subsequently breached the RESPOND model, ‘Antigena Watched Domain Block’. 

Figure 7: History log of the Darktrace RESPOND action applied to the device breaching the Darktrace RESPOND model, Antigena Watched Domain Block and applying the action, “Block connections to 89.45.67[.]160 port 443 and pbxphonenetwork[.]com on port 443” on March 31, 2023.

Darktrace Coverage 

Utilizing integrations with Darktrace such as those with CrowdStrike and SentinelOne, Darktrace was able to detect and respond to activity identified as malicious 3CX activity by CrowdStrike and SentinelOne as seen in Figures 1, 2, 3, and 4. This activity breached the following Darktrace DETECT models: 

  • Integration / CrowdStrike Alert
  • Security Integration / High Severity Integration Malware Detection

Darktrace was also able to identify lateral movement activity such as in the case illustrated in Figure 5.

  • Compliance / SMB Drive Write

Lastly, C2 beaconing activity from malicious endpoints associated with the 3CX compromise was also detected as seen in Figure 6, this activity breached the following Darktrace DETECT model:

  • Anomalous Connection / Suspicious Self-Signed SSL

For customers with Darktrace RESPOND configured in autonomous response mode, Darktrace RESPOND models also breached to activity related to the 3CX supply chain compromise as seen in Figures 3, 4, and 7. Below are the models that breached and the following autonomous actions that were applied:

  • Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block, “Enforce pattern of life”
  • Antigena / Network / External Threat / Antigena Watched Domain Block, “Block connections to 89.45.67[.]160 port 443 and pbxphonenetwork[.]com on port 443”

Conclusion 

The first known cascading supply chain compromise occurred inopportunely for 3CX but conveniently for UNC 4736 North Korean threat actors. This “SmoothOperator” compromise was detected by endpoint security platforms such as CrowdStrike who was at the cusp of this discovery when it became one of the first platforms to report on malicious activity related to the 3CX DesktopApp supply chain compromise.  

Although still novel at the time and largely without reported indicators of compromise, Darktrace was able to capture and identify activity related to the 3CX compromise across its customer base, as well as respond autonomously to contain it. Darktrace was able to amplify security integrations with CrowdStrike and SentinelOne, and via anomaly-based model breaches, contribute unique insights by highlighting activity in varied parts of the 3CX supply chain compromise kill chain. The “SmoothOperator” supply chain attack proves that the Darktrace suite of products, including DETECT and RESPOND, can not only act autonomously to identify and respond to novel threats, but also work with security integrations to further amplify intervention and prevent cyber disruption on customer networks. 

Credit to Nahisha Nobregas, SOC Analyst and Trent Kessler, SOC Analyst.

Appendices

MITRE ATT&CK Framework

Resource Development

  • T1588 Obtain Capabilities  
  • T1588.004 Digital Certificates
  • T1608 Stage Capabilities  
  • T1608.003 Install Digital Certificate

Initial Access

  • T1190 Exploit Public-Facing Application
  • T1195 Supply Chain Compromise  
  • T1195.002 Compromise Software Supply Chain

Persistence

  • T1574 Hijack Execution Flow
  • T1574.002 DLL Side-Loading

Privilege Escalation

  • T1055 Process Injection
  • T1574 Hijack Execution Flow  
  • T1574.002 DLL Side-Loading

Command and Control

  • T1071 Application Layer Protocol
  • T1071.001 Web Protocols
  • T1071.004 DNS  
  • T1105 Ingress Tool Transfer
  • T1573 Encrypted Channel

List of IOCs

C2 Hostnames

  • journalide[.]org
  • pbxphonenetwork[.]com

Likely C2 IP address

  • 89.45.67[.]160

References

  1. https://www.crowdstrike.com/blog/crowdstrike-detects-and-prevents-active-intrusion-campaign-targeting-3cxdesktopapp-customers/
  2. https://www.bleepingcomputer.com/news/security/3cx-confirms-north-korean-hackers-behind-supply-chain-attack/
  3. https://www.mandiant.com/resources/blog/3cx-software-supply-chain-compromise
  4. https://www.securityweek.com/cascading-supply-chain-attack-3cx-hacked-after-employee-downloaded-trojanized-app/
  5. https://securelist.com/gopuram-backdoor-deployed-through-3cx-supply-chain-attack/109344/
  6. https://www.bleepingcomputer.com/news/security/3cx-hack-caused-by-trading-software-supply-chain-attack/
  7. https://www.sentinelone.com/blog/smoothoperator-ongoing-campaign-trojanizes-3cx-software-in-software-supply-chain-attack/
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.
AUTHOR
ABOUT ThE AUTHOR
Nahisha Nobregas
SOC Analyst
Book a 1-1 meeting with one of our experts
share this article
COre coverage

More in this series

항목을 찾을 수 없습니다.

Blog

항목을 찾을 수 없습니다.

The State of AI in Cybersecurity: How AI will impact the cyber threat landscape in 2024

Default blog imageDefault blog image
22
Apr 2024

About the AI Cybersecurity Report

We surveyed 1,800 CISOs, security leaders, administrators, and practitioners from industries around the globe. Our research was conducted to understand how the adoption of new AI-powered offensive and defensive cybersecurity technologies are being managed by organizations.

This blog is continuing the conversation from our last blog post “The State of AI in Cybersecurity: Unveiling Global Insights from 1,800 Security Practitioners” which was an overview of the entire report. This blog will focus on one aspect of the overarching report, the impact of AI on the cyber threat landscape.

To access the full report click here.

Are organizations feeling the impact of AI-powered cyber threats?

Nearly three-quarters (74%) state AI-powered threats are now a significant issue. Almost nine in ten (89%) agree that AI-powered threats will remain a major challenge into the foreseeable future, not just for the next one to two years.

However, only a slight majority (56%) thought AI-powered threats were a separate issue from traditional/non AI-powered threats. This could be the case because there are few, if any, reliable methods to determine whether an attack is AI-powered.

Identifying exactly when and where AI is being applied may not ever be possible. However, it is possible for AI to affect every stage of the attack lifecycle. As such, defenders will likely need to focus on preparing for a world where threats are unique and are coming faster than ever before.

a hypothetical cyber attack augmented by AI at every stage

Are security stakeholders concerned about AI’s impact on cyber threats and risks?

The results from our survey showed that security practitioners are concerned that AI will impact organizations in a variety of ways. There was equal concern associated across the board – from volume and sophistication of malware to internal risks like leakage of proprietary information from employees using generative AI tools.

What this tells us is that defenders need to prepare for a greater volume of sophisticated attacks and balance this with a focus on cyber hygiene to manage internal risks.

One example of a growing internal risks is shadow AI. It takes little effort for employees to adopt publicly-available text-based generative AI systems to increase their productivity. This opens the door to “shadow AI”, which is the use of popular AI tools without organizational approval or oversight. Resulting security risks such as inadvertent exposure of sensitive information or intellectual property are an ever-growing concern.

Are organizations taking strides to reduce risks associated with adoption of AI in their application and computing environment?

71.2% of survey participants say their organization has taken steps specifically to reduce the risk of using AI within its application and computing environment.

16.3% of survey participants claim their organization has not taken these steps.

These findings are good news. Even as enterprises compete to get as much value from AI as they can, as quickly as possible, they’re tempering their eager embrace of new tools with sensible caution.

Still, responses varied across roles. Security analysts, operators, administrators, and incident responders are less likely to have said their organizations had taken AI risk mitigation steps than respondents in other roles. In fact, 79% of executives said steps had been taken, and only 54% of respondents in hands-on roles agreed. It seems that leaders believe their organizations are taking the needed steps, but practitioners are seeing a gap.

Do security professionals feel confident in their preparedness for the next generation of threats?

A majority of respondents (six out of every ten) believe their organizations are inadequately prepared to face the next generation of AI-powered threats.

The survey findings reveal contrasting perceptions of organizational preparedness for cybersecurity threats across different regions and job roles. Security administrators, due to their hands-on experience, express the highest level of skepticism, with 72% feeling their organizations are inadequately prepared. Notably, respondents in mid-sized organizations feel the least prepared, while those in the largest companies feel the most prepared.

Regionally, participants in Asia-Pacific are most likely to believe their organizations are unprepared, while those in Latin America feel the most prepared. This aligns with the observation that Asia-Pacific has been the most impacted region by cybersecurity threats in recent years, according to the IBM X-Force Threat Intelligence Index.

The optimism among Latin American respondents could be attributed to lower threat volumes experienced in the region, but it's cautioned that this could change suddenly (1).

What are biggest barriers to defending against AI-powered threats?

The top-ranked inhibitors center on knowledge and personnel. However, issues are alluded to almost equally across the board including concerns around budget, tool integration, lack of attention to AI-powered threats, and poor cyber hygiene.

The cybersecurity industry is facing a significant shortage of skilled professionals, with a global deficit of approximately 4 million experts (2). As organizations struggle to manage their security tools and alerts, the challenge intensifies with the increasing adoption of AI by attackers. This shift has altered the demands on security teams, requiring practitioners to possess broad and deep knowledge across rapidly evolving solution stacks.

Educating end users about AI-driven defenses becomes paramount as organizations grapple with the shortage of professionals proficient in managing AI-powered security tools. Operationalizing machine learning models for effectiveness and accuracy emerges as a crucial skill set in high demand. However, our survey highlights a concerning lack of understanding among cybersecurity professionals regarding AI-driven threats and the use of AI-driven countermeasures indicating a gap in keeping pace with evolving attacker tactics.

The integration of security solutions remains a notable problem, hindering effective defense strategies. While budget constraints are not a primary inhibitor, organizations must prioritize addressing these challenges to bolster their cybersecurity posture. It's imperative for stakeholders to recognize the importance of investing in skilled professionals and integrated security solutions to mitigate emerging threats effectively.

To access the full report click here.

References

1. IBM, X-Force Threat Intelligence Index 2024, Available at: https://www.ibm.com/downloads/cas/L0GKXDWJ

2. ISC2, Cybersecurity Workforce Study 2023, Available at: https://media.isc2.org/-/media/Project/ISC2/Main/Media/ documents/research/ISC2_Cybersecurity_Workforce_Study_2023.pdf?rev=28b46de71ce24e6ab7705f6e3da8637e

Continue reading
About the author

Blog

Inside the SOC

Sliver C2: How Darktrace Provided a Sliver of Hope in the Face of an Emerging C2 Framework

Default blog imageDefault blog image
17
Apr 2024

Offensive Security Tools

As organizations globally seek to for ways to bolster their digital defenses and safeguard their networks against ever-changing cyber threats, security teams are increasingly adopting offensive security tools to simulate cyber-attacks and assess the security posture of their networks. These legitimate tools, however, can sometimes be exploited by real threat actors and used as genuine actor vectors.

What is Sliver C2?

Sliver C2 is a legitimate open-source command-and-control (C2) framework that was released in 2020 by the security organization Bishop Fox. Silver C2 was originally intended for security teams and penetration testers to perform security tests on their digital environments [1] [2] [5]. In recent years, however, the Sliver C2 framework has become a popular alternative to Cobalt Strike and Metasploit for many attackers and Advanced Persistence Threat (APT) groups who adopt this C2 framework for unsolicited and ill-intentioned activities.

The use of Sliver C2 has been observed in conjunction with various strains of Rust-based malware, such as KrustyLoader, to provide backdoors enabling lines of communication between attackers and their malicious C2 severs [6]. It is unsurprising, then, that it has also been leveraged to exploit zero-day vulnerabilities, including critical vulnerabilities in the Ivanti Connect Secure and Policy Secure services.

In early 2024, Darktrace observed the malicious use of Sliver C2 during an investigation into post-exploitation activity on customer networks affected by the Ivanti vulnerabilities. Fortunately for affected customers, Darktrace DETECT™ was able to recognize the suspicious network-based connectivity that emerged alongside Sliver C2 usage and promptly brought it to the attention of customer security teams for remediation.

How does Silver C2 work?

Given its open-source nature, the Sliver C2 framework is extremely easy to access and download and is designed to support multiple operating systems (OS), including MacOS, Windows, and Linux [4].

Sliver C2 generates implants (aptly referred to as ‘slivers’) that operate on a client-server architecture [1]. An implant contains malicious code used to remotely control a targeted device [5]. Once a ‘sliver’ is deployed on a compromised device, a line of communication is established between the target device and the central C2 server. These connections can then be managed over Mutual TLS (mTLS), WireGuard, HTTP(S), or DNS [1] [4]. Sliver C2 has a wide-range of features, which include dynamic code generation, compile-time obfuscation, multiplayer-mode, staged and stageless payloads, procedurally generated C2 over HTTP(S) and DNS canary blue team detection [4].

Why Do Attackers Use Sliver C2?

Amidst the multitude of reasons why malicious actors opt for Sliver C2 over its counterparts, one stands out: its relative obscurity. This lack of widespread recognition means that security teams may overlook the threat, failing to actively search for it within their networks [3] [5].

Although the presence of Sliver C2 activity could be representative of authorized and expected penetration testing behavior, it could also be indicative of a threat actor attempting to communicate with its malicious infrastructure, so it is crucial for organizations and their security teams to identify such activity at the earliest possible stage.

Darktrace’s Coverage of Sliver C2 Activity

Darktrace’s anomaly-based approach to threat detection means that it does not explicitly attempt to attribute or distinguish between specific C2 infrastructures. Despite this, Darktrace was able to connect Sliver C2 usage to phases of an ongoing attack chain related to the exploitation of zero-day vulnerabilities in Ivanti Connect Secure VPN appliances in January 2024.

Around the time that the zero-day Ivanti vulnerabilities were disclosed, Darktrace detected an internal server on one customer network deviating from its expected pattern of activity. The device was observed making regular connections to endpoints associated with Pulse Secure Cloud Licensing, indicating it was an Ivanti server. It was observed connecting to a string of anomalous hostnames, including ‘cmjk3d071amc01fu9e10ae5rt9jaatj6b.oast[.]live’ and ‘cmjft14b13vpn5vf9i90xdu6akt5k3pnx.oast[.]pro’, via HTTP using the user agent ‘curl/7.19.7 (i686-redhat-linux-gnu) libcurl/7.63.0 OpenSSL/1.0.2n zlib/1.2.7’.

Darktrace further identified that the URI requested during these connections was ‘/’ and the top-level domains (TLDs) of the endpoints in question were known Out-of-band Application Security Testing (OAST) server provider domains, namely ‘oast[.]live’ and ‘oast[.]pro’. OAST is a testing method that is used to verify the security posture of an application by testing it for vulnerabilities from outside of the network [7]. This activity triggered the DETECT model ‘Compromise / Possible Tunnelling to Bin Services’, which breaches when a device is observed sending DNS requests for, or connecting to, ‘request bin’ services. Malicious actors often abuse such services to tunnel data via DNS or HTTP requests. In this specific incident, only two connections were observed, and the total volume of data transferred was relatively low (2,302 bytes transferred externally). It is likely that the connections to OAST servers represented malicious actors testing whether target devices were vulnerable to the Ivanti exploits.

The device proceeded to make several SSL connections to the IP address 103.13.28[.]40, using the destination port 53, which is typically reserved for DNS requests. Darktrace recognized that this activity was unusual as the offending device had never previously been observed using port 53 for SSL connections.

Model Breach Event Log displaying the ‘Application Protocol on Uncommon Port’ DETECT model breaching in response to the unusual use of port 53.
Figure 1: Model Breach Event Log displaying the ‘Application Protocol on Uncommon Port’ DETECT model breaching in response to the unusual use of port 53.

Figure 2: Model Breach Event Log displaying details pertaining to the ‘Application Protocol on Uncommon Port’ DETECT model breach, including the 100% rarity of the port usage.
Figure 2: Model Breach Event Log displaying details pertaining to the ‘Application Protocol on Uncommon Port’ DETECT model breach, including the 100% rarity of the port usage.

Further investigation into the suspicious IP address revealed that it had been flagged as malicious by multiple open-source intelligence (OSINT) vendors [8]. In addition, OSINT sources also identified that the JARM fingerprint of the service running on this IP and port (00000000000000000043d43d00043de2a97eabb398317329f027c66e4c1b01) was linked to the Sliver C2 framework and the mTLS protocol it is known to use [4] [5].

An Additional Example of Darktrace’s Detection of Sliver C2

However, it was not just during the January 2024 exploitation of Ivanti services that Darktrace observed cases of Sliver C2 usages across its customer base.  In March 2023, for example, Darktrace detected devices on multiple customer accounts making beaconing connections to malicious endpoints linked to Sliver C2 infrastructure, including 18.234.7[.]23 [10] [11] [12] [13].

Darktrace identified that the observed connections to this endpoint contained the unusual URI ‘/NIS-[REDACTED]’ which contained 125 characters, including numbers, lower and upper case letters, and special characters like “_”, “/”, and “-“, as well as various other URIs which suggested attempted data exfiltration:

‘/upload/api.html?c=[REDACTED] &fp=[REDACTED]’

  • ‘/samples.html?mx=[REDACTED] &s=[REDACTED]’
  • ‘/actions/samples.html?l=[REDACTED] &tc=[REDACTED]’
  • ‘/api.html?gf=[REDACTED] &x=[REDACTED]’
  • ‘/samples.html?c=[REDACTED] &zo=[REDACTED]’

This anomalous external connectivity was carried out through multiple destination ports, including the key ports 443 and 8888.

Darktrace additionally observed devices on affected customer networks performing TLS beaconing to the IP address 44.202.135[.]229 with the JA3 hash 19e29534fd49dd27d09234e639c4057e. According to OSINT sources, this JA3 hash is associated with the Golang TLS cipher suites in which the Sliver framework is developed [14].

Conclusion

Despite its relative novelty in the threat landscape and its lesser-known status compared to other C2 frameworks, Darktrace has demonstrated its ability effectively detect malicious use of Sliver C2 across numerous customer environments. This included instances where attackers exploited vulnerabilities in the Ivanti Connect Secure and Policy Secure services.

While human security teams may lack awareness of this framework, and traditional rules and signatured-based security tools might not be fully equipped and updated to detect Sliver C2 activity, Darktrace’s Self Learning AI understands its customer networks, users, and devices. As such, Darktrace is adept at identifying subtle deviations in device behavior that could indicate network compromise, including connections to new or unusual external locations, regardless of whether attackers use established or novel C2 frameworks, providing organizations with a sliver of hope in an ever-evolving threat landscape.

Credit to Natalia Sánchez Rocafort, Cyber Security Analyst, Paul Jennings, Principal Analyst Consultant

Appendices

DETECT Model Coverage

  • Compromise / Repeating Connections Over 4 Days
  • Anomalous Connection / Application Protocol on Uncommon Port
  • Anomalous Server Activity / Server Activity on New Non-Standard Port
  • Compromise / Sustained TCP Beaconing Activity To Rare Endpoint
  • Compromise / Quick and Regular Windows HTTP Beaconing
  • Compromise / High Volume of Connections with Beacon Score
  • Anomalous Connection / Multiple Failed Connections to Rare Endpoint
  • Compromise / Slow Beaconing Activity To External Rare
  • Compromise / HTTP Beaconing to Rare Destination
  • Compromise / Sustained SSL or HTTP Increase
  • Compromise / Large Number of Suspicious Failed Connections
  • Compromise / SSL or HTTP Beacon
  • Compromise / Possible Malware HTTP Comms
  • Compromise / Possible Tunnelling to Bin Services
  • Anomalous Connection / Low and Slow Exfiltration to IP
  • Device / New User Agent
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Anomalous File / EXE from Rare External Location
  • Anomalous File / Numeric File Download
  • Anomalous Connection / Powershell to Rare External
  • Anomalous Server Activity / New Internet Facing System

List of Indicators of Compromise (IoCs)

18.234.7[.]23 - Destination IP - Likely C2 Server

103.13.28[.]40 - Destination IP - Likely C2 Server

44.202.135[.]229 - Destination IP - Likely C2 Server

References

[1] https://bishopfox.com/tools/sliver

[2] https://vk9-sec.com/how-to-set-up-use-c2-sliver/

[3] https://www.scmagazine.com/brief/sliver-c2-framework-gaining-traction-among-threat-actors

[4] https://github[.]com/BishopFox/sliver

[5] https://www.cybereason.com/blog/sliver-c2-leveraged-by-many-threat-actors

[6] https://securityaffairs.com/158393/malware/ivanti-connect-secure-vpn-deliver-krustyloader.html

[7] https://www.xenonstack.com/insights/out-of-band-application-security-testing

[8] https://www.virustotal.com/gui/ip-address/103.13.28.40/detection

[9] https://threatfox.abuse.ch/browse.php?search=ioc%3A107.174.78.227

[10] https://threatfox.abuse.ch/ioc/1074576/

[11] https://threatfox.abuse.ch/ioc/1093887/

[12] https://threatfox.abuse.ch/ioc/846889/

[13] https://threatfox.abuse.ch/ioc/1093889/

[14] https://github.com/projectdiscovery/nuclei/issues/3330

Continue reading
About the author
Natalia Sánchez Rocafort
Cyber Security Analyst
Our ai. Your data.

Elevate your cyber defenses with Darktrace AI

무료 평가판 시작
Darktrace AI protecting a business from cyber threats.