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Detecting & Containing Gootloader Malware

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15
Feb 2024
15
Feb 2024
Learn how Darktrace helps detect and contain multi-functional threats like the Gootloader malware. Stay ahead of cyber threats with Darktrace AI solutions.

What is multi-functional malware?

While traditional malware variants were designed with one specific objective in mind, the emergence of multi-functional malware, such as loader malware, means that organizations are likely to be confronted with multiple malicious tools and strains of malware at once. These threats often have non-linear attack patterns and kill chains that can quickly adapt and progress quicker than human security teams are able to react. Therefore, it is more important than ever for organizations to adopt an anomaly approach to combat increasingly versatile and fast-moving threats.

Example of Multi-functional malware

One example of a multi-functional malware recently observed by Darktrace can be seen in Gootloader, a multi-payload loader variant that has been observed in the wild since 2020. It is known to primarily target Windows-based systems across multiple industries in the US, Canada, France, Germany, and South Korea [1].  

How does Gootloader malware work?

Once installed on a target network, Gootloader can download additional malicious payloads that allow threat actors to carry out a range of harmful activities, such as stealing sensitive information or encrypting files for ransom.

The Gootloader malware is known to infect networks via search engine optimization (SEO) poisoning, directing users searching for legitimate documents to compromised websites hosting a malicious payload masquerading as the desired file.

If the malware remains undetected, it paves the way for a second stage payload known as Gootkit, which functions as a banking trojan and information-stealer, or other malware tools including Cobalt Strike and Osiris [2].

Darktrace detection of Gootloader malware

In late 2023, Darktrace observed one instance of Gootloader affecting a customer in the US. Thanks to its anomaly-focused approach, Darktrace DETECT™ quickly identified the anomalous activity surrounding this emerging attack and brought it to the immediate attention of the customer’s security team. All the while, Darktrace RESPOND™ was in place and able to autonomously intervene, containing the suspicious activity and ensuring the Gootloader compromise could not progress any further.

In September 2023, Darktrace identified an instance of the Gootloader malware attempting to propagate within the network of a customer in the US. Darktrace identified the first indications of the compromise when it detected a device beaconing to an unusual external location and performing network scanning. Following this, the device was observed making additional command-and-control (C2) connections, before finally downloading an executable (.exe) file which likely represented the download of a further malicious payload.

As this customer had subscribed to the Proactive Notification Service (PTN), the suspicious activity was escalated to the Darktrace Security Operations Center (SOC) for further investigation by Darktrace’s expert analysts. The SOC team were able to promptly triage the incident and advise urgent follow-up actions.

Gootloader Attack Overview

Figure 1: Timeline of Anomalous Activities seen on the breach device.

Initial Beaconing and Scanning Activity

On September 21, 2023, Darktrace observed the first indications of compromise on the network when a device began to make regular connections to an external endpoint that was considered extremely rare for the network, namely ‘analyzetest[.]ir’.

Although the endpoint did not overtly seem malicious in nature (it appeared to be related to laboratory testing), Darktrace recognized that it had never previously been seen on the customer’s network and therefore should be treated with caution.  This initial beaconing activity was just the beginning of the malicious C2 communications, with several additional instances of beaconing detected to numerous suspicious endpoints, including funadhoo.gov[.]mv, tdgroup[.]ru’ and ‘army.mil[.]ng.

Figure 2: Initial beaconing activity detected on the breach device.

Soon thereafter, Darktrace detected the device performing internal reconnaissance, with an unusually large number of connections to other internal locations observed. This scanning activity appeared to primarily be targeting the SMB protocol by scanning port 445.

Within seconds of DETECT’s detection of this suspicious SMB scanning activity, Darktrace RESPOND moved to contain the compromise by blocking the device from connecting to port 445 and enforcing its ‘pattern of life’. Darktrace’s Self-Learning AI enables it to learn a device’s normal behavior and recognize if it deviates from this; by enforcing a pattern of life on an affected device, malicious activity is inhibited but the device is allowed to continue its expected activity, minimizing disruption to business operations.

Figure 3: The breach device Model Breach Event Log showing Darktrace DETECT identifying suspicious SMB scanning activity and the corresponding RESPOND actions.

Following the initial detection of this anomalous activity, Darktrace’s Cyber AI Analyst launched an autonomous investigation into the beaconing and scanning activity and was able to connect these seemingly separate events into one incident. AI Analyst analyzes thousands of connections to hundreds of different endpoints at machine speed and then summarizes its findings in a single pane of glass, giving customers the necessary information to assess the threat and begin remediation if necessary. This significantly lessens the burden for human security teams, saving them previous time and resources, while ensuring they maintain full visibility over any suspicious activity on their network.

Figure 4: Cyber AI Analyst incident log summarizing the technical details of the device’s beaconing and scanning behavior.

Beaconing Continues

Darktrace continued to observe the device carrying out beaconing activity over the next few days, likely representing threat actors attempting to establish communication with their malicious infrastructure and setting up a foothold within the customer’s environment. In one such example, the device was seen connecting to the suspicious endpoint ‘fysiotherapie-panken[.]nl’. Multiple open-source intelligence (OSINT) vendors reported this endpoint to be a known malware delivery host [3].

Once again, Darktrace RESPOND was in place to quickly intervene in response to these suspicious external connection attempts. Over the course of several days, RESPOND blocked the offending device from connecting to suspicious endpoints via port 443 and enforced its pattern of life. These autonomous actions by RESPOND effectively mitigated and contained the attack, preventing it from escalating further along the kill chain and providing the customer’s security team crucial time to take act and employ their own remediation.

Figure 5: A sample of the autonomous RESPOND actions that was applied on the affected device.

Possible Payload Retrieval

A few days later, on September 26, 2023, Darktrace observed the affected device attempting to download a Windows Portable Executable via file transfer protocol (FTP) from the external location ‘ftp2[.]sim-networks[.]com’, which had never previously been seen on the network. This download likely represented the next step in the Gootloader infection, wherein additional malicious tooling is downloaded to further cement the malicious actors’ control over the device. In response, Darktrace RESPOND immediately blocked the device from making any external connections, ensuring it could not download any suspicious files that may have rapidly escalated the attackers’ efforts.

Figure 6: DETECT’s identification of the offending device downloading a suspicious executable file via FTP.

The observed combination of beaconing activity and a suspicious file download triggered an Enhanced Monitoring breach, a high-fidelity DETECT model designed to detect activities that are more likely to be indicative of compromise. These models are monitored by the Darktrace SOC round the clock and investigated by Darktrace’s expert team of analysts as soon as suspicious activity emerges.

In this case, Darktrace’s SOC triaged the emerging activity and sent an additional notice directly to the customer’s security team, informing them of the compromise and advising on next steps. As this customer had subscribed to Darktrace’s Ask the Expert (ATE) service, they also had a team of expert analysts available to them at any time to aid their investigations.

Figure 7: Enhanced Monitoring Model investigated by the Darktrace SOC.

Conclusion

Loader malware variants such as Gootloader often lay the groundwork for further, potentially more severe threats to be deployed within compromised networks. As such, it is crucial for organizations and their security teams to identify these threats as soon as they emerge and ensure they are effectively contained before additional payloads, like information-stealing malware or ransomware, can be downloaded.

In this instance, Darktrace demonstrated its value when faced with a multi-payload threat by detecting Gootloader at the earliest stage and responding to it with swift targeted actions, halting any suspicious connections and preventing the download of any additional malicious tooling.

Darktrace DETECT recognized that the beaconing and scanning activity performed by the affected device represented a deviation from its expected behavior and was indicative of a potential network compromise. Meanwhile, Darktrace RESPOND ensured that any suspicious activity was promptly shut down, buying crucial time for the customer’s security team to work with Darktrace’s SOC to investigate the threat and quarantine the compromised device.

Credit to: Ashiq Shafee, Cyber Security Analyst, Qing Hong Kwa, Senior Cyber Analyst and Deputy Analyst Team Lead, Singapore

Appendices

Darktrace DETECT Model Detections

Anomalous Connection / Rare External SSL Self-Signed

Device / Suspicious SMB Scanning Activity

Anomalous Connection / Young or Invalid Certificate SSL Connections to Rare

Compromise / High Volume of Connections with Beacon Score

Compromise / Beacon to Young Endpoint

Compromise / Beaconing Activity To External Rare

Compromise / Slow Beaconing Activity To External Rare

Compromise / Beacon for 4 Days

Anomalous Connection / Suspicious Expired SSL

Anomalous Connection / Multiple Failed Connections to Rare Endpoint

Compromise / Sustained SSL or HTTP Increase

Compromise / Large Number of Suspicious Successful Connections

Compromise / Large Number of Suspicious Failed Connections

Device / Large Number of Model Breaches

Anomalous File / FTP Executable from Rare External Location

Device / Initial Breach Chain Compromise

RESPOND Models

Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block

Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block

Antigena / Network/Insider Threat/Antigena Network Scan 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

Antigena / Network / External Threat / Antigena Suspicious Activity Block

List of Indicators of Compromise (IoCs)

Type

Hostname

IoCs + Description

explorer[.]ee - C2 Endpoint

fysiotherapie-panken[.]nl- C2 Endpoint

devcxp2019.theclearingexperience[.]com- C2 Endpoint

campsite.bplaced[.]net- C2 Endpoint

coup2pompes[.]fr- C2 Endpoint

analyzetest[.]ir- Possible C2 Endpoint

tdgroup[.]ru- C2 Endpoint

ciedespuys[.]com- C2 Endpoint

fi.sexydate[.]world- C2 Endpoint

funadhoo.gov[.]mv- C2 Endpoint

geying.qiwufeng[.]com- C2 Endpoint

goodcomix[.]fun- C2 Endpoint

ftp2[.]sim-networks[.]com- Possible Payload Download Host

MITRE ATT&CK Mapping

Tactic – Technique

Reconnaissance - Scanning IP blocks (T1595.001, T1595)

Command and Control - Web Protocols , Application Layer Protocol, One-Way Communication, External Proxy, Non-Application Layer Protocol, Non-Standard Port (T1071.001/T1071, T1071, T1102.003/T1102, T1090.002/T1090, T1095, T1571)

Collection – Man in the Browser (T1185)

Resource Development - Web Services, Malware (T1583.006/T1583, T1588.001/T1588)

Persistence - Browser Extensions (T1176)

References

1.     https://www.blackberry.com/us/en/solutions/endpoint-security/ransomware-protection/gootloader

2.     https://redcanary.com/threat-detection-report/threats/gootloader/

3.     https://www.virustotal.com/gui/domain/fysiotherapie-panken.nl

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.
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Ashiq Shafee
Cyber Security Analyst
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Inside the SOC

Post-Exploitation Activities on PAN-OS Devices: A Network-Based Analysis

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20
Jun 2024

Introduction

Perimeter devices such as firewalls, virtual private networks (VPNs), and intrusion prevention systems (IPS), have long been the target of adversarial actors attempting to gain access to internal networks. However, recent publications and public service announcements by leading public institutions underscore the increased emphasis threat actors are putting on leveraging such products to initiate compromises.

A blog post by the UK National Cyber Security Center (NCSC) released in early 2024 notes that as improvements are made in the detection of phishing email payloads, threat actors have again begun re-focusing efforts to exploiting network edge devices, many of which are not secure by design, as a means of breach initiation.[i] As such, it comes as no surprise that new Common Vulnerabilities and Exposures (CVEs) are constantly discovered that exploit such internet-exposed systems.

Darktrace analysts frequently observe the impacts of such CVEs first through their investigations via Darktrace’s Security Operations Center (SOC), sometimes even before the public disclosure of proof of concepts for such exploits.  Beginning April 2024, Darktrace’s SOC began handling alerts and customer requests for potential incidents involving Palo Alto Networks firewall devices.  It was during this time that external researchers publicly disclosed what would later be classified as PAN-OS CVE-2024-3400, a form of remote command execution vulnerability that affects several versions of Palo Alto Networks’ firewall operating System (PAN-OS), namely PAN-OS 11.1, 11.0 and 10.2.

The increase in observed SOC activity for Palo Alto firewall devices, coupled with the public announcement of the new CVE, prompted Darktrace researchers to look for evidence of PAN-OS exploitation on customer networks. Researchers also focused on documenting post-exploitation activity from threat actors leveraging the recently disclosed vulnerability.

As such, this blog highlights the network-based behaviors involved in the CVE-2024-3400 attack chains investigated by Darktrace’s SOC and Threat Research teams. Moreover, this investigation also provides a deeper insight into the post-compromise activities of threat actors leveraging the novel CVE.  Such insights will not only prove relevant for cybersecurity teams looking to inhibit compromises in this specific instance, but also highlights general patterns of behavior by threat actors utilizing such CVEs to target internet-facing systems.

CVE-2024-3400

In April 2024, the Darktrace SOC observed an uptick in activity involving recurring patterns of malicious activity from Palo Alto firewall appliances. In response to this trend, Darktrace initiated a Threat Research investigation into such activity to try and identify common factors and indicators across seemingly parallel events. As the Threat Research team opened their investigation, external researchers concurrently provided public details of CVE-2024-3400, a form of remote command execution vulnerability in the GlobalProtect feature on Palo Alto Network firewall devices running PAN-OS versions: 10.2, 11.0, and 11.1.[ii]

In their proof of concept, security researchers at watchTowr demonstrated how an attacker can pass session ID (SESSID) values to these PAN-OS devices to request files that do not exist. In response, the system creates a zero-byte file with root privileges with the same name.[iii] Log data is passed on devices running telemetry services to external servers through command line functionality.[iv] Given this functionality, external actors could then request non-existent files in the SESSID containing command parameters which then be interpreted by the command line functionality.[v] Although researchers first believed the exploit could only be used against devices running telemetry services, this was later discovered to be untrue.[vi]

As details of CVE-2024-3400 began to surface, Darktrace’s Threat Research analysts quickly identified distinct overlaps in the observed activity on specific customer deployments and the post-exploitation behavior reported by external researchers. Given the parallels, Darktrace correlated the patterns of activity observed by the SOC team to exploitation of the newly discovered vulnerability in PAN-OS firewall appliances.

Campaign Analysis

Between the April and May 2024, Darktrace identified four main themes of post-exploitation activity involving Palo Alto Network firewall devices likely targeted via CVE-2024-3400: exploitation validation, shell command and tool retrieval, configuration data exfiltration, and ongoing command and control through encrypted channels and application protocols.

1. Exploit Validation and Further Vulnerability Enumeration

Many of the investigated attack chains began with malicious actors using out-of-band application security testing (OAST) services such as Interactsh to validate exploits against Palo Alto firewall appliances. This exploit validation activity typically resulted in devices attempting to contact unusual external endpoints (namely, subdomains of ‘oast[.]pro’, ‘oast[.]live’, ‘oast[.]site’, ‘oast[.]online’, ‘oast[.]fun’, ‘oast[.]me’, and ‘g3n[.]in’) associated with OAST services such as Interactsh. These services can be used by developers to inspect and debug internet traffic, but also have been easily abused by threat actors.

While attempted connections to OAST services do not alone indicate CVE-2024-3400 exploitation, the prevalence of such activities in observed Palo Alto firewall attack chains suggests widespread usage of these OAST services to validate initial access methods and possibly further enumerate systems for additional vulnerabilities.

Figure 1: Model alert log details showcasing a PAN-OS device making DNS queries for Interactsh domain names in what could be exploit validation, and/or further host enumeration.

2. Command and Payload Transmission

The most common feature across analyzed incidents was HTTP GET requests for shell scripts and Linux executable files (ELF) from external IPs associated with exploitation of the CVE. These HTTP requests were frequently initiated using the utilities, cURL and wget. On nearly every device likely targeted by threat actors leveraging the CVE, Darktrace analysts highlighted the retrieval of shell scripts that either featured enumeration commands, the removal of evidence of compromise activity, or commands to retrieve and start binaries on the destination device.

a) Shell Script Retrieval

Investigated devices commonly performed HTTP GET requests to retrieve shell command scripts. Despite this commonality, there was some degree of variety amongst the retrieved payloads and their affiliation with certain command tools. Several distinct types of shell commands and files were identified during the analyzed breaches. For example, some firewall devices were seen requesting .txt files associated with both Sliver C2, whose malicious use has previously been investigated by Darktrace, and Cobalt Strike. The target URIs of devices’ HTTP requests for these files included, “36shr.txt”, “2.txt”, “bin.txt”, and “data.txt”.

More interestingly, though, was the frequency with which analyzed systems requested bash scripts from rare external IP addresses, sometimes over non-standard ports for the HTTP protocol. These bash scripts would feature commands usually for the recipient system to check for certain existing files and or running processes. If the file did not exist, the system would then use cURL or wget to obtain content from external sites, change the permissions of the file, and then execute, sending output to dev/null as a means of likely defense evasion. In some scripts, the system would first make a new folder, and change directories prior to acquiring external content. Additionally, some samples highlighted multiple attempts at enumeration of the host system.

Figure 2: Packet capture (PCAP) data highlighting the incoming shell scripts providing instructions to use cURL to obtain external content, change the permissions of the file to execute, and then run the binary using the credentials and details provided.
Figure 3: PCAP data highlighting a variation of a shell script seen in an HTTP response processed by compromised devices. The script provides instructions to make a directory, retrieve and execute external content, and to hide the output.

Not every retrieved file that was not explicitly a binary featured bash scripts. Model alerts on some deployments also included file masquerading attempts by threat actors, whereby the Palo Alto firewall device would request content with a misleading extension in the URI. In one such instance, the requested URI, and HTTP response header suggests the returned content is an image/png, but the actual body response featured configuration parameters for a new daemon service to be run on the system.

Figure 4: PCAP data indicating configuration details likely for a new daemon on an investigated host. Such HTTP body content differs from the image/png extension within the request URI and declared content type in the HTTP response header.

Bash scripts analyzed across customer deployments also mirrored those identified by external security teams. External researchers previously reported on a series of identifiable shell commands in some cases of CVE-2024-3400 exploitation analyzed by their teams. Commands frequently involved a persistence mechanism they later labeled as the “UPSTYLE” backdoor.[vii]  This python-based program operates by reading commands hidden in error logs generated by 404 requests to the compromised server. The backdoor interprets the requests and writes the output to CSS files on the device. In many cases, Darktrace’s Threat Research team noted clear parallels between shell commands retrieved via HTTP GET request with those directly involving UPSTYLE. There were also matches with some URI patterns identified with the backdoor and requests observed on Darktrace deployments.

Figure 5: HTTP response data containing shell commands potentially relating to the UPSTYLE backdoor.

The presence of these UPSTYLE-related shell commands in response to Palo Alto firewall devices’ HTTP requests provides further evidence for initial exploitation of the CVE. Many bash scripts in examined cases interacted with folders and files likely related to CVE-2024-3400 exploitation. These scripts frequently sought to delete contents of certain folders, such as “/opt/panlogs/tmp/device_telemetry/minute/*” where evidence of exploitation would likely reside. Moreover, recursive removal and copy commands were frequently seen targeting CSS files within the GlobalProtect folder, already noted as the vulnerable element within PAN-OS versions. This evidence is further corroborated by host-based forensic analysis conducted by external researchers.[viii]

Figure 6: PCAP data from investigated system indicating likely defense evasion by removing content on folders where CVE exploitation occurred.

b) Executable File Retrieval

Typically, following command processing, compromised Palo Alto firewall devices proceeded to make web requests for several unusual and potentially malicious files. Many such executables would be retrieved via processed scripts. While there a fair amount of variety in specific executables and binaries obtained, overall, these executables involved either further command tooling such as Sliver C2 or Cobalt Strike payloads, or unknown executables. Affected systems would also employ uncommon ports for HTTP connections, in a likely attempt to evade detection. Extensions featured within the URI, when visible, frequently noted ‘.elf’ (Linux executable) or ‘.exe’ payloads. While most derived hashes did not feature identifiable open-source intelligence (OSINT) details, some samples did have external information tying the sample to specific malware. For example, one such investigation featured a compromised system requesting a file with a hash identified as the Spark malware (backdoor) while another investigated case included a host requesting a known crypto-miner.

Figure 7: PCAP data highlighting compromised system retrieving ELF content from a rare external server running a simple Python HTTP server.
Figure 8: Darktrace model alert logs highlighting a device labeled “Palo Alto” making a HTTP request on an uncommon port for an executable file following likely CVE exploitation.

3. Configuration Data Exfiltration and Unusual HTTP POST Activity

During Darktrace’s investigations, there were also several instances of sensitive data exfiltration from PAN-OS firewall devices. Specifically, targeted systems were observed making HTTP POST requests via destination port 80 to rare external endpoints that OSINT sources associate with CVE-2024-3400 exploitation and activity. PCAP analysis of such HTTP requests revealed that they often contained sensitive configuration details of the targeted Palo Alto firewall devices, including the IP address, default gateway, domain, users, superusers, and password hashes, to name only a few. Threat actors frequently utilized Target URIs such as “/upload” in their HTTP POST requests of this multi-part boundary form data. Again, the User-Agent headers of these HTTP requests largely involved versions of cURL, typically 7.6.1, and wget.

Figure 9: PCAP datahighlighting Palo Alto Firewall device running vulnerable version of PAN-OSposting configuration details to rare external services via HTTP.
Figure 10: Model alert logs highlighting a Palo Alto firewall device performing HTTP POSTs to a rare external IP, without a prior hostname lookup, on an uncommon port using a URI associated with configuration data exfiltration across analyzed incidents
Figure 11: Examples of TargetURIs of HTTP POST requests involving base64 encoded IPs and potential dataegress.

4. Ongoing C2 and Miscellaneous Activity

Lastly, a smaller number of affected Palo Alto firewall devices were seen engaging in repeated beaconing and/or C2 communication via both encrypted and unencrypted protocols during and following the initial series of kill chain events. Such encrypted channels typically involved protocols such as TLS/SSL and SSH. This activity likely represented ongoing communication of targeted systems with attacker infrastructure. Model alerts typically highlighted unusual levels of repeated external connectivity to rare external IP addresses over varying lengths of time. In some investigated incidents, beaconing activity consisted of hundreds of thousands of connections over several days.

Figure 12:  Advanced search details highlighting high levels of ongoing external communication to endpoints associated with C2 infrastructure exploiting CVE-2024-3400.

Some beaconing activity appears to have involved the use of the WebSocket protocol, as indicated by the appearance of “/ws” URIs and validated within packet captures. Such connections were then upgraded to an encrypted connection.

Figure 13:  PCAP highlighting use of WebSocket protocol to engage in ongoing external connectivity to likely C2 infrastructure following CVE-2024-3400 compromise.

While not directly visible in all the deployments, some investigations also yielded evidence of attempts at further post-exploitation activity. For example, a handful of the analyzed binaries that were downloaded by examined devices had OSINT information suggesting a relation to crypto-mining malware strains. However, crypto-mining activity was not directly observed at this time. Furthermore, several devices also triggered model alerts relating to brute-forcing activity via several authentication protocols (namely, Keberos and RADIUS) during the time of compromise. This brute-force activity likely represented attempts to move laterally from the affected firewall system to deeper parts of the network.

Figure 14: Model alert logs noting repeated SSL connectivity to a Sliver C2-affiliated endpoint in what likely constitutes C2 connectivity.
Figure 15: Model alert logs featuring repeated RADIUS login failures from a compromised PAN-OS device using generic usernames, suggesting brute-force activity.

Conclusion

Between April and late May 2024, Darktrace’s SOC and Threat Research teams identified several instances of likely PAN-OS CVE-2024-3400 exploitation across the Darktrace customer base. The subsequent investigation yielded four major themes that categorize the observed network-based post-exploitation activity. These major themes were exploit validation activity, retrieval of binaries and shell scripts, data exfiltration via HTTP POST activity, and ongoing C2 communication with rare external endpoints. The insights shared in this article will hopefully contribute to the ongoing discussion within the cybersecurity community about how to handle the likely continued exploitation of this vulnerability. Moreover, this article may also help cybersecurity professionals better respond to future exploitation of not only Palo Alto PAN-OS firewall devices, but also of edge devices more broadly.

Threat actors will continue to discover and leverage new CVEs impacting edge infrastructure. Since it is not yet known which CVEs threat actors will exploit next, relying on rules and signatures for the detection of exploitation of such CVEs is not a viable approach. Darktrace’s anomaly-based approach to threat detection, however, is well positioned to robustly adapt to threat actors’ changing methods, since although threat actors can change the CVEs they exploit, they cannot change the fact that their exploitation of CVEs results in highly unusual patterns of activity.

Credit to Adam Potter, Cyber Analyst, Sam Lister, Senior Cyber Analyst

Appendices

Indicators of Compromise

Indicator – Type – Description

94.131.120[.]80              IP             C2 Endpoint

94.131.120[.]80:53/?src=[REDACTED]=hour=root                  URL        C2/Exfiltration Endpoint

134.213.29[.]14/?src=[REDACTED]min=root             URL        C2/Exfiltration Endpoint

134.213.29[.]14/grep[.]mips64            URL        Payload

134.213.29[.]14/grep[.]x86_64             URL        Payload

134.213.29[.]14/?deer               URL        Payload

134.213.29[.]14/?host=IDS   URL        Payload

134.213.29[.]14/ldr[.]sh           URL        Payload

91ebcea4e6d34fd6e22f99713eaf67571b51ab01  SHA1 File Hash               Payload

185.243.115[.]250/snmpd2[.]elf        URL        Payload

23.163.0[.]111/com   URL        Payload

80.92.205[.]239/upload            URL        C2/Exfiltration Endpoint

194.36.171[.]43/upload            URL        C2/Exfiltration Endpoint

update.gl-protect[.]com          Hostname         C2 Endpoint

update.gl-protect[.]com:63869/snmpgp      URL        Payload

146.70.87[.]237              IP address         C2 Endpoint

146.70.87[.]237:63867/snmpdd         URL        Payload

393c41b3ceab4beecf365285e8bdf0546f41efad   SHA1 File Hash               Payload

138.68.44[.]59/app/r URL        Payload

138.68.44[.]59/app/clientr     URL        Payload

138.68.44[.]59/manage            URL        Payload

72.5.43[.]90/patch      URL        Payload

217.69.3[.]218                 IP             C2 Endpoint

5e8387c24b75c778c920f8aa38e4d3882cc6d306                  SHA1 File Hash               Payload

217.69.3[.]218/snmpd[.]elf   URL        Payload

958f13da6ccf98fcaa270a6e24f83b1a4832938a    SHA1 File Hash               Payload

6708dc41b15b892279af2947f143af95fb9efe6e     SHA1 File Hash               Payload

dc50c0de7f24baf03d4f4c6fdf6c366d2fcfbe6c       SHA1 File Hash               Payload

109.120.178[.]253:10000/data[.]txt                  URL        Payload

109.120.178[.]253:10000/bin[.]txt   URL        Payload

bc9dc2e42654e2179210d98f77822723740a5ba6                 SHA1 File Hash               Payload

109.120.178[.]253:10000/123              URL        Payload

65283921da4e8b5eabb926e60ca9ad3d087e67fa                 SHA1 File Hash               Payload

img.dxyjg[.]com/6hiryXjZN0Mx[.]sh                  URL        Payload

149.56.18[.]189/IC4nzNvf7w/2[.]txt                 URL        Payload

228d05fd92ec4d19659d71693198564ae6f6b117 SHA1 File Hash               Payload

54b892b8fdab7c07e1e123340d800e7ed0386600                 SHA1 File Hash               Payload

165.232.121[.]217/rules          URL        Payload

165.232.121[.]217/app/request          URL        Payload

938faec77ebdac758587bba999e470785253edaf SHA1 File Hash               Payload

165.232.121[.]217/app/request63   URL        Payload

165.232.121[.]217:4443/termite/165.232.121[.]217             URL        Payload

92.118.112[.]60/snmpd2[.]elf               URL        Payload

2a90d481a7134d66e8b7886cdfe98d9c1264a386                 SHA1 File Hash               Payload

92.118.112[.]60/36shr[.]txt   URL        Payload

d6a33673cedb12811dde03a705e1302464d8227f                 SHA1 File Hash               Payload

c712712a563fe09fa525dfc01ce13564e3d98d67  SHA1 File Hash               Payload

091b3b33e0d1b55852167c3069afcdb0af5e5e79 SHA1 File Hash               Payload

5eebf7518325e6d3a0fd7da2c53e7d229d7b74b6                  SHA1 File Hash               Payload

183be7a0c958f5ed4816c781a2d7d5aa8a0bca9f SHA1 File Hash               Payload

e7d2f1224546b17d805617d02ade91a9a20e783e                 SHA1 File Hash               Payload

e6137a15df66054e4c97e1f4b8181798985b480d SHA1 File Hash               Payload

95.164.7[.]33:53/sea[.]png    URL        Payload

95.164.7[.]33/rules     URL        Payload

95.164.7[.]33:53/lb64                URL        Payload

c2bc9a7657bea17792048902ccf2d77a2f50d2d7 SHA1 File Hash               Payload

923369bbb86b9a9ccf42ba6f0d022b1cd4f33e9d SHA1 File Hash               Payload

52972a971a05b842c6b90c581b5c697f740cb5b9                 SHA1 File Hash               Payload

95d45b455cf62186c272c03d6253fef65227f63a    SHA1 File Hash               Payload

322ec0942cef33b4c55e5e939407cd02e295973e                  SHA1 File Hash               Payload

6335e08873b4ca3d0eac1ea265f89a9ef29023f2  SHA1 File Hash               Payload

134.213.29[.]14              IP             C2 Endpoint

185.243.115[.]250       IP             C2 Endpoint

80.92.205[.]239              IP             C2 Endpoint

194.36.171[.]43              IP             C2 Endpoint

92.118.112[.]60              IP             C2 Endpoint

109.120.178[.]253       IP             C2 Endpoint

23.163.0[.]111                 IP             C2 Endpoint

72.5.43[.]90     IP             C2 Endpoint

165.232.121[.]217       IP             C2 Endpoint

8.210.242[.]112              IP             C2 Endpoint

149.56.18[.]189              IP             C2 Endpoint

95.164.7[.]33  IP             C2 Endpoint

138.68.44[.]59                 IP             C2 Endpoint

Img[.]dxyjg[.]com         Hostname         C2 Endpoint

Darktrace Model Alert Coverage

·      Anomalous File / Masqueraded File Transfer

·      Anomalous File / EXE from Rare External Location

·      Anomalous File / Multiple EXE from Rare External Locations

·      Anomalous File / Script from Rare External Location

·      Anomalous File / Script and EXE from Rare External

·      Anomalous File / Suspicious Octet Stream Download

·      Anomalous File / Numeric File Download

·      Anomalous Connection / Application Protocol on Uncommon Port

·      Anomalous Connection / New User Agent to IP Without Hostname

·      Anomalous Connection / Posting HTTP to IP Without Hostname

·      Anomalous Connection / Multiple Failed Connections to Rare Endpoint

·      Anomalous Connection / Suspicious Self-Signed SSL

·      Anomalous Connection / Anomalous SSL without SNI to New External

·      Anomalous Connection / Multiple Connections to New External TCP Port

·      Anomalous Connection / Rare External SSL Self-Signed

·      Anomalous Server Activity / Outgoing from Server

·      Anomalous Server Activity / Rare External from Server

·      Compromise / SSH Beacon

·      Compromise / Beacon for 4 Days

·      Compromise / Sustained TCP Beaconing Activity To Rare Endpoint

·      Compromise / High Priority Tunnelling to Bin Services

·      Compromise / Sustained SSL or HTTP Increase

·      Compromise / Connection to Suspicious SSL Server

·      Compromise / Suspicious File and C2

·      Compromise / Large Number of Suspicious Successful Connections

·      Compromise / Slow Beaconing Activity To External Rare

·      Compromise / HTTP Beaconing to New Endpoint

·      Compromise / SSL or HTTP Beacon

·      Compromise / Suspicious HTTP and Anomalous Activity

·      Compromise / Beacon to Young Endpoint

·      Compromise / High Volume of Connections with Beacon Score

·      Compromise / Suspicious Beaconing Behaviour

·      Compliance / SSH to Rare External Destination

·      Compromise / HTTP Beaconing to Rare Destination

·      Compromise / Beaconing Activity To External Rare

·      Device::New User Agent

·      Device / Initial Breach Chain Compromise

·      Device / Multiple C2 Model Breaches

MITRE ATTACK Mapping

Tactic – Technique

Initial Access  T1190 – Exploiting Public-Facing Application

Execution           T1059.004 – Command and Scripting Interpreter: Unix Shell

Persistence      T1543.002 – Create or Modify System Processes: Systemd Service

Defense Evasion           T1070.004 – Indicator Removal: File Deletion

Credential Access       T1110.001 – Brute Force: Password Guessing

Discovery           T1083 – File and System Discovery

T1057 – Process Discovery

Collection         T1005 – Data From Local System

Command and Control             T1071.001 – Application Layer Protocol:  Web Protocols

T1573.002 – Encrypted Channel: Asymmetric Cryptography

T1571 – Non-Standard Port

T1105 – Ingress Tool Transfer

Exfiltration         T1041 – Exfiltration over C2 Protocol

T1048.002 - Exfiltration Over Alternative Protocol: Exfiltration Over Asymmetric Encrypted Non-C2 Protocol

References

[i]  https://www.ncsc.gov.uk/blog-post/products-on-your-perimeter

[ii] https://security.paloaltonetworks.com/CVE-2024-3400

[iii] https://labs.watchtowr.com/palo-alto-putting-the-protecc-in-globalprotect-cve-2024-3400/

[iv] https://labs.watchtowr.com/palo-alto-putting-the-protecc-in-globalprotect-cve-2024-3400/

[v] https://labs.watchtowr.com/palo-alto-putting-the-protecc-in-globalprotect-cve-2024-3400/

[vi] https://security.paloaltonetworks.com/CVE-2024-3400

[vii] https://www.volexity.com/blog/2024/04/12/zero-day-exploitation-of-unauthenticated-remote-code-execution-vulnerability-in-globalprotect-cve-2024-3400/

[viii] https://www.volexity.com/blog/2024/05/15/detecting-compromise-of-cve-2024-3400-on-palo-alto-networks-globalprotect-devices/

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About the author
Adam Potter
Cyber Analyst

Safeguarding Distribution Centers in the Digital Age

Default blog imageDefault blog image
12
Jun 2024

Challenges securing distribution centers

For large retail providers, e-commerce organizations, logistics & supply chain organizations, and other companies who rely on the distribution of goods to consumers cybersecurity efforts are often focused on an immense IT infrastructure. However, there's a critical, often overlooked segment of infrastructure that demands vigilant monitoring and robust protection: distribution centers.

Distribution centers play a critical role in the business operations of supply chains, logistics, and the retail industry. They serve as comprehensive logistics hubs, with many organizations operating multiple centers worldwide to meet consumer needs. Depending on their size and hours of operation, even just one hour of downtime at these centers can result in significant financial losses, ranging from tens to hundreds of thousands of dollars per hour.

Due to the time-sensitive nature and business criticality of distribution centers, there has been a rise in applying modern technologies now including AI applications to enhance efficiency within these facilities. Today’s distribution centers are increasingly connected to Enterprise IT networks, the cloud and the internet to manage every stage of the supply chain. Additionally, it is common for organizations to allow 3rd party access to the distribution center networks and data for reasons including allowing them to scale their operations effectively.

However, this influx of new technologies and interconnected systems across IT, OT and cloud introduces new risks on the cybersecurity front. Distribution center networks include industrial operational technologies ICS/OT, IoT technologies, enterprise network technology, and cloud systems working in coordination. The convergence of these technologies creates a greater chance that blind spots exist for security practitioners and this increasing presence of networked technology increases the attack surface and potential for vulnerability. Thus, having cybersecurity measures that cover IT, OT or Cloud alone is not enough to secure a complex and dynamic distribution center network infrastructure.  

The OT network encompasses various systems, devices, hardware, and software, such as:

  • Enterprise Resource Planning (ERP)
  • Warehouse Execution System (WES)
  • Warehouse Control System (WCS)
  • Warehouse Management System (WMS)
  • Energy Management Systems (EMS)
  • Building Management Systems (BMS)
  • Distribution Control Systems (DCS)
  • Enterprise IT devices
  • OT and IoT: Engineering workstations, ICS application and management servers, PLCs, HMI, access control, cameras, and printers
  • Cloud applications

Distribution centers: An expanding attack surface

As these distribution centers have become increasingly automated, connected, and technologically advanced, their attack surfaces have inherently increased. Distribution centers now have a vastly different potential for cyber risk which includes:  

  • More networked devices present
  • Increased routable connectivity within industrial systems
  • Externally exposed industrial control systems
  • Increased remote access
  • IT/OT enterprise to industrial convergence
  • Cloud connectivity
  • Contractors, vendors, and consultants on site or remoting in  

Given the variety of connected systems, distribution centers are more exposed to external threats than ever before. Simultaneously, distribution center’s business criticality has positioned them as interesting targets to cyber adversaries seeking to cause disruption with significant financial impact.

Increased connectivity requires a unified security approach

When assessing the unique distribution center attack surface, the variety of interconnected systems and devices requires a cybersecurity approach that can cover the diverse technology environment.  

From a monitoring and visibility perspective, siloed IT, OT or cloud security solutions cannot provide the comprehensive asset management, threat detection, risk management, and response and remediation capabilities across interconnected digital infrastructure that a solution natively covering IT, cloud, OT, and IoT can provide.  

The problem with using siloed cybersecurity solutions to cover a distribution center is the visibility gaps that are inherently created when using multiple solutions to try and cover the totality of the diverse infrastructure. What this means is that for cross domain and multi-stage attacks, depending on the initial access point and where the adversary plans on actioning their objectives, multiple stages of the attack may not be detected or correlated if they security solutions lack visibility into OT, IT, IoT and cloud.

Comprehensive security under one solution

Darktrace leverages Self-Learning AI, which takes a new approach to cybersecurity. Instead of relying on rules and signatures, this AI trains on the specific business to learn a ‘pattern of life’ that models normal activity for every device, user, and connection. It can be applied anywhere an organization has data, and so can natively cover IT, OT, IoT, and cloud.  

With these models, Darktrace /OT provides improved visibility, threat detection and response, and risk management for proactive hardening recommendations.  

Visibility: Darktrace is the only OT security solution that natively covers IT, IoT and OT in unison. AI augmented workflows ensure OT cybersecurity analysts and operation engineers can manage IT and OT environments, leveraging a live asset inventory and tailored dashboards to optimize security workflows and minimize operator workload.

Threat detection, investigation, and response: The AI facilitates anomaly detection capable of detecting known, unknown, and insider threats and precise response for OT environments that contains threats at their earliest stages before they can jeopardize control systems. Darktrace immediately understands, identifies, and investigates all anomalous activity in OT networks, whether human or machine driven and uses Explainable AI to generate investigation reports via Darktrace’s Cyber AI Analyst.

Proactive risk identification: Risk management capabilities like attack path modeling can prioritize remediation and mitigation that will most effectively reduce derived risk scores. Rather than relying on knowledge of past attacks and CVE lists and scores, Darktrace AI learns what is ‘normal’ for its environment, discovering previously unknown threats and risks by detecting subtle shifts in behavior and connectivity. Through the application of Darktrace AI for OT environments, security teams can investigate novel attacks, discover blind spots, get live-time visibility across all their physical and digital assets, and reduce the time to detect, respond to, and triage security events.

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About the author
Daniel Simonds
Director of Operational Technology
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