Stay updated on the latest cybersecurity threats and learn how AI detected a vulnerability exploit in GitLab.
Darktrace has discovered a significant number of cases involving a successful exploit of GitLab servers — a common open source software used by developers. The vulnerability, tracked as CVE-2021-22205, allows an unauthenticated, remote attacker to execute arbitrary commands as the ‘git’ user, giving them full access to the repository, including deleting, modifying, and exfiltrating source code.
In each case discovered by Darktrace AI, attackers successfully exploited servers and ran crypto-mining malware. However, this vulnerability opens the door into a wider range of possibilities, including data exfiltration, ransomware, and supply chain attacks.
The flaw was fixed on April 14, 2021, but recent research has revealed that this vulnerability is still exploitable with over 30,000 GitLab servers remaining unpatched.
The vulnerability has affected customers in every corner of the world, with Darktrace customers in the US, EMEA and APAC all targeted. Affected industries include technology, transportation, and education.
Attack details
The cases detailed below generally follow the same pattern. First, user accounts with admin privileges are registered on a publicly accessible GitLab server belonging to an unnamed customer. This is followed by a remote execution of commands that grant the rogue accounts elevated permissions.
Figure 1: Multiple model breaches firing on an unusual data egress event on October 30, which resulted in a Proactive Threat Notification model breach.
After multiple model breaches on malicious EXE downloads and command and control (C2) activities with the TOR network, the organization received a Proactive Threat Notification (PTN) from Darktrace that immediately alerted them to the issue. This enabled the customer to remove the compromised device from the network.
The next day, Darktrace discovered cryptocurrency mining occurring on a compromised server that was communicating on a non-standard port. This triggered alerts to the customer through Darktrace’s Proactive Threat Notification service, immediately escalating the threat to their security team.
Figure 2: Multiple cryptocurrency mining model breaches from the same server firing on November 3.
The related breaches include scripts from rare external locations and rare endpoints (endpoints that have never been contacted by the breach devices in the past). Not surprisingly, the endpoints in question are crypto-mining pools.
It is important to note that this GitLab vulnerability represents only the initial attack vector, which could result in a number of scenarios. In the customer environment detailed above, crypto-mining has occurred; however, exploitation of this vulnerability could serve as the first stage of a more destructive ransomware attack, or result in stolen intellectual property.
Lastly, throughout the compromises identified across Darktrace’s customer base, it appears that the Interactsh tool was leveraged by the threat actors in the attack. Interactsh is an open-source tool for out of band data transfers and validation of security flaws, and it is commonly used by both researchers and hackers. Darktrace was easily able to identify this tool as part of the larger threat.
Cyber AI Analyst investigates
Darktrace’s Cyber AI Analyst launched an immediate investigation, stitching together different events across a five-day period and revealing four stages of the attack. This presented the security team with all the information they needed to perform effective investigation and clean up, including isolating the infected devices.
Figure 3: Cyber AI Analyst automatically investigates, piecing together the events into a single narrative.
In another customer environment, Cyber AI Analyst was again able to piece together multiple security events to present a coherent security narrative, determining that the suspicious file downloads likely contained malicious software, and recommending immediate attention from security staff.
Figure 4: In a different case, Cyber AI Analyst surfaces a summary and key metrics around the suspicious file downloads.
Cyber AI Analyst made stellar detections and Proactive Threat Notification alerted affected clients ASAP. Clients were then supported through Ask the Expert (ATE) services. There has been no evidence of ransomware thus far, but these types of attacks typically gain a foothold on Internet-exposed servers and then pivot internally to deploy ransomware.
In a third example with a separate customer, Cyber AI Analyst stitched together six different security events into a single security narrative. Here, Darktrace’s technology was able to connect the dots between C2 behavior, suspicious file downloads, unusual connections, and Tor activity, eventually leading to its discovery of cryptocurrency mining.
Cyber AI Analyst specifically identified GitLab in the suspicious file downloads from a rare external endpoint. The fact that Darktrace was able to identify this in the context of a holistic view of threatening activity across this organization’s digital ecosystem — stretching from suspicious SSL connections to the eventual crypto-mining activity — presents a remarkable picture of Cyber AI Analyst in action.
Figure 5: Cyber AI Analyst identifying the GitLab activity in the context of the wider security narrative.
Concluding thoughts
Though the patch was released in April, over 50% of deployments remain unpatched. There are potential reasons why they remain unpatched — overworked security staff, or simply negligence.
Even when CVEs are mapped and patched promptly, however, novel and never-before-seen attacks can still slip through the cracks. Before the Gitlab flaw was publicly disclosed and fixed, this vulnerability was a zero-day.
And so, rather than wait for CVEs to be publicly disclosed, organizations would be prudent to adopt technologies that can detect and respond to emerging attacks at their earliest stages — regardless of whether they are exploiting known or unknown vulnerabilities.
At Darktrace we talk a lot about the problems novel and unknown threats pose for traditional security solutions. This case shows that even when a threat is known for over six months, difficulties in implementing and rolling out patching mean it can still cause issues.
Thanks to Darktrace’s AI continuously monitoring the behavior of our customer’s devices, they were able to identify the threat at its earliest stages, before it could develop into something more disruptive like ransomware. And had the customers had Darktrace Antigena configured, the technology would have responded autonomously to contain the malicious behavior before the attackers could get past stage one.
Thanks to Darktrace analyst Waseem Akhter for his insights on the above threat find.
Device / Large Number of Model Breaches from Critical Network Device
Unusual Activity / Enhanced Unusual External Data Transfer
Other Darktrace model detections:
Anomalous Connection / Anomalous SSL without SNI to New External
Anomalous Connection / Application Protocol on Uncommon Port
Anomalous Connection / Callback on Web Facing Device
Anomalous Connection / Data Sent to Rare Domain
Anomalous Connection / New User Agent to IP Without Hostname
Anomalous Connection / Posting HTTP to IP Without Hostname
Anomalous File / Multiple EXE from Rare External Locations
Anomalous File / Internet Facing System File Download
Anomalous File / Script from Rare Location
Anomalous Server Activity / Outgoing from Serve
Compromise / Beaconing Activity To External Rare
Compliance / Crypto Currency Mining Activity
Compromise / High Volume of Connections with Beacon Score
Compromise / Large DNS Volume for Suspicious Domain
Compromise / Monero Mining
Compliance / Possible Tor Usage
Device / Internet Facing Device with High Priority Alert
Device / Large Number of Model Breaches
Device / Large Number of Connections to New Endpoints
Device / Suspicious Domain
Unusual Activity / Unusual External Data to New IPs
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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.
Darktrace's Detection of State-Linked ShadowPad Malware
An integral part of cybersecurity is anomaly detection, which involves identifying unusual patterns or behaviors in network traffic that could indicate malicious activity, such as a cyber-based intrusion. However, attribution remains one of the ever present challenges in cybersecurity. Attribution involves the process of accurately identifying and tracing the source to a specific threat actor(s).
Given the complexity of digital networks and the sophistication of attackers who often use proxies or other methods to disguise their origin, pinpointing the exact source of a cyberattack is an arduous task. Threat actors can use proxy servers, botnets, sophisticated techniques, false flags, etc. Darktrace’s strategy is rooted in the belief that identifying behavioral anomalies is crucial for identifying both known and novel threat actor campaigns.
The ShadowPad cluster
Between July 2024 and November 2024, Darktrace observed a cluster of activity threads sharing notable similarities. The threads began with a malicious actor using compromised user credentials to log in to the target organization's Check Point Remote Access virtual private network (VPN) from an attacker-controlled, remote device named 'DESKTOP-O82ILGG'. In one case, the IP from which the initial login was carried out was observed to be the ExpressVPN IP address, 194.5.83[.]25. After logging in, the actor gained access to service account credentials, likely via exploitation of an information disclosure vulnerability affecting Check Point Security Gateway devices. Recent reporting suggests this could represent exploitation of CVE-2024-24919 [27,28]. The actor then used these compromised service account credentials to move laterally over RDP and SMB, with files related to the modular backdoor, ShadowPad, being delivered to the ‘C:\PerfLogs\’ directory of targeted internal systems. ShadowPad was seen communicating with its command-and-control (C2) infrastructure, 158.247.199[.]185 (dscriy.chtq[.]net), via both HTTPS traffic and DNS tunneling, with subdomains of the domain ‘cybaq.chtq[.]net’ being used in the compromised devices’ TXT DNS queries.
Figure 1: Darktrace’s Advanced Search data showing the VPN-connected device initiating RDP connections to a domain controller (DC). The device subsequently distributes likely ShadowPad-related payloads and makes DRSGetNCChanges requests to a second DC.
Figure 2: Event Log data showing a DC making DNS queries for subdomains of ‘cbaq.chtq[.]net’ to 158.247.199[.]185 after receiving SMB and RDP connections from the VPN-connected device, DESKTOP-O82ILGG.
Additional cases of ShadowPad were observed across Darktrace’s customer base in 2024. In some cases, common C2 infrastructure with the cluster discussed above was observed, with dscriy.chtq[.]net and cybaq.chtq[.]net both involved; however, no other common features were identified. These ShadowPad infections were observed between April and November 2024, with customers across multiple regions and sectors affected. Darktrace’s observations align with multiple other public reports that fit the timeframe of this campaign.
Darktrace has also observed other cases of ShadowPad without common infrastructure since September 2024, suggesting the use of this tool by additional threat actors.
The data theft thread
One of the Darktrace customers impacted by the ShadowPad cluster highlighted above was a European manufacturer. A distinct thread of activity occurred within this organization’s network several months after the ShadowPad intrusion, in October 2024.
The thread involved the internal distribution of highly masqueraded executable files via Sever Message Block (SMB) and WMI (Windows Management Instrumentation), the targeted collection of sensitive information from an internal server, and the exfiltration of collected information to a web of likely compromised sites. This observed thread of activity, therefore, consisted of three phrases: lateral movement, collection, and exfiltration.
The lateral movement phase began when an internal user device used an administrative credential to distribute files named ‘ProgramData\Oracle\java.log’ and 'ProgramData\Oracle\duxwfnfo' to the c$ share on another internal system.
Figure 3: Darktrace model alert highlighting an SMB write of a file named ‘ProgramData\Oracle\java.log’ to the c$ share on another device.
Over the next few days, Darktrace detected several other internal systems using administrative credentials to upload files with the following names to the c$ share on internal systems:
ProgramData\Adobe\ARM\webservices.dll
ProgramData\Adobe\ARM\wksprt.exe
ProgramData\Oracle\Java\wksprt.exe
ProgramData\Oracle\Java\webservices.dll
ProgramData\Microsoft\DRM\wksprt.exe
ProgramData\Microsoft\DRM\webservices.dll
ProgramData\Abletech\Client\webservices.dll
ProgramData\Abletech\Client\client.exe
ProgramData\Adobe\ARM\rzrmxrwfvp
ProgramData\3Dconnexion\3DxWare\3DxWare.exe
ProgramData\3Dconnexion\3DxWare\webservices.dll
ProgramData\IDMComp\UltraCompare\updater.exe
ProgramData\IDMComp\UltraCompare\webservices.dll
ProgramData\IDMComp\UltraCompare\imtrqjsaqmm
Figure 4: Cyber AI Analyst highlighting an SMB write of a file named ‘ProgramData\Adobe\ARM\webservices.dll’ to the c$ share on an internal system.
The threat actor appears to have abused the Microsoft RPC (MS-RPC) service, WMI, to execute distributed payloads, as evidenced by the ExecMethod requests to the IWbemServices RPC interface which immediately followed devices’ SMB uploads.
Figure 5: Cyber AI Analyst data highlighting a thread of activity starting with an SMB data upload followed by ExecMethod requests.
Several of the devices involved in these lateral movement activities, both on the source and destination side, were subsequently seen using administrative credentials to download tens of GBs of sensitive data over SMB from a specially selected server. The data gathering stage of the threat sequence indicates that the threat actor had a comprehensive understanding of the organization’s system architecture and had precise objectives for the information they sought to extract.
Immediately after collecting data from the targeted server, devices went on to exfiltrate stolen data to multiple sites. Several other likely compromised sites appear to have been used as general C2 infrastructure for this intrusion activity. The sites used by the threat actor for C2 and data exfiltration purport to be sites for companies offering a variety of service, ranging from consultancy to web design.
Figure 6: Screenshotof one of the likely compromised sites used in the intrusion.
At least 16 sites were identified as being likely data exfiltration or C2 sites used by this threat actor in their operation against this organization. The fact that the actor had such a wide web of compromised sites at their disposal suggests that they were well-resourced and highly prepared.
Figure 7: Darktrace model alert highlighting an internal device slowly exfiltrating data to the external endpoint, yasuconsulting[.]com.
Figure 8: Darktrace model alert highlighting an internal device downloading nearly 1 GB of data from an internal system just before uploading a similar volume of data to another suspicious endpoint, www.tunemmuhendislik[.]com
Cyber AI Analyst spotlight
Figure 9: Cyber AI Analyst identifying and piecing together the various steps of a ShadowPad intrusion.
Figure 10: Cyber AI Analyst Incident identifying and piecing together the various steps of the data theft activity.
As shown in the above figures, Cyber AI Analyst’s ability to thread together the different steps of these attack chains are worth highlighting.
In the ShadowPad attack chains, Cyber AI Analyst was able to identify SMB writes from the VPN subnet to the DC, and the C2 connections from the DC. It was also able to weave together this activity into a single thread representing the attacker’s progression.
Similarly, in the data exfiltration attack chain, Cyber AI Analyst identified and connected multiple types of lateral movement over SMB and WMI and external C2 communication to various external endpoints, linking them in a single, connected incident.
These Cyber AI Analyst actions enabled a quicker understanding of the threat actor sequence of events and, in some cases, faster containment.
Attribution puzzle
Publicly shared research into ShadowPad indicates that it is predominantly used as a backdoor in People’s Republic of China (PRC)-sponsored espionage operations [5][6][7][8][9][10]. Most publicly reported intrusions involving ShadowPad are attributed to the China-based threat actor, APT41 [11][12]. Furthermore, Google Threat Intelligence Group (GTIG) recently shared their assessment that ShadowPad usage is restricted to clusters associated with APT41 [13]. Interestingly, however, there have also been public reports of ShadowPad usage in unattributed intrusions [5].
The data theft activity that later occurred in the same Darktrace customer network as one of these ShadowPad compromises appeared to be the targeted collection and exfiltration of sensitive data. Such an objective indicates the activity may have been part of a state-sponsored operation. The tactics, techniques, and procedures (TTPs), artifacts, and C2 infrastructure observed in the data theft thread appear to resemble activity seen in previous Democratic People’s Republic of Korea (DPRK)-linked intrusion activities [15] [16] [17] [18] [19].
The distribution of payloads to the following directory locations appears to be a relatively common behavior in DPRK-sponsored intrusions.
Observed examples:
C:\ProgramData\Oracle\Java\
C:\ProgramData\Adobe\ARM\
C:\ProgramData\Microsoft\DRM\
C:\ProgramData\Abletech\Client\
C:\ProgramData\IDMComp\UltraCompare\
C:\ProgramData\3Dconnexion\3DxWare\
Additionally, the likely compromised websites observed in the data theft thread, along with some of the target URI patterns seen in the C2 communications to these sites, resemble those seen in previously reported DPRK-linked intrusion activities.
No clear evidence was found to link the ShadowPad compromise to the subsequent data theft activity that was observed on the network of the manufacturing customer. It should be noted, however, that no clear signs of initial access were found for the data theft thread – this could suggest the ShadowPad intrusion itself represents the initial point of entry that ultimately led to data exfiltration.
Motivation-wise, it seems plausible for the data theft thread to have been part of a DPRK-sponsored operation. DPRK is known to pursue targets that could potentially fulfil its national security goals and had been publicly reported as being active in months prior to this intrusion [21]. Furthermore, the timing of the data theft aligns with the ratification of the mutual defense treaty between DPRK and Russia and the subsequent accused activities [20].
Darktrace assesses with medium confidence that a nation-state, likely DPRK, was responsible, based on our investigation, the threat actor applied resources, patience, obfuscation, and evasiveness combined with external reporting, collaboration with the cyber community, assessing the attacker’s motivation and world geopolitical timeline, and undisclosed intelligence.
Conclusion
When state-linked cyber activity occurs within an organization’s environment, previously unseen C2 infrastructure and advanced evasion techniques will likely be used. State-linked cyber actors, through their resources and patience, are able to bypass most detection methods, leaving anomaly-based methods as a last line of defense.
Two threads of activity were observed within Darktrace’s customer base over the last year: The first operation involved the abuse of Check Point VPN credentials to log in remotely to organizations’ networks, followed by the distribution of ShadowPad to an internal domain controller. The second operation involved highly targeted data exfiltration from the network of one of the customers impacted by the previously mentioned ShadowPad activity.
Despite definitive attribution remaining unresolved, both the ShadowPad and data exfiltration activities were detected by Darktrace’s Self-Learning AI, with Cyber AI Analyst playing a significant role in identifying and piecing together the various steps of the intrusion activities.
Credit to Sam Lister (R&D Detection Analyst), Emma Foulger (Principal Cyber Analyst), Nathaniel Jones (VP), and the Darktrace Threat Research team.
Appendices
Darktrace / NETWORK model alerts
User / New Admin Credentials on Client
Anomalous Connection / Unusual Admin SMB Session
Compliance / SMB Drive Write
Device / Anomalous SMB Followed By Multiple Model Breaches
Survey findings: AI Cyber Threats are a Reality, the People are Acting Now
Artificial intelligence is changing the cybersecurity field as fast as any other, both on the offensive and defensive side. We surveyed over 1,500 cybersecurity professionals from around the world to uncover their attitudes, understanding, and priorities when it comes to AI cybersecurity in 2025. Our full report, unearthing some telling trends, is out now.
Nearly 74% of participants say AI-powered threats are a major challenge for their organization and 90% expect these threats to have a significant impact over the next one to two years, a slight increase from last year. These statistics highlight that AI is not just an emerging risk but a present and evolving one.
As attackers harness AI to automate and scale their operations, security teams must adapt just as quickly. Organizations that fail to prioritize AI-specific security measures risk falling behind, making proactive defense strategies more critical than ever.
Some of the most pressing AI-driven cyber threats include:
AI-powered social engineering: Attackers are leveraging AI to craft highly personalized and convincing phishing emails, making them harder to detect and more likely to bypass traditional defenses.
More advanced attacks at speed and scale: AI lowers the barrier for less skilled threat actors, allowing them to launch sophisticated attacks with minimal effort.
Attacks targeting AI systems: Cybercriminals are increasingly going after AI itself, compromising machine learning models, tampering with training data, and exploiting vulnerabilities in AI-driven applications and APIs.
Safe and secure use of AI
AI is having an effect on the cyber-threat landscape, but it also is starting to impact every aspect of a business – from marketing to HR to operations. The accessibility of AI tools for employees improves workflows, but also poses risks like data privacy violations, shadow AI, and violation of industry regulations.
How are security practitioners accommodating for this uptick in AI use across business?
Among survey participants 45% of security practitioners say they had already established a policy on the safe and secure use of AI and around 50% are in discussions to do so.
While almost all participants acknowledge that this is a topic that needs to be addressed, the gap between discussion and execution could underscore a need for greater insight, stronger leadership commitment, and adaptable security frameworks to keep pace with AI advancements in the workplace. The most popular actions taken are:
Implemented security controls to prevent unwanted exposure of corporate data when using AI technology (67%)
Implemented security controls to protect against other threats/risks associated with using AI technology (62%)
This year specifically, we see further action being taken with the implementation of security controls, training, and oversight.
For a more detailed breakdown that includes results based on industry and organizational size, download the full report here.
AI threats are rising, but security teams still face major challenges
78% of CISOs say AI-powered cyber-threats are already having a significant impact on their organization, a 5% increase from last year.
While cyber professionals feel more prepared for AI powered threats than they did 12 months ago, 45% still say their organization is not adequately prepared—down from 60% last year.
Despite this optimism, key challenges remain, including:
A shortage of personnel to manage tools and alerts
Gaps in knowledge and skills related to AI-driven countermeasures
Confidence in traditional security tools vs. new AI based tools
This year, 73% of survey participants expressed confidence in their security team’s proficiency in using AI within their tool stack, marking an increase from the previous year.
However, only 50% of participants have confidence in traditional cybersecurity tools to detect and block AI-powered threats. In contrast, 75% of participants are confident in AI-powered security solutions for detecting and blocking such threats and attacks.
As leading organizations continue to implement and optimize their use of AI, they are incorporating it into an increasing number of workflows. This growing familiarity with AI is likely to boost the confidence levels of practitioners even further.
The data indicates a clear trend towards greater reliance on AI-powered security solutions over traditional tools. As organizations become more adept at integrating AI into their operations, their confidence in these advanced technologies grows.
This shift underscores the importance of staying current with AI advancements and ensuring that security teams are well-trained in utilizing these tools effectively. The increasing confidence in AI-driven solutions reflects their potential to enhance cybersecurity measures and better protect against sophisticated threats.
The full report for Darktrace’s State of AI Cybersecurity is out now. Download the paper to dig deeper into these trends, and see how results differ by industry, region, organization size, and job title.
![Event Log data showing a DC making DNS queries for subdomains of ‘cbaq.chtq[.]net’ to 158.247.199[.]185 after receiving SMB and RDP connections from the VPN-connected device, DESKTOP-O82ILGG.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/67d1dbda2020b168dc1d83c2_Screenshot%202025-03-12%20at%2012.07.41%E2%80%AFPM.png)
![Darktrace model alert highlighting an internal device slowly exfiltrating data to the external endpoint, yasuconsulting[.]com.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/67d1dd09fe9cf54a67c48f13_Screenshot%202025-03-12%20at%2012.14.04%E2%80%AFPM.png)
![Darktrace model alert highlighting an internal device downloading nearly 1 GB of data from an internal system just before uploading a similar volume of data to another suspicious endpoint, www.tunemmuhendislik[.]com](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/67d1dd45f3743ebe06d81d89_Screenshot%202025-03-12%20at%2012.14.46%E2%80%AFPM.png)
