How Darktrace Could Have Stopped a Surprise DDoS Incident
23
Nov 2022
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When is the best time to be hit with a cyber-attack?
The answer that springs to most is ‘Never’, however in today’s threat landscape, this is often wishful thinking. The next best answer is ‘When we’re ready for it’. Yet, this does not take into account the intention of those committing attacks. The reality is that the best time for a cyber-attack is when no one else is around to stop it.
When do cyber attacks happen?
Previous analysis from Mandiant reveals that over half of ransomware compromises occur at out of work hours, a trend Darktrace has also witnessed in the past two years [1]. This is deliberate, as the fewer people that are online, the harder it is to get ahold of security teams and the higher the likelihood there is of an attacker achieving their goals. Given this landscape, it is clear that autonomous response is more important than ever. In the absence of human resources, autonomous security can fill in the gap long enough for IT teams to begin remediation.
This blog will detail an incident where autonomous response provided by Darktrace RESPOND would have entirely prevented an infection attempt, despite it occurring in the early hours of the morning. Because the customer had RESPOND in human confirmation mode (AI response must first be approved by a human), the attempt by XorDDoS was ultimately successful. Given that the attack occurred in the early hours of the morning, there was likely no one around to confirm Darktrace RESPOND actions and prevent the attack.
XorDDoS Primer
XorDDoS is a botnet, a type of malware that infects devices for the purpose of controlling them as a collective to carry out specific actions. In the case of XorDDoS, it infects devices in order to carry out denial of service attacks using said devices. This year, Microsoft has reported a substantial increase in activity from this malware strain, with an increased focus on Linux based operating systems [2]. XorDDoS most commonly finds its way onto systems via SSH brute-forcing, and once deployed, encrypts its traffic with an XOR cipher. XorDDoS has also been known to download additional payloads such as backdoors and cryptominers. Needless to say, this is not something you have on a corporate network.
Initial Intrusion of XorDDoS
The incident begins with a device first coming online on 10th August. The device appeared to be internet facing and Darktrace saw hundreds of incoming SSH connections to the device from a variety of endpoints. Over the course of the next five days, the device received thousands of failed SSH connections from several IP addresses that, according to OSINT, may be associated with web scanners [3]. Successful SSH connections were seen from internal IP addresses as well as IP addresses associated with IT solutions relevant to Asia-Pacific (the customer’s geographic location). On midnight of 15th August, the first successful SSH connection occurred from an IP address that has been associated with web scanning. This connection lasted around an hour and a half, and the external IP uploaded around 3.3 MB of data to the client device. Given all of this, and what the industry knows about XorDDoS, it is likely that the client device had SSH exposed to the Internet which was then brute-forced for initial access.
There were a few hours of dwell until the device downloaded a ZIP file from an Iraqi mirror site, mirror[.]earthlink[.]iq at around 6AM in the customer time zone. The endpoint had only been seen once before and was 100% rare for the network. Since there has been no information on OSINT around this particular endpoint or the ZIP files downloaded from the mirror site, the detection was based on the unusualness of the download.
Following this, Darktrace saw the device make a curl request to the external IP address 107.148.210[.]218. This was highlighted as the user agent associated with curl had not been seen on the device before, and the connection was made directly to an IP address without a hostname (suggesting that the connection was scripted). The URIs of these requests were ‘1.txt’ and ‘2.txt’.
The ‘.txt’ extensions on the URIs were deceiving and it turned out that both were executable files masquerading as text files. OSINT on both of the hashes revealed that the files were likely associated with XorDDoS. Additionally, judging from packet captures of the connection, the true file extension appeared to be ‘.ELF’. As XorDDoS primarily affects Linux devices, this would make sense as the true extension of the payload.
Figure 1: Packet capture of the curl request made by the breach device.
C2 Connections
Immediately after the ‘.ELF’ download, Darktrace saw the device attempting C2 connections. This included connections to DGA-like domains on unusual ports such as 1525 and 8993. Luckily, the client’s firewall seems to have blocked these connections, but that didn’t stop XorDDoS. XorDDoS continued to attempt connections to C2 domains, which triggered several Proactive Threat Notifications (PTNs) that were alerted by SOC. Following the PTNs, the client manually quarantined the device a few hours after the initial breach. This lapse in actioning was likely due to an early morning timing with the customer’s employees not being online yet. After the device was quarantined, Darktrace still saw XorDDoS attempting C2 connections. In all, hundreds of thousands of C2 connections were detected before the device was removed from the network sometime on 7th September.
Figure 2: AI Analyst was able to identify the anomalous activity and group it together in an easy to parse format.
An Alternate Timeline
Although the device was ultimately removed, this attack would have been entirely prevented had RESPOND/Network not been in human confirmation mode. Autonomous response would have kicked in once the device downloaded the ‘.ZIP file’ from the Iraqi mirror site and blocked all outgoing connections from the breach device for an hour:
Figure 3: Screenshot of the first Antigena (RESPOND) breach that would have prevented all subsequent activity.
The model breach in Figure 3 would have prevented the download of the XorDDoS executables, and then prevented the subsequent C2 connections. This hour would have been crucial, as it would have given enough time for members of the customer’s security team to get back online should the compromised device have attempted anything else. With everyone attentive, it is unlikely that this activity would have lasted as long as it did. Had the attack been allowed to progress further, the infected device would have at the very least been an unwilling participant in a future DDoS attack. Additionally, the device could have a backdoor placed within it, and additional malware such as cryptojackers might have been deployed.
Conclusions
Unfortunately, we do not exist in the alternate timeline that autonomous response would have prevented this whole series of events.Luckily, although it was not in place, the PTN alerts provided by Darktrace’s SOC team still sped up the process of remediation in an event that was never intended to be discovered given the time it occurred. Unusual times of attack are not just limited to ransomware, so organizations need to have measures in place for the times that are most inconvenient to them, but most convenient to attackers. With Darktrace/RESPOND however, this is just one click away.
Thanks to Brianna Leddy for their contribution.
Appendices
Darktrace Model Detections
Below is a list of model breaches in order of trigger. The Proactive Threat Notification models are in bold and only the first Antigena [RESPOND] breach that would have prevented the initial compromise has been included. A manual quarantine breach has also been added to show when the customer began remediation.
Compliance / Incoming SSH, August 12th 23:39 GMT +8
Anomalous File / Zip or Gzip from Rare External Location, August 15th, 6:07 GMT +8
Antigena / Network / External Threat / Antigena File then New Outbound Block, August 15th 6:36 GMT +8 [part of the RESPOND functionality]
Anomalous Connection / New User Agent to IP Without Hostname, August 15th 6:59 GMT +8
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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.
There’s no question that AI is already impacting the SOC – augmenting, assisting, and filling the gaps left by staff and skills shortages. We surveyed over 1,500 cybersecurity professionals from around the world to uncover their attitudes to AI cybersecurity in 2025. Our findings revealed striking trends in how AI is changing the way security leaders think about hiring and SOC transformation. Download the full report for the big picture, available now.
Let’s start with some context. As the cybersecurity sector has rapidly evolved to integrate AI into all elements of cyber defense, the pace of technological advancement is outstripping the development of necessary skills. Given the ongoing challenges in security operations, such as employee burnout, high turnover rates, and talent shortages, recruiting personnel to bridge these skills gaps remains an immense challenge in today’s landscape.
But here, our main findings on this topic seem to contradict each other.
There’s no question over the impact of AI-powered threats – nearly three-quarters (74%) agree that AI-powered threats now pose a significant challenge for their organization.
When we look at how security leaders are defending against AI-powered threats, over 3 out of 5 (62%) see insufficient personnel to manage tools and alerts as the biggest barrier.
Yet at the same time, increasing cyber security staff is at the bottom of the priority list for survey participants, with only 11% planning to increase cybersecurity staff in 2025 – less than in 2024. What 64% of stakeholders are committed to, however, is adding new AI-powered tools onto their existing security stacks.
The conclusion? Due to pressures around hiring, defensive AI is becoming integral to the SOC as a means of augmenting understaffed teams.
How is AI plugging skills shortages in the SOC?
As explored in our recent white paper, the CISO’s Guide to Navigating the Cybersecurity Skills Shortage, 71% of organizations report unfilled cybersecurity positions, leading to the estimation that less than 10% of alerts are thoroughly vetted. In this scenario, AI has become an essential multiplier to relieve the burden on security teams.
95% of respondents agree that AI-powered solutions can significantly improve the speed and efficiency of their defenses. But how?
The area security leaders expect defensive AI to have the biggest impact is on improving threat detection, followed by autonomous response to threats and identifying exploitable vulnerabilities.
Interestingly, the areas that participants ranked less highly (reducing alert fatigue and running phishing simulation), are the tasks that AI already does well and can therefore be used already to relieve the burden of manual, repetitive work on the SOC.
Different perspectives from different sides of the SOC
CISOs and SecOps teams aren’t necessarily aligned on the AI defense question – while CISOs tend to see it as a strategic game-changer, SecOps teams on the front lines may be more sceptical, wary of its real-world reliability and integration into workflows.
From the data, we see that while less than a quarter of execs doubt that AI-powered solutions will block and automatically respond to AI threats, about half of SecOps aren’t convinced. And only 17% of CISOs lack confidence in the ability of their teams to implement and use AI-powered solutions, whereas over 40% those in the team doubt their own ability to do so.
This gap feeds into the enthusiasm that executives share about adding AI-driven tools into the stack, while day-to-day users of the tools are more interested in improving security awareness training and improving cybersecurity tool integration.
Levels of AI understanding in the SOC
AI is only as powerful as the people who use it, and levels of AI expertise in the SOC can make or break its real-world impact. If security leaders want to unlock AI’s full potential, they must bridge the knowledge gap—ensuring teams understand not just the different types of AI, but where it can be applied for maximum value.
Only 42% of security professionals are confident that they fully understand all the types of AI in their organization’s security stack.
This data varies between job roles – executives report higher levels of understanding (60% say they know exactly which types of AI are being used) than participants in other roles. Despite having a working knowledge of using the tools day-to-day, SecOps practitioners were more likely to report having a “reasonable understanding” of the types of AI in use in their organization (42%).
Whether this reflects a general confidence in executives rather than technical proficiency it’s hard to say, but it speaks to the importance of AI-human collaboration – introducing AI tools for cybersecurity to plug the gaps in human teams will only be effective if security professionals are supported with the correct education and training.
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.
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