Blog
/
Network
/
March 12, 2025

Darktrace's Detection of State-Linked ShadowPad Malware

In 2024, Darktrace identified a cluster of intrusions involving the state-linked malware, ShadowPad. This blog will detail ShadowPad and the associated activities detected by Darktrace.
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.
Written by
Sam Lister
SOC Analyst
Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
12
Mar 2025


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.

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 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.
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.
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.

Darktrace observed these ShadowPad activity threads within the networks of European-based customers in the manufacturing and financial sectors.  One of these intrusions was followed a few months later by likely state-sponsored espionage activity, as detailed in the investigation of the year in Darktrace’s Annual Threat Report 2024.

[related-resource]

Related ShadowPad activity

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.  

Darktrace model alert highlighting an SMB write of a file named ‘ProgramData\Oracle\java.log’ to the c$ share on another device.
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

Cyber AI Analyst highlighting an SMB write of a file named ‘ProgramData\Adobe\ARM\webservices.dll’ to the c$ share on an internal system.
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.  

Cyber AI Analyst data highlighting a thread of activity starting with an SMB data upload followed by ExecMethod requests.
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.

Screenshot of one of the likely compromised sites used in the intrusion. 
Figure 6: Screenshot of 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.  

Darktrace model alert highlighting an internal device slowly exfiltrating data to the external endpoint, yasuconsulting[.]com.
Figure 7: Darktrace model alert highlighting an internal device slowly exfiltrating data to the external endpoint, yasuconsulting[.]com.
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    
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

Cyber AI Analyst identifying and piecing together the various steps of a ShadowPad intrusion.
Figure 9: Cyber AI Analyst identifying and piecing together the various steps of a ShadowPad intrusion.  
Cyber AI Analyst Incident identifying and piecing together the various steps of the data theft activity.
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

Anomalous File / Internal / Unusual SMB Script Write

User / New Admin Credentials on Client  

Anomalous Connection / Unusual Admin SMB Session

Compliance / SMB Drive Write

Device / Anomalous SMB Followed By Multiple Model Breaches

Anomalous File / Internal / Unusual SMB Script Write

Device / New or Uncommon WMI Activity

Unusual Activity / Internal Data Transfer

Anomalous Connection / Download and Upload

Anomalous Server Activity / Rare External from Server

Compromise / Beacon to Young Endpoint

Compromise / Agent Beacon (Short Period)

Anomalous Server Activity / Anomalous External Activity from Critical Network Device

Anomalous Connection / POST to PHP on New External Host

Compromise / Sustained SSL or HTTP Increase

Compromise / Sustained TCP Beaconing Activity To Rare Endpoint

Anomalous Connection / Multiple Failed Connections to Rare Endpoint

Device / Multiple C2 Model Alerts

Anomalous Connection / Data Sent to Rare Domain

Anomalous Connection / Download and Upload

Unusual Activity / Unusual External Data Transfer

Anomalous Connection / Low and Slow Exfiltration

Anomalous Connection / Uncommon 1 GiB Outbound  

MITRE ATT&CK mapping

(Technique name – Tactic ID)

ShadowPad malware threads

Initial Access - Valid Accounts: Domain Accounts (T1078.002)

Initial Access - External Remote Services (T1133)

Privilege Escalation - Exploitation for Privilege Escalation (T1068)

Privilege Escalation - Valid Accounts: Default Accounts (T1078.001)

Defense Evasion - Masquerading: Match Legitimate Name or Location (T1036.005)

Lateral Movement - Remote Services: Remote Desktop Protocol (T1021.001)

Lateral Movement - Remote Services: SMB/Windows Admin Shares (T1021.002)

Command and Control - Proxy: Internal Proxy (T1090.001)

Command and Control - Application Layer Protocol: Web Protocols (T1071.001)

Command and Control - Encrypted Channel: Asymmetric Cryptography (T1573.002)

Command and Control - Application Layer Protocol: DNS (T1071.004)

Data theft thread

Resource Development - Compromise Infrastructure: Domains (T1584.001)

Privilege Escalation - Valid Accounts: Default Accounts (T1078.001)

Privilege Escalation - Valid Accounts: Domain Accounts (T1078.002)

Execution - Windows Management Instrumentation (T1047)

Defense Evasion - Masquerading: Match Legitimate Name or Location (T1036.005)

Defense Evasion - Obfuscated Files or Information (T1027)

Lateral Movement - Remote Services: SMB/Windows Admin Shares (T1021.002)

Collection - Data from Network Shared Drive (T1039)

Command and Control - Application Layer Protocol: Web Protocols (T1071.001)

Command and Control - Encrypted Channel: Asymmetric Cryptography (T1573.002)

Command and Control - Proxy: External Proxy (T1090.002)

Exfiltration - Exfiltration Over C2 Channel (T1041)

Exfiltration - Data Transfer Size Limits (T1030)

List of indicators of compromise (IoCs)

IP addresses and/or domain names (Mid-high confidence):

ShadowPad thread

- dscriy.chtq[.]net • 158.247.199[.]185 (endpoint of C2 comms)

- cybaq.chtq[.]net (domain name used for DNS tunneling)  

Data theft thread

- yasuconsulting[.]com (45.158.12[.]7)

- hobivan[.]net (94.73.151[.]72)

- mediostresbarbas.com[.]ar (75.102.23[.]3)

- mnmathleague[.]org (185.148.129[.]24)

- goldenborek[.]com (94.138.200[.]40)

- tunemmuhendislik[.]com (94.199.206[.]45)

- anvil.org[.]ph (67.209.121[.]137)

- partnerls[.]pl (5.187.53[.]50)

- angoramedikal[.]com (89.19.29[.]128)

- awork-designs[.]dk (78.46.20[.]225)

- digitweco[.]com (38.54.95[.]190)

- duepunti-studio[.]it (89.46.106[.]61)

- scgestor.com[.]br (108.181.92[.]71)

- lacapannadelsilenzio[.]it (86.107.36[.]15)

- lovetamagotchith[.]com (203.170.190[.]137)

- lieta[.]it (78.46.146[.]147)

File names (Mid-high confidence):

ShadowPad thread:

- perflogs\1.txt

- perflogs\AppLaunch.exe

- perflogs\F4A3E8BE.tmp

- perflogs\mscoree.dll

Data theft thread

- ProgramData\Oracle\java.log

- ProgramData\Oracle\duxwfnfo

- 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

- temp\HousecallLauncher64.exe

Attacker-controlled device hostname (Mid-high confidence)

- DESKTOP-O82ILGG

References  

[1] https://www.kaspersky.com/about/press-releases/shadowpad-how-attackers-hide-backdoor-in-software-used-by-hundreds-of-large-companies-around-the-world  

[2] https://media.kasperskycontenthub.com/wp-content/uploads/sites/43/2017/08/07172148/ShadowPad_technical_description_PDF.pdf

[3] https://blog.avast.com/new-investigations-in-ccleaner-incident-point-to-a-possible-third-stage-that-had-keylogger-capacities

[4] https://securelist.com/operation-shadowhammer-a-high-profile-supply-chain-attack/90380/

[5] https://assets.sentinelone.com/c/Shadowpad?x=P42eqA

[6] https://www.cyfirma.com/research/the-origins-of-apt-41-and-shadowpad-lineage/

[7] https://www.csoonline.com/article/572061/shadowpad-has-become-the-rat-of-choice-for-several-state-sponsored-chinese-apts.html

[8] https://global.ptsecurity.com/analytics/pt-esc-threat-intelligence/shadowpad-new-activity-from-the-winnti-group

[9] https://cymulate.com/threats/shadowpad-privately-sold-malware-espionage-tool/

[10] https://www.secureworks.com/research/shadowpad-malware-analysis

[11] https://blog.talosintelligence.com/chinese-hacking-group-apt41-compromised-taiwanese-government-affiliated-research-institute-with-shadowpad-and-cobaltstrike-2/

[12] https://hackerseye.net/all-blog-items/tails-from-the-shadow-apt-41-injecting-shadowpad-with-sideloading/

[13] https://cloud.google.com/blog/topics/threat-intelligence/scatterbrain-unmasking-poisonplug-obfuscator

[14] https://www.domaintools.com/wp-content/uploads/conceptualizing-a-continuum-of-cyber-threat-attribution.pdf

[15] https://www.nccgroup.com/es/research-blog/north-korea-s-lazarus-their-initial-access-trade-craft-using-social-media-and-social-engineering/  

[16] https://www.microsoft.com/en-us/security/blog/2021/01/28/zinc-attacks-against-security-researchers/

[17] https://www.microsoft.com/en-us/security/blog/2022/09/29/zinc-weaponizing-open-source-software/  

[18] https://www.welivesecurity.com/en/eset-research/lazarus-luring-employees-trojanized-coding-challenges-case-spanish-aerospace-company/  

[19] https://blogs.jpcert.or.jp/en/2021/01/Lazarus_malware2.html  

[20] https://usun.usmission.gov/joint-statement-on-the-unlawful-arms-transfer-by-the-democratic-peoples-republic-of-korea-to-russia/

[21] https://media.defense.gov/2024/Jul/25/2003510137/-1/-1/1/Joint-CSA-North-Korea-Cyber-Espionage-Advance-Military-Nuclear-Programs.PDF  

[22] https://kyivindependent.com/first-north-korean-troops-deployed-to-front-line-in-kursk-oblast-ukraines-military-intelligence-says/

[23] https://www.microsoft.com/en-us/security/blog/2024/12/04/frequent-freeloader-part-i-secret-blizzard-compromising-storm-0156-infrastructure-for-espionage/  

[24] https://www.microsoft.com/en-us/security/blog/2024/12/11/frequent-freeloader-part-ii-russian-actor-secret-blizzard-using-tools-of-other-groups-to-attack-ukraine/  

[25] https://www.sentinelone.com/labs/chamelgang-attacking-critical-infrastructure-with-ransomware/    

[26] https://thehackernews.com/2022/06/state-backed-hackers-using-ransomware.html/  

[27] https://blog.checkpoint.com/security/check-point-research-explains-shadow-pad-nailaolocker-and-its-protection/

[28] https://www.orangecyberdefense.com/global/blog/cert-news/meet-nailaolocker-a-ransomware-distributed-in-europe-by-shadowpad-and-plugx-backdoors

[related-resource]

AI Cybersecurity: Insights for 2025

We surveyed 1,500+ cybersecurity professionals globally to explore their views, knowledge, and priorities on AI cybersecurity in 2025.

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.
Written by
Sam Lister
SOC Analyst

More in this series

No items found.

Blog

/

Network

/

August 15, 2025

From Exploit to Escalation: Tracking and Containing a Real-World Fortinet SSL-VPN Attack

Fortinet SSL-VPN AttackDefault blog imageDefault blog image

Threat actors exploiting Fortinet CVEs

Over the years, Fortinet has issued multiple alerts about a wave of sophisticated attacks targeting vulnerabilities in its SSL-VPN infrastructure. Despite the release of patches to address these vulnerabilities, threat actors have continued to exploit a trio of Common Vulnerabilities and Exposures (CVEs) disclosed between 2022 and 2024 to gain unauthorized access to FortiGate devices.

Which vulnerabilities are exploited?

The vulnerabilities—CVE-2022-42475, CVE-2023-27997, and CVE-2024-21762—affect Fortinet’s SSL-VPN services and have been actively exploited by threat actors to establish initial access into target networks.

The vulnerabilities affect core components of FortiOS, allowing attackers to execute remote code on affected systems.

CVE-2022-42475

Type: Heap-Based Buffer Overflow in FortiOS SSL-VPN

Impact: Remote Code Execution (Actively Exploited)

This earlier vulnerability also targets the SSL-VPN interface and has been actively exploited in the wild. It allows attackers to execute arbitrary code remotely by overflowing a buffer in memory, often used to deploy malware or establish persistent backdoors [6].

CVE-2023-27997

Type: Heap-Based Buffer Overflow in FortiOS and FortiProxy

Impact: Remote Code Execution

This flaw exists in the SSL-VPN component of both FortiOS and FortiProxy. By exploiting a buffer overflow in the heap memory, attackers can execute malicious code remotely. This vulnerability is particularly dangerous because it can be triggered without authentication, making it ideal for an initial compromise [5].

CVE-2024-21762

Type: Out-of-Bounds Write in sslvpnd

Impact: Remote Code Execution

This vulnerability affects the SSL-VPN daemon (sslvpnd) in FortiOS. It allows unauthenticated remote attackers to send specially crafted HTTP requests that write data outside of allocated memory bounds. This can lead to arbitrary code execution, giving attackers full control over a device [4].

In short, these flaws enable remote attackers to execute arbitrary code without authentication by exploiting memory corruption issues such as buffer overflows and out-of-bounds writes. Once inside, threat actors use symbolic link (symlink) in order to maintain persistence on target devices across patches and firmware updates. This persistence then enables them to bypass security controls and manipulate firewall configurations, effectively turning patched systems into long-term footholds for deeper network compromise [1][2][3].

Darktrace’s Coverage

Darktrace detected a series of suspicious activities originating from a compromised Fortinet VPN device, including anomalous HTTP traffic, internal network scanning, and SMB reconnaissance, all indicative of post-exploitation behavior. Following initial detection by Darktrace’s real-time models, its Autonomous Response capability swiftly acted on the malicious activity, blocking suspicious connections and containing the threat before further compromise could occur.

Further investigation by Darktrace’s Threat Research team uncovered a stealthy and persistent attack that leveraged known Fortinet SSL-VPN vulnerabilities to facilitate lateral movement and privilege escalation within the network.

Phase 1: Initial Compromise – Fortinet VPN Exploitation

The attack on a Darktrace customer likely began on April 11 with the exploitation of a Fortinet VPN device running an outdated version of FortiOS. Darktrace observed a high volume of HTTP traffic originating from this device, specifically targeting internal systems. Notably, many of these requests were directed at the /cgi-bin/ directory,  a common target for attackers attempting to exploit web interfaces to run unauthorized scripts or commands. This pattern strongly indicated remote code execution attempts via the SSL-VPN interface [7].

Once access was gained, the threat actor likely modified existing firewall rules, a tactic often used to disable security controls or create hidden backdoors for future access. While Darktrace does not have direct visibility into firewall configuration changes, the surrounding activity and post-exploitation behavior indicated that such modifications were made to support long-term persistence within the network.

HTTP activity from the compromised Fortinet device, including repeated requests to /cgi-bin/ over port 8080.
Figure 1: HTTP activity from the compromised Fortinet device, including repeated requests to /cgi-bin/ over port 8080

Phase 2: Establishing Persistence & Lateral Movement

Shortly after the initial compromise of the Fortinet VPN device, the threat actor began to expand their foothold within the internal network. Darktrace detected initial signs of network scanning from this device, including the use of Nmap to probe the internal environment, likely in an attempt to identify accessible services and vulnerable systems.

Darktrace’s detection of unusual network scanning activities on the affected device.
Figure 2: Darktrace’s detection of unusual network scanning activities on the affected device.

Around the same time, Darktrace began detecting anomalous activity on a second device, specifically an internal firewall interface device. This suggested that the attacker had established a secondary foothold and was leveraging it to conduct deeper reconnaissance and move laterally through the network.

In an effort to maintain persistence within the network, the attackers likely deployed symbolic links in the SSL-VPN language file directory on the Fortinet device. While Darktrace did not directly observe symbolic link abuse, Fortinet has identified this as a known persistence technique in similar attacks [2][3]. Based on the observed post-exploitation behavior and likely firewall modifications, it is plausible that such methods were used here.

Phase 3: Internal Reconnaissance & Credential Abuse

With lateral movement initiated from the internal firewall interface device, the threat actor proceeded to escalate their efforts to map the internal network and identify opportunities for privilege escalation.

Darktrace observed a successful NTLM authentication from the internal firewall interface to the domain controller over the outdated protocol SMBv1, using the account ‘anonymous’. This was immediately followed by a failed NTLM session connection using the hostname ‘nmap’, further indicating the use of Nmap for enumeration and brute-force attempts. Additional credential probes were also identified around the same time, including attempts using the credential ‘guest’.

Darktrace detection of a series of login attempts using various credentials, with a mix of successful and unsuccessful attempts.
Figure 3: Darktrace detection of a series of login attempts using various credentials, with a mix of successful and unsuccessful attempts.

The attacker then initiated DCE_RPC service enumeration, with over 300 requests to the Endpoint Mapper endpoint on the domain controller. This technique is commonly used to discover available services and their bindings, often as a precursor to privilege escalation or remote service manipulation.

Over the next few minutes, Darktrace detected more than 1,700 outbound connections from the internal firewall interface device to one of the customer’s subnets. These targeted common services such as FTP (port 21), SSH (22), Telnet (23), HTTP (80), and HTTPS (443). The threat actor also probed administrative and directory services, including ports 135, 137, 389, and 445, as well as remote access via RDP on port 3389.

Further signs of privilege escalation attempts were observed with the detection of over 300 Netlogon requests to the domain controller. Just over half of these connections were successful, indicating possible brute-force authentication attempts, credential testing, or the use of default or harvested credentials.

Netlogon and DCE-RPC activity from the affected device, showing repeated service bindings to epmapper and Netlogon, followed by successful and failed NetrServerAuthenticate3 attempts.
Figure 4: Netlogon and DCE-RPC activity from the affected device, showing repeated service bindings to epmapper and Netlogon, followed by successful and failed NetrServerAuthenticate3 attempts.

Phase 4: Privilege Escalation & Remote Access

A few minutes later, the attacker initiated an RDP session from the internal firewall interface device to an internal server. The session lasted over three hours, during which more than 1.5MB of data was uploaded and over 5MB was downloaded.

Notably, no RDP cookie was observed during this session, suggesting manual access, tool-less exploitation, or a deliberate attempt to evade detection. While RDP cookie entries were present on other occasions, none were linked to this specific session—reinforcing the likelihood of stealthy remote access.

Additionally, multiple entries during and after this session show SSL certificate validation failures on port 3389, indicating that the RDP connection may have been established using self-signed or invalid certificates, a common tactic in unauthorized or suspicious remote access scenarios.

Darktrace’s detection of an RDP session from the firewall interface device to the server, lasting over 3 hours.
Figure 5: Darktrace’s detection of an RDP session from the firewall interface device to the server, lasting over 3 hours.

Darktrace Autonomous Response

Throughout the course of this attack, Darktrace’s Autonomous Response capability was active on the customer’s network. This enabled Darktrace to autonomously intervene by blocking specific connections and ports associated with the suspicious activity, while also enforcing a pre-established “pattern of life” on affected devices to ensure they were able to continue their expected business activities while preventing any deviations from it. These actions were crucial in containing the threat and prevent further lateral movement from the compromised device.

Darktrace’s Autonomous Response targeted specific connections and restricted affected devices to their expected patterns of life.
Figure 6: Darktrace’s Autonomous Response targeted specific connections and restricted affected devices to their expected patterns of life.

Conclusion

This incident highlights the importance of important staying on top of patching and closely monitoring VPN infrastructure, especially for internet-facing systems like Fortinet devices. Despite available patches, attackers were still able to exploit known vulnerabilities to gain access, move laterally and maintain persistence within the customer’s network.

Attackers here demonstrated a high level of stealth and persistence. Not only did they gain access to the network and carry out network scans and lateral movement, but they also used techniques such as symbolic link abuse, credential probing, and RDP sessions without cookies to avoid detection.  Darktrace’s detection of the post-exploitation activity, combined with the swift action of its Autonomous Response technology, successfully blocked malicious connections and contained the attack before it could escalate

Credit to Priya Thapa (Cyber Analyst), Vivek Rajan (Cyber Analyst), and Ryan Traill (Analyst Content Lead)

Appendices

Real-time Detection Model Alerts

·      Device / Suspicious SMB Scanning Activity

·      Device / Anomalous Nmap Activity

·      Device / Network Scan

·      Device / RDP Scan

·      Device / ICMP Address Scan

Autonomous Response Model Alerts:  

·      Antigena / Network / Insider Threat / Antigena Network Scan Block

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

MITRE ATT&CK Mapping

Initial Access – External Remote Services – T1133

Initial Access – Valid Accounts – T1078

Execution – Exploitation for Client Execution – T1203

Persistence – Account Manipulation – T1098

Persistence – Application Layer Protocol – T1071.001

Privilege Escalation – Exploitation for Privilege Escalation – T1068

Privilege Escalation – Valid Accounts – T1078

Defense Evasion – Masquerading – T1036

Credential Access – Brute Force – T1110

Discovery – Network Service Scanning – T1046

Discovery – Remote System Discovery – T1018

Lateral Movement – Remote Services – T1021

Lateral Movement – Software Deployment Tools – T1072

Collection – Data from Local System – T1005

Collection – Data Staging – T1074

Exfiltration – Exfiltration Over Alternative Protocol – T1048

References

[1]  https://www.tenable.com/blog/cve-2024-21762-critical-fortinet-fortios-out-of-bound-write-ssl-vpn-vulnerability

[2] https://thehackernews.com/2025/04/fortinet-warns-attackers-retain.html

[3] https://www.cisa.gov/news-events/alerts/2025/04/11/fortinet-releases-advisory-new-post-exploitation-technique-known-vulnerabilities

[4] https://www.fortiguard.com/psirt/FG-IR-24-015

[5] https://www.tenable.com/blog/cve-2023-27997-heap-based-buffer-overflow-in-fortinet-fortios-and-fortiproxy-ssl-vpn-xortigate

[6]  https://www.tenable.com/blog/cve-2022-42475-fortinet-patches-zero-day-in-fortios-ssl-vpns

[7] https://www.fortiguard.com/encyclopedia/ips/12475

The content provided in this blog is published by Darktrace for general informational purposes only and reflects our understanding of cybersecurity topics, trends, incidents, and developments at the time of publication. While we strive to ensure accuracy and relevance, the information is provided “as is” without any representations or warranties, express or implied. Darktrace makes no guarantees regarding the completeness, accuracy, reliability, or timeliness of any information presented and expressly disclaims all warranties.

Nothing in this blog constitutes legal, technical, or professional advice, and readers should consult qualified professionals before acting on any information contained herein. Any references to third-party organizations, technologies, threat actors, or incidents are for informational purposes only and do not imply affiliation, endorsement, or recommendation.

Darktrace, its affiliates, employees, or agents shall not be held liable for any loss, damage, or harm arising from the use of or reliance on the information in this blog.

The cybersecurity landscape evolves rapidly, and blog content may become outdated or superseded. We reserve the right to update, modify, or remove any content without notice.

Continue reading
About the author
Priya Thapa
Cyber Analyst

Blog

/

Cloud

/

August 15, 2025

How Organizations are Addressing Cloud Investigation and Response

Cloud investigation and responseDefault blog imageDefault blog image

Why cloud investigation and response needs to evolve

As organizations accelerate their move to the cloud, they’re confronting two interrelated pressures: a rapidly expanding attack surface and rising regulatory scrutiny. The dual pressure is forcing security practitioners to evolve their strategies in the cloud, particularly around investigation and response, where we see analysts spending the most time. This work is especially difficult in the cloud, often requiring experienced analysts to manually stitch together evidence across fragmented systems, unfamiliar platforms, and short-lived assets.

However, adapting isn’t easy. Many teams are operating with limited budgets and face a shortage of cloud-specific security talent. That’s why more organizations are now prioritizing tools that not only deliver deep visibility and rapid response in the cloud, but also help upskill their analysts to keep pace with threats and compliance demands.

Our 2024 survey report highlights just how organizations are recognizing gaps in their cloud security, feeling the heat from regulators, and making significant investments to bolster their cloud investigation capabilities.

In this blog post, we’ll explore the current challenges, approaches, and strategies organizations are employing to enhance their cloud investigation and incident response.

Recognizing the gaps in current cloud investigation and response methods

Complex environments & static tools

Due to the dynamic nature of cloud infrastructure, ephemeral assets, autoscaling environments, and multi-cloud complexity, traditional investigation and response methods which rely on static snapshots and point-in-time data, are fundamentally mismatched. And with Cloud environment APIs needing deep provider knowledge and scripting skills to extract much needed evidence its unrealistic for one person to master all aspects of cloud incident response.

Analysts are still stitching together logs from fragmented systems, manually correlating events, and relying on post-incident forensics that often arrive too late to drive meaningful response. These approaches were built for environments that rarely changed. In the cloud, where assets may only exist for minutes and attacker movement can span regions or accounts in seconds, point-in-time visibility simply can’t keep up. As a result, critical evidence is missed, timelines are incomplete, and investigations drag on longer than they should.

Even some modern approaches still depend heavily on static configurations, delayed snapshots, or siloed visibility that can’t keep pace with real-time attacker movement.

There is even the problem of  identifying what cloud data sources hold the valuable information needed to investigate in the first place. With AWS alone having over 200 products, each with its own security practices and data sources.It can be challenging to identify where you need to be looking.  

To truly secure the cloud, investigation and response must be continuous, automated, and context-rich. Tools should be able to surface the signal from the noise and support analysts at every step, even without deep forensics expertise.

Increasing compliance pressure

With the rise of data privacy regulations and incident reporting mandates worldwide, organizations face heightened scrutiny. Noncompliance can lead to severe penalties, making it crucial to have robust cloud investigation and response mechanisms in place. 74% of organizations surveyed reported that data privacy regulations complicate incident response, underscoring the urgency to adapt to regulatory requirements.

In addition, a majority of organizations surveyed (89%) acknowledged that they suffer damage before they can fully contain and investigate incidents, particularly in cloud environments, highlighting the need for enhanced cloud capabilities.  

Enhancing cloud investigation and response

To address these challenges, organizations are actively growing their capabilities to perform investigations in the cloud. Key steps include:

Allocating and increasing budgets:  

Recognizing the importance of cloud-specific investigation tools, many organizations have started to allocate dedicated budgets for cloud forensics. 83% of organizations have budgeted for cloud forensics, with 77% expecting this budget to increase. This reflects a strong commitment to improving cloud security.

Implementing automation that understands cloud behavior

Automation isn’t just about speeding up tasks. While modern threats require speed and efficiency from defenders, automation aims to achieve this by enabling consistent decision making across unique and dynamic environments. Traditional SOAR platforms, often designed for static on-prem environments, struggle to keep pace with the dynamic and ephemeral nature of the cloud, where resources can disappear before a human analyst even has a chance to look at them. Cloud-native automation, designed to act on transient infrastructure and integrate seamlessly with cloud APIs, is rapidly emerging as the more effective approach for real-time investigation and response. Automation can cover collection, processing, and storage of incident evidence without ever needing to wait for human intervention and the evidence is ready and waiting all in once place, regardless of if the evidence is cloud-provider logs, disk images, or  memory dumps. With the right automation tools you can even go further and automate the full process from end to end covering acquisition, processing, analysis, and response.

Artificial Intelligence (AI) that augments analysts’ intuition not just adds speed

While many vendors tout AI’s ability to “analyze large volumes of data,” that’s table stakes. The real differentiator is how AI understands the narrative of an incident, surfacing high-fidelity alerts, correlating attacker movement across cloud and hybrid environments, and presenting findings in a way that upskills rather than overwhelms analysts.  

In this space, AI isn’t just accelerating investigations, it’s democratizing them by reducing the reliance on highly specialized forensic expertise.  

Strategies for effective cloud investigation and response

Organizations are also exploring various strategies to optimize their cloud investigation and response capabilities:

Enhancing visibility and control:

  • Unified platforms: Implementing platforms that provide a unified view across multiple cloud environments can help organizations achieve better visibility and control. This consolidation reduces the complexity of managing disparate tools and data sources.
  • Improved integration: Ensuring that all security tools and platforms are seamlessly integrated is critical. This integration facilitates better data sharing and cohesive incident management.
  • Cloud specific expertise: Training and Recruitment: Investing in training programs to develop cloud-specific skills among existing staff and recruiting experts with cloud security knowledge can bridge the skill gap.
  • Continuous learning: Given the constantly evolving nature of cloud threats, continuous learning and adaptation are essential for maintaining effective security measures.

Leveraging automation and AI:

  • Automation solutions: Automation solutions for cloud environments can significantly speed up and simplify incident response efficiency. These solutions can handle repetitive tasks, allowing security teams to focus on more complex issues.
  • AI powered analysis: AI can assist in rapidly analyzing incident data, identifying anomalies, and predicting potential threats. This proactive approach can help prevent incidents before they escalate.

Cloud investigation and response with Darktrace

Darktrace’s  forensic acquisition & investigation capabilities helps organizations address the complexities of cloud investigations and incident response with ease. The product seamlessly integrates with AWS, GCP, and Azure, consolidating data from multiple cloud environments into one unified platform. This integration enhances visibility and control, making it easier to manage and respond to incidents across diverse cloud infrastructures.

By leveraging machine learning and automation, Forensic Acquisition & Investigation accelerates the investigation process by quickly analyzing vast amounts of data, identifying patterns, and providing actionable insights. Automation reduces manual effort and response times, allowing your security team to focus on the most pressing issues.

Forensic Acquisition & Investigation can help you stay ahead of threats whilst also meeting regulatory requirements, helping you to maintain a robust cloud security position.

Continue reading
About the author
Calum Hall
Technical Content Researcher
Your data. Our AI.
Elevate your network security with Darktrace AI