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December 12, 2022

ML Integration for Third-Party EDR Alerts

The advantages and benefits of combining EDR technologies with Darktrace: how this integration can enhance your cybersecurity strategy.
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
Max Heinemeyer
Global Field CISO
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12
Dec 2022

This blog demonstrates how we use EDR integration in Darktrace for detection & investigation. We’ll look at four key features, which are summarized with an example below:  

1)    Contextualizing existing Darktrace information – E.g. ‘There was a Microsoft Defender for Endpoint (MDE) alert 5 minutes after Darktrace saw the device beacon to an unusual destination on the internet. Let me pivot back into the Defender UI’
2)    Cross-data detection engineering
‘Darktrace, create an alert or trigger a response if you see a specific MDE alert and a native Darktrace detection on the same entity over a period of time’
3)    Applying unsupervised machine learning to third-party EDR alerts
‘Darktrace, create an alert or trigger a response if there is a specific MDE alert that is unusual for the entity, given the context’
4)    Use third-party EDR alerts to trigger AI Analyst
‘AI Analyst, this low-fidelity MDE alert flagged something on the endpoint. Please take a deep look at that device at the time of the Defender alert, conduct an investigation on Darktrace data and share your conclusions about whether there is more to it or not’ 

MDE is used as an example above, but Darktrace’s EDR integration capabilities extend beyond MDE to other EDRs as well, for example to Sentinel One and CrowdStrike EDR.

Darktrace brings its Self-Learning AI to your data, no matter where it resides. The data can be anywhere – in email environments, cloud, SaaS, OT, endpoints, or the network, for example. Usually, we want to get as close to the raw data as possible to get the maximum context for our machine learning. 

We will explain how we leverage high-value integrations from our technology partners to bring further context to Darktrace, but also how we apply our Self-Learning AI to third-party data. While there are a broad range of integrations and capabilities available, we will primarily look at Microsoft Defender for Endpoint, CrowdStrike, and SentinelOne and focus on detection in this blog post. 

The Nuts and Bolts – Setting up the Integration

Darktrace is an open platform – almost everything it does is API-driven. Our system and machine learning are flexible enough to ingest new types of data & combine it with already existing information.  

The EDR integrations mentioned here are part of our 1-click integrations. All it requires is the right level of API access from the EDR solutions and the ability for Darktrace to communicate with the EDR’s API. This type of integration can be setup within minutes – it currently doesn’t require additional Darktrace licenses.

Figure 1: Set-up of Darktrace Graph Security API integration

As soon as the setup is complete, it enables various additional capabilities. 
Let’s look at some of the key detection & investigation-focussed capabilities step-by-step.

Contextualizing Existing Darktrace Information

The most basic, but still highly-useful integration is enriching existing Darktrace information with EDR alerts. Darktrace shows a chronological history of associated telemetry and machine learning for each entity observed in the entities event log. 

With an EDR integration enabled, we now start to see EDR alerts for the respective entities turn up in the entity’s event log at the correct point in time – with a ton of context and a 1-click pivot back to the native EDR console: 

Figure 2: A pivot from the Darktrace Threat Visualizer to Microsoft Defender

This context is extremely useful to have in a single screen during investigations. Context is king – it reduces time-to-meaning and skill required to understand alerts.

Cross-Data Detection Engineering

When an EDR integration is activated, Darktrace enables an additional set of detections that leverage the new EDR alerts. This comes out of the box and doesn’t require any further detection engineering. It is worth mentioning though that the new EDR information is being made available in the background for bespoke detection engineering, if advanced users want to leverage these as custom metrics.

The trick here is that the added context provided by the additional EDR alerts allows for more refined detections – primarily to detect malicious activity with higher confidence. A network detection showing us beaconing over an unusual protocol or port combination to a rare destination on the internet is great – but seeing within Darktrace that CrowdStrike detected a potentially hostile file or process three minutes prior to the beaconing detection on the same device will greatly help to prioritize the detections and aid a subsequent investigation.

Here is an example of what this looks like in Darktrace:

Figure 3: A combined model breach in the Threat Visualizer

Applying Unsupervised Machine Learning to Third-Party EDR Alerts


Once we start seeing EDR alerts in Darktrace, we can start treating it like any other data – by applying unsupervised machine learning to it. This means we can then understand how unusual a given EDR detection is for each device in question. This is extremely powerful – it allows to reduce noisy alerts without requiring ongoing EDR alert tuning and opens a whole world of new detection capabilities.

As an example – let’s imagine a low-level malware alert keeps appearing from the EDR on a specific device. This might be a false-positive in the EDR, or just not of interest for the security team, but they may not have the resources or knowledge to further tune their EDR and get rid of this noisy alert.

While Darktrace keeps adding this as contextual information in the device’s event log, it could, depending on the context of the device, the EDR alert, and the overall environment, stop alerting on this particular EDR malware alert on this specific device if it stops being unusual. Over time, noise is reduced across the environment – but if that particular EDR alert appears on another device, or on the same device in a different context, it might get flagged again, as it now is unusual in the given context.

Darktrace then goes a step further, taking those unusual EDR alerts and combining them with unusual activity seen in other Darktrace coverage areas, like the network for example. Combining an unusual EDR alert with an unusual lateral movement attempt, for example, allows it to find these combined, high-precision, cross-data set anomalous events that are highly indicative of an active cyber-attack – without having to pre-define the exact nature of what ‘unusual’ looks like.

Figure 4: Combined EDR & network detection using unsupervised machine learning in Darktrace

Use Third-Party EDR Alerts to Trigger AI Analyst

Everything we discussed so far is great for improving precision in initial detections, adding context, and cutting through alert-noise. We don’t stop there though – we can also now use the third-party EDR alerts to trigger our investigation engine, the AI Analyst.

Cyber AI Analyst replicates and automates typical level 1 and level 2 Security Operations Centre (SOC) workflows. It is usually triggered by every native Darktrace detection. This is not a SOAR where playbooks are statically defined – AI Analyst builds hypotheses, gathers data, evaluates the data & reports on its findings based on the context of each individual scenario & investigation. 

Darktrace can use EDR alerts as starting points for its investigation, with every EDR alert ingested now triggering AI Analyst. This is similar to giving a (low-level) EDR alert to a human analyst and telling them: ‘Go and take a look at information in Darktrace and try to conclude whether there is more to this EDR alert or not.’

The AI Analyst subsequently looks at the entity which had triggered the EDR alert and investigates all available Darktrace data on that entity, over a period of time, in light of that EDR alert. It does not pivot outside Darktrace itself for that investigation (e.g. back into the Microsoft console) but looks at all of the context natively available in Darktrace. If concludes that there is more to this EDR alert – e.g. a bigger incident – it will report on that and clearly flag it. The report can of course be directly downloaded as a PDF to be shared with other stakeholders.

This comes in handy for a variety of reasons – primarily to further automate security operations and alleviate pressure from human teams. AI Analyst’s investigative capabilities sit on top of everything we discussed so far (combining EDR detections with detections from other coverage areas, applying unsupervised machine learning to EDR detections, …).

However, it can also come in handy to follow up on low-severity EDR alerts for which you might not have the human resources to do so.

The below screenshot shows an example of a concluded AI Analyst investigation that was triggered by an EDR alert:

Figure 5: An AI Analyst incident trained on third-party data

The Impact of EDR Integrations

The purpose behind all of this is to augment human teams, save them time and drive further security automation.

By ingesting third-party endpoint alerts, combining it with our existing intelligence and applying unsupervised machine learning to it, we achieve that further security automation. 

Analysts don’t have to switch between consoles for investigations. They can leverage our high-fidelity detections that look for unusual endpoint alerts, in combination with our already powerful detections across cloud and email systems, zero trust architecture, IT and OT networks, and more. 

In our experience, this pinpoints the needle in the haystack – it cuts through noise and reduces the mean-time-to-detect and mean-time-to-investigate drastically.

All of this is done out of the box in Darktrace once the endpoint integrations are enabled. It does not need a data scientist to make the machine learning work. Nor does it need a detection engineer or threat hunter to create bespoke, meaningful detections. We want to reduce the barrier to entry for using detection and investigation solutions – in terms of skill and experience required. The system is still flexible, transparent, and open, meaning that advanced users can create their own combined detections, leveraging unsupervised machine learning across different data sets with a few clicks.

There are of course more endpoint integration capabilities available than what we covered here, and we will explore these in future blog posts.

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
Max Heinemeyer
Global Field CISO

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June 17, 2025

Tracking CVE-2025-31324: Darktrace’s detection of SAP Netweaver exploitation before and after disclosure 

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Introduction: Exploiting SAP platforms

Global enterprises depend extensively on SAP platforms, such as SAP NetWeaver and Visual Composer, to run critical business processes worldwide. These systems; however, are increasingly appealing targets for well-resourced adversaries:

What is CVE-2025-31324?

CVE-2025-31324 affects SAP’s NetWeaver Visual Composer, a web-based software modeling tool. SAP NetWeaver is an application server and development platform that runs and connects SAP and non-SAP applications across different technologies [2]. It is commonly used by process specialists to develop application components without coding in government agencies, large enterprises, and by critical infrastructure operators [4].

CVE-2025-31324 affects SAP’s Netweaver Visual Composer Framework 7.1x (all SPS) and above [4]. The vulnerability in a Java Servlet (/irj/servlet_jsp) would enable an unauthorized actor to upload arbitrary files to the /developmentserver/metadatauploader endpoint, potentially resulting in remote code execution (RCE) and full system compromise [3]. The issue stems from an improper authentication and authorization check in the SAP NetWeaver Application Server Java systems [4].

What is the severity rating of CVE-2025-31324?

The vulnerability, first disclosed on April 24, 2025, carries the highest severity rating (CVSS v3 score: 10.0) and could allow remote attackers to upload malicious files without requiring authentication [1][5]. Although SAP released a workaround on April 8, many organizations are hesitant to take their business-critical SAP NetWeaver systems offline, leaving them exposed to potential exploitation [2].

How is CVE-2025-31324 exploited?

The vulnerability is exploitable by sending specifically crafted GET, POST, or HEAD HTTP requests to the /developmentserver/metadatauploader URL using either HTTP or HTTPS. Attackers have been seen uploading malicious files (.jsp, .java, or .class files to paths containing “\irj\servlet_jsp\irj\”), most of them being web shells, to publicly accessible SAP NetWeaver systems.

External researchers observed reconnaissance activity targeting this vulnerability in late January 2025, followed by a surge in exploitation attempts in February. The first confirmed compromise was reported in March [4].

Multiple threat actors have reportedly targeted the vulnerability, including Chinese Advanced Persistent Threats (APTs) groups Chaya_004 [7], UNC5221, UNC5174, and CL-STA-0048 [8], as well as ransomware groups like RansomEXX, also known as Storm-2460, BianLian [4] or Qilin [6] (the latter two share the same indicators of  compromise (IoCs)).

Following the initial workaround published on April 8, SAP released a security update addressing CVE-2025-31324 and subsequently issued a patch on May 13 (Security Note 3604119) to resolve the root cause of the vulnerability [4].

Darktrace’s coverage of CVE-2025-31324 exploitation

Darktrace has observed activity indicative of threat actors exploiting CVE-2025-31324, including one instance detected before the vulnerability was publicly disclosed.

In April 2025, the Darktrace Threat Research team investigated activity related to the CVE-2025-31324 on SAP devices and identified two cases suggesting active exploitation of the vulnerability. One case was detected prior to the public disclosure of the vulnerability, and the other just two days after it was published.

Early detection of CVE 2025-31324 by Darktrace

Figure 1: Timeline of events for an internet-facing system, believed to be a SAP device, exhibiting activity indicative of CVE-2025-31324 exploitation.
Figure 1: Timeline of events for an internet-facing system, believed to be a SAP device, exhibiting activity indicative of CVE-2025-31324 exploitation.

On April 18, six days prior to the public disclosure of CVE-2025-31324, Darktrace began to detect unusual activity on a device belonging to a logistics organization in the Europe, the Middle East and Africa (EMEA) region. Multiple IoCs observed during this incident have since been linked via OSINT to the exploitation of CVE-2025-31324. Notably, however, this reporting was not available at the time of detection, highlighting Darktrace’s ability to detect threats agnostically, without relying on threat intelligence.

The device was observed making  domain name resolution request for the Out-of-Band Application Security Testing (OAST) domain cvvr9gl9namk9u955tsgaxy3upyezhnm6.oast[.]online. OAST is often used by security teams to test if exploitable vulnerabilities exist in a web application but can similarly be used by threat actors for the same purpose [9].

Four days later, on April 22, Darktrace observed the same device, an internet-facing system believed to be a SAP device, downloading multiple executable (.exe) files from several Amazon Simple Storage Service (S3). Darktrace’s Threat Research team later found these files to be associated with the KrustyLoader  malware [23][24][25].

KrustyLoader is known to be associated with the Chinese threat actor UNC5221, also known as UTA0178, which has been reported to aggressively target devices exposed to the internet [10] [14] [15]. It is an initial-stage malware which downloads and launches a second-stage payload – Sliver C2. Sliver is a similar tool to Cobalt Strike (an open-source post-exploitation toolkit). It is used for command-and-control (C2) connections [11][12]13]. After its successful download, KrustyLoader deletes itself to evade detection.  It has been reported that multiple Chinese APT groups have deployed KrustyLoader on SAP Netweaver systems post-compromise [8].

The actors behind KrustyLoader have also been associated with the exploitation of zero-day vulnerabilities in other enterprise systems, including Ivanti devices [12]. Notably, in this case, one of the Amazon S3 domains observed (abode-dashboard-media.s3.ap-south-1.amazonaws[.]com ) had previously been investigated by Darktrace’s Threat Research team as part of their investigation into Ivanti Connect Secure (CS) and Policy Secure (PS) appliances.

In addition to the download of known malicious files, Darktrace also detected new IoCs, including several executable files that could not be attributed to any known malware families or previous attacks, and for which no corresponding OSINT reporting was available.

Post-CVE publication detection

Exploit Validation

Between April 27 and 29, Darktrace observed unusual activity from an SAP device on the network of a manufacturing customer in EMEA.

Darktrace / NETWORK’s detection of an SAP device performing a large volume of suspicious activity between April 27 and April 29.
Figure 2: Darktrace / NETWORK’s detection of an SAP device performing a large volume of suspicious activity between April 27 and April 29.

The device was observed making DNS requests for OAST domains (e.g. aaaaaaaa.d06qqn7pu5a6u25tv9q08p5xhbjzw33ge.oast[.]online and aaaaaaaaaaa.d07j2htekalm3139uk2gowmxuhapkijtp.oast[.]pro), suggesting that a threat actor was testing for exploit validation [9].

Darktrace / NETWORK’s detection of a SAP device making suspicious domain name resolution requests for multiple OAST domains.
Figure 3: Darktrace / NETWORK’s detection of a SAP device making suspicious domain name resolution requests for multiple OAST domains.

Privilege escalation tool download attempt

One day later, Darktrace observed the same device attempting to download an executable file from hxxp://23.95.123[.]5:666/xmrigCCall/s.exe (SHA-1 file hash: e007edd4688c5f94a714fee036590a11684d6a3a).

Darktrace / NETWORK identified the user agents Microsoft-CryptoAPI/10.0 and CertUtil URL Agent during the connections to 23.95.123[.]5. The connections were made over port 666, which is not typically used for HTTP connections.

Multiple open-source intelligence (OSINT) vendors have identified the executable file as either JuicyPotato or SweetPotato, both Windows privilege escalation tools[16][17][18][19]. The file hash and the unusual external endpoint have been associated with the Chinese APT group Gelsemium in the past, however, many threat actors are known to leverage this tool in their attacks [20] [21].

Figure 4: Darktrace’s Cyber AI Analyst’s detection of a SAP device downloading a suspicious executable file from hxxp://23.95.123[.]5:666/xmrigCCall/s.exe on April 28, 2025.

Darktrace deemed this activity highly suspicious and triggered an Enhanced Monitoring model alert, a high-priority security model designed to detect activity likely indicative of compromise. As the customer was subscribed to the Managed Threat Detection service, Darktrace’s Security Operations Centre (SOC) promptly investigated the alert and notified the customer for swift remediation. Additionally, Darktrace’s Autonomous Response capability automatically blocked connections to the suspicious IP, 23.95.123[.]5, effectively containing the compromise in its early stages.

Actions taken by Darktrace’s Autonomous Response to block connections to the suspicious external endpoint 23.95.123[.]5. This event log shows that the connections to 23.95.123[.]5 were made over a rare destination port for the HTTP protocol and that new user agents were used during the connections.
Figure 5: Actions taken by Darktrace’s Autonomous Response to block connections to the suspicious external endpoint 23.95.123[.]5. This event log shows that the connections to 23.95.123[.]5 were made over a rare destination port for the HTTP protocol and that new user agents were used during the connections.

Conclusion

The exploitation of CVE-2025-31324 to compromise SAP NetWeaver systems highlights the persistent threat posed by vulnerabilities in public-facing assets. In this case, threat actors leveraged the flaw to gain an initial foothold, followed by attempts to deploy malware linked to groups affiliated with China [8][20].

Crucially, Darktrace demonstrated its ability to detect and respond to emerging threats even before they are publicly disclosed. Six days prior to the public disclosure of CVE-2025-31324, Darktrace detected unusual activity on a device believed to be a SAP system, which ultimately represented an early detection of the CVE. This detection was made possible through Darktrace’s behavioral analysis and anomaly detection, allowing it to recognize unexpected deviations in device behavior without relying on signatures, rules or known IoCs. Combined with its Autonomous Response capability, this allowed for immediate containment of suspicious activity, giving security teams valuable time to investigate and mitigate the threat.

Credit to Signe Zaharka (Principal Cyber Analyst), Emily Megan Lim, (Senior Cyber Analyst) and Ryan Traill (Analyst Content Lead)

Appendices

List of IoCs

23.95.123[.]5:666/xmrigCCall/s.exe - URL- JuicyPotato/SweetPotato - high confidence

29274ca90e6dcf5ae4762739fcbadf01- MD5 file hash - JuicyPotato/SweetPotato - high confidence

e007edd4688c5f94a714fee036590a11684d6a3a - SHA-1 file hash - JuicyPotato/SweetPotato -high confidence

3268f269371a81dbdce8c4eedffd8817c1ec2eadec9ba4ab043cb779c2f8a5d2 - SHA-256 file hash - JuicyPotato/SweetPotato -high confidence

abode-dashboard-media.s3.ap-south-1.amazonaws[.]com/nVW2lsYsYnv58 - URL- high confidence

applr-malbbal.s3.ap-northeast-2.amazonaws[.]com/7p3ow2ZH - URL- high confidence

applr-malbbal.s3.ap-northeast-2.amazonaws[.]com/UUTICMm - URL- KrustyLoader - high confidence

beansdeals-static.s3.amazonaws[.]com/UsjKy - URL- high confidence

brandnav-cms-storage.s3.amazonaws[.]com/3S1kc - URL- KrustyLoader - high confidence

bringthenoiseappnew.s3.amazonaws[.]com/pp79zE - URL- KrustyLoader - high confidence

f662135bdd8bf792a941ea222e8a1330 - MD5 file hash- KrustyLoader - high confidence

fa645f33c0e3a98436a0161b19342f78683dbd9d - SHA-1 file hash- KrustyLoader - high confidence

1d26fff4232bc64f9ab3c2b09281d932dd6afb84a24f32d772d3f7bc23d99c60 - SHA-256 file hash- KrustyLoader - high confidence

6900e844f887321f22dd606a6f2925ef - MD5 file hash- KrustyLoader - high confidence

da23dab4851df3ef7f6e5952a2fc9a6a57ab6983 - SHA-1 file hash- KrustyLoader - high confidence

1544d9392eedf7ae4205dd45ad54ec67e5ce831d2c61875806ce4c86412a4344 - SHA-256 file hash- KrustyLoader - high confidence

83a797e5b47ce6e89440c47f6e33fa08 - MD5 file hash - high confidence

a29e8f030db8990c432020441c91e4b74d4a4e16 - SHA-1 file hash - high confidence

72afde58a1bed7697c0aa7fa8b4e3b03 - MD5 file hash- high confidence

fe931adc0531fd1cb600af0c01f307da3314c5c9 - SHA-1 file hash- high confidence

b8e56de3792dbd0f4239b54cfaad7ece3bd42affa4fbbdd7668492de548b5df8 - SHA-256 file hash- KrustyLoader - high confidence

17d65a9d8d40375b5b939b60f21eb06eb17054fc - SHA-1 file hash- KrustyLoader - high confidence

8c8681e805e0ae7a7d1a609efc000c84 - MD5 file hash- KrustyLoader - high confidence

29274ca90e6dcf5ae4762739fcbadf01 - MD5 file hash- KrustyLoader - high confidence

Darktrace Model Detections

Anomalous Connection / CertUtil Requesting Non Certificate

Anomalous Connection / CertUtil to Rare Destination

Anomalous Connection / Powershell to Rare External

Anomalous File / EXE from Rare External Location

Anomalous File / Multiple EXE from Rare External Locations

Anomalous File / Internet Facing System File Download

Anomalous File / Masqueraded File Transfer (Enhanced Monitoring)

Anomalous Server Activity / New User Agent from Internet Facing System

Compliance / CertUtil External Connection

Compromise / High Priority Tunnelling to Bin Services (Enhanced Monitoring)

Compromise / Possible Tunnelling to Bin Services

Device / Initial Attack Chain Activity (Enhanced Monitoring)

Device / Suspicious Domain

Device / Internet Facing Device with High Priority Alert

Device / Large Number of Model Alerts

Device / Large Number of Model Alerts from Critical Network Device (Enhanced Monitoring)

Device / New PowerShell User Agent

Device / New User Agent

Autonomous Response Model Alerts

Antigena / Network / External Threat / Antigena Suspicious File Block

Antigena / Network / Significant Anomaly / Antigena Controlled and Model Alert

Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Server Block

Antigena / Network / Significant Anomaly / Antigena Significant Server Anomaly Block

Antigena/ Network / External Threat / Antigena Suspicious File Block

Antigena/ Network / External Threat / Antigena Suspicious File Pattern of Life Block

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

Antigena/ Network / Significant Anomaly / Antigena Controlled and Model Alert

Antigena/ Network / Significant Anomaly / Antigena Enhanced Monitoring from Server Block

Antigena/ Network / Significant Anomaly / Antigena Significant Server Anomaly Block

Cyber AI Analyst Incidents

Possible HTTP Command and Control

Suspicious File Download

MITRE ATT&CK Mapping

Malware - RESOURCE DEVELOPMENT - T1588.001

PowerShell - EXECUTION - T1059.001

Drive-by Compromise - INITIAL ACCESS - T1189

Ingress Tool Transfer - COMMAND AND CONTROL - T1105

Application Layer Protocol - COMMAND AND CONTROL - T1071

Exploitation of Remote Services - LATERAL MOVEMENT - T1210

Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol - EXFILTRATION - T1048.003

References

1. https://nvd.nist.gov/vuln/detail/CVE-2025-31324

2. https://www.bleepingcomputer.com/news/security/over-1-200-sap-netweaver-servers-vulnerable-to-actively-exploited-flaw/

3. https://reliaquest.com/blog/threat-spotlight-reliaquest-uncovers-vulnerability-behind-sap-netweaver-compromise/

4. https://onapsis.com/blog/active-exploitation-of-sap-vulnerability-cve-2025-31324/

5. https://www.bleepingcomputer.com/news/security/sap-fixes-suspected-netweaver-zero-day-exploited-in-attacks/

6. https://op-c.net/blog/sap-cve-2025-31324-qilin-breach/

7. https://www.forescout.com/blog/threat-analysis-sap-vulnerability-exploited-in-the-wild-by-chinese-threat-actor/

8. https://blog.eclecticiq.com/china-nexus-nation-state-actors-exploit-sap-netweaver-cve-2025-31324-to-target-critical-infrastructures

9. https://portswigger.net/burp/application-security-testing/oast

10. https://www.picussecurity.com/resource/blog/unc5221-cve-2025-22457-ivanti-connect-secure  

11. https://malpedia.caad.fkie.fraunhofer.de/details/elf.krustyloader

12. https://www.broadcom.com/support/security-center/protection-bulletin/krustyloader-backdoor

13. https://labs.withsecure.com/publications/new-krustyloader-variant-dropped-via-screenconnect-exploit

14. https://blog.eclecticiq.com/china-nexus-threat-actor-actively-exploiting-ivanti-endpoint-manager-mobile-cve-2025-4428-vulnerability

15. https://thehackernews.com/2024/01/chinese-hackers-exploiting-critical-vpn.html

16. https://www.virustotal.com/gui/file/3268f269371a81dbdce8c4eedffd8817c1ec2eadec9ba4ab043cb779c2f8a5d2

17. https://bazaar.abuse.ch/sample/3268f269371a81dbdce8c4eedffd8817c1ec2eadec9ba4ab043cb779c2f8a5d2/

18. https://www.fortinet.com/content/dam/fortinet/assets/analyst-reports/report-juicypotato-hacking-tool-discovered.pdf

19. https://www.manageengine.com/log-management/correlation-rules/detecting-sweetpotato.html

20. https://unit42.paloaltonetworks.com/rare-possible-gelsemium-attack-targets-se-asia/

21. https://assets.kpmg.com/content/dam/kpmg/in/pdf/2023/10/kpmg-ctip-gelsemium-apt-31-oct-2023.pdf

22. https://securityaffairs.com/177522/hacking/experts-warn-of-a-second-wave-of-attacks-targeting-sap-netweaver-bug-cve-2025-31324.html

23. https://www.virustotal.com/gui/file/b8e56de3792dbd0f4239b54cfaad7ece3bd42affa4fbbdd7668492de548b5df8

24. https://www.virustotal.com/gui/file/1d26fff4232bc64f9ab3c2b09281d932dd6afb84a24f32d772d3f7bc23d99c60/detection

25. https://www.virustotal.com/gui/file/1544d9392eedf7ae4205dd45ad54ec67e5ce831d2c61875806ce4c86412a4344/detection

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About the author
Signe Zaharka
Senior Cyber Security Analyst

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June 17, 2025

Proactive OT Security: Lessons on Supply Chain Risk Management from a Rogue Raspberry Pi

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Understanding supply chain risk in manufacturing

For industries running Industrial Control Systems (ICS) such as manufacturing and fast-moving consumer goods (FMCG), complex supply chains mean that disruption to one weak node can have serious impacts to the entire ecosystem. However, supply chain risk does not always originate from outside an organization’s ICS network.  

The implicit trust placed on software or shared services for maintenance within an ICS can be considered a type of insider threat [1], where defenders also need to look ‘from within’ to protect against supply chain risk. Attackers have frequently mobilised this form of insider threat:

  • Many ICS and SCADA systems were compromised during the 2014 Havex Watering Hole attack, where via operators’ implicit trust in the trojanized versions of legitimate applications, on legitimate but compromised websites [2].
  • In 2018, the world’s largest manufacturer of semiconductors and processers shut down production for three days after a supplier installed tainted software that spread to over 10,000 machines in the manufacturer’s network [3].
  • During the 2020 SolarWinds supply chain attack, attackers compromised a version of Orion software that was deployed from SolarWinds’ own servers during a software update to thousands of customers, including tech manufacturing companies such as Intel and Nvidia [4].

Traditional approaches to ICS security have focused on defending against everything from outside the castle walls, or outside of the ICS network. As ICS attacks become more sophisticated, defenders must not solely rely on static perimeter defenses and prevention. 

A critical part of active defense is understanding the ICS environment and how it operates, including all possible attack paths to the ICS including network connections, remote access points, the movement of data across zones and conduits and access from mobile devices. For instance, original equipment manufacturers (OEMs) and vendors often install remote access software or third-party equipment in ICS networks to facilitate legitimate maintenance and support activities, which can unintentionally expand the ICS’ attack surface.  

This blog describes an example of the convergence between supply chain risk and insider risk, when a vendor left a Raspberry Pi device in a manufacturing customer’s ICS network without the customer’s knowledge.

Case study: Using unsupervised machine learning to detect pre-existing security issues

Raspberry Pi devices are commonly used in SCADA environments as low-cost, remotely accessible data collectors [5][6][7]. They are often paired with Industrial Internet of Things (IIoT) for monitoring and tracking [8]. However, these devices also represent a security risk because their small physical size and time-consuming nature of physical inspection makes them easy to overlook. This poses a security risk, as these devices have previously been used to carry out USB-based attacks or to emulate Ethernet-over-USB connections to exfiltrate sensitive data [8][9].

In this incident, a Darktrace customer was unaware that their supplier had installed a Raspberry Pi device on their ICS network. Crucially, the installation occurred prior to Darktrace’s deployment on the customer’s network. 

For other anomaly detection tools, this order of events meant that this third-party device would likely have been treated as part of the customer’s existing infrastructure. However, after Darktrace was deployed, it analyzed the metadata from the encrypted HTTPS and DNS connections that the Raspberry Pi made to ‘call home’ to the supplier and determined that these connections were  unusual compared to the rest of the devices in the network, even in the absence of any malicious indicators of compromise (IoCs).  

Darktrace triggered the following alerts for this unusual activity that consequently notified the customer to the pre-existing threat of an unmanaged device already present in their network:

  • Compromise / Sustained SSL or HTTP Increase
  • Compromise / Agent Beacon (Short Period)
  • Compromise / Agent Beacon (Medium Period)
  • Compromise / Agent Beacon (Long Period)
  • Tags / New Raspberry Pi Device
  • Device / DNS Requests to Unusual Server
  • Device / Anomaly Indicators / Spike in Connections to Rare Endpoint Indicator
Darktrace’s External Sites Summary showing the rarity of the external endpoint that the Raspberry Pi device ‘called home’ to and the model alerts triggered.  
Figure 1: Darktrace’s External Sites Summary showing the rarity of the external endpoint that the Raspberry Pi device ‘called home’ to and the model alerts triggered.  

Darktrace’s Cyber AI Analyst launched an autonomous investigation into the activity, correlating related events into a broader incident and generating a report outlining the potential threat along with supporting technical details.

Darktrace’s anomaly-based detection meant that the Raspberry Pi device did not need to be observed performing clearly malicious behavior to alert the customer to the security risk, and neither can defenders afford to wait for such escalation.

Why is this significant?

In 2021 a similar attack took place. Aiming to poison a Florida water treatment facility, attackers leveraged a TeamViewer instance that had been dormant on the system for six months, effectively allowing the attacker to ‘live off the land’ [10].  

The Raspberry Pi device in this incident also remained outside the purview of the customer’s security team at first. It could have been leveraged by a persistent attacker to pivot within the internal network and communicate externally.

A proactive approach to active defense that seeks to minimize and continuously monitor the attack surface and network is crucial.  

The growing interest in manufacturing from attackers and policymakers

Significant motivations for targeting the manufacturing sector and increasing regulatory demands make the convergence of supply chain risk, insider risk, and the prevalence of stealthy living-off-the-land techniques particularly relevant to this sector.

Manufacturing is consistently targeted by cybercriminals [11], and the sector’s ‘just-in-time’ model grants attackers the opportunity for high levels of disruption. Furthermore, under NIS 2, manufacturing and some food and beverage processing entities are now designated as ‘important’ entities. This means stricter incident reporting requirements within 24 hours of detection, and enhanced security requirements such as the implementation of zero trust and network segmentation policies, as well as measures to improve supply chain resilience [12][13][14].

How can Darktrace help?

Ultimately, Darktrace successfully assisted a manufacturing organization in detecting a potentially disruptive 'near-miss' within their OT environment, even in the absence of traditional IoCs.  Through passive asset identification techniques and continuous network monitoring, the customer improved their understanding of their network and supply chain risk.  

While the swift detection of the rogue device allowed the threat to be identified before it could escalate, the customer could have reduced their time to respond by using Darktrace’s built-in response capabilities, had Darktrace’s Autonomous Response capability been enabled.  Darktrace’s Autonomous Response can be configured to target specific connections on a rogue device either automatically upon detection or following manual approval from the security team, to stop it communicating with other devices in the network while allowing other approved devices to continue operating. Furthermore, the exportable report generated by Cyber AI Analyst helps security teams to meet NIS 2’s enhanced reporting requirements.  

Sophisticated ICS attacks often leverage insider access to perform in-depth reconnaissance for the development of tailored malware capabilities.  This case study and high-profile ICS attacks highlight the importance of mitigating supply chain risk in a similar way to insider risk.  As ICS networks adapt to the introduction of IIoT, remote working and the increased convergence between IT and OT, it is important to ensure the approach to secure against these threats is compatible with the dynamic nature of the network.  

Credit to Nicole Wong (Principal Cyber Analyst), Matthew Redrup (Senior Analyst and ANZ Team Lead)

[related-resource]

Appendices

MITRE ATT&CK Mapping

  • Infrastructure / New Raspberry Pi Device - INITIAL ACCESS - T1200 Hardware Additions
  • Device / DNS Requests to Unusual Server - CREDENTIAL ACCESS, COLLECTION - T1557 Man-in-the-Middle
  • Compromise / Agent Beacon - COMMAND AND CONTROL - T1071.001 Web Protocols

References

[1] https://www.cisa.gov/topics/physical-security/insider-threat-mitigation/defining-insider-threats

[2] https://www.trendmicro.com/vinfo/gb/threat-encyclopedia/web-attack/139/havex-targets-industrial-control-systems

[3]https://thehackernews.com/2018/08/tsmc-wannacry-ransomware-attack.html

[4] https://www.theverge.com/2020/12/21/22194183/intel-nvidia-cisco-government-infected-solarwinds-hack

[5] https://www.centreon.com/monitoring-ot-with-raspberry-pi-and-centreon/

[6] https://ieeexplore.ieee.org/document/9107689

[7] https://www.linkedin.com/pulse/webicc-scada-integration-industrial-raspberry-pi-devices-mryff

[8] https://www.rowse.co.uk/blog/post/how-is-the-raspberry-pi-used-in-the-iiot

[9] https://sepiocyber.com/resources/whitepapers/raspberry-pi-a-friend-or-foe/#:~:text=Initially%20designed%20for%20ethical%20purposes,as%20cyberattacks%20and%20unauthorized%20access

[10] https://edition.cnn.com/2021/02/10/us/florida-water-poison-cyber/index.html

[11] https://www.mxdusa.org/2025/02/13/top-cyber-threats-in-manufacturing/

[12] https://www.shoosmiths.com/insights/articles/nis2-what-manufacturers-and-distributors-need-to-know-about-europes-new-cybersecurity-regime

[13] https://www.goodaccess.com/blog/nis2-require-zero-trust-essential-security-measure#zero-trust-nis2-compliance

[14] https://logisticsviewpoints.com/2024/11/06/the-impact-of-nis-2-regulations-on-manufacturing-supply-chains/

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About the author
Nicole Wong
Cyber Security Analyst
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