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February 29, 2024

Protecting Against AlphV BlackCat Ransomware

Learn how Darktrace AI is combating AlphV BlackCat ransomware, including the details of this ransomware and how to protect yourself from it.
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
Specialist Security Researcher
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29
Feb 2024

As-a-Service malware trending

Throughout the course of 2023, “as-a-Service” strains of malware remained the most consistently observed threat type to affect Darktrace customers, mirroring their overall prominence across the cyber threat landscape. With this trend expected to continue throughout 2024, organizations and their security teams should be prepared to defend their network against increasingly versatile and tailorable malware-as-a-service (MaaS) and ransomware-as-a-service (RaaS) strains [1].

What is ALPHV ransomware?

The ALPHV ransomware, also known as ‘BlackCat’ or ‘Noberus’, is one example of a RaaS strain that has been prominent across the threat landscape over the last few years.

ALPHV is a ransomware strain coded in the Rust programming language. The ransomware is sold as part of the RaaS economy [2], with samples of the ransomware being provided and sold by a criminal group (the RaaS ‘operator’) to other cybercriminals (the RaaS ‘affiliates’) who then gain entry to organizations' networks with the intention of detonating the ransomware and demanding ransom payments.

ALPHV was likely first used in the wild back in November 2021 [3]. Since then, it has become one of the most prolific ransomware strains, with the Federal Bureau of Investigation (FBI) reporting nearly USD 300 million in ALPHV ransom payments as of September 2023 [4].

In December 2023, the FBI and the US Department of Justice announced a successful disruption campaign against the ALPHV group, which included a takedown of the their data leak site, and the release of a decryption tool for the ransomware strain [5], and in February 2024, the US Department of State announced  a reward of up to USD 10 million for information leading to the identification or location of anyone occupying a key leadership position in the group operating the ALPHV ransomware strain [6].

The disruption campaign against the ransomware group appeared to have been successful, as evidenced by the recent, significant decline in ALPHV attacks, however, it would not be surprising for the group to simply return with new branding, in a similar vein to its apparent predecessors, DarkSide and BlackMatter [7].

How does ALPHV ransomware work?

ALPHV affiliates have been known to employ a variety of methods to progress towards their objective of detonating ALPHV ransomware [4]. In the latter half of 2023, ALPHV affiliates were observed using malicious advertising (i.e, malvertising) to deliver a Python-based backdoor-dropper known as 'Nitrogen' to users' devices [8][12]. These malvertising operations consisted in affiliates setting up malicious search engine adverts for tools such as WinSCP and AnyDesk.

Users' interactions with these adverts led them to sites resembling legitimate software distribution sites. Users' attempts to download software from these spoofed sites resulted in the delivery of a backdoor-dropping malware sample dubbed 'Nitrogen' to their devices. Nitrogen has been observed dropping a variety of command-and-control (C2) implants onto users' devices, including Cobalt Strike Beacon and Sliver C2. ALPHV affiliates often used the backdoor access afforded to them by these C2 implants to conduct reconnaissance and move laterally, in preparation for detonating ALPHV ransomware payloads.

Darktrace Detection of ALPHV Ransomware

During October 2023, Darktrace observed several cases of ALPHV affiliates attempting to infiltrate organizations' networks via the use of malvertising to socially engineer users into downloading and installing Nitrogen from impersonation websites such as 'wireshhark[.]com' and wìnscp[.]net (i.e, xn--wnscp-tsa[.]net).

While the attackers managed to bypass traditional security measures and evade detection by using a device from the customer’s IT team to perform its malicious activity, Darktrace DETECT™ swiftly identified the subtle indicators of compromise (IoCs) in the first instance. This swift detection of ALPHV, along with Cyber AI Analyst™ autonomously investigating the wide array of post-compromise activity, provided the customer with full visibility over the attack enabling them to promptly initiate their remediation and recovery efforts.

Unfortunately, in this incident, Darktrace RESPOND™ was not fully deployed within their environment, hindering its ability to autonomously counter emerging threats. Had RESPOND been fully operational here, it would have effectively contained the attack in its early stages, avoiding the eventual detonation of the ALPHV ransomware.

Figure 1: Timeline of the ALPHV ransomware attack.

In mid-October, a member of the IT team at a US-based Darktrace customer attempted to install the network traffic analysis software, Wireshark, onto their desktop. Due to the customer’s configuration, Darktrace's visibility over this device was limited to its internal traffic, despite this it was still able to identify and alert for a string of suspicious activity conducted by the device.

Initially, Darktrace observed the device making type A DNS requests for 'wiki.wireshark[.]org' immediately before making type A DNS requests for the domain names 'www.googleadservices[.]com', 'allpcsoftware[.]com', and 'wireshhark[.]com' (note the two 'h's). This pattern of activity indicates that the device’s user was redirected to the website, wireshhark[.]com, as a result of the user's interaction with a sponsored Google Search result pointing to allpcsoftware[.]com.

At the time of analysis, navigating to wireshhark[.]com directly from the browser search bar led to a YouTube video of Rick Astley's song "Never Gonna Give You Up". This suggests that the website, wireshhark[.]com, had been configured to redirect users to this video unless they had arrived at the website via the relevant sponsored Google Search result [8].

Although it was not possible to confirm this with certainty, it is highly likely that users who visited the website via the appropriate sponsored Google Search result were led to a fake website (wireshhark[.]com) posing as the legitimate website, wireshark[.]com. It seems that the actors who set up this fake version of wireshark[.]com were inspired by the well-known bait-and-switch technique known as 'rickrolling', where users are presented with a desirable lure (typically a hyperlink of some kind) which unexpectedly leads them to a music video of Rick Astley's "Never Gonna Give You Up".

After being redirected to wireshhark[.]com, the user unintentionally installed a malware sample which dropped what appears to be Cobalt Strike onto their device. The presence of Cobalt Strike on the user's desktop was evidenced by the subsequent type A DNS requests which the device made for the domain name 'pse[.]ac'. These DNS requests were responded to with the likely Cobalt Strike C2 server address, 194.169.175[.]132. Given that Darktrace only had visibility over the device’s internal traffic, it did not observe any C2 connections to this Cobalt Strike endpoint. However, the desktop's subsequent behavior suggests that a malicious actor had gained 'hands-on-keyboard' control of the device via an established C2 channel.

Figure 2: Advanced Search data showing an customer device being tricked into visiting the fake website, wireshhark[.]com.

Since the malicious actor had gained control of an IT member's device, they were able to abuse the privileged account credentials to spread Python payloads across the network via SMB and the Windows Management Instrumentation (WMI) service. The actor was also seen distributing the Windows Sys-Internals tool, PsExec, likely in an attempt to facilitate their lateral movement efforts. It was normal for this IT member's desktop to distribute files across the network via SMB, which meant that this malicious SMB activity was not, at first glance, out of place.

Figure 3: Advanced Search data showing that it was normal for the IT member's device to distribute files over SMB.

However, Darktrace DETECT recognized that the significant spike in file writes being performed here was suspicious, even though, on the surface, it seemed ‘normal’ for the device. Furthermore, Darktrace identified that the executable files being distributed were attempting to masquerade as a different file type, potentially in an attempt to evade the detection of traditional security tools.

Figure 4: Event Log data showing several Model Breaches being created in response to the IT member's DEVICE's SMB writes of Python-based executables.

An addition to DETECT’s identification of this unusual activity, Darktrace’s Cyber AI Analyst launched an autonomous investigation into the ongoing compromise and was able to link the SMB writes and the sharing of the executable Python payloads, viewing the connections as one lateral movement incident rather than a string of isolated events. After completing its investigation, Cyber AI Analyst was able to provide a detailed summary of events on one pane of glass, ensuring the customer could identify the affected device and begin their remediation.

Figure 5: Cyber AI Analyst investigation summary highlighting the IT member's desktop’s lateral movement activities.

C2 Activity

The Python payloads distributed by the IT member’s device were likely related to the Nitrogen malware, as evidenced by the payloads’ names and by the network behaviours which they engendered.  

Figure 6: Advanced Search data showing the affected device reaching out to the C2 endpoint, pse[.]ac, and then distributing Python-based executable files to an internal domain controller.

The internal devices to which these Nitrogen payloads were distributed immediately went on to contact C2 infrastructure associated with Cobalt Strike. These C2 connections were made over SSL on ports 443 and 8443.  Darktrace identified the attacker moving laterally to an internal SQL server and an internal domain controller.

Figure 7: Advanced Search data showing an internal SQL server contacting the Cobalt Strike C2 endpoint, 194.180.48[.]169, after receiving Python payloads from the IT member’s device.
Figure 8: Event Log data showing several DETECT model breaches triggering in response to an internal SQL server’s C2 connections to 194.180.48[.]169.

Once more, Cyber AI Analyst launched its own investigation into this activity and was able to successfully identify a series of separate SSL connections, linking them together into one wider C2 incident.

Figure 9: Cyber AI Analyst investigation summary highlighting C2 connections from the SQL server.

Darktrace observed the attacker using their 'hands-on-keyboard' access to these systems to elevate their privileges, conduct network reconnaissance (primarily port scanning), spread Python payloads further across the network, exfiltrate data from the domain controller and transfer a payload from GitHub to the domain controller.

Figure 10: Cyber AI Analyst investigation summary an IP address scan carried out by an internal domain controller.
Figure 12: Event Log data showing an internal domain controller contacting GitHub around the time that it was in communication with the C2 endpoint, 194.180.48[.]169.
Figure 13: Event Log data showing a DETECT model breach being created in response to an internal domain controller's large data upload to the C2 endpoint, 194.180.48[.]169.

After conducting extensive reconnaissance and lateral movement activities, the attacker was observed detonating ransomware with the organization's VMware environment, resulting in the successful encryption of the customer’s VMware vCenter server and VMware virtual machines. In this case, the attacker took around 24 hours to progress from initial access to ransomware detonation.  

If the targeted organization had been signed up for Darktrace's Proactive Threat Notification (PTN) service, they would have been promptly notified of these suspicious activities by the Darktrace Security Operations Center (SOC) in the first instance, allowing them to quickly identify affected devices and quarantine them before the compromise could escalate.

Additionally, given the quantity of high-severe alerts that triggered in response to this attack, Darktrace RESPOND would, under normal circumstances, have inhibited the attacker's activities as soon as they were identified by DETECT. However, due to RESPOND not being configured to act on server devices within the customer’s network, the attacker was able to seamlessly move laterally through the organization's server environment and eventually detonate the ALPHV ransomware.

Nevertheless, Darktrace was able to successfully weave together multiple Cyber AI Analyst incidents which it generated into a thread representing the chain of behavior that made up this attack. The thread of Incident Events created by Cyber AI Analyst provided a substantial account of the attack and the steps involved in it, which significantly facilitated the customer’s post-incident investigation efforts.  

Figure 14: Darktrace's AI Analyst weaved together 33 of the Incident Events it created together into a thread representing the attacker’s chain of behavior.

Conclusion

It is expected for malicious cyber actors to revise and upgrade their methods to evade organizations’ improving security measures. The continued improvement of email security tools, for example, has likely created a need for attackers to develop new means of Initial Access, such as the use of Microsoft Teams-based malware delivery.

This fast-paced ALPHV ransomware attack serves as a further illustration of this trend, with the actor behind the attack using malvertising to convince an unsuspecting user to download the Python-based malware, Nitrogen, from a fake Wireshark site. Unbeknownst to the user, this stealthy malware dropped a C2 implant onto the user’s device, giving the malicious actor the ‘hands-on-keyboard’ access they needed to move laterally, conduct network reconnaissance, and ultimately detonate ALPHV ransomware.

Despite the non-traditional initial access methods used by this ransomware actor, Darktrace DETECT was still able to identify the unusual patterns of network traffic caused by the attacker’s post-compromise activities. The large volume of alerts created by Darktrace DETECT were autonomously investigated by Darktrace’s Cyber AI Analyst, which was able to weave together related activities of different devices into a comprehensive timeline of the attacker’s operation. Given the volume of DETECT alerts created in response to this ALPHV attack, it is expected that Darktrace RESPOND would have autonomously inhibited the attacker’s operation had the capability been appropriately configured.

As the first post-compromise activities Darktrace observed in this ALPHV attack were seemingly performed by a member of the customer’s IT team, it may have looked normal to a human or traditional signature and rules-based security tools. To Darktrace’s Self-Learning AI, however, the observed activities represented subtle deviations from the device’s normal pattern of life. This attack, and Darktrace’s detection of it, is therefore a prime illustration of the value that Self-Learning AI can bring to the task of detecting anomalies within organizations’ digital estates.

Credit to Sam Lister, Senior Cyber Analyst, Emma Foulger, Principal Cyber Analyst

Appendices

Darktrace DETECT Model Breaches

- Compliance / SMB Drive Write

- Compliance / High Priority Compliance Model Breach

- Anomalous File / Internal / Masqueraded Executable SMB Write

- Device / New or Uncommon WMI Activity

- Anomalous Connection / New or Uncommon Service Control

- Anomalous Connection / High Volume of New or Uncommon Service Control

- Device / New or Uncommon SMB Named Pipe

- Device / Multiple Lateral Movement Model Breaches

- Device / Large Number of Model Breaches  

- SMB Writes of Suspicious Files (Cyber AI Analyst)

- Suspicious Remote WMI Activity (Cyber AI Analyst)

- Suspicious DCE-RPC Activity (Cyber AI Analyst)

- Compromise / Connection to Suspicious SSL Server

- Compromise / High Volume of Connections with Beacon Score

- Anomalous Connection / Suspicious Self-Signed SSL

- Anomalous Connection / Anomalous SSL without SNI to New External

- Compromise / Suspicious TLS Beaconing To Rare External

- Compromise / Beacon to Young Endpoint

- Compromise / SSL or HTTP Beacon

- Compromise / Agent Beacon to New Endpoint

- Device / Long Agent Connection to New Endpoint

- Compromise / SSL Beaconing to Rare Destination

- Compromise / Large Number of Suspicious Successful Connections

- Compromise / Slow Beaconing Activity To External Rare

- Anomalous Server Activity / Outgoing from Server

- Device / Multiple C2 Model Breaches

- Possible SSL Command and Control (Cyber AI Analyst)

- Unusual Repeated Connections (Cyber AI Analyst)

- Device / ICMP Address Scan

- Device / RDP Scan

- Device / Network Scan

- Device / Suspicious Network Scan Activity

- Scanning of Multiple Devices (Cyber AI Analyst)

- ICMP Address Scan (Cyber AI Analyst)

- Device / Anomalous Github Download

- Unusual Activity / Unusual External Data Transfer

- Device / Initial Breach Chain Compromise

MITRE ATT&CK Mapping

Resource Development techniques:

- Acquire Infrastructure: Malvertising (T1583.008)

Initial Access techniques:

- Drive-by Compromise (T1189)

Execution techniques:

- User Execution: Malicious File (T1204.002)

- System Services: Service Execution (T1569.002)

- Windows Management Instrumentation (T1047)

Defence Evasion techniques:

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

Discovery techniques:

- Remote System Discovery (T1018)

- Network Service Discovery (T1046)

Lateral Movement techniques:

- Remote Services: SMB/Windows Admin Shares

- Lateral Tool Transfer (T1570)

Command and Control techniques:

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

- Encrypted Channel: Asymmetric Cryptography (T1573.002)

- Non-Standard Port (T1571)

- Ingress Tool Channel (T1105)

Exfiltration techniques:

- Exfiltration Over C2 Channel (T1041)

Impact techniques:

- Data Encrypted for Impact (T1486)

List of Indicators of Compromise

- allpcsoftware[.]com

- wireshhark[.]com

- pse[.]ac • 194.169.175[.]132

- 194.180.48[.]169

- 193.42.33[.]14

- 141.98.6[.]195

References  

[1] https://darktrace.com/threat-report-2023

[2] https://www.microsoft.com/en-us/security/blog/2022/05/09/ransomware-as-a-service-understanding-the-cybercrime-gig-economy-and-how-to-protect-yourself/

[3] https://www.bleepingcomputer.com/news/security/alphv-blackcat-this-years-most-sophisticated-ransomware/

[4] https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-353a

[5] https://www.justice.gov/opa/pr/justice-department-disrupts-prolific-alphvblackcat-ransomware-variant

[6] https://www.state.gov/u-s-department-of-state-announces-reward-offers-for-criminal-associates-of-the-alphv-blackcat-ransomware-variant/

[7] https://www.bleepingcomputer.com/news/security/blackcat-alphv-ransomware-linked-to-blackmatter-darkside-gangs/

[8] https://www.trendmicro.com/en_us/research/23/f/malvertising-used-as-entry-vector-for-blackcat-actors-also-lever.html

[9] https://news.sophos.com/en-us/2023/07/26/into-the-tank-with-nitrogen/

[10] https://www.esentire.com/blog/persistent-connection-established-nitrogen-campaign-leverages-dll-side-loading-technique-for-c2-communication

[11] https://www.esentire.com/blog/nitrogen-campaign-2-0-reloads-with-enhanced-capabilities-leading-to-alphv-blackcat-ransomware

[12] https://www.esentire.com/blog/the-notorious-alphv-blackcat-ransomware-gang-is-attacking-corporations-and-public-entities-using-google-ads-laced-with-malware-warns-esentire

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
Specialist Security Researcher

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April 17, 2026

Why Behavioral AI Is the Answer to Mythos

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How AI is breaking the patch-and-prevent security model

The business world was upended last week by the news that Anthropic has developed a powerful new AI model, Claude Mythos, which poses unprecedented risk because of its ability to expose flaws in IT systems.  

Whether it’s Mythos or OpenAI’s GPT-5.4-Cyber, which was just announced on Tuesday, supercharged AI models in the hands of hackers will allow them to carry out attacks at machine speed, much faster than most businesses can stop them.  

This news underscores a stark reality for all leaders: Patching holes alone is not a sufficient control against modern cyberattacks. You must assume that your software is already vulnerable right now. And while LLMs are very good at spotting vulnerabilities, they’re pretty bad at reliably patching them.

Project Glasswing members say it could take months or years for patches to be applied. While that work is done, enterprises must be protected against Zero-Day attacks, or security holes that are still undiscovered.  

Most cybersecurity strategies today are built like a daily multivitamin: broad, preventative, and designed to keep the system generally healthy over time. Patch regularly. Update software. Reduce known vulnerabilities. It’s necessary, disciplined, and foundational. But it’s also built for a world where the risks are well known and defined, cycles are predictable, and exposure unfolds at a manageable pace.

What happens when that model no longer holds?

The AI cyber advantage: Behavioral AI

The vulnerabilities exposed by AI systems like Mythos aren’t the well-understood risks your “multivitamin” was designed to address. They are transient, fast-emerging entry points that exist just long enough to be exploited.

In that environment, prevention alone isn’t enough. You don’t need more vitamins—you need a painkiller. The future of cybersecurity won’t be defined by how well you maintain baseline health. It will be defined by how quickly you respond when something breaks and every second counts.

That’s why behavioral AI gives businesses a durable cyber advantage. Rather than trying to figure out what the attacker looks like, it learns what “normal” looks like across the digital ecosystem of each individual business.  

That’s exactly how behavioral AI works. It understands the self, or what's normal for the organization, and then it can spot deviations in from normal that are actually early-stage attacks.

The Darktrace approach to cybersecurity

At Darktrace, we’ve been defending our 10,000 customers using behavioral AI cybersecurity developed in our AI Research Centre in Cambridge, U.K.

Darktrace was built on the understanding that attacks do not arrive neatly labeled, and that the most damaging threats often emerge before signatures, indicators, or public disclosures can catch up.  

Our AI algorithms learn in real time from your personalized business data to learn what’s normal for every person and every asset, and the flows of data within your organization. By continuously understanding “normal” across your entire digital ecosystem, Darktrace identifies and contains threats emerging from unknown vulnerabilities and compromised supply chain dependencies, autonomously curtailing attacks at machine speed.  

Security for novel threats

Darktrace is built for a world where AI is not just accelerating attacks, but fundamentally reshaping how they originate. What makes our AI so unique is that it's proven time and again to identify cyber threats before public vulnerability disclosures, such as critical Ivanti vulnerabilities in 2025 and SAP NetWeaver exploitations tied to nation-state threat actors.  

As AI reshapes how vulnerabilities are found and exploited, cybersecurity must be anchored in something more durable than a list of known flaws. It requires a real-time understanding of the business itself: what belongs, what does not, and what must be stopped immediately.

What leaders should do right now

The leadership priority must shift accordingly.

First, stop treating unknown vulnerabilities as an edge case. AI‑driven discovery makes them the norm. Security programs built primarily around known flaws, signatures, and threat intelligence will always lag behind an attacker that is operating in real time.

Second, insist on an understanding of what is actually normal across the business. When threats are novel, labels are useless. The earliest and most reliable signal of danger is abnormal behavior—systems, users, or data flows that suddenly depart from what is expected. If you cannot see that deviation as it happens, you are effectively blind during the most critical window.

Finally, assume that the next serious incident will occur before remediation guidance is available. Ask what happens in those first minutes and hours. The organizations that maintain resilience are not the ones waiting for disclosure cycles to catch up—they are the ones that can autonomously identify and contain emerging threats as they unfold.

This is the reality of cybersecurity in an AI‑shaped world. Patching and prevention remain important foundations, but the advantage now belongs to those who can respond instantly when the unpredictable occurs.

Behavioral AI is security designed not just for known threats, but for the ones that AI will discover next.

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Ed Jennings
President and CEO

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April 17, 2026

Inside ZionSiphon: Darktrace’s Analysis of OT Malware Targeting Israeli Water Systems

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What is ZionSiphon?

Darktrace recently analyzed a malware sample, which identifies itself as ZionSiphon. This sample combines several familiar host-based capabilities, including privilege escalation, persistence, and removable-media propagation, with targeting logic themed around water treatment and desalination environments.

This blog details Darktrace’s investigation of ZionSiphon, focusing on how the malware identifies targets, establishes persistence, attempts to tamper with local configuration files, and scans for Operational Technology (OT)-relevant services on the local subnet. The analysis also assesses what the code suggests about the threat actor’s intended objectives and highlights where the implementation appears incomplete.

Function “ZionSiphon()” used by the malware author.
Figure 1: Function “ZionSiphon()” used by the malware author.

Targets and motivations

Israel-Focused Targeting and Messaging

The clearest indicators of intent in this sample are its hardcoded Israel-focused targeting checks and the strong political messaging found in some strings in the malware’s binary.

In the class initializer, the malware defines a set of IPv4 ranges, including “2.52.0.0-2.55.255.255”, “79.176.0.0-79.191.255.255”, and “212.150.0.0-212.150.255.255”, indicating that the author intended to restrict execution to a narrow range of addresses. All of the specified IP blocks are geographically located within Israel.

The malware obfuscates the IP ranges by encoding them in Base64.
Figure 2: The malware obfuscates the IP ranges by encoding them in Base64.

The ideological motivations behind this malware are also seemingly evident in two Base64-encoded strings embedded in the binary. The first (shown in Figure 1) is:

Netanyahu = SW4gc3VwcG9ydCBvZiBvdXIgYnJvdGhlcnMgaW4gSXJhbiwgUGFsZXN0aW5lLCBhbmQgWWVtZW4gYWdhaW5zdCBaaW9uaXN0IGFnZ3Jlc3Npb24uIEkgYW0gIjB4SUNTIi4=“, which decodes to “In support of our brothers in Iran, Palestine, and Yemen against Zionist aggression. I am "0xICS".

The second string, “Dimona = UG9pc29uaW5nIHRoZSBwb3B1bGF0aW9uIG9mIFRlbCBBdml2IGFuZCBIYWlmYQo=“, decodes to “Poisoning the population of Tel Aviv and Haifa”.  These strings do not appear to be used by the malware for any operational purpose, but they do offer an indication of the attacker’s motivations. Dimona, referenced in the second string, is an Israeli city in the Negev desert, primarily known as the site of the Shimon Peres Negev Nuclear Research Center.

The Dimona string as it appears in the decompiled malware, with the Base64-decoded text.
Figure 3: The Dimona string as it appears in the decompiled malware, with the Base64-decoded text.

The hardcoded IP ranges and propaganda‑style text suggest politically motivated intent, with Israel appearing to be a likely target.

Water and desalination-themed targeting?

The malware also includes Israel-linked strings in its target list, including “Mekorot, “Sorek”, “Hadera”, “Ashdod”, “Palmachim”, and “Shafdan”. All of the strings correspond to components of Israel’s national water infrastructure: Mekorot is Israel’s national water company responsible for managing the country’s water system, including major desalination and wastewater projects. Sorek, Hadera, Ashdod, and Palmachim are four of Israel’s five major seawater desalination plants, each producing tens of millions of cubic meters of drinking water annually. Shafdan is the country’s central wastewater treatment and reclamation facility. Their inclusion in ZionSiphon’s targeting list suggests an interest in infrastructure linked to Israel’s water sector.

Strings in the target list, all related to Israel and water treatment.
Figure 4: Strings in the target list, all related to Israel and water treatment.

Beyond geographic targeting, the sample contains a second layer of environment-specific checks aimed at water treatment and desalination systems. In the function ”IsDamDesalinationPlant()”, the malware first inspects running process names for strings such as “DesalPLC”, “ROController”, “SchneiderRO”, “DamRO”, “ReverseOsmosis”, “WaterGenix”, “RO_Pump”, “ChlorineCtrl”, “WaterPLC”, “SeaWaterRO”, “BrineControl”, “OsmosisPLC”, “DesalMonitor”, “RO_Filter”, “ChlorineDose”, “RO_Membrane”, “DesalFlow”, “WaterTreat”, and “SalinityCtrl”. These strings are directly related to desalination, reverse osmosis, chlorine handling, and plant control components typically seen in the water treatment industry.

The filesystem checks reinforce this focus. The code looks for directories such as “C:\Program Files\Desalination”, “C:\Program Files\Schneider Electric\Desal”, “C:\Program Files\IDE Technologies”, “C:\Program Files\Water Treatment”, “C:\Program Files\RO Systems”, “C:\Program Files\DesalTech”, “C:\Program Files\Aqua Solutions”, and “C:\Program Files\Hydro Systems”, as well as files including “C:\DesalConfig.ini”, “C:\ROConfig.ini”, “C:\DesalSettings.conf”, “C:\Program Files\Desalination\system.cfg”, “C:\WaterTreatment.ini”, “C:\ChlorineControl.dat”, “C:\RO_PumpSettings.ini”, and “C:\SalinityControl.ini.”

Malware Analysis

Privilege Escalation

The “RunAsAdmin” function from the malware sample.
Figure 5: The “RunAsAdmin” function from the malware sample.


The malware’s first major action is to check whether it is running with administrative rights. The “RunAsAdmin()” function calls “IsElevated()”, which retrieves the current Windows identity and checks whether it belongs to the local Administrators group. If the process is already elevated, execution proceeds normally.

The “IsElevated” function as seen in the sample.
Figure 6: The “IsElevated” function as seen in the sample.


If not, the code waits on the named mutex and launches “powershell.exe” with the argument “Start-Process -FilePath <current executable> -Verb RunAs”, after which it waits for that process to finish and then exits.

Persistence and stealth installation

Registry key creation.
Figure 7: Registry key creation.

Persistence is handled by “s1()”. This routine opens “HKCU\Software\Microsoft\Windows\CurrentVersion\Run”, retrieves the current process path, and compares it to “stealthPath”. If the current file is not already running from that location, it copies itself to the stealth path and sets the copied file’s attributes to “hidden”.

The code then creates a “Run” value named “SystemHealthCheck” pointing to the stealth path. Because “stealthPath” is built from “LocalApplicationData” and the hardcoded filename “svchost.exe”, the result is a user-level persistence mechanism that disguises the payload under a familiar Windows process name. The combination of a hidden file and a plausible-sounding autorun value suggests an intent to blend into ordinary Windows artifacts rather than relying on more complex persistence methods.

Target determination

The malware’s targeting determination is divided between “IsTargetCountry()” and “IsDamDesalinationPlant()”. The “IsTargetCountry()” function retrieves the local IPv4 address, converts it to a numeric value, and compares it against each of the hardcoded ranges stored in “ipRanges”. Only if the address falls within one of these ranges does the code move on to next string-comparison step, which ultimately determines whether the country check succeeded.

The main target validation function.
Figure 8: The main target validation function.
 The “IsTargetCountry” function.
Figure 9 : The “IsTargetCountry” function.


IsDamDesalinationPlant()” then assesses whether the host resembles a relevant OT environment. It first scans running process names for the hardcoded strings previously mentioned, followed by checks for the presence of any of the hardcoded directories or files. The intended logic is clear: the payload activates only when both a geographic condition and an environment specific condition related to desalination or water treatment are met.

Figure. 10: An excerpt of the list of strings used in the “IsDamDesalinationPlant” function

Why this version appears dysfunctional

Although the file contains sabotage, scanning, and propagation functions, the current sample appears unable to satisfy its own target-country checking function even when the reported IP falls within the specified ranges. In the static constructor, every “ipRanges” entry is associated with the same decoded string, “Nqvbdk”, derived from “TnF2YmRr”. Later, “IsTargetCountry()” (shown in Figure 8) compares that stored value against “EncryptDecrypt("Israel", 5)”.

The “EncryptDecrypt” function
Figure 11: The “EncryptDecrypt” function

As implemented, “EncryptDecrypt("Israel", 5)” does not produce “Nqvbdk”, it produces a different string. This function seems to be a basic XOR encode/decode routine, XORing the string “Israel” with value of 5. Because the resulting output does not match “Nqvbdk” the comparison always fails, even when the host IP falls within one of the specified ranges. As a result, this build appears to consistently determine that the device is not a valid target. This behavior suggests that the version is either intentionally disabled, incorrectly configured, or left in an unfinished state. In fact, there is no XOR key that would transform “Israel” into “Nqvbdk” using this function.

Self-destruct function

The “SelfDestruct” function
Figure 12: The “SelfDestruct” function

If IsTargetCountry() returns false, the malware invokes “SelfDestruct()”. This routine removes the SystemHealthCheck value from “HKCU\Software\Microsoft\Windows\CurrentVersion\Run”, writes a log file to “%TEMP%\target_verify.log” containing the message “Target not matched. Operation restricted to IL ranges. Self-destruct initiated.” and creates the batch file “%TEMP%\delete.bat”. This file repeatedly attempts to delete the malware’s executable, before deleting itself.

Local configuration file tampering

If the malware determines that the system it is on is a valid target, its first action is local file tampering. “IncreaseChlorineLevel()” checks a hardcoded list of configuration files associated with desalination, reverse osmosis, chlorine control, and water treatment OT/Industrial Control Systems (ICS).  As soon as it finds any one of these file present, it appends a fixed block of text to it and returns immediately.

The block of text appended to relevant configuration files.
Figure 13: The block of text appended to relevant configuration files.

The appended block of text contains the following entries: “Chlorine_Dose=10”, “Chlorine_Pump=ON”, “Chlorine_Flow=MAX”, “Chlorine_Valve=OPEN”, and “RO_Pressure=80”. Only if none of the hardcoded files are found does the malware proceed to its network-based OT discovery logic.

OT discovery and protocol logic

This section of the code attempts to identify devices on the local subnet, assign each one a protocol label, and then attempt protocol-specific communication. While the overall structure is consistent across protocols, the implementation quality varies significantly.

Figure 14: The ICS scanning function.

The discovery routine, “UZJctUZJctUZJct()”, obtains the local IPv4 address, reduces it to a /24 prefix, and iterates across hosts 1 through 255. For each host, it probes ports 502 (Modbus), 20000 (DNP3), and 102 (S7comm), which the code labels as “Modbus”, “DNP3”, and “S7” respectively if a valid response is received on the relevant port.

The probing is performed in parallel. For every “ip:port” combination, the code creates a task and attempts a TCP connection. The “100 ms” value in the probe routine is a per-connection timeout on “WaitOne(100, ...)”, rather than a delay between hosts or protocols. In practice, this results in a burst of short-lived OT-focused connection attempts across the local subnet.

Protocol validation and device classification

When a connection succeeds, the malware does not stop at the open port. It records the endpoint as an “ICSDevice” with an IP address, port, and protocol label. It then performs a second-stage validation by writing a NULL byte to the remote stream and reading the response that comes back.

For Modbus, the malware checks whether the first byte of the reply is between 1 and 255, for DNP3, it checks whether the first two bytes are “05 64”, and for S7comm, it checks whether the first byte is “03”. These checks are not advanced parsers, but they do show that the author understood the protocols well enough to add lightweight confirmation before sending follow-on data.

 The Modbus read request along with unfinished code for additional protocols.
Figure 15: The Modbus read request along with unfinished code for additional protocols.  

The most developed OT-specific logic is the Modbus-oriented path. In the function “IncreaseChlorineLevel(string targetIP, int targetPort, string parameter)”, the malware connects to the target and sends “01 03 00 00 00 0A”. It then reads the response and parses register values in pairs. The code then uses some basic logic to select a register index: for “Chlorine_Dose”, it looks for values greater than 0 and less than 1000; for “Turbine_Speed”, it looks for values greater than 100.

The Modbus command observed in the sample (01 03 00 00 00 0A) is a Read Holding Registers request. The first byte (0x01) represents the unit identifier, which in traditional Modbus RTU specifies the addressed slave device; in Modbus TCP, however, this value is often ignored or used only for gateway routing because device addressing is handled at the IP/TCP layer.

The second byte (0x03) is the Modbus function code indicating a Read Holding Registers request. The following two bytes (0x00 0x00) specify the starting register address, indicating that the read begins at address zero. The final two bytes (0x00 0A) define the number of registers to read, in this case ten consecutive registers. Taken together, the command requests the contents of the first ten holding registers from the target device and represents a valid, commonly used Modbus operation.

If a plausible register is found, the malware builds a six-byte Modbus write using function code “6” (Write)” and sets the value to 100 for “Chlorine_Dose”, or 0 for any other parameter. If no plausible register is found, it falls back to using hardcoded write frames. In the main malware path, however, the code only calls this function with “Chlorine_Dose".

If none of the ten registers meets the expected criteria, the malware does not abandon the operation. Instead, it defaults to a set of hardcoded Modbus write frames that specify predetermined register addresses and values. This behavior suggests that the attacker had only partial knowledge of the target environment. The initial register-scanning logic appears to be an attempt at dynamic discovery, while the fallback logic ensures that a write operation is still attempted even if that discovery fails.

Incomplete DNP3 and S7comm Logic

The DNP3 and S7comm branches appear much less complete. In “GetCommand()”, the DNP3 path returns the fixed byte sequence “05 64 0A 0C 01 02”, while the S7comm path returns “03 00 00 13 0E 00”. Neither sequence resembles a fully formed command for the respective protocol.

In the case of the S7comm section, the five byte‑ sequence found in the malware sample (05 00 1C 22 1E) most closely matches the beginning of an S7comm parameter block, specifically the header of a “WriteVar (0x05)” request, which is the S7comm equivalent of a Modbus register write operation. In the S7comm protocol, the first byte of a parameter block identifies the function code,  but the remaining bytes in this case do not form a valid item definition. A vaild S7 WriteVar parameter requires at least one item and a full 11-byte variable-specification structure. By comparison this 5‑ byte array is far too short to be a complete or usable command.

The zero item count (0x00) and the trailing three bytes appear to be either uninitialized data or the beginning of an incomplete address field. Together, these details suggest that the attacker likely intended to implement S7 WriteVar functionality, like the Modbus function, but left this portion of the code unfinished.

The DNP3 branch of the malware also appears to be only partially implemented. The byte sequence returned by the DNP3 path (05 64 0A 0C 01 02) begins with the correct two‑byte DNP3 link‑layer sync header (0x05 0x64) and includes additional bytes that resemble the early portion of a link‑layer header. However, the sequence is far too short to constitute a valid DNP3 frame. It lacks the required destination and source address fields, the 16‑bit CRC blocks, and any application‑layer payload in which DNP3 function code would reside. As a result, this fragment does not represent a meaningful DNP3 command.

The incomplete S7 and DNP3 fragments suggest that these protocol branches were still in a developmental or experimental state when the malware was compiled. Both contain protocol‑accurate prefixes, indicating an intent to implement multi‑protocol OT capabilities, however for reasons unknow, these sections were not fully implemented or could not be completed prior to deployment.

USB Propagation

The malware also includes a removable-media propagation mechanism. The “sdfsdfsfsdfsdfqw()” function scans for drives, selects those identified as removable, and copies the hidden payload to each one as “svchost.exe” if it is not already present. The copied executable is marked with the “Hidden” and “System” attributes to reduce visibility.

The malware then calls “CreateUSBShortcut()”, which uses “WScript.Shell” to create .lnk files for each file in the removable drive root. Each shortcut’s TargetPath is set to the hidden malware copy, the icon is set to “shell32.dll, 4” (this is the windows genericfile icon), and the original file is hidden. Were a victim to click this “file,” they would unknowingly run the malware.

Figure 14:The creation of the shortcut on the USB device.

Key Insights

ZionSiphon represents a notable, though incomplete, attempt to build malware capable of malicious interaction with OT systems targeting water treatment and desalination environments.

While many of ZionSiphon’s individual capabilities align with patterns commonly found in commodity malware, the combination of politically motivated messaging, Israel‑specific IP targeting, and an explicit focus on desalination‑related processes distinguishes it from purely opportunistic threats. The inclusion of Modbus sabotage logic, filesystem tampering targeting chlorine and pressure control, and subnet‑wide ICS scanning demonstrates a clear intent to interact directly with industrial processes controllers and to cause significant damage and potential harm, rather than merely disrupt IT endpoints.

At the same time, numerous implementation flaws, most notably the dysfunctional country‑validation logic and the placeholder DNP3 and S7comm components, suggest that analyzed version is either a development build, a prematurely deployed sample, or intentionally defanged for testing purposes. Despite these limitations, the overall structure of the code likely indicates a threat actor experimenting with multi‑protocol OT manipulation, persistence within operational networks, and removable‑media propagation techniques reminiscent of earlier ICS‑targeting campaigns.

Even in its unfinished state, ZionSiphon underscores a growing trend in which threat actors are increasingly experimenting with OT‑oriented malware and applying it to the targeting of critical infrastructure. Continued monitoring, rapid anomaly detection, and cross‑visibility between IT and OT environments remain essential for identifying early‑stage threats like this before they evolve into operationally viable attacks.

Credit to Calum Hall (Cyber Analyst)
Edited by Ryan Traill (Content Manager)

References

1.        https://www.virustotal.com/gui/file/07c3bbe60d47240df7152f72beb98ea373d9600946860bad12f7bc617a5d6f5f/details

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