Blog
/
Email
/
July 6, 2023

How Darktrace Foiled QR Code Phishing

Explore Darktrace's successful detection of QR code phishing. Understand the methods used to thwart these sophisticated cyber threats.
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
Alexandra Sentenac
Cyber Analyst
Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
06
Jul 2023

What is a QR Code?

Invented by a Japanese company in 1994 to label automobile parts, Quick Response codes, best known as QR codes, are rapidly becoming ubiquitous everywhere in the world. Their design, inspired by the board and black and white pieces of the game of Go, permits the storage of more information than regular barcodes and to access that information more quickly. The COVID-19 pandemic contributed to their increased popularity as it conveniently replaced physical media of all types for the purpose of content sharing. It is now common to see them in restaurant menus, plane tickets, advertisements and even in stickers containing minimal to no text pasted on lamp posts and other surfaces, enticing passers-by to scan its content. 

QR Code Phishing Attacks (Quishing)

Recently, threat actors have been identified using QR codes too to embed malicious URLs leading the unsuspecting user to compromised websites containing malware or designed to harvest credentials. In the past month, Darktrace has observed an increase in the number of phishing emails leveraging malicious QR codes for malware distribution and/or credential harvesting, a new form of social engineering attack labelled “Quishing” (i.e., QR code phishing).

Between June 13 and June 22, 2023, Darktrace protected a tech company against one such Quishing attack when five of its senior employees were sent malicious emails impersonating the company’s IT department. The emails contained a QR code that led to a login page designed to harvest the credentials of these senior staff members. Fortunately for the customer, Darktrace / EMAIL thwarted this phishing campaign in the first instance and the emails never reached the employee inboxes. 

Trends in Quishing Attacks

The Darktrace/Email team have noticed a recent and rapid increase in QR code abuse, suggesting that it is a growing tactic used by threat actors to deliver malicious payload links. This trend has also been observed by other security solutions [1] [2] [3] [4]. The Darktrace/Email team has identified malicious emails abusing QR codes in multiple ways. Examples include embedded image links which load a QR code and QR code images being delivered as attachments, such as those explored in this case study. Darktrace/Email is continually refining its detection of malicious QR codes and QR code extraction capabilities so that it can detect and block them regardless of their size and location within the email.   

Quishing Attack Overview

The attack consisted of five emails, each sent from different sender and envelope addresses, displayed common points between them. The emails all conveyed a sense of urgency, either via the use of words such as “urgent”, “now”, “required” or “important” in the subject field or by marking the email as high priority, thus making the recipient believe the message is pressing and requires immediate attention. 

Additionally, the subject of three of the emails directly referred to two factor authentication (2FA) enabling or QR code activation. Another particularity of these emails was that three of them attempted to impersonate the internal IT team of the company by inserting the company domain alongside strings, such as “it-desk” and “IT”, into the personal field of the emails. Email header fields like this are often abused by attackers to trick users by pretending to be an internal department or senior employee, thus avoiding more thorough validation checks. Both instilling a sense of urgency and including a known domain or name in the personal field are techniques that help draw attention to the email and maximize the chances that it is opened and engaged by the recipient. 

However, threat actors also need to make sure that the emails actually reach the intended inboxes, and this can be done in several ways. In this case, several tactics were employed. Two of the five emails were sent from legitimate sender addresses that successfully passed SPF validation, suggesting they were sent from compromised accounts. SPF is a standard email authentication method that tells the receiving email servers whether emails have been sent from authorized servers for a given domain. Without SPF validation, emails are more likely to be categorized as spam and be sent to the junk folder as they do not come from authorized sources.

Another of the malicious emails, which also passed SPF checks, used a health care facility company domain in the header-from address field but was actually sent from a different domain (i.e., envelope domain), which lowers the value of the SPF authentication. However, the envelope domain observed in this instance belonged to a company recently acquired by the tech company targeted by the campaign.

This shows a high level of targeting from the attackers, who likely hoped that this detail would make the email more familiar and less suspicious. In another case, the sender domain (i.e., banes-gn[.]com) had been created just 6 days prior, thus lowering the chances of there being open-source intelligence (OSINT) available on the domain. This reduces the chances of the email being detected by traditional email security solutions relying on signatures and known-bad lists.

Darktrace Detects Quishing Attack

Despite its novelty, the domain was detected and assessed as highly suspicious by Darktrace. Darktrace/Email was able to recognize all of the emails as spoofing and impersonation attempts and applied the relevant tags to them, namely “IT Impersonation” and “Fake Account Alert”, depending on the choice of personal field and subject. The senders of the five emails had no prior history or association with the recipient nor the company as no previous correspondence had been observed between the sender and recipient. The tags applied informed on the likely intent and nature of the suspicious indicators present in the email, as shown in Figure 1. 

Darktrace/Email UI
Figure 1: Email log overview page, displaying important information clearly and concisely. 

Quishing Attack Tactics

Minimal Plain Text

Another characteristic shared by these emails was that they had little to no text included in the body of the email and they did not contain a plain text portion, as shown in Figure 2. For most normal emails sent by email clients and most automated programs, an email will contain an HTML component and a text component, in addition to any potential attachments present. All the emails had one image attachment, suggesting the bulk of the message was displayed in the image rather than the email body. This hinders textual analysis and filtering of the email for suspicious keywords and language that could reveal its phishing intent. Additionally, the emails were well-formatted and used the logo of the well-known corporation Microsoft, suggesting some level of technical ability on the part of the attackers. 

Figure 2: Email body properties giving additional insights into the content of the email. 

Attachment and link payloads

The threat actors employed some particularly innovative and novel techniques with regards to the attachments and link payloads within these emails. As previously stated, all emails contained an image attachment and one or two links. Figure 3 shows that Darktrace/Email detected that the malicious links present in these emails were located in the attachments, rather than the body of the email. This is a technique often employed by threat actors to bypass link analysis by security gateways. Darktrace/Email was also able to detect this link as a QR code link, as shown in Figure 4.

Figure 3: Further properties and metrics regarding the location of the link within the email. 
Figure 4: Darktrace / EMAIL analyzes multiple metrics and properties related to links, some of which are detailed here. 

The majority of the text, as well as the malicious payload, was contained within the image attachment, which for one of the emails looked like this: 

example of quishing email
Figure 5: Redacted screenshot of the image payload contained in one of the emails. 

Convincing Appearance

As shown, the recipient is asked to setup 2FA authentication for their account within two days if they don’t want to be locked out. The visual formatting of the image, which includes a corporate logo and Privacy Statement and Acceptable Use Policy notices, is well balanced and convincing. The payload, in this case the QR code containing a malicious link, is positioned in the centre so as to draw attention and encourage the user to scan and click. This is a type of email employees are increasingly accustomed to receiving in order to log into corporate networks and applications. Therefore, recipients of such malicious emails might assume represents expected business activity and thus engage with the QR code without questioning it, especially if the email is claiming to be from the IT department.  

Malicious Redirection

Two of the Quishing emails contained links to legitimate file storage and sharing solutions Amazon Web Services (AWS) and and InterPlanetary File System (IPFS), whose domains are less likely to be blocked by traditional security solutions. Additionally, the AWS domain link contained a redirect to a different domain that has been flagged as malicious by multiple security vendors [5]. Malicious redirection was observed in four of the five emails, initially from well-known and benign services’ domains such as bing[.]com and login[.]microsoftonline[.]com. This technique allows attackers to hide the real destination of the link from the user and increase the likelihood that the link is clicked. In two of the emails, the redirect domain had only recently been registered, and in one case, the redirect domain observed was hosted on the new .zip top level domain (i.e., docusafe[.]zip). The domain name suggests it is attempting to masquerade as a compressed file containing important documentation. As seen in Figure 6, a new Darktrace/Email feature allows customers to safely view the final destination of the link, which in this case was a seemingly fake Microsoft login page which could be used to harvest corporate credentials.

Figure 6: Safe preview available from the Darktrace/Email Console showing the destination webpage of one of the redirect links observed.

Gathering Account Credentials

Given the nature of the landing page, it is highly likely that this phishing campaign had the objective of stealing the recipients’ credentials, as further indicated by the presence of the recipients’ email addresses in the links. Additionally, these emails were sent to senior employees, likely in an attempt to gather high value credentials to use in future attacks against the company. Had they succeeded, this would have represented a serious security incident, especially considering that 61% of attacks in 2023 involved stolen or hacked credentials according to Verizon’s 2023 data breach investigations report [6]. However, these emails received the highest possible anomaly score (100%) and were held by Darktrace/Email, thus ensuring that their intended recipients were never exposed to them. 

Looking at the indicators of compromise (IoCs) identified in this campaign, it appears that several of the IPs associated with the link payloads have been involved in previous phishing campaigns. Exploring the relations tab for these IPs in Virus Total, some of the communicating files appear to be .eml files and others have generic filenames including strings such as “invoice” “remittance details” “statement” “voice memo”, suggesting they have been involved in other phishing campaigns seemingly related to payment solicitation and other fraud attempts.

Figure 7: Virus Total’s relations tab for the IP 209.94.90[.]1 showing files communicating with the IP. 

Conclusion

Even though the authors of this Quishing campaign used all the tricks in the book to ensure that their emails would arrive unactioned by security tools to the targeted high value recipients’ inboxes, Darktrace/Email was able to immediately recognize the phishing attempts for what they were and block the emails from reaching their destination. 

This campaign used both classic and novel tactics, techniques, and procedures, but ultimately were detected and thwarted by Darktrace/Email. It is yet another example of the increasing attack sophistication mentioned in a previous Darktrace blog [7], wherein the attack landscape is moving from low-sophistication, low-impact, and generic phishing tactics to more targeted, sophisticated and higher impact attacks. Darktrace/Email does not rely on historical data nor known-bad lists and is best positioned to protect organizations from these highly targeted and sophisticated attacks.

References

[1] https://www.infosecurity-magazine.com/opinions/qr-codes-vulnerability-cybercrimes/ 

[2] https://www.helpnetsecurity.com/2023/03/21/qr-scan-scams/ 

[3] https://www.techtarget.com/searchsecurity/feature/Quishing-on-the-rise-How-to-prevent-QR-code-phishing 

[4] https://businessplus.ie/tech/qr-code-phishing-hp/ 

[5] https://www.virustotal.com/gui/domain/fistulacure.com

[6] https://www.verizon.com/business/en-gb/resources/reports/dbir/ ; https://www.verizon.com/business/en-gb/resources/reports/dbir/

[7] https://darktrace.com/blog/shifting-email-conversation 

Darktrace Model Detections 

Association models

No Sender or Content Association

New Sender

Unknown Sender

Low Sender Association

Link models

Focused Link to File Storage

Focused Rare Classified Links

New Unknown Hidden Redirect

High Risk Link + Low Sender Association

Watched Link Type

High Classified Link

File Storage From New

Hidden Link To File Storage

New Correspondent Classified Link

New Unknown Redirect

Rare Hidden Classified Link

Rare Hidden Link

Link To File Storage

Link To File Storage and Unknown Sender

Open Redirect

Unknown Sender Isolated Rare Link

Visually Prominent Link

Visually Prominent Link Unexpected For Sender

Low Link Association

Low Link Association and Unknown Sender

Spoof models

Fake Support Style

External Domain Similarities

Basic Known Entity Similarities

Unusual models

Urgent Request Banner

Urgent Request Banner + Basic Suspicious Sender

Very Young Header Domain

Young Header Domain

Unknown User Tracking

Unrelated Personal Name Address

Unrelated Personal Name Address + Freemail

Unusual Header TLD

Unusual Connection From Unknown

Unbroken Personal

Proximity models

Spam + Unknown Sender

Spam

Spam models

Unlikely Freemail Correspondence

Unlikely Freemail Personalization

General Indicators models

Incoming Mail Security Warning Message

Darktrace Model Tags

Credential Harvesting

Internal IT Impersonation

Multistage payload

Lookalike Domain

Phishing Link

Email Account Takeover

Fake Account Alert

Low Mailing History

No Association

Spoofing Indicators

Unknown Correspondent

VIP

Freemail

IoC - Type - Description & Confidence

fistulacure[.]com

domain

C2 Infrastructure

docusafe[.]zip

domain

Possible C2 Infrastructure

mwmailtec[.]com

domain

Possible C2 Infrastructure

czeromedia[.]com

domain

Possible C2 Infrastructure

192.40.165[.]109

IP address

Probable C2 Infrastructure

209.94.90[.]1

IP address

C2 Infrastructure

52.61.107[.]58

IP address

Possible C2 Infrastructure

40.126.32[.]133

IP address

Possible C2 Infrastructure

211.63.158[.]157

IP address

Possible C2 Infrastructure

119.9.27[.]129

IP address

Possible C2 Infrastructure

184.25.204[.]33

IP address

Possible C2 Infrastructure

40.107.8[.]107

IP address

Probable C2 Infrastructure

40.107.212[.]111

IP address

Possible Infrastructure

27.86.113[.]2

IP address

Possible C2 Infrastructure

192.40.191[.]19

IP address

Possible C2 Infrastructure

157.205.202[.]217

IP address

Possible C2 Infrastructure

a31f1f6063409ecebe8893e36d0048557142cbf13dbaf81af42bf14c43b12a48

SHA256 hash

Possible Malicious File

4c4fb35ab6445bf3749b9d0ab1b04f492f2bc651acb1bbf7af5f0a47502674c9

SHA256 hash

Possible Malicious File

f9c51d270091c34792b17391017a09724d9a7890737e00700dc36babeb97e252

SHA256 hash

Possible Malicious File

9f8ccfd616a8f73c69d25fd348b874d11a036b4d2b3fc7dbb99c1d6fa7413d9a

SHA256 hash

Possible Malicious File

b748894348c32d1dc5702085d70d846c6dd573296e79754df4857921e707c439

SHA256 hash

Possible Malicious File

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
Alexandra Sentenac
Cyber Analyst

More in this series

No items found.

Blog

/

/

April 17, 2026

Why Behavioral AI Is the Answer to Mythos

mythos behavioral aiDefault blog imageDefault blog image

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.

[related-resource]

Continue reading
About the author
Ed Jennings
President and CEO

Blog

/

OT

/

April 17, 2026

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

zionsiphonDefault blog imageDefault blog image

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

Continue reading
About the author
Your data. Our AI.
Elevate your network security with Darktrace AI