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
Written by
Adam Stevens
Senior Director of Product, Cloud | Darktrace
Share
09
Aug 2023
TLDR: There are too many technical vulnerabilities and there is too little organizational context for IT teams to patch effectively. Attack path modelling provides the organizational context, allowing security teams to prioritize vulnerabilities. The result is a system where CVEs can be parsed in, organizational context added, and attack paths considered, ultimately providing a prioritized list of vulnerabilities that need to be patched.
Figure 1: The Darktrace user interface presents risk-prioritized vulnerabilities
This blog post explains how Darktrace addresses the challenge of vulnerability prioritization. Most of the industry focusses on understanding the technical impact of vulnerabilities globally (‘How could this CVE generally be exploited? Is it difficult to exploit? Are there pre-requisites to exploitation? …’), without taking local context of a vulnerability into account. We’ll discuss here how we create that local context through attack path modelling and map it to technical vulnerability information. The result is a stunningly powerful way to prioritize vulnerabilities.
We will explore:
1) The challenge and traditional approach to vulnerability prioritization 2) Creating local context through machine learning and attack path modelling 3) Examining the result – contextualized, vulnerability prioritization
The Challenge
Anyone dealing with Threat and Vulnerability Management (TVM) knows this situation:
You have a vulnerability scanning report with dozens or hundreds of pages. There is a long list of ‘critical’ vulnerabilities. How do you start prioritizing these vulnerabilities, assuming your goal is reducing the most risk?
Sometimes the challenge is even more specific – you might have 100 servers with the same critical vulnerability present (e.g. MoveIT). But which one should you patch first, as all of those have the same technical vulnerability priority (‘critical’)? Which one will achieve the biggest risk reduction (critical asset e.g.)? Which one will be almost meaningless to patch (asset with no business impact e.g.) and thus just a time-sink for the patch and IT team?
There have been recent improvements upon flat CVE-scoring for vulnerability prioritization by adding threat-intelligence about exploitability of vulnerabilities into the mix. This is great, examples of that additional information are Exploit Prediction Scoring System (EPSS) and Known Exploited Vulnerabilities Catalogue (KEV).
With CVE and CVSS scores we have the theoretical technical impact of vulnerabilities, and with EPSS and KEV we have information about the likelihood of exploitation of vulnerabilities. That’s a step forward, but still doesn’t give us any local context. Now we know even more about the global and generic technical risk of a vulnerability, but we still lack the local impact on the organization.
Let’s add that missing link via machine learning and attack path modelling.
Adding Attack Path Modelling for Local Context
To prioritize technical vulnerabilities, we need to know as much as we can about the asset on which the vulnerability is present in the context of the local organization. Is it a crown jewel? Is it a choke point? Does it sit on a critical attack path? Is it a dead end, never used and has no business relevance? Does it have organizational priority? Is the asset used by VIP users, as part of a core business or IT process? Does it share identities with elevated credentials? Is the human user on the device susceptible to social engineering?
Those are just a few typical questions when trying to establish local context of an asset. Knowing more about the threat landscape, exploitability, or technical information of a CVE won’t help answer any of the above questions. Gathering, evaluating, maintaining, and using this local context for vulnerability prioritization is the hard part. This local context often resides informally in the head of the TVM or IT team member, having been assembled by having been at the organization for a long time, ‘knowing’ systems, applications and identities in question and talking to asset and application owners if time permits. This does unfortunately not scale, is time-consuming and heavily dependent on individuals.
Understanding all attack paths for an organization provides this local context programmatically.
We discover those attack paths, and these are bespoke for each organization through Darktrace PREVENT™, using the following method (simplified):
1) Build an adaptive model of the local business. Collect, combine, and analyze (using machine learning and non-machine learning techniques) data from various data domains:
a. Network, Cloud, IT, and OT data (network-based attack paths, communication patterns, peer-groups, choke-points, …). Natively collected by Darktrace technology.
b. Email data (social engineering attack paths, phishing susceptibility, external exposure, security awareness level, …). Natively collected by Darktrace technology.
c. Identity data (account privileges, account groups, access levels, shared permissions, …). Collected via various integrations, e.g. Active Directory.
d. Attack surface data (internet-facing exposure, high-impact vulnerabilities, …). Natively collected by Darktrace technology.
e. SaaS information (further identity context). Natively collected by Darktrace
f. Vulnerability information (CVEs, CVSS, EPSS, KEV, …). Collected via integrations, e.g. Vulnerability Scanners or Endpoint products.
Figure 3: Darktrace PREVENT revealing each stage of an attack path
2) Understand what ‘crown jewels’ are and how to get to them. Calculate entity importance (user, technical asset), exposure levels, potential damage levels (blast radius) weakness levels, and other scores to identify most important entities and their relationships to each other (‘crown jewels’).
Various forms of machine learning and non-machine learning techniques are used to achieve this. Further details on some of the exact methods can be found here. The result is a holistic, adaptive and dynamic model of the organization that shows most important entities and how to get to them across various data domains.
The combination of local context and technical context, around the severity and likelihood of exploitation, creates the Darktrace Vulnerability Score. This enables effective risk-based prioritisation of CVE patching.
Figure 4: List of devices with the highest damage potential in the organization - local context
3) Map the attack path model of the organization to common cyber domain knowledge. We can then combine things like MITRE ATT&CK techniques with those identified connectivity patterns and attack paths – making it easy to understand which techniques, tools and procedures (TTPs) can be used to move through the organization, and how difficult it is to exploit each TTP.
Figure 5: An example attack path with associated MITRE techniques and difficulty scores for each TTP
We can now easily start prioritizing CVE patching based on actual, organizational risk and local context.
Bringing It All Together
Finally, we overlay the attack paths calculated by Darktrace with the CVEs collected from a vulnerability scanner or EDR. This can either happen as a native integration in Darktrace PREVENT, if we are already ingesting CVE data from another solution, or via CSV upload.
Figure 6: Darktrace's global CVE prioritization in action.
But you can also go further than just looking at the CVE that delivers the biggest risk reduction globally in your organization if it is patched. You can also look only at certain group of vulnerabilities, or a sub-set of devices to understand where to patch first in this reduced scope:
Figure 7: An example of the information Darktrace reveals around a CVE
This also provides the TVM team clear justification for the patch and infrastructure teams on why these vulnerabilities should be prioritized and what the positive impact will be on risk reduction.
Attack path modelling can be utilized for various other use cases, such as threat modelling and improving SOC efficiency. We’ll explore those in more depth at a later stage.
Want to explore more on using machine learning for vulnerability prioritization? Want to test it on your own data, for free? Arrange a demo today.
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.
AI-powered security for a rapidly growing grocery enterprise
By combining AI-driven detection and autonomous response, this leading grocery holding group has built a security model that delivers continuous protection, accelerates growth, and empowers a small, highly efficient team to safeguard a complex retail ecosystem.
How a Leading Bank is Prioritizing Risk Management to Power a Resilient Future
This influential southern European bank has strengthened its cyber resilience with Darktrace, unifying its risk landscape, reducing manual effort, and empowering teams to proactively prioritize and mitigate exposures with confidence.
Patch Smarter, Not Harder: Now Empowering Security Teams with Business-Aligned Threat Context Agents
This blog introduces new innovations in Darktrace / Proactive Exposure Management that bring precision and clarity to vulnerability prioritization. Learn how No-Telemetry Endpoints provide real device context without network data and how new Cost-Benefit Analysis capabilities quantify patching ROI—helping teams cut noise, act faster, and strengthen proactive risk management.
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.
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.
Inside ZionSiphon: Darktrace’s Analysis of OT Malware Targeting Israeli Water Systems
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.
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.
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.
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.
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
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.
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
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.
Figure 8: The main target validation 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)”.
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
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.
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.
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)
.avif)
