Find High-Impact Attack Paths with Darktrace / Proactive Exposure Management
Understand high-impact attack paths with Darktrace / Proactive Exposure Management. Learn from detailed use cases and improve your cybersecurity measures effectively.
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
Elliot Stocker
Product SME
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22
Feb 2023
What are the people, process, and technology assets that would do the most harm, if compromised by an attacker?
Attack path modeling provides a detailed map of all the roads that lead to an organization's crown jewels, prioritized in order of likelihood and potential impact. CISO's are increasingly looking to this kind of solution to complement their security stack because it highlights risks that are specific to this organization's structure, as well as potentially unexpected relationships between devices or users that would prove catastrophic if they were exploited.
What makes Darktrace's Attack Path Modeling solution stand out?
Data sources are varied and information from the entire digital estate is considered
Modeling is real-time and continuously re-evaluated
Output does not require expert technical knowledge to be leveraged
Valuable as a standalone for vulnerability prioritization
As a component of the Darktrace ActiveAI Security Platform, the solution provides immediate value by feeding back into detection and response engines (e.g. tag critical assets for detection) but also provides long term systemic improvements as outcomes are followed up.
Thinking like an attacker
It is anticipated that CISOs will soon move beyond just insurance and checkbox compliance, as underwriters include more and more exclusions for certain types of cyber-attacks and the limits of compliance ticking the protection box rather than bolstering operational assurance become more apparent. They will push their teams to opt for more proactive cyber security measures to maximize ROI in the face of budget cuts, shifting investment into tools and capabilities that continuously improve their cyber resilience and demonstrate cyber risk reduction.
While red teams can provide insight into where effort and resource should be most immediately applied, the exercises themselves are often costly, non-exhaustive and infrequently run.
Hackers are constantly seeking pathways, preferably those of least resistance, to compromise a system by exploiting its vulnerabilities. Attack path modeling enables security teams to look at their environment from the perspective of the attacker. In turn, this helps them eliminate attack paths progressively, reducing the options an attacker would have, should they breach the walls.
A deeper dive into Attack Path Modeling
An attack path is a visual representation of the path that an attacker takes to exploit a weakness in the system. It highlights the series of steps (attack vectors) that a threat actor might take from one of the doors into the organization (attack surface) to access valuable assets.
It is typically unusual for an attacker to have a boulevard straight down to the crown jewels. They will most likely leverage a couple of loopholes, unexpected relationships and blind spots in the security stack to piece together a path to these confidential assets. Attack path modeling can help to highlight the attack vectors that connect, to form this path to compromise.
Figure 1: The Darktrace / Proactive Exposure Management user interface.
How to model attack paths
Darktrace's proprietary Self-Learning AI models relationships, and graph theory is incorporated to understand the importance of users, documents and relationships between these.
Darktrace's Attack Path Modeling component identifies target nodes (users, accounts, devices), it then calculates the shortest paths to these target nodes and weights the results according to the likelihood of this attack path and the damage caused if the target asset was compromised. This is exactly what an attacker would do when planning an attack, albeit with a significant advantage to Darktrace's AI Engine, which has access to more information than the attacker. For the first time, defenders have the upper hand against attackers.
Avoiding siloed efforts
According to a Gartner survey, 75% of organizations are looking at consolidating security tools, not primarily because of cost, but because it helps drive cyber risk reduction. Ensuring that security efforts are part of a wider security ecosystem, rather than siloed efforts, is crucial to maximize the return on these investments.
Darktrace / Proactive Exposure Management integrates with Darktrace's detection and response to ensure that the organization's security posture is hardened, even if the team doesn't have time to eliminate the attack path.
Defensive superiority is key, and Attack Path Modeling is one way to help security teams gain back an advantage. Find out how you can test it in your own environment.
Attack Path Modeling is an objective, however, and there are a few important questions to consider when assessing the different methods of creating these models.
Are we considering all the relevant data when building my attack paths map?
Consider the case where one of your marketing executives has a close friendship with someone in your development team. How do you model that into your attack paths cartography? Attack paths encompass the full digital estate, so the attack path modeling solution should consider information from various parts, internal and external. This may include data from the Email environment, the Network, Endpoints, SaaS & Cloud, Active Directory, Vulnerability Scanners, etc.
Cross-data analysis is the only way to understand holistic attack paths.
Are we looking at the most up to date map of attack paths?
Relationships between users, devices and other sensitive assets can evolve on a daily basis, this implies attack paths evolve on a daily basis. Ensuring that the methods or solutions used update their understanding continuously and in real-time is vital if security teams want the most up to date understanding of their organization's risk posture.
To improve our security posture, how do we know which attack paths to start with?
One thing is to map the sum-total of attack paths, another is to prioritize them. Attack path modeling gives you the map but adding a risk-assessment (explored in more depth below) layer on top is how you prioritize. This is where graph theory can be very useful to identify choke points that you may want to strengthen.
Does this output yield actionable insights?
The prime objective of this solution is not simply to provide an assessment of cyber risk posture, but rather to help drive security efforts in the right direction. To that end, the output needs to be accessible to team members that may not have expert cyber skills. Lowering barriers to entry with usable insights and mitigation advice is key to successfully improve the organization's security posture.
Assessing risk to prioritize attack paths
Darktrace Attack Path Modeling (APM) is a risk-based approach to assessing cyber-attack pathways, thinking like an attacker, and probing the path of least resistance. 'Risk' in this case is defined as the product of two factors: Probability and Impact. By using this information to categorize possible attack paths in the risk matrix below, Darktrace's APM can prioritize attack paths to ensure security team efforts are spent on controlling for the most relevant risks for their organization.
Figure 2: Risk matrix for attack path prioritization
A: Defining Probability
There are two types of probability to consider:
The likelihood of one particular door being chosen by an attacker to infiltrate the organization (among the assets at the attack surface - this could be an internet-facing server, an inbox, a SaaS/Cloud account, etc). And,
The likelihood of one particular node (defined as a device or user account) being compromised next, via lateral movement.
Figure 3: Simplified example of calculating probability of lateral movement from a compromised agent to one of two servers
B: Defining Impact
Impact refers to the overall impact of an asset being compromised and unusable. In the case of an asset (e.g.: a key server), the bigger the disruption if this asset goes down, the higher the impact score. If considering a particular document, restricted access and sensitivity score of users accessing it are some of the variables used to estimate impact.
Figure 4: Diagram showing a simplified example of mapping access volume and sensitivity to estimate document value.
Both variables are calculated by the AI autonomously, without requiring human input. Security teams can of course reinforce the AI's understanding of the organization with their business expertise (by tagging additional sensitive devices for example).
A more in-depth description of how impact is propagated to identify key servers or sensitive documents, as well as other components that comprise the Darktrace Attack Path Modeling module can be found in this white paper.
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)
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