April 16, 2026

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

Darktrace analysis reveals ZionSiphon, an OT‑focused malware targeting Israeli water treatment and desalination systems. The malware combines privilege escalation, persistence, USB propagation, and ICS scanning with sabotage capabilities aimed at chlorine and pressure controls, highlighting growing experimentation with politically motivated critical ‑infrastructure attacks against industrial operational technologies globally.

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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.

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Apr 2026

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.*

ZionSiphon 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

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.*

“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

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.

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)

References

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

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Inside the SOC

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October 29, 2026

Daniel Levy

Threat Hunting Data Scientist

•

October 22, 2026

Nathaniel Jones

SVP, Global Threat Intelligence

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November 3, 2026

From One Alert to a Wider Campaign: Uncovering Novel Threats Through Behavioral Analysis

Key Findings

Darktrace’s Threat Research team is exploring a new method to identify malicious campaigns by correlating similar anomalous activity across its customer fleet.
The method uses unsupervised clustering to identify shared behavioral patterns across multiple customer deployments without relying on prior indicators of compromise (IoCs).
The approach identified shared infrastructure across dozens of customers, with activity linked to the likely POPA botnet.
A second cluster identified anomalous activity associated with Monero cryptocurrency mining.

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How does Darktrace conduct threat research?

The Darktrace Threat Research team aims to identify malicious campaign activity affecting multiple customers. By their nature, these campaigns will lead to shared behavioral patterns across multiple victims.

In this piece, we describe a method for identifying malicious campaign activity that uses this simple observation as a guiding principle. We begin by outlining a few key facts about the modern threat landscape and why behavioral detection is necessary to meet the challenges it presents. We then explore how fleet-wide behavioral detections can be leveraged using the principle above to systematically identify multi-deployment campaigns impacting the Darktrace customer fleet. Finally, we demonstrate that this research is not purely theoretical, using real examples of campaign activity detected through this novel approach.

Shifting to a behavioral-focused approach

The modern era of cyber-defense necessitates detection capabilities beyond traditional signature-based methods. Technologies such as antivirus signatures, hash-based malware identification, and IP blocklists have historically been successful in defending against known threats. However, they are easily circumvented by attackers and inherently limited in defending against the novel attack techniques that today's threat landscape is producing.

Recent breakthroughs in AI represent a transformational risk multiplier that cannot be ignored. AI systems do not merely execute existing attacks at machine speed; there are signs they can also synthesize novel tradecraft [1], increasing the likelihood that defenders will encounter attack patterns that have never previously been observed. Meanwhile vulnerability disclosures continue to accelerate. More than 48,000 CVEs were published in 2025, and published statistics to date indicate that 2026 is already on track to significantly exceed that figure [2]. While multiple factors may contribute to vulnerability discovery, AI is seen as a key factor in this rapid growth. Organizations should expect a growing proportion of malicious activity to fall outside the scope of detections that rely solely on prior knowledge of known threats.

By design, Darktrace’s behavioral and anomaly-focused detection capabilities are well-suited to this testing landscape. With a focus on detecting unknown and unexpected activity that deviates from the baseline of “normal” behavior, detection of CVE exploitation without prior knowledge of the vulnerability and before public disclosure, for example, is achievable [3][4].

Darktrace’s diverse customer base benefits from a further layer of protection: working with only deidentified alert metadata, our threat researchers hunt proactively across the fleet. Beyond the level of the individual customer, there is a second, larger vantage to be leveraged.

Darktrace data provides unique insight into the unexpected behavioral patterns that occur across its customer fleet. What can we see if we look at this fleet-wide telemetry and hunt for systematic behavioral campaigns that impact our customer base?

Correlating across the customer base

At the deployment level, Darktrace focuses on whether activity is unusual in the context of an organization's normal behavior, but as we zoom out and look at these alerts at the fleet level, the problem fundamentally changes . We are no longer seeking anomalies in largely normal data; we are seeking systematic patterns within data already known to be anomalous. An isolated behavior anomaly may be notable for a single customer; the same fingerprint across dozens is a signal of a structured campaign. Hunting for such campaigns and sharing our findings are key areas of focus for Darktrace’s Threat Research team. In this spirit, Darktrace is actively exploring a a new research approach that leverages deidentified alert metadata to increase campaign detection ability.

This approach is based on the premise that an expert human analyst presented with a set of alerts will identify likely related activity by assessing whether the metadata fields indicate a relationship. For example, two connections to a rare endpoint that exhibits naming structure that is likely from a single algorithm are a strong indicator, as is the use of an unusual port carrying a given protocol (e.g. port 6000 for SSL). Darktrace’s approach to campaign detection encodes this expert human judgement process through deterministic embedding functions that obtain reproducible vector representations of alert data. Unsupervised clustering is then applied to these vector representations to obtain groups of alerts that describe semantically similar alert data across multiple customers. These clusters therefore represent similar anomalous activity that has occurred across multiple customers. In other words, these clusters are candidates for malicious campaign activity.

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Figure 1: Layers of AI-based analysis provided through Darktrace products and cross-customer correlation.

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Case study 1 – unusual connections to shared underlying infrastructure

In one recent case, the clustering produced several tight groups of alerts spanning dozens of customers. Each group centered on a single domain whose subdomains were algorithmically generated (e.g. s01699, s1736, s91). Examined side by side, these clusters shared an unmistakable behavioral shape: each involved a domain with a clear naming pattern (e.g. s016841786.net-echo[.]com, s1670.pixellog[.]io, s217.pixellog[.]io) , with connections over SSL on the unusual port 6000 to servers hosted by non-mainstream providers (e.g. OVH, Hetzner, GTHost, Leaseweb), rather than generally expected content-delivery networks.

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Figure 2: An example model alert triggering on unusual outbound connections and data transfer to s1670.pixellog[.]io.

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Closer inspection revealed that the same subdomain label appeared under different domains (e.g. s1786.net-echo[.]com and s1786.pixellog[.]io). Following this lead, Darktrace’s threat researchers pivoted across the customer base and found numerous distinct subdomain labels reused under more than one registered domain. In several cases, the same label resolved to the same IP across different domains (e.g. s1704 under both pixellog[.]io and net-echo[.]com → 148.113.222[.]244; s01684 → 15.235.67[.]95), suggesting the use of ephemeral subdomain infrastructure in an attempt to avoid signature-based defense techniques.

This example demonstrates how multi-deployment data can uncover shared infrastructure that may be difficult to identify at the individual deployment level alone. In particular, the presented method facilitated this discovery by producing coherent, tight clusters of geometrically similar vector embeddings of malicious activity from large-scale fleet data, which could then be presented to a researcher for further investigation. This activity highlighted across the Darktrace customer base was ultimately linked to likely POPA botnet activity through use of open-source intelligence (OSINT) [5].

Case Study 2 – anomalous user agent and txt URI

A second example shows the method surfacing a single, self-contained cluster. The clustering was not given any indicators to look for; instead, it grouped devices that shared an unusual combination of features. On inspection, that shared consisted of:

- unusual user agent (tes)

- specific request path (/md5_GIP.txt)

- uncommon destination port (83)

- hostname impersonating Google (matching regular expression ^www\.google\.com\.[a-zA-Z0-9]+\.fast-dns-host\.com$)

Treating these shared features as IoCs, Darktrace’s threat researchers then pivoted to an exploration across the customer base to investigate the full breadth of the campaign. This search identified further commonalities; additional domains which shared the same IP as the domain family highlighted in the cluster, as well as evidence of cryptocurrency mining on affected devices. Additional OSINT research also aligned with these findings, linking the discovered payloads and infrastructure to Monero cryptocurrency mining [6].

Conclusion

The method outlined in this blog is under active development, with ongoing technical refinement providing opportunities to further enhance its capabilities. While the capability continues to mature, this approach to threat hunting leverages the powerful vantage point of fleetwide telemetry across a diverse customer base to help protect Darktrace customers. This method allows campaigns that at the deployment level appear as isolated alerts to be seen at their true scale and spread. It enables proactive fleet-wide analysis without prior IoCs to identify campaigns impacting Darktrace customers.

This is possible because of what the method is built on: the atomic unit is the Darktrace alert, a signal that encodes years of cyber expertise, while the multi-deployment nature of the method allows organizations to indirectly benefit from findings from other networks through shared intelligence by Darktrace’s Threat Research team.

Credit to Daniel Levy (Threat Hunting Data Scientist), Paul Jennings (Principal Cyber Analyst), Emma Foulger (Global Head, Threat Operations)

Edited by Ryan Traill (Content Manager)

References

1. https://www.aisi.gov.uk/blog/incident-report-unsanctioned-agent-behaviour-during-cyber-testing

2. https://www.cvefind.com/en/cve-statistic-date.html

3. https://www.darktrace.com/blog/security-after-signatures-operating-in-a-world-of-pre-cve-disclosure-exploitation-collapsed-trust-boundaries-and-autonomous-systems

4. https://www.darktrace.com/blog/pre-cve-threat-detection-10-examples-identifying-malicious-activity-prior-to-public-disclosure-of-a-vulnerability

5. https://github.com/synthient/public-research/blob/main/2026/06/popa/Popa.md

6. https://www.deepwatch.com/blog/pirated-adobe-photoshop-2023-installers-lead-to-monero-cryptocurrency-mining/

About the author

Daniel Levy

Threat Hunting Data Scientist

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AI

November 2, 2026

PHP Vulnerabilities, ARTEX and the Growing Role of AI in Cyber Attacks

The disclosure of CVE-2026-109618 and CVE-2026-109619 placed immediate pressure on organizations running PHP. Both vulnerabilities can allow unauthenticated remote code execution, including in products and appliances where PHP is embedded, and updates depend on third-party vendors [1].

Early guidance focused on patching internet-facing systems and monitoring for behavior associated with successful exploitation, including unexpected command execution, Webroot changes and outbound connections from PHP servers.

Darktrace observed widespread scanning across monitored environments involving hundreds of distinct source IP addresses that public reporting has associated with activity targeting the PHP vulnerabilities. The volume of scanning was significantly greater than the evidence available for successful exploitation. For analysts, that distinction is important. Scanning demonstrates interest in exposed infrastructure; establishing compromise requires evidence of subsequent behavior on the targeted system, including command execution, file modification or unexpected outbound communication.

PHP is not an isolated example. Imperva subsequently demonstrated remote code execution against a vulnerable Node.js build through malicious Brotli data, reproducing execution through compressed HTTP responses and configured request parsers. The same inputs were rejected by the patched runtime. Imperva linked its Node2Shell and PHP2Shell research to the increasing speed of vulnerability discovery and exploit development. [2]

While Darktrace has not observed direct evidence of CVE-2026-109618 or CVE-2026-109619 exploitation, on October 21^st we observed a six-month high in detections consistent with zero-day exploitation activity, representing a 64% increase compared to historic data. This increase has not been confirmed as related to the PHP vulnerabilities. Darktrace’s behavioral approach is designed to detect anomalous activity without relying solely on rules written for a specific exploit. Comparable increases were observed around the Log4j disclosure and other globally significant vulnerability disclosures.

AI-assisted vulnerability research

The PHP disclosure arrives as AI systems have become more capable at vulnerability research [3]. OpenAI launched Daybreak in June 2026, combining GPT-5.5-Cyber, Codex Security and partnerships with security researchers and software maintainers. OpenAI says its models have been applied to discovering and generating patches for vulnerabilities in browsers, network infrastructure, FreeBSD and the Linux kernel. Darktrace joined the OpenAI Daybreak Cyber Partner Program in June to explore how OpenAI's cyber capabilities could be integrated within Darktrace products and services [4][10].

OpenAI reported in August that Codex Security Cloud had analyzed more than 30 million commits across more than 30,000 codebases. According to OpenAI, Cloudflare uses models accessed through Daybreak to investigate customer-authorized code and combines findings with live traffic and security signals to assess whether affected routes are active or receiving suspicious traffic, with proposed mitigations and patches remaining subject to customer review [5][6].

Faster discovery still leaves defenders with the work of locating affected software, testing fixes and completing remediation. The PHP case shows that gap clearly, particularly when vulnerable runtimes are embedded inside other products.

Enterprise agents as an attack surface

Darktrace Signal Labs has also examined how a trusted enterprise agent could be abused after an attacker has already obtained access to an environment. In a controlled simulation, researchers demonstrated that, in that post-compromise scenario, the Claude platform’s memory stores feature could be abused to establish a live C2 channel between an external operator and internal Claude Code agents. The external operator was able to dynamically assign network discovery, credential access, and data exfiltration tasks to the agents via this C2 channel. Detection of the agents’ activities relied on the correlation of process creation and network traffic activity from agent harness processes, alongside behavioral analysis to identify deviations from expected behavior. Darktrace has not claimed it observed this memory-store command-and-control technique in operational attacks [7].

AI-assisted attacks in Japan

Evidence from Japan indicates AI being used for vulnerability scanning and attacks outside controlled research. On 21 October, JPCERT/CC published findings from an investigation into recent unauthorized access incidents affecting organizations in Japan. JPCERT/CC states that it confirmed attackers had used AI to scan for known vulnerabilities and conduct attacks through APIs. According to JPCERT/CC, its examination of attack infrastructure found evidence of Android application analysis, API discovery and testing for weaknesses including server-side request forgery, and the infrastructure also targeted services intended for users outside Japan [8].

JPCERT/CC does not identify ARTEX by name in that publication. The named ARTEX attribution comes from separate investigations, including Microsoft's analysis of Storm-0940.

AI in offensive operations

Microsoft describes ARTEX as an open-source, multi-agent penetration-testing system capable of automating activity with limited human interaction. Microsoft reported that Storm-0940, which it assesses to be a China state-aligned actor active since 2021, used ARTEX against hundreds of organizations, and Microsoft attributed the ConnectWise supply-chain compromise [9].

Microsoft observed ARTEX accelerating scanning, reconnaissance, password spraying and OAuth application abuse. Several ARTEX-driven operations reached privileged cloud access less than three hours after initial targeting. Following the ConnectWise compromise, Storm-0940 attempted to authenticate to more than 2,250 tenants at rates of up to 2,900 attempts per minute [9].

Darktrace Threat Research found activity in its own telemetry involving infrastructure later associated with the campaign. On 13 October, Darktrace recorded failed authentication activity involving 144.172.64[.]128, one of the addresses Microsoft later identified as performing sign-ins and Microsoft Graph activity in ConnectWise customer environments. Darktrace analysis identified 65 failed authentication attempts in around 20 minutes.

*Figure 1: Darktrace Advanced Search showing failed authentication events involving 144.172.64[.]128 on 13 October 2026, (customer-identifying information redacted).*

The telemetry establishes repeated failed authentication from infrastructure later associated with the campaign. It does not establish ARTEX execution or successful compromise. At the time of observation, Microsoft's later attribution was unavailable. Analysts therefore had the behavior itself: repeated failed authentication from the same source over a short period, together with any related identity, application or network activity inside the environment.

Microsoft documented a wider password-spray campaign against 327 accounts across eight organizations between 10 and 14 October. It recorded more than 151,000 failed logons, including more than 111,000 on 11 October, but no successful authentication, token issuance, Graph call or directory change from that campaign. Microsoft also reported that most Storm-0940 targeting was unsuccessful [9].

Where the actor progressed beyond authentication, Microsoft observed service-principal sign-ins followed by Graph and directory operations. Activity included reconnaissance, application credential changes and attempts to modify privileges. Microsoft recommends correlating service-principal sign-ins with Graph activity, directory changes and resource access [9].

Behavior before attribution

The PHP and ARTEX cases begin with different information. PHP defenders knew which software required urgent remediation but initially had limited information about exploitation. Process execution, file modification and outbound communication could still provide evidence of compromise.

The PHP and ARTEX observations also illustrate the difference between volume and impact. Darktrace observed broad scanning around the PHP disclosure, while the ARTEX-associated infrastructure produced concentrated authentication activity. Neither observation independently demonstrates compromise and in each case examining the behavior that follows the initial interaction is the investigative clue.

The Darktrace ARTEX-related observation began with authentication behavior. Microsoft publicly attributed the source infrastructure eight days later. That attribution added context, but it did not change what had originally been recorded in the environment.

AI is now present at several points in this cycle. Daybreak applies models to vulnerability discovery and remediation. Darktrace Signal Labs demonstrated how a trusted enterprise agent could be co-opted in a controlled environment. JPCERT/CC confirmed AI-assisted vulnerability scanning and API attacks in recent incidents. Microsoft documented ARTEX being used by Storm-0940 in active operations [4][7][8][9].

Microsoft's findings also show why scale and impact must be assessed separately. One ARTEX-associated campaign produced more than 151,000 failed logons without successful access, while other operations reached privileged cloud access in less than three hours. Security teams have to distinguish high-volume attempts from activity that progresses through authentication, privilege changes and resource access.

The evidence required to assess these cases remains familiar: authentication records, process execution, application and directory changes, file activity and network communication. AI can increase the rate at which those behaviors occur. Defenders still need to establish their sequence and impact.

Credit to Emma Foulger, Sam Lister, Dylan Evans, Moishe Tajtelbaum

References

[1] NCSC-NL, php2shell vulnerabilities (CVE-2026-109618, CVE-2026-109619), 21 October 2026. https://github.com/NCSC-NL/php2shell

[2] Imperva, https://www.imperva.com/blog/node2shell-from-brotli-data-to-remote-code-execution/

[3] Cyber Security Agency of Singapore, https://www.csa.gov.sg/alerts-and-advisories/alerts/al-2026-151/

[4] OpenAI, Daybreak: Tools for securing every organization in the world, 22 June 2026. https://openai.com/index/daybreak-securing-the-world/

[5] OpenAI Developers, Scaling cyber defenders with Daybreak, 21 August 2026. https://developers.openai.com/blog/scaling-cyber-defenders-with-daybreak

[6] OpenAI, Cloudflare links vulnerability discovery to defense with OpenAI, 21 October 2026. https://openai.com/index/cloudflare-cyber/

[7] Darktrace Signal Labs, Shared Memories as a Covert C2 Channel, draft research supplied for review.

[8] JPCERT/CC, Recent AI-Assisted Cyber Attacks in Japan: Investigating Attack Infrastructure and Techniques, 21 October 2026. https://blogs.jpcert.or.jp/en/2026/10/web_api_attacks.html

[9] Microsoft Security Research and Microsoft Threat Intelligence, AI-driven, large-scale nation-state campaign using ARTEX enables supply chain compromise, 21 October 2026. https://www.microsoft.com/en-us/security/blog/2026/10/21/storm-0940-uses-artex-agentic-ai-connectwise-supply-chain-compromise/

[10] Darktrace, Advancing the Use of Frontier AI in Cybersecurity: Darktrace Joins the OpenAI Daybreak Cyber Partner Program to Explore Defensive AI Integrations, 22 June 2026. https://www.darktrace.com/blog/advancing-the-use-of-frontier-ai-in-cybersecurity-darktrace-joins-the-openai-daybreak-cyber-partner-program-to-explore-defensive-ai-integrations

About the author

Nathaniel Jones

SVP, Global Threat Intelligence

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