Research by: Sven Rath (@eversinc33), Jaromír Hořejší (@JaromirHorejsi)

Key Points

• The YouTube Ghost Network is a malware distribution network that uses compromised accounts to promote malicious videos and spread malware, such as infostealers.
• One of the observed campaigns uses a new, heavily obfuscated loader malware written in Node.js, which we call GachiLoader.
• To make it easier to analyze obfuscated Node.js malware, Check Point Research developed an open-source Node.js tracer, which significantly reduces the effort needed to analyze this type of malware and extract configurations.
• One variant of GachiLoader deploys a second stage malware, Kidkadi, that implements a novel technique for Portable Executable (PE) injection. This technique loads a legitimate DLL and abuses Vectored Exception Handling to replace it on-the-fly with a malicious payload.

Introduction

In a previous publication, we examined the YouTube Ghost Network, a coordinated collection of compromised accounts that abuse the platform to promote malware. In our current research, we analyze one specific campaign of this network, which stood out as the deployed malware implements a previously undocumented PE injection method which abuses Vectored Exception Handling to load its malicious payload.

Campaign Overview

Similar to campaigns we previously documented, the infection chain begins with compromised accounts that host videos designed to lure viewers into downloading malware from an external file hosting platform. The theme of this campaign are game cheats and various cracked software:

The video’s descriptions then provide a password for the archive containing the malware, as well as instructions that usually include disabling Windows Defender.

We identified more than a hundred videos belonging to this campaign, which collected approximately 220.000 views. The videos were spread across 39 compromised accounts, with the first video uploaded on December 22, 2024. This means that this campaign has been running for more than 9 months. After we reported these videos to YouTube, most have been taken offline, although new videos will continue to appear on newly compromised accounts.

Since we started monitoring this specific campaign, it deployed the Rhadamanthys infostealer as a final payload, which is distributed through a custom loader which we call GachiLoader.

GachiLoader

GachiLoader is a heavily obfuscated Node.js JavaScript malware used to deploy additional payloads to an infected machine. Node.js is one of a long line of threat actors always adapt their arsenal using non-traditional programming languages and platforms adapted by threat actors in their quest to spread malware.

As obfuscated JavaScript requires a lot of time and effort to manually deobfuscate, we developed a tracer for Node.js scripts to dynamically analyze this type of malware, defeat common anti-analysis tricks and significantly reduce the manual analysis effort. This tool is not only useful for GachiLoader, but is useful for anyone analyzing heavily obfuscated Node.js malware. Therefore, we decided to share it with the research community here:

• https://github.com/CheckPointSW/Nodejs-Tracer

Some of the analyzed GachiLoader samples drop a second-stage loader, which we call Kidkadi. This loader is particularly interesting, as it implements a novel technique for PE injection, which tricks the Windows loader into loading a malicious PE from memory instead of a legitimate DLL. We analyzed this technique, which we call Vectored Overloading and reimplemented it in a Proof-of-Concept (PoC) shared below.

Technical Analysis

GachiLoader’s JavaScript module is bundled into a self-contained executable, using the nexe packer, with sizes roughly between 60 and 90 MB. nexe is an open-source project, that compiles a Node.js application into a single executable file, bundled with a Node.js runtime, so that the file can run on a host without Node.js installed. While the size of the executable is quite big, it isn’t suspicious as the victim expects to receive a software package. The tool nexe_unpacker can be used to extract the obfuscated JavaScript source code from the PE.

Anti-Analysis Features

To avoid analysis by a security researcher or an automated sandbox, the GachiLoader JavaScript module employs several anti-VM and anti-analysis checks:

• Checks if the total amount of RAM is at least 4GB
• Checks if at least 2 CPU cores are available
• Compares the username against a list of usernames, that can be associated with various sandboxes or analysis systems (see Appendix A for a list of all names).
• Checks the hostname against a similar list of hostnames (see Appendix B for a list of all hostnames).
• Probes the running programs and compares against a list of programs, such as analysis tools, sandbox indicators or common programs running on VMs (see Appendix C for a list of all process names).

The malware then proceeds to run several PowerShell commands to enumerate the system resources and capabilities over WMI .

• Check if at least one port connector object exists: (Get-WmiObject Win32_PortConnector).Count
• Get drive manufacturers and compare against a blacklist: Get-WmiObject Win32_DiskDrive | Select-Object -ExpandProperty Model (See Appendix D for a list of all drive manufacturers).
• Resolve video controllers via Get-WmiObject Win32_VideoController | Select-Object -ExpandProperty Name, and check the names against a blacklist associated with VM environments (See Appendix E for a list of all video controller names).

If any of these checks indicate a virtual machine, sandbox or analysis environment the malware enters a loop of sending HTTP GET requests to benign websites such as linkedin.com, grok.com, whatsapp.com or twitter.com :

Finally, to avoid running multiple times in a short period of time, a mutex file with a random-per-sample name and the .lock extension is created in the %TEMP% directory on running for the first time. If this file already exists or was modified within the last 5 minutes, the program terminates.

We were able to easily bypass all of these anti-analysis with Node.js Tracer: the tool hooks into the respective methods and spoofs the results to the caller, in this case the malware, allowing the script to run and expose its malicious actions:

Privilege Elevation via UAC Prompt

If the malware decides that the environment is not that a sandbox, it then checks if it is running in an elevated context by running net session , a command that is expected to fail if run by a non-administrative user. If the command fails, the malware tries to restart itself in an elevated context using the following PowerShell command:

powershell -WindowStyle Hidden -Command "Start-Process cmd.exe -Verb RunAs -WindowStyle Hidden -ArgumentList '/c \"<path_to_program_itself>\"'"

While this triggers a UAC prompt, that prompt is likely to be accepted by the victim, as they expect to run an installer for some sort of software, which usually requires administrative privileges.

Defense Evasion

To avoid detections of subsequent payloads, the malware attempts to kill Windows Defender’s SecHealthUI.exe process by running taskkill /F /IM SecHealthUI.exe and adds Defender exclusions via Add-MpPreference -ExclusionPath for the following paths:

• C:\Users\
• C:\ProgramData\
• C:\Windows\
• For all other existing drives, at the root (e.g. D:\ )

In addition, an exclusion for *.sys files is added via Add-MpPreference -ExclusionExtension '.sys', although we have not observed any *.sys files being dropped by the analyzed samples.

Payload Delivery and Execution

To retrieve the next stage’s payload, the malware comes in two variants.

• One variant gets the payload from a remote URL
• The other variant drops another loader, kidkadi.node, which loads the final payload using the Vectored Overloading method. This payload is embedded in the loader’s JavaScript source.

First Variant – Remote Payload

GachiLoader first obtains information about the host it is running on, such as antivirus products and the OS version, and sends them via a POST request to the /log endpoint of its C2 (Command and Control) addresses. The samples all have multiple C2 addresses embedded for redundancy and try out each one in succession, as we saw when tracing the calls through our tracer:

Next, a GET request to the /richfamily/<key> endpoint (where <key> is a value unique to each sample) with the X-Secret: gachifamily header gets the URL of the final payload to download, encoded in Base64. This final payload can only be retrieved if using the correct X-Secret header again – this time using a unique key embedded in the binary, e.g. X-Secret: 5FZQY1gYj0UKw4ZC99d1oNYR8LvTPtrfN357Eh5gmRvsMaPYgXtMxRXpMb2bTFOb2h2HqMnvUKT9CUpj9864gckmPUzf9uLIIU9. Otherwise, the web server returns a Forbidden error.

The final payload is then downloaded to the %TEMP% directory and saved with a random name, mimicking legitimate software such as KeePass.exe, GoogleDrive.exe , UnrealEngine.exe or others which contain the Rhadamanthys infostealer, packed and protected with VMProtect or Themida.

Second Variant – Kidkadi

The second variant we observed in the wild did not reach out to a C2 server to get the second payload, but instead had an embedded payload which is executed through another loader that is dropped to disk under %TEMP% as kidkadi.node:

.node files are native addons for Node.js, which are essentially just DLLs that can be called from Node.js code via dlopen. Therefore, they can be used by developers whenever the Node-API does not expose sufficient functionality.

The malware exposes a function for Node.js to call, where the name of the method differs across samples. In some cases, the name as well as the error messages in some samples are of Russian origin:

The loader passes the payload PE as a binary buffer to Kidkadi through this exposed function, which then runs this payload via reflective PE loading. We found that this loader uses a novel spin on Module Overloading, abusing Vectored Exception Handlers (VEHs) to trick the Windows operating system to run the final payload, when invoking LoadLibrary to load an arbitrary DLL. This technique, not yet documented, shows that the author has a decent understanding of Windows internals. We named this method Vectored Overloading.

PE Loading via Vectored Overloading

The malware first creates a new section with SEC_IMAGE from the legitimate wmp.dll, a DLL used by Windows Media Player. It then overwrites this section with the content of the payload (the PE to be loaded) and maps a view of that section into the process via NtMapViewOfSection. The PE’s sections are then copied into memory one by one and relocations as well as the correct protections are applied:

This results in a view of the malicious PE, mapped to the process, which is backed by the legitimate DLL wmp.dll. This section view is what the Windows loader (meaning ntdll!Ldr*) will be tricked into loading later on.

Since the Windows loader, called via LoadLibrary, does not load arbitrary PEs, but only those that have DLL characteristics, the Characteristics of the FileHeader are set to IMAGE_FILE_DLL , if the payload is not a DLL. Additionally, the entry point is zeroed out, likely to avoid the loader calling an entry point that is not that of a DLL. If the payload is a DLL, the header is not changed.

Afterwards, the malware registers a Vectored Exception Handler (VEH).

VEHs are user-mode callbacks that are invoked by the OS when an exception occurs. A common malware technique abusing VEHs is to register a hardware breakpoint on a specific instruction, which triggers an exception whenever this instruction is reached. This exception is then handled by the VEH, which can intercept the call and, for example, change the parameters. This essentially allows hooking functions without patching memory, such as when using classic trampoline hooks.

In this case, the hardware breakpoint (HWBP) is set on NtOpenSection :

The malware then loads amsi.dll via LoadLibrary , which kicks off the injection:

A call to LoadLibrary internally ends up in the Windows loader creating a section object of the target DLL to load, which is opened through a call to NtOpenSection . This triggers the hardware breakpoint, and subsequently the VEH, which were registered earlier. This is where the main injection logic is implemented.

To make the loader map the malicious PE instead of the actual amsi.dll section, the section object pointing to amsi.dll is swapped with the malicious payload section from earlier. The VEH simply places the section handle created earlier on the stack position that corresponds to the [out] PHANDLE SectionHandle argument of NtOpenSection. The VEH then advances the instruction pointer eip to the ret instruction and resumes execution. This skips the actual call to the kernel while still giving back a valid handle, essentially emulatingNtOpenSection:

Before stepping out of the VEH, the hardware breakpoint is re-set to NtMapViewOfSection.

NtMapViewOfSection is then used by the Windows loader to map the section into the process, which again triggers the hardware breakpoint. To make sure the malicious payload is mapped, the syscall is again emulated by advancing the instruction pointer and replacing the [out] arguments with the relevant values, such as the section base address or the section size. This is possible, because the section view was mapped by the malware earlier, when the malicious payload was written into the view of wmp.dll:

A final hardware breakpoint is then set on NtClose , where the malware simply verifies that the correct section handle is closed.

Back in the regular flow of the program, outside the VEH, the entry point will be invoked if the payload is a regular PE. If it is a DLL, the loader expects it to be another .node module and resolves the correct exports to invoke:

Completely unrelated to this campaign, we found a file with an original filename of HookPE.exe, which is a 64-bit PoC version of the technique with debug prints that uses the technique to load calc.exe into memory. Error strings in this binary indicate that the loader uses code from libpeconv for PE manipulation.

This injection technique has multiple advantages over “classic” RunPE-style reflective loading:

• Just like when using the Module Overloading technique, the injected DLL will show up as backed by a legitimate image (such as wmp.dll), since the section was originally created for this DLL. However, since the code in memory will differ from the code on disk, tools such as Moneta are able to detect it:

• Some loader work is offloaded to the Windows loader. This significantly reduces complexity for the malware author as they do not have to implement e.g. resolving imports or TLS callbacks, which in turn increases payload compatibility. For example, many publicly available PE loaders do not properly handle TLS callbacks.
• By emulating syscalls, the respective kernel side callbacks such as ETWti are not invoked, as the call to the kernel is skipped entirely. This may fool security solutions that rely only on these section ETWti events. Of course, the earlier calls before the injection (when mapping the image) still trigger those events, but not in the order usually expected.

We published a reimplementation of the 64bit variant of this injection method as a tool for security researchers to analyze the technique and test detections:

• https://github.com/CheckPointSW/VectoredOverloading

Dynamic Config Extraction at Scale

As deobfuscation of the JavaScript source is a tedious and partially manual process, we decided to run all available samples of GachiLoader through Node.js Tracer to bypass the anti-analysis checks and receive the final payloads. By hooking filesystem-related Node APIs, the downloaded files are saved for the analyst before they can be deleted by the malware trying to remove its traces.

The final payloads of both variants of GachiLoader were all packed and protected by Themida or VMProtect. Dumping the unprotected configuration from memory when running them in an automated sandbox then allowed us to extract the C2 servers of the final payloads.

All the analyzed samples that were part of this campaign dropped Rhadamanthys as the final malware. The extracted C2 servers can be found in the IoC section below.

Conclusion

Malware written for the Node.js platform has become increasingly common and is mostly found in obfuscated form, which is tedious to statically deobfuscate and analyze. By enabling analysts to trace and hook Node-API execution dynamically with our open source Node.js Tracer, the time that has to be spent on triage and analysis is significantly reduced, and common anti-analysis checks can easily be defeated.

The threat actor behind GachiLoader demonstrated proficiency with Windows internals, coming up with a new variation of a known technique. This highlights the need for security researchers to stay up-to-date with malware techniques such as PE injections and to proactively look for new ways in which malware authors try to evade detections.

The threat actors behind the YouTube Ghost Network exploit the trust in the YouTube platform to trick victims into downloading malware. Users should be particularly cautious of offers for cracked software, cracks, trainers, or cheats, as these files are frequently laced with malware designed to steal data and/or compromise a device. While both the security community and YouTube actively work to identify and remove such content, these attacks remain persistent.

Protections

Check Point Threat Emulation and Harmony Endpoint provide comprehensive coverage of attack tactics, filetypes, and operating systems, and protect against the attacks and threats described in this report.

Indicators of Compromise

Description	Value
.zip Archives	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
Variant 1 – GachiLoader	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GachiLoader’s C2 Servers	davpniktonevidit[.]cfdnupogodi[.]cfd94[.]154[.]35[.]99nexus-cloud-360[.]comglobalmarket247online[.]com176[.]46[.]152[.]18213[.]209[.]150[.]104[vault-360-nexus[.]com]iietrich[.]cfdmceenzie[.]sbs62[.]60[.]226[.]23366[.]63[.]187[.]72digitalservice365cloud[.]com178[.]16[.]52[.]231
Variant 2 – Kidkadi Dropper	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
Rhadamanthys C2 Servers	176[.]46[.]152[.]18:8181/gDatFeDway/r26ggaap[.]dssde178[.]16[.]53[.]193/mK2k20ajW7kairt1mg88vT1aT9vwU5AZN9AkYYs2QBNbnXV3ph/YEr2KP0jEBhSDdVcS9cWNhbKUgDxcEm9kqxLwFAdHgmKyw7FZq[.]exe180[.]178[.]189[.]34:8181/gDatFeDway/mh3af5md[.]wg4ja180[.]178[.]189[.]34:8181/gDatFeDway/ujp8k5q9[.]kbtsk185[.]141[.]216[.]120:1888/gateway/st2jdbg8[.]gsg453[.]126[.]4378[.]16[.]53[.]193/mK2k20ajW7kairt1mg88vT1aT9vwU5AZN9AkYYs2QBNbnXV3ph/YEr2KP0jEBhSDdVcS9cWNhbKUgDxcEm9kqxLwFAdHgmKyw7FZq[.]exe94[.]154[.]35[.]99:1888/gateway/el3tkioe[.]xcg4w94[.]154[.]35[.]99:1888/gateway/mbw0n34s[.]gibis94[.]154[.]35[.]99:1888/gateway/wwpac3ey[.]q23nfcxbnqdytjgrxutmzawczv[.]cg/gateway/0f4m3h8r[.]trz19jfbcrmphnnikoktsmcpzirlplkwp[.]zl/gateway/8pv47lge[.]93qfg
Kidkadi.node	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
HookPE.exe	ded68a8f5d0765740d469c08bd66270097f3474eab92ee1e65ddcdd6d15fca6e

Appendix A – Username Blocklist

mashinesssss	sandbox	honey
vmware	currentuser	nepenthes
andy	hal9th	johndoe
wdagutilityaccount	abby	peter wilson
hmarc	patex	john-pc
rdhj0cnfevzx	keecfmwgqj	frank
8nl0colnq5bq	lisa	john
pxmduopvyx	8vizsm	w0fjuovmcpa
lmvwjj9b	pqonjhvwxss	3u2v9m8
julia	heuerzl	harry johnson
j.seance	a.monaldo	tvmt
johanna	johnson	miller
malware	maltest	virus
test user	sand bog	bruno
anand	it-admin	walker

Appendix B – Hostname Blocklist

b30f0242-1c6a-4	desktop-vrsqlag	q9itrkphr
xc64zb	desktop-d019gdm	desktop-wi8clet
server1	lisa-pc	john-pc
desktop-b0t93d6	desktop-1pypk29	desktop-1y2433r
wileypc	wok	6c4e733f-c2d9-4
ralphs-pc	desktop-wg3myjs	desktop-7xc6gez
desktop-5oy9s0o	qarzhrdbj	orelee pc
archibaldpc	julia-pc	d1b_coursek
comname_5076	ralphs-pc	desktop-vkeons4
tdt-eff-2w11wss	work	q9iatrkphr

Appendix C – Process Blocklist

human.exe	cred-store.exe	device-sense.exe
private-cloud-proxy.exe	tib_monitor_monitor.exe	tmsmonitor.exe
vmtoolsd.exe	adpagent.exe	fakenet.exe
dumpcap.exe	httpdebugger.exe	wireshark.exe
fiddler.exe	vboxservice.exe	df5serv.exe
vboxtray.exe	ollydbg.exe	pestudio.exe
vmwareuser.exe	vgautservice.exe	vmacthlp.exe
x96dbgn.exe	vmsrvc.exe	x32dbgn.exe
vmusrvc.exe	prl_cc.exe	prl_tools.exe
xenservice.exe	qemu-ga.exe	joeboxcontrol.exe
ksdumperclient.exe	ksdumper.exe	joeboxserver.exe
vmwareservice.exe	vmwaretray.exe	todaydeathdo.exe
mitmdump.exe	idaw.exe	vxtkernelsvcntmgr.exe
windbg.exe	dumpit.exe	procmon.exe
rammap.exe	rammap64.exe	inetsim.exe
hvix64.exe	ida64.exe	x64dbg.exe
cutter.exe	r2.exe	binaryninja.exe
dbgview.exe	tcpdump.exe	netcat.exe
idaq64.exe	frida-server.exe	frida-inject.exe
frida.exe	pin.exe	drrun.exe
apimonitor.exe	volatility.exe	rekall

Appendix D – Drive Manufacturer Blocklist

vmware	xen	msft virtual
hyper-v	kvm	red hat
aws	azure	google
gcp	openstack	cinder
ovirt	citrix	virtuozzo
virtio

Appendix E – Video Controller Blocklist

virtualbox graphics adapter	vbox disp adapter	qemu virtual video
hyper-v video	parallels display adapter wddm	red hat qxl
xen vga	citrix display adapter

The post GachiLoader: Defeating Node.js Malware with API Tracing appeared first on Check Point Research.

Key Findings

• In recent months, Check Point Research has identified a new wave of attacks attributed to the Chinese threat actor Ink Dragon. Ink Dragon overlaps with threat clusters publicly reported as Earth Alux, Jewelbug, REF7707, CL-STA-0049, among others.
• Ink Dragon has expanded its operational focus to new regions – In the last few months, the threat actor’s activities show increased focus on government targets in Europe in addition to continued activities in Southeast Asia and South America.
• Ink Dragon builds a victim-based relay network – Ink Dragon leverages a custom ShadowPad IIS Listener module to turn compromised servers into active nodes within a distributed mesh, allowing each victim to forward commands and traffic, effectively transforming targets into part of their C2 infrastructure.
• Ink Dragon continues to exploit long-known IIS misconfigurations for initial access – Despite years of public reporting and awareness within the security community, Ink Dragon still relies on predictable or mismanaged ASP.NET machineKey values to perform ViewState deserialization attacks against vulnerable IIS and SharePoint servers.
• Ink Dragon is evolving its operations with new TTPs and tools – The cluster has introduced a new variant of FinalDraft malware with enhanced stealth and higher exfiltration throughput, along with advanced evasion techniques that enable stealthy lateral movement and multi-stage malware deployment across compromised networks.

Introduction

Check Point Research tracks a sustained, highly capable espionage cluster, which we refer to as Ink Dragon, and is referenced in other reports as CL-STA-0049, Earth Alux, or REF7707. This cluster is assessed by several vendors to be PRC-aligned. Since at least early 2023, Ink Dragon has repeatedly targeted government, telecom, and public-sector infrastructure, initially concentrating on Southeast Asia and South America, but with an increasing footprint in Europe and other regions. The actor’s campaigns combine solid software engineering, disciplined operational playbooks, and a willingness to reuse platform-native tools to blend into normal enterprise telemetry. This mix makes their intrusions both effective and stealthy.

A notable characteristic of Ink Dragon’s operations is their tendency to convert compromised environments into part of a larger, distributed relay network. By deploying a ShadowPad IIS Listener Module across multiple victims, the group effectively turns each breached server into a communication node capable of receiving, forwarding, and proxying commands. This design allows attackers to route traffic not only deeper inside a single organization’s network, but also across different victim networks entirely. As a result, one compromise can quietly become another hop in a global, multi-layered infrastructure supporting ongoing campaigns elsewhere, blending operational control with strategic reuse of previously breached assets.

This blog also presents the forensic story of a high‑stakes compromise of a European government office, highlighting recurring methods observed across different victims. We walk through the entire kill chain observed in the field, including web-centric initial access, hands-on-keyboard activity, staged loaders, privilege escalation, and credential-harvesting components, as well as aggressive lateral movement that culminated in domain dominance. We also document multiple delivery and persistence patterns that Ink Dragon favors, and unpack a new variant of the FinalDraft backdoor, which is used as a resilient, cloud-native command-and-control platform.

Beyond the technical details, this article shows how Ink Dragon’s tooling and repeatable TTPs reflect a mature, modular development model that steadily expands in capability while maintaining a consistent operational philosophy.

Attack Chain

Attackers begin by gaining initial access through ViewState deserialization or ToolShell-based exploits, then deploy ShadowPad on the compromised server. They harvest IIS worker credentials and establish an RDP proxy to move laterally, using RDP and ShadowPad’s built-in capabilities along with reused credentials. After obtaining access to a domain admin account, they achieve domain dominance. From there, they deploy FinalDraft on strategic machines and install a ShadowPad IIS listener on public-facing servers, enabling new victims to connect to the attackers’ infrastructure as the campaign continues.

Initial Access

In the environments we investigated, the common initial access vector is exploitation of ASP.NET ViewState deserialization via publicly disclosed machine keys. In this scenario, the __VIEWSTATE parameter, normally protected using the application’s machineKey, can be forged if the key is copied from public sources. Once the attacker can generate a valid signature, they can inject a crafted ViewState payload that the server deserializes, leading to remote code execution.

In other cases, we’ve also observed the actor abusing the ToolShell SharePoint vulnerability. ToolShell is an exploit chain targeting on-premises Microsoft SharePoint that combines authentication bypass and unsafe deserialization (CVE-2025-49706 / CVE-2025-53771 and CVE-2025-49704 / CVE-2025-53770, among others) to enable unauthenticated remote code execution and web shell deployment on vulnerable servers. In July 2025, we observed the actor conducting mass scanning for the ToolShell vulnerability during the initial waves of exploitation, indicating the actor was among a limited set of actors with early access to the exploit.

This demonstrates that the attackers have multiple web-facing options for initial compromise. The practical workflow is straightforward: enumerate internet-facing IIS/SharePoint servers, test for predictable machine keys or vulnerable SharePoint endpoints (often using publicly available fuzzing lists), and submit crafted POSTs or payloads that trigger deserialization/RCE. These attacks are stealthy and scale well against large organizations with inconsistent web configurations.

Internal Operation & Kill Chain Overview

Lateral Movement

In these campaigns, the adversary leverages two complementary strengths of a web compromise after gaining an initial foothold: privileged local artifacts (the IIS application/service credentials and configuration) and visibility into active administrative sessions.

By obtaining the IIS machineKey/DecryptionKey or otherwise recovering the site’s cryptographic secrets, the attacker can decrypt locally stored configuration blobs and credentials that the site or its worker processes store. In practice, this frequently yields the IIS worker/app-pool account password or other local secrets that carry elevated rights on the host and often across other IIS servers that reuse the same service account or credential material.

With a local administrative credential in hand, Ink Dragon escalates from code execution in w3wp.exe to full system control, and then leverages that control to create a persistent remote access channel (commonly an RDP tunnel or scheduled task that launches an administrative payload). Because many organizations reuse service credentials across web farms for management convenience, obtaining a single IIS credential can allow the actor to authenticate to sibling IIS hosts and pivot laterally with minimal network noise. Ink Dragon frequently tunnels RDP traffic to reach internal hosts from a remote workstation, exposing their machine names and enabling direct, interactive sessions that appear superficially legitimate.

From there, the attacker’s lateral playbook becomes straightforward: stage a resilient implant (ShadowPad/FinalDraft variants are common) and propagate it using native protocols such as SMB. The operator copies the triad (EXE + side-loaded DLL + encrypted blob) into writable shares, creating a service or scheduled task to run the payload, and attempting to disguise the service/process under a plausible name.

Persistence

The most common patterns we observed:

1. Scheduled tasks – the actor created tasks with benign-looking names (notably SYSCHECK) set to run under SYSTEM and pointing to staged loader hosts such as conhost.exe. Tasks were often created to run once at a chosen time, allowing the operator to bootstrap wide-scale re-execution while minimizing noisy periodic callbacks.
2. Service installation – on several machines, Ink Dragon installed services to launch their loaders as persistent system services, with service names disguised as Windows updates or temporary maintenance (e.g., WindowsTempUpdate). These services were used to run side-loaded triads (EXE + malicious DLL + encrypted blob), guaranteeing automatic restart and SYSTEM execution after reboots.

It’s essential to note that many of the staged executables (e.g., conhost.exe) were renamed to resemble native Windows binaries, yet they were digitally signed by legitimate vendors such as Advanced Micro Devices, Realtek Semiconductor Corp, and NVIDIA. Their OriginalFileName metadata differed from the on-disk names, indicating deliberate masquerading and abuse of trusted signatures to blend with the operating system.

Privilege Escalation

Ink Dragon combines targeted exploitation with heavy credential harvesting to quickly escalate local control into domain-level dominance.

1. Local escalation via exploitation: In multiple incidents, the initial __VIEWSTATE RCE was followed by local escalation tooling such as PrintNotifyPotato to obtain SYSTEM from a web server context. This allowed full control over the host and the rights to create persistent services and change firewall settings.
2. Credential harvesting and LSASS dumping: the operators deployed custom tools (we observed variants of LalsDumper) to create LSASS dumps and extract registry hives (SAM, SYSTEM) into ProgramData or user-profile directories for offline cracking. Dump files and registry hive exports were then used to recover NTLM hashes and Kerberos material.
3. Leveraging idle sessions: Ink Dragon actively enumerated Remote Desktop sessions and administrative consoles on key servers. In at least one instance, the actor located an idle RDP session belonging to a Domain Administrator that had authenticated via Network Level Authentication (CredSSP) using NTLMv2 fallback. Since the session remained disconnected but not logged off, it is highly likely that LSASS retained the associated logon token and NTLM verifier in memory. Ink Dragon obtained SYSTEM-level access to the host, extracted the token (and possibly the NTLM key material), and reused it to perform authenticated SMB operations. Through these actions, they were able to write to administrative shares and exfiltrate NTDS.dit and registry hives, marking the point at which they achieved domain-wide privilege escalation and control.

Enabling Egress

Ink Dragon modified host firewall rules to permit broad outbound traffic and effectively turned compromised hosts into unconstrained exfiltration/proxy nodes.

The group created a permissive outbound rule labeled to resemble legitimate software (we observed a rule named Microsoft MsMpEng, associated with MsMpEng.exe) that allowed Any → Any outbound traffic across all profiles. The rule was created locally (not via GPO), enabled, and applied to the Defender process in SYSTEM context. This bypasses upstream egress controls that would otherwise block custom ports or tunneling traffic.

Building the Relay Network

During the investigation, we discovered that Ink Dragon is actively converting compromised organizations into functional communication nodes within a distributed ShadowPad relay network. This capability is powered by an IIS Listener Module. Instead of serving as a traditional backdoor, the module enables the malware to register new URL listeners directly through the HttpAddUrl API, which lets processes bind HTTP(S) endpoints dynamically, including wildcard patterns that match all sub-paths or hostnames. This means the malicious listener seamlessly coexists with legitimate IIS behavior, silently intercepting any incoming HTTP requests whose URL matches its configuration. When a request arrives, the module decrypts the payload and evaluates whether the structure fits its proprietary protocol. If not, it falls back to genuine IIS logic, serving real web content or returning legitimate error codes. The result is a hard-to-detect implant that blends into the server’s normal traffic patterns while retaining full control over its hidden C2 channel.

Where this becomes significantly more strategic is in how the module manages remote peers. The listener can categorize external IP addresses into two functional roles: servers and clients. It automatically pairs nodes from each list to relay traffic between them. Once two peers are matched, the compromised host becomes a live forwarding point: data arriving from the “server” side is streamed directly to the “client,” and responses are returned in the same manner, effectively turning the victim into a transparent communication bridge. We observed multiple instances where government entities were inadvertently serving as C2 relays. In some instances, the victim machine is leveraged as a hop, serving as an access node for ShadowPad clients active in other, unrelated target environments. This chaining effect forms a multi-layered mesh of compromised infrastructure, allowing the operator to issue commands to downstream implants without direct communication with them.

Beyond its role in constructing a large-scale external relay network, the ShadowPad IIS module also exposes a conventional internal proxy capability, designed to route commands toward ShadowPad nodes located deeper within the same network. When the attackers need to deliver instructions to implants that do not have direct external reachability, the IIS module simply relays the traffic internally, behaving much like a pivot point inside the compromised environment. This capability is not new in concept, but its integration into a legitimate IIS worker process makes it extremely difficult to distinguish malicious lateral communication from normal internal service traffic.

Beyond the relay logic itself, the module leaves behind an unusually rich forensic artifact: debug strings that document the number of bytes it forwards between external and internal IP addresses. These strings include source, destination, and payload size. During our investigation, this telemetry proved essential in reconstructing the attackers’ communication graph, enabling us to map exactly how commands entered the victim, how they were relayed, and which internal hosts were drawn into the chain. The presence of such granular logging underscores just how central the relay mechanism is to the operator’s workflow: the victim is not merely compromised, but actively repurposed as a communication bridge that keeps the broader ShadowPad infrastructure stitched together.

This architecture enhances stealth and survivability. By routing commands through unrelated victims, the true controlling IP is never exposed, and network defenders have difficulty distinguishing malicious cross-organization traffic from legitimate inter-government or inter-infrastructure communication. Most significantly, every newly compromised perimeter system can be repurposed immediately as another hop, allowing Ink Dragon to expand its operational reach while obscuring both the origin and the direction of command flow.

Tools & Post‑Exploitation Components

As we expanded our analysis beyond the initial access vector, a broader toolset began to emerge. Ink Dragon does not operate with a single backdoor or a monolithic framework; instead, the intrusions feature a sequence of purpose-built components that activate at different stages of the operation. What follows is a breakdown of the post-exploitation tooling we recovered, from IIS-embedded ShadowPad modules to debugger-based loaders, credential-harvesting implants, and long-term command-and-control platforms.

Summary of Observed Components

Category	Name	Function	Brief Description
IIS Backdoors & C2	ShadowPad IIS Listener Module	Core C2 & relay node	Intercepts selected IIS traffic, decrypts commands, builds a distributed relay network, and exposes a full ShadowPad backdoor on IIS servers.
Loader	ShadowPad Loader	Payload delivery	Triad structure (EXE + DLL + TMP) that decrypts and runs the ShadowPad core in memory while deleting artifacts.
Loader	CDBLoader	Memory-resident payload execution	Uses cdb.exe scripts to patch memory, run shellcode, and load AES-encrypted payloads via a debugger session.
Credential Access	LalsDumper	LSASS dump extraction	Registers a malicious SSP DLL in LSASS, extracts a compressed LSASS memory dump via custom direct-syscall logic.
Loader	032Loader	Host-dependent payload loader	RC4-like decryption using the system’s InstallDate as entropy, delivering payloads via shared memory mapping.
Modular RAT	FinalDraft	Long-term espionage & cloud C2	Modular RAT using Microsoft Graph API; supports exfiltration, RDP history harvesting, tunneling, scheduling, and mailbox-based command exchange.

ShadowPad IIS Listener Module

Module Initialization

The ShadowPad IIS Listener Module deployed in this intrusion is a fully integrated component designed to masquerade as part of the legitimate IIS stack while quietly providing command-and-control and relay capabilities. Its configuration block defines several operational parameters that determine both how it responds to benign web traffic and how it handles attacker-controlled messages. These include a server type string (used in HTTP response headers), a document root path, and a fallback error page path. Even when configuration fields are absent, the module falls back to realistic defaults — "C:\\inetpub\\wwwroot", "C:\\inetpub\\custerr\\en-US\\404.htm", and "Microsoft-IIS/10.0". Those values allow the module to blend seamlessly with a standard Windows Server installation.

The most important part of the configuration is the list of URL patterns the module will intercept. These patterns are stored as wildcard-enabled strings, separated by semicolons, and are used to register listeners via the Windows HttpAddUrl API. This API allows the module to attach itself to arbitrary HTTP URL prefixes, including those containing wildcards for hostnames or paths, making the listener hard to detect unless the exact bindings are enumerated at the OS level. Any inbound request matching one of these patterns is captured and passed through the module’s internal decryption and message-parsing routine.

def decrypt_first_packet(buf: bytearray, seed: int, length: int):
    """
    buf     = bytearray starting at a1->type (first byte = LOBYTE(seed), second = HIBYTE(seed))
    length  = total length of buffer (rdx)
    """
    
    count = length - 2
    seed_lo = buf[0]
    seed_hi = buf[1]
    num = (seed_hi << 8) | seed_lo
    num &= 0xFFFFFFFF
    
    pos = 2
    for _ in range(count):
        hi = (num >> 16) & 0xFFFF
        num = (hi * 0x7093915D - num * 0x6EA30000 + 0x06B0F0E3) & 0xFFFFFFFF
        buf[pos] ^= num & 0xFF
        pos += 1

    return buf

Requests that do not conform to the attacker’s expected encrypted structure are quietly handled as normal IIS traffic: static files are served from the configured webroot folder when available, and legitimate error pages are returned otherwise. This fallback behavior ensures the listener remains operationally stealthy, presenting a legitimate façade while still acting as a covert interception point.

Split Command Architecture

Once a request passes the module’s URL‑matching, decryption, and structural checks, the ShadowPad IIS Listener evaluates the embedded command ID. These commands fall into two broad categories, both of which are handled by the same component.

The first category contains the instructions responsible for building and maintaining the distributed relay network. These include commands that register an endpoint as a “server,” commands that register an endpoint as a “client,” and the logic that pairs nodes, forwards traffic, and maintains the two queues that drive the hop‑to‑hop communication model.

The second category is entirely separate in purpose: a full backdoor command set enabling the operator to interact directly with the local system. These commands cover host reconnaissance, file operations, data collection, payload staging, configuration updates, process control, and network‑level actions. In other words, the same module responsible for linking compromised machines into a cross‑victim relay chain is also fully capable of operating as a traditional ShadowPad backdoor on that same host.

Distributed Relay Network Construction

The most strategically significant capability lies in the module’s role in building and maintaining the operator’s distributed relay network. The IIS Listener maintains two concurrent registries of peers: a server list and a client list. Nodes can add themselves to either list via dedicated command IDs. Entries in the server list are periodically revalidated every 30 seconds, and the module issues acknowledgments to confirm ongoing availability. Nodes placed into the client list behave differently; if a client remains unpaired for 30 seconds, it is automatically pruned to prevent stale links.

Whenever a new node is inserted into one of the lists and the Listener detects that both lists contain at least one live entry, it pairs the first server node with the first client node. At that moment, it sends the server the victim’s IP address. After this handshake, the module establishes bidirectional relaying between the two sockets, shuttling each packet from server → client and client → server with no further processing. The result is a fully transparent hop that allows an upstream operator to deliver commands to a downstream client without ever interacting with that client directly.

Backdoor Features

The IIS Listener Module of ShadowPad also includes features of the ShadowPad client, letting the attackers run different commands on the IIS machine. This embedded command set provides the operator with extensive control over the compromised host, enabling everything from reconnaissance to interactive access to payload staging.

The breadth of these command IDs illustrates how ShadowPad’s IIS Listener is more than a simple traffic forwarder; it is a self-contained control surface capable of both maintaining the distributed relay network and exerting fine-grained control over any machine running it. This duality is central to Ink Dragon’s operational philosophy: relay-capable nodes double as fully functional access points, allowing the operator to maintain stealthy persistence, collect intelligence, deploy additional tooling, and issue high-privilege instructions without ever exposing their true command infrastructure.

ShadowPad’s backdoor exposes a broad command surface designed to give operators full, hands-on control of a compromised system. It begins with basic orchestration capabilities, retrieving the full command map, gathering a detailed system snapshot, launching an interactive reverse shell, or cleanly shutting down the implant when needed. From there, the malware offers an extensive file-management layer that can enumerate drives, walk directories, manipulate timestamps, create or remove files and folders, and read, write, move, or download data on demand. This is complemented by rich process and service control, allowing operators to list and kill processes, inspect loaded modules, and start, stop, or delete Windows services with the same precision seen in legitimate administration tools.

ShadowPad also supports multiple execution models, including running commands with output capture, spawning interactive console processes with full screen-buffer access, and maintaining long-lived execution channels through pipes. The networking side is equally capable: the implant can inspect local TCP/UDP tables, alter network entries, and even serve as a pivot point by proxying or tunneling traffic through the infected host. In practice, these capabilities turn the backdoor into a complete remote operations platform that blends system administration, espionage tooling, and covert tunneling into a single, tightly integrated module.

ShadowPad Loaders

ShadowPad is a modular, multi-layered backdoor framework widely attributed to Chinese state-sponsored threat groups and frequently used in long-term espionage campaigns. ShadowPad allows operators to deploy customized modules for data exfiltration, credential harvesting, and lateral movement.

In Ink Dragon’s operations, we observed numerous ShadowPad deployments following a recurring triad sideloading structure: an executable, a malicious DLL, and an encrypted TMP payload. The legitimate or masqueraded executable loads the malicious DLL, which in turn decrypts and executes the ShadowPad core from the TMP file directly in memory, erasing the payload afterward to minimize forensic artifacts.

A notable detail in Ink Dragon’s ShadowPad loader is that many of the malicious DLLs across different incidents shared the same generic name, DLL.dll. The DLL loaders were MFC-based binaries, where the malicious code was heavily obfuscated using ScatterBrain.

Once executed, these loaders perform on-the-fly decryption of configuration data and payloads stored in the same directory.

Path	Executable	DLL
C:\Users\Public	vncutil64.exe	vncutil64loc.dll
C:\Program Files\Microsoft\Edge	ApplicationLogs.exe	atiadlxy.dll
C:\Program Files\Microsoft\Edge\Application	msedge_proxyLog.exe	msedge_proxyLogLOC.dll

CDBLoader

One of Ink Dragon’s most distinctive TTPs is leveraging the Microsoft debugger (cdb.exe) as an execution host.

Rather than launching a typical EXE, the operator runs a WinDbg/CDB scripted session, such as: c:\users\public\cdb.exe -cf C:\Users\Public\config.ini -o C:\Users\Public\cdb.exe.

The shipped config.ini is not an innocuous INI file. It contains a sequence of memory-edit / write-bytes commands followed by a change to the instruction pointer (RIP) so execution continues at the attacker-controlled shellcode. The shellcode, in turn, reads an auxiliary file (wmsetup.log) from disk, decrypts it with an AES key hard-coded into the shellcode, and loads the real payload into memory for execution. After the payload runs, the debugger instance and helper files are often removed, leaving only transient memory-resident code.

LalsDumper

LalsDumper is Ink Dragon’s in-house LSASS extraction chain observed during multiple intrusions. The sequence starts with a small loader (lals.exe) that manipulates a companion DLL (fdp.dll) in the same folder. The loader locates a placeholder (32 ‘d’ characters) inside fdp.dll, overwrites that placeholder with the path to an auxiliary file (rtu.txt), and produces a patched DLL named nfdp.dll.

Crucially, the loader calls AddSecurityPackageA to register the DLL as a Security Support Provider (SSP) so that lsass.exe will load it, a technique that forces LSASS itself to load attacker code in-process.

The registered DLL reads rtu.txt, applies a simple XOR with 0x20 to recover a secondary payload, and then maps that payload into memory. The payload implements a custom MiniDumpWriteDump-like routine (not using the Windows API directly) to create a compressed dump of lsass.exe, writing a ZIP-like dump file named <%TEMP%>\<pid>.ddt.

The chain uses direct syscalls (hashed at runtime) to evade API-based hooking and EDR detection.

def hash_syscall(name: str) -> int:
    h = 0xCD7815D6                # constant seed
    for (*WORD) ch in name:       # iterate Unicode code-points
        h ^= (ord(ch) & 0xFFFF) + ror32(h, 8)
    return h & 0xFFFFFFFF

The DLL exposes a function called Tom, which serves as the payload’s runtime entry point and orchestrates the loader’s core logic: when invoked, Tom creates a file in the system TEMP folder named using the victim process ID (for example %TEMP%\<pid>.ddt), captures a memory dump of lsass.exe and writes that dump into the .ddt file using a ZIP-style archive format.

032Loader

032Loader is a sideload-based loader that uses host-specific entropy to decrypt and execute payloads. After being sideloaded by a legitimate executable, the loader patches the host EXE’s code flow so that control transfers to its own logic immediately after load.

The loader then queries the system’s InstallDate from the registry and derives a decryption key from that value. It uses the InstallDate as the seed for RC4 (or RC4-like) decryption of a third-file blob (the encrypted payload), often accessed via the SystemFunction032 API to perform the decryption step. Once decrypted, the loader maps the payload via CreateFileMappingW / MapViewOfFile and adjusts memory protections with VirtualProtect, then transfers execution to the in-memory payload.

FinalDraft: New Version

FinalDraft is a well-engineered, full-featured remote administration tool with the ability to accept add-on modules that extend functionality and proxy network traffic internally, mostly by abusing Outlook mail service via the Microsoft Graph API. The samples and behavior we analyzed are consistent with previous public write-ups, but Ink Dragon’s deployments show continued feature expansion and careful operational tuning to reduce telemetry and maximize resilience.

FinalDraft begins by locating and decrypting its configuration blob. The configuration is XORed with either a hash derived from the host ProductId (making the config per-machine) or the hash of a hardcoded string.

Once decrypted, the config exposes a unique GUID for the implant, the preferred communication transport, an AES key for message encryption, C2 endpoints and refresh tokens, and other operational metadata.

00000000 struct __fixed config_structure
00000000 {
00000000     char refresh_token[5000];
00001388     char backup_refresh_token_url[200];
00001450     char guid[36];
00001474     int unknown;
00001478     __int16 build_id;
0000147A     int sleep_val;
0000147E     char communication_method;
0000147F     char aes_key[16];                
0000148F     char external_ip_or_not;
00001490     char self_remove_flag;
00001491     char exit_byte __bin;
00001492     __int64 unknown;
0000149A     int interval_val;
0000149E     int repeat_val;
000014A2     int[7] time_list;
000014B2     __int64 time1;
000014BA };

The most commonly used transport we observed is COutlookTrans, which leverages the Microsoft Graph API (the token endpoint at https://login.microsoftonline.com/common/oauth2/token) to hide command-and-control traffic inside legitimate cloud mail flows. FinalDraft uses a refresh token embedded in its configuration to acquire OAuth access tokens, which it stores locally under HKCU\SOFTWARE\Microsoft\Windows\CurrentVersion\Explorer\UUID\ for later use. Commands and responses are exchanged as email drafts (messages whose subject starts with r_<session_id> and p_<session_id>). The payloads inside those messages are base64-encoded, AES-encrypted (with the config key), and compressed, which helps the operator hide instructions inside otherwise normal mailbox data and bypass network filtering that whitelists Microsoft cloud endpoints.

FinalDraft implements a modular command framework in which operators push encoded command documents to the victim’s mailbox, and the implant pulls, decrypts, and executes them. Beyond the standard toolkit (process/service control, file/registry access, tunneling, and file transfer), the Ink Dragon variants we analyzed introduce several extensions that increase stealth, operational control, and data-theft efficiency.

Flexible Beacon Scheduling

The malware supports highly granular callback controls, allowing operators to define daily beaconing windows or specify an explicit list of times when the implant will attempt to reach its C2. This schedule-based design helps blend command traffic into predictable operational patterns.

RDP Activity Harvesting

DumpRDPHistory extracts evidence of both outbound and inbound RDP usage.

1. RDP OUT (Outbound RDP targets) – MRU-like entries in the user registry hives that record the servers the user has connected to (the built-in RDP client’s Recent Server list). For each entry, the tool reads UserNameHint and the registry key’s last write time to produce lines like: <ServerName>:<UserNameHint>:<Timestamp>. This data is queried from HKCU\SOFTWARE\Microsoft\Terminal Server Client\Servers\<ServerName>\
2. RDP IN (Inbound RDP sessions) – Event log entries from the Terminal Services Local Session Manager operational channel that record session connect/disconnect events. The tool queries Windows Event Log (via EvtQuery / Get-WinEvent) for Event ID 21 (Remote Desktop Services: Session logon succeeded) and 25 (disconnect/other session events depending on Windows version). From these, it extracts records like: <UserName>:<IP>:<TimeStamp>.

Security Control Downgrades

Several commands intentionally weaken Windows security posture to assist persistence and lateral movement:

• DisableRestrictedAdmin – makes RDP connections authenticate with reusable credentials or delegated tokens, enabling credential hopping instead of “restricted” mode authentication. It makes RDP more attractive for lateral movement because an attacker who already controls an endpoint can initiate Restricted Admin connections using local credentials or stolen hashes to hop to other systems without needing cleartext passwords.

• DisableTokenFiltering – disables remote token filtering so local admin accounts receive full, not restricted, admin tokens over the network. This allows attackers to use reused or harvested local credentials to perform truly administrative remote actions without requiring domain credentials.
• EnableDSRMAdmin – allows the powerful DSRM recovery account to log in even when the DC is not in Directory Services Restore Mode. It gives an attacker a powerful, low-audited way to gain full local control of a DC using an otherwise seldom-monitored credential or hash.

• DisableRunAsPPL – removes Process Protection Level safeguards, enabling injection and tampering with protected processes such as AV/EDR components. Adversaries do this to defeat endpoint defenses (allowing in-memory loaders, hooking, or live patching of AV and EDR components), to persist by replacing signed binaries, or to extract secrets from processes that would normally be protected.

High-Throughput Exfiltration

BackgroundFileTransfer introduces a dedicated asynchronous worker for large-scale exfiltration. It streams data in sizeable chunks, reports progress in the background, and minimizes the number of required outbound connections. This is ideal for moving large archives over cloud-proxied channels without attracting attention.

Host Profiling & Inventory

Ink Dragon consolidates multiple reconnaissance routines such as system fingerprinting, adapter enumeration, and installed software collection into a unified host profile. This captures IP configuration details (similar to ipconfig /all), network adapter metadata, installed applications (from HKLM and HKCU uninstall keys), system identifiers (MachineGuid, ProductId, InstallDate, and System uptime), and other indicators useful for victim classification and targeting.

Victimology

Ink Dragon’s targeting patterns show a consistent emphasis on government organizations, but beyond that shared characteristic, there are no clear indicators of how the actor selects its victims. Most affected organizations appear to serve a specific operational purpose for the actor rather than reflecting broad, industry-wide targeting.

Geographically, Ink Dragon has focused heavily on government entities in Southeast Asia and Africa, and in recent months has expanded its activity into Europe. Since the ToolShell exploitation wave in July, the actor has steadily increased its operations in the region, with a growing concentration on European government-sector targets. A key aspect of Ink Dragon’s tradecraft is its use of compromised organizations as C2 relay nodes. As a result, we have observed European victims being leveraged to launch activity not only against additional European institutions, but also against targets in Africa and Southeast Asia.

Overlap With RudePanda / REF3927 Activity

During our investigation, we also observed evidence of a second intrusion set commonly tracked as RudePanda / REF3927 on several of the same victim environments compromised by Ink Dragon. While we do not assess these activity clusters to be operationally linked, the victimology overlap is notable: both actors exploited the same internet-facing server vulnerability to gain footholds in identical organizations and systems.

Upon gaining access, RudePanda relied on its customary toolset, which included:

• Godzilla-derived webshells used to execute in-memory .NET payloads and deploy secondary components.
• Malicious IIS modules belonging to the TOLLBOOTH family embed command-and-control logic directly inside the web server’s processing pipeline. Several modules contained PDB references to dongtai, consistent with tooling previously documented in public reporting.
• Configuration tampering of IIS’s applicationHost.config, where a simple diff against previous snapshots exposes the unauthorized module insertions.

In addition, the actor deployed a kernel-mode rootkit driver, wingtb.sys (2965ddbcd11a08a3ca159af187ef754c), a modified and signed variant of Winkbj.sys. Its installation uses an INF file (Hidden.inf) naming the service “Wingtb.” The driver is derived from the open-source “Hidden” rootkit project, used to conceal files, processes, and registry entries after being activated on the system.

Finally, several .lnk artifacts referencing the operator’s backdoor kit that is consistent with the GoToHTTP tooling previously documented by both HarfangLab and Elastic were recovered on disk. Although the original payloads were deleted, these link files served as additional indicators of RudePanda’s presence.

Conclusion

Ink Dragon’s operations illustrate a shift in how modern espionage clusters weaponize compromised infrastructure. Rather than treating each victim as an endpoint to be monitored or harvested, the group systematically folds every breached perimeter server into a distributed ShadowPad relay fabric. The IIS Listener Module sits at the center of this strategy: a stealthy, low-friction component that binds hidden URL prefixes, intercepts traffic without disrupting legitimate services, and quietly links victims together into a multi-hop communication grid.

This architecture transforms the threat landscape in several ways. First, it grants the operators resilient command paths that do not rely on fixed infrastructure. Even if a downstream implant has no internet reachability or if upstream servers are blocked, the attacker can simply reroute through other victims. Second, it provides natural operational camouflage. Traffic tunneled between unrelated government or enterprise networks appears outwardly benign and often blends into the expected profile of inter-organizational HTTP flow. Third, it enables strategic re-use of compromised assets: once a host is enrolled as a ShadowPad relay, it becomes an evergreen pivot point that continues to serve campaigns long after the initial intrusion.

The rest of the kill chain reinforces this design philosophy. Mature sideloading patterns, modular loader triads, memory-resident payloads, compartmentalized privilege escalation, and disciplined credential harvesting all serve one purpose: establish durable, high-privilege access long enough to turn the victim into reliable infrastructure. Whether deploying FinalDraft, staging ShadowPad loaders, or extracting domain-wide secrets, each action feeds back into the broader communication mesh that Ink Dragon relies on to maintain persistence across environments.

Taken together, Ink Dragon presents a threat model in which the boundary between “compromised host” and “command infrastructure” no longer exists. Each foothold becomes a node in a larger, operator-controlled network – a living mesh that grows stronger with every additional victim. Defenders must therefore view intrusions not only as local breaches but as potential links in an external, attacker-managed ecosystem, where shutting down a single node is insufficient unless the entire relay chain is identified and dismantled. Ink Dragon’s relay-centric architecture is among the more mature uses of ShadowPad observed to date. A blueprint for long-term, multi-organizational access built on the victims themselves.

IOCs

2e84ea5cef8a9a8a60c7553b5878a349a037cffeab4c7f40da5d0873ede7ff72 - dongtai module
e2f6e722c26e19b76396c2502cacf2aaceaaa1486865578c665ebf0065641ffa - dongtai module
f9dd0b57a5c133ca0c4cab3cca1ac8debdc4a798b452167a1e5af78653af00c1 - Wingtb.sys
a86e72ca58de6d215a59ae233963eaea27fe47ef0c9f43938e27339df4a86732 - 032Loader
7efe5c1229178c1b48f6750c846575e7f48d17ea817997bd7acba0e5ecf1e577 - 032Loader
D88115113E274071B03A3B4C1DA99EAEA7B8D94ADF833DFD26943AF0A6D78B4D - 032Loader
f094ff83d4b7d06bc17b15db7d7dc0e622778b0eda71e8fc9fdf7db83c460426 - nfdp.dll
36f00887f6c0af63ef3c70a60a540c64040b13a4209b975e96ce239e65548d4a - fdp.dll
ecf0fbd72aac684b03930ad2ff9cdd386e9c13ddf449f27918f337dc8963590e - LalsDumper
2b57deb1f6f7d5448464b88bd96b47c5e2bd6e1c64c1b9214b57c4d35a591279 - LalsDumper
b4a53f117722fb4af0a64d30ec8aa4c4c82f456e3d2a5c5111c63ce261f3b547 - ShadowPad Loader
866fde351251092fb5532e743459ba80968cd5516cce813c8755467f5e8a47a1 - ShadowPad Loader
188ab2d68f17ecf08a7a4cfc6457c79b0a5117b3277352a7371a525416129114 - ShadowPad Loader
809ddcbb64d6f2ccc4a8909068da60e6ea8b3ebd9c09dd826def0e188c7a2da2 - config.ini
f438ca355e6888c4c9cd7287b22cfe5773992ef83f0b16e72fb9ae239d85586c - FinalDraft
c305b3b3f9426d024cdd262497a5d196264397bfed445705759d0a793a58fe6e - Encrypted FinalDraft

The post Inside Ink Dragon: Revealing the Relay Network and Inner Workings of a Stealthy Offensive Operation appeared first on Check Point Research.

By: Dikla Barda, Roman Zaikin, and Oded Vanunu

On November 30, 2025, Check Point Research detected a critical exploit targeting Yearn Finance’s yETH pool on Ethereum. Within hours, approximately $9 million was stolen from the protocol. The attacker achieved this by minting an astronomical number of tokens—235 septillion yETH (a 41-digit number)—while depositing only 16 wei, worth approximately $0.000000000000000045. This represents one of the most capital-efficient exploits in DeFi history.

The Vulnerability: Cached Storage Flaw

The attack exploited a critical flaw in how the protocol manages its internal accounting. The yETH pool caches calculated values in storage variables called packed_vbs[] to save on gas costs. These variables store virtual balance information that tells the protocol how much value exists in the pool. The vulnerability emerged when the pool was completely emptied—while the main supply counter correctly reset to zero, the cached packed_vbs[] values were never cleared.

How the Attack Was Executed

The attacker executed the exploit in three stages: First, they performed over ten deposit-and-withdrawal cycles using flash-loaned funds, deliberately leaving small residual values in the packed_vbs[] storage with each iteration. Second, they withdrew all remaining liquidity, bringing the supply to zero while the cached values remained populated with accumulated phantom balances. Finally, they deposited just 16 wei across eight tokens. The protocol detected that supply was zero and triggered its “first-ever deposit” logic, which read the cached values from storage. Instead of minting tokens based on the 16 wei actually deposited, the protocol read the accumulated phantom values and minted trillions upon trillions of LP tokens, giving the attacker control over the entire pool.

Background: The yETH Ecosystem

Protocol Architecture

Yearn Finance’s yETH is a liquid staking token representing a basket of Ethereum-based liquid staking derivatives (LSDs). The protocol consists of three main components:

1. yETH Token – A standard ERC20 token with minter privileges
2. yETH Pool – A weighted stableswap AMM (Automated Market Maker) pool
3. Rate Providers – Oracle contracts that provide exchange rates for various LSDs

The pool contract implements a complex mathematical invariant based on weighted pool mechanics (similar to Balancer), adapted with Curve-style virtual balances for gas optimization.

The Pool’s Core Mechanism

Unlike simple constant-product AMMs (x × y = k), the yETH pool uses a sophisticated invariant that accounts for:

• Multiple assets (up to 32)
• Weighted ratios for each asset
• Exchange rates for LSDs (wstETH, rETH, cbETH, etc.)
• Virtual balances calculated as: vb_i = balance_i × rate_i / PRECISION

The pool stores these virtual balances in state variables to avoid recalculating them on every operation—a gas optimization that became the source of the vulnerability.

The Vulnerability: Incomplete State Cleanup

The Core Bug

The vulnerability exists in the interaction between two functions: remove_liquidity() and add_liquidity().

In remove_liquidity() (lines 590-654):

The Problem: When ALL LP tokens are burned (supply == 0), the virtual balances are decremented proportionally but never explicitly reset to zero. Due to rounding, tiny amounts remain in self.packed_vbs[].S

In add_liquidity() (lines 523-528): 

In _calc_vb_prod_sum() (lines 729-744):

The Fatal Flaw: This function reads self.packed_vbs[asset] from storage, expecting them to be zero for a “first deposit” scenario. However, after multiple deposit/withdrawal cycles, these storage slots contain accumulated residual values that were never reset.

The Exploit Transaction: A Technical Walkthrough

Phase 1: Capital Acquisition

The attacker borrowed assets via flash loans from Balancer and Aave, obtaining wstETH, rETH, WETH, ETHx, and cbETH without upfront capital.

Phase 2: State Poisoning

The attacker executed multiple deposit-withdrawal cycles to accumulate residual values in packed_vbs[] storage. Each cycle deposited assets into vaults and the yETH pool, then withdrew portions. The virtual balances decremented but never fully reset.

Phase 3: Pool Drain

The attacker burned all remaining LP tokens, setting self.supply = 0 while self.packed_vbs[] retained accumulated values and was NOT reset.

Phase 4: Exploit

The attacker deposited minimal wei amounts across all supported tokens. The protocol treated this as an initial deposit and read stale storage values, minting septillions of yETH tokens instead of calculating from the actual dust deposit.

Phase 5: Fund Extraction

The attacker swapped the minted yETH tokens for WETH on Balancer pools and withdrew the underlying assets (sfrxETH, wstETH, ETHx, cbETH, rETH, apxETH, wOETH, mETH) from the pool.

Phase 6: Cleanup

The attacker converted all stolen assets to ETH via Uniswap V3 and other DEXs, repaid all flash loans with fees, and sent a portion to Tornado Cash for laundering while retaining the remainder as profit.

The Design Bug

1 wstETH ≈ 1.15 ETH

1 rETH ≈ 1.08 ETH

1 cbETH ≈ 1.00 ETH

To calculate how many LP tokens to give you, the pool needs to:

1. Get the exchange rate for each token (expensive!)
2. Calculate: virtual_balance = actual_balance × rate / PRECISION
3. Sum all virtual balances
4. Use this for the invariant calculation

Doing this EVERY time is expensive gas-wise, so instead of recalculating every time, the pool:

• Calculates once when you deposit/withdraw
• Stores the result in packed_vbs[]
• Reuses this cached value in future calculations

Expensive (done every operation without caching): 

Cheap (with caching): 

What Happens When It’s Not Zero When It Should Be?

Normal Flow (Working Correctly), scenario: Pool has 100 ETH worth of assets

Bug Scenario (When Not Reset) What the code ASSUMES when supply == 0:

What ACTUALLY happens after full withdrawal:

The pool was designed to store virtual balances in state to save gas on recalculations. This is a common optimization pattern in DeFi:

The Missing Edge Case

The developers correctly handled the normal flow:

• Adding liquidity updates virtual balances ✓
• Removing liquidity decrements virtual balances ✓
• Swapping updates virtual balances ✓

But they missed the edge case:

• Removing ALL liquidity should RESET virtual balances to zero ✗

The Implicit Assumption

The code assumed that when prev_supply == 0, this meant a “first-ever deposit” to a pristine pool. But after a full withdrawal, prev_supply == 0 while packed_vbs[] contained residual state from previous operations.

Conclusion

The yETH exploit stands as a masterclass in finding and exploiting subtle state management bugs. The attacker demonstrated deep understanding of:

• The protocol’s mathematical invariants
• Storage layout and state persistence
• How to manipulate state across multiple transactions
• How to maximize impact with minimal capital

For defenders, this exploit reinforces that correctness in complex systems requires explicit handling of ALL state transitions, not just the happy path. A missing state reset—a single oversight in 1000+ lines of code—enabled the theft of $9 million.

As DeFi protocols grow more complex, incorporating novel AMM designs and mathematical optimizations, the attack surface for such subtle bugs expands. The only defense is rigorous engineering discipline: explicit state management, comprehensive testing, and the humility to assume that if something CAN go wrong, eventually someone will find a way to exploit it.

How this could have been prevented

Onchain security must evolve from post-incident forensics to real-time prevention:

→ Simulate transactions before execution to catch abnormal token minting ratios (16 wei in → septillions out is not normal)

→ Track state across transaction sequences — this attack required 10+ deposit/withdrawal cycles to poison packed_vbs[]. Single-transaction monitoring would miss it

→ Block execution automatically when drain patterns emerge, not just alert after the fact

The difference:

• Seeing the exploit after $9M is gone vs.
• Stopping the malicious add_liquidity() before it executes

The Lesson: A single missing state reset — packed_vbs[] not clearing when supply hit zero — enabled this entire attack. Complex DeFi systems need runtime protection that understands protocol logic, not just signature-based detection.

Learn more about Check Point’s Blockchain Security solution here.

The post The $9M yETH Exploit: How 16 Wei Became Infinite Tokens appeared first on Check Point Research.

By: Isabel Mill & Oded Vanunu

OpenAI Codex CLI is OpenAI’s command-line tool that brings AI model-backed reasoning into developer workflows. It can read, edit, and run code directly from the terminal, making it possible to interact with projects using natural language commands, automate tasks, and streamline day-to-day development One of its key features is MCP (Model Context Protocol) – a standardized way to integrate external tools and services into the Codex environment, allowing developers to extend the CLI’s capabilities with custom functionality and automated workflows.

Research Motivation

We tested whether Codex safely handles project-supplied configuration and environment overrides automatically loaded at runtime, and whether implicit trust in those project files, which the CLI may read and execute without explicit user consent or provenance checks, can be abused in collaborative workflows.

Our Research Findings

During testing, we found that Codex CLI will automatically load and execute MCP server entries from a project-local configuration whenever codex is run inside that repository., Concretely, if a repository contains a .env that sets CODEX_HOME=./.codex and an accompanying ./.codex/config.toml with mcp_servers entries, Codex CLI resolves its config to that local folder, parses the MCP definitions, and invokes the declared command/args immediately at startup. There is no interactive approval, no secondary validation of the command or arguments, and no re-check when those values change — the CLI treats the project-local MCP configuration as trusted execution material.

This sequence turns ordinary repository files into an execution vector: an attacker who can commit or merge a .env and a ./.codex/config.toml can cause arbitrary commands to run on any developer who clones the repo and runs codex. In practice, we demonstrated this with deterministic payloads (file-creation) and by replacing benign commands with reverse-shell payloads; both executed without user prompts. Because the behavior binds trust to the presence of the MCP entry under the resolved CODEX_HOME rather than to the contents of the entry, an initially innocuous config can be swapped for a malicious one post-approval or post-merge, creating a stealthy, reproducible supply-chain backdoor that triggers on normal developer workflows.

Technical Deep Dive

Codex resolves its configuration path at startup, then parses and materializes any MCP server entries it finds so they’re available to the runtime. When the effective CODEX_HOME points at a repository folder, Codex treats that repo-level config as the authoritative source and will invoke the command + args listed under mcp_servers as part of expected startup/automation flows. In the vulnerable behavior, there is no secondary validation, no interactive approval, and no re-check when the command/args change.  The CLI simply runs what the project config declares.

This means an attacker can perform the following steps:

1. Prepare a repository with a benign-looking project structure.

2. Add a .env that redirects configuration to the repo:

3. Commit a ./.codex/config.toml containing an mcp_servers entry that declares command + args. In this example, we used a harmless file-creation payload, but the same chain can be swapped for a reverse shell.

4. When a developer clones or updates the project and runs codex, the repo .env setting CODEX_HOME=./.codex causes Codex to load ./.codex/config.toml and execute its mcp_servers.*.command immediately, without prompting. The command runs in the user’s context; an attacker can silently swap in a reverse shell, exfiltrate data, or harvest credentials. In the image example below, we demonstrate this by opening Calculator on the victim machine.

Real World Consequences

This vulnerability enables silent, repeatable remote code execution in any environment where developers run codex against a repository. By abusing project-local config loading, an attacker who can land a commit or PR can turn an otherwise innocent repo into a persistent backdoor that triggers whenever a developer runs codex, with no additional prompts or approvals.

An attacker with write or PR access can:

• Achieve persistent remote access: Embed a reverse shell or persistent payload in ./.codex/config.toml (delivered alongside a .env that redirects CODEX_HOME) and regain access each time a developer runs codex.
• Execute arbitrary commands silently: Any shell command defined in an MCP entry runs immediately in the user’s context whenever codex loads the project config.
• Escalate and exfiltrate: Developer machines frequently hold cloud tokens, SSH keys, and source; attackers can harvest credentials, exfiltrate secrets, or push further exploits.
• Persist and swap payloads post-merge: Because trust is tied to the resolved config location rather than the contents, an initially harmless entry can be replaced later with malicious commands without triggering re-approval.
• Propagate via supply-chain artifacts: Compromised templates, starter repos, or popular open-source projects can weaponize many downstream consumers with a single commit.
• Contaminate CI and build pipelines: If CI, automation, or build agents run codex on checked-out code, the compromise can move from workstations into build artifacts and downstream deployments.
• Enable lateral movement and privilege escalation: With harvested credentials and local access, an attacker can pivot to cloud resources, repositories, or internal networks.

This breaks the CLI’s expected security boundary: project-supplied files become trusted execution material, and that implicit trust can be exploited with minimal effort and no user interaction beyond standard development workflow.

Responsible disclosure timeline:

• Check Point Research responsibly disclosed the issue to the OpenAI Codex CLI team on August 7, 2025.
• OpenAI issued a fix on August 20, 2025, in Codex CLI version 0.23.0. The patch prevents .env files from silently redirecting CODEX_HOME into project directories, closing the automatic execution path we demonstrated.
• Our testing confirmed the fix is effective. Codex CLI now blocks project-local redirection of CODEX_HOME, requiring safer defaults and stopping immediate execution of attacker-supplied project files.

To ensure protection, we strongly recommend all users update to Codex CLI version 0.23.0 or later.

The post CVE-2025-61260 — OpenAI Codex CLI: Command Injection via Project-Local Configuration appeared first on Check Point Research.

Key Findings

• Record fragmentation and decentralization: The number of active extortion groups in Q3 2025 rose to a record of 85 groups, the highest number observed to date. The top 10 groups accounted only for 56% of all published victims, down from 71% in Q1.
• Stable high activity: Ransomware victim postings stabilized at an average of 535 victims per month, up from 420 year-over-year (YoY) in Q2–Q3 2024, but below the Q1 2025 peak.
• LockBit’s return: Lockbit’s reappearance with the release of LockBit 5.0 in September 2025 potentially signals affiliate re-centralization under a major brand
• Emerging dominance: Qilin became the most active group, averaging 75 victims per month, followed by Akira, INC Ransom, and Play.
• Regional concentration shifts: South Korea entered the top 10 list of most attacked countries following a focused campaign by Qilin targeting its financial sector.
• Sector impact: Manufacturing and business services remained the most affected sectors. Healthcare held steady at 8% of total victims, despite selective avoidance by major actors like Play.

Ransomware in Q3 2025: RaaS fragmentation increases and Lockbit is back

During the third quarter of 2025, we monitored more than 85 active data leak sites (DLS) that collectively listed 1,592 new victims. Compared to the 1,607 victims reported in Q2 2025, the publication rate remained stable though it is still notably higher than the 1,270 victims recorded in Q3 2024 (a 25% increase YoY). Overall, there are approximately 520 to 540 new victims per month, indicating that ransomware activity has plateaued albeit at historically high levels.

Figure 1 – Total Number of Reported Ransomware Victims in DLS, per month.

Despite the termination of several prominent ransomware groups, the overall number of active threat actors continues to grow, with new ones appearing every month.

During Q3 2025, we observed a steady expansion of double-extortion activity, driven mostly by small and emerging operators. Of the 85 data leak sites tracked this quarter, 47 groups published fewer than ten victims, suggesting a growing number of affiliates moving beyond established ransomware-as-a-service (RaaS) programs to conduct attacks independently.

This fragmentation follows the closure or dormancy of several major RaaS brands during the year, including RansomHub, 8Base, BianLian, Cactus, and others. In Q3 alone, 14 new groups began publishing victims, bringing the total number of newly observed actors in 2025 to 45. In Q1, the ten most active groups accounted for 71% of all DLS postings. In Q2, their share fell to 63%, and by Q3, to just 56%.

These findings illustrate the limited long-term impact of law-enforcement operations on the overall number of ransomware victims. Despite several high-profile takedowns during the past year—most of them directed at large RaaS operations such as LockBit, 8Base, and Blacksuit—the total volume of attacks did not significantly decline. Instead, the attacks continue a gradual upward trend, from an average of  approximately 420 victims per month in Q2–Q3 2024 to about 535 per month in the same period of 2025.

This limited effect appears to stem from the focus of enforcement efforts: takedown operations primarily target RaaS infrastructure and administrators, which does not affect the affiliate operators who conduct the intrusions and drive the operational execution. When a major RaaS platform is disrupted, these affiliates typically migrate to alternative programs or establish their own data-leak sites, resulting in only short-term interruptions to overall activity levels.

The effects of affiliate mobility are evident both in the proliferation of new leak sites and in the rising activity of existing groups. Qilin, the most active actor in Q3 2025—and one of the most aggressive in recruiting former RansomHub affiliates—averaged around 75 victims per month, up from 36 in Q1 prior to RansomHub’s closure in April. INC Ransomware increased its monthly total from 23 to 39 victims, and Play went from 28 to 33 during the same period.

This ongoing fragmentation of the ecosystem may further erode ransomware operators’ reliability. Victims traditionally rely on attackers reputation to supply decryption keys after payment. Large RaaS brands have a commercial incentive to maintain credibility and provide the keys, but smaller, short-lived groups do not, leading to reduced payment rates which are currently estimated at 25–40% of total attacks.

In this context, the re-centralization of affiliates around major, recognizable brands remains strategically significant. Large RaaS programs preserve their market advantage through stability, reputation, and structured affiliate infrastructure. The re-emergence of LockBit, discussed in the following section, may represent precisely such a re-consolidation of affiliates under a stable and trusted brand identity.

Figure 2 – Ransomware Groups by Publicly Claimed Victims – Q3 2025.

Qilin, Akira, INC Ransom, Play, and Safepay maintained their positions among the most active groups in Q3. Warlock and The Gentlemen emerged more recently, both demonstrating rapid early activity: Warlock began posting victims in June 2025 and reached 43 total listings in Q3, while The Gentlemen claimed 38 victims during a single month of operation in September 2025.

Lockbit 5.0 is here – is LockBit truly back?

Until its disruption during Operation Cronos in early 2024, LockBit dominated the RaaS ecosystem, accounting for 20–30% of all published victims. Following the takedown, several arrests were announced, and successor groups — first RansomHub, then Qilin — attempted to inherit its affiliate base. However, the group’s core administrator, known as LockBitSupp, was never apprehended and in underground forums continued to hint at an eventual comeback.

The release of LockBit 5.0 in September 2025 marks the group’s return to active operations, reigniting questions about whether the RaaS landscape may again consolidate around this long-standing brand.

Figure 3 – LockBit’s share of all DLS-published victims.

LockBit had long promised a comeback. In May 2025, following the latest in a series of public setbacks, the group’s administrator, LockBitSupp, responded on the RAMP underground forum, declaring that they would “always rise up after being hacked.”

Figure 4 – LockBit administrator vowing to return on the RAMP forum.

Figure 5 – LockBit administrator announcing the group’s return on RAMP chat.

The XSS Russian-language cybercriminal forum previously banned LockBitSupp following a dispute with another user and reaffirmed that “explicit advertising of RaaS will remain prohibited.” RaaS programs depend on affiliate recruitment for their business model and maintaining visibility on prominent criminal forums such as XSS or RAMP is essential to their work.

By early September, the XSS administrator reported that LockBitSupp requested to be reinstated and opened the decision to a community vote.

Figure 6 – XSS forum administrator polling members on whether to reinstate LockBitSupp.

Despite this effort, the vote ultimately failed, and LockBit remains banned from XSS.

Figure 7 – Voting results on LockBitSupp’s proposed return to XSS.

In early September 2025, LockBit announced on RAMP the official launch of LockBit 5.0, coinciding with the sixth anniversary of the operation. New affiliates were asked to provide a Bitcoin deposit of roughly US $500 for access to a new encryptor and an updated control panel.

Figure 8 – LockBit 5.0 affiliate registration screen.

Since that announcement, we identified more than 15 distinct victims affected by LockBit 5.0, which replaced the earlier 4.0 builds that were still active until April 2025. LockBit continues to enforce strict operational security: all affiliate interfaces require individualized credentials, and no victims have been publicly listed on the group’s data-leak site.

Figure 9 – LockBit 5.0 ransom note from an attack in mid-September 2025

Updated ransom notes now explicitly identify themselves as “LockBit 5.0” and include a unique personal identifier that allows each victim to access a private negotiation portal. Victims are typically granted a 30-day grace period before the stolen data is published.

Figure 10 – Screenshot of LockBit 5.0 negotiations with a victim, mid-September 2025.

Analysis of the initial campaign shows that approximately 65 percent of identified attacks targeted organizations in the United States, with the remainder affecting Mexico, Indonesia, and several European countries.

LockBit 5.0 represents an upgraded evolution of the previous 4.0 version, incorporating Windows, Linux, and ESXi variants. The new build introduces enhanced evasion and anti-analysis mechanisms, faster encryption routines, and the use of a randomized 16-character file extension to disrupt signature-based detection. Most confirmed infections were deployed on Windows systems, while roughly 20 percent targeted ESXi virtual infrastructure.

Historically, LockBit has been among the most active and disruptive RaaS programs. With its extensive experience and the lower entry barrier for new affiliates, the re-emergence of the group poses a renewed risk to organizations across many sectors. The actions observed in September likely represent only the leading edge of a larger campaign, and the October victim postings on LockBit’s data-leak site are expected to confirm its full operational return.

DragonForce’s marketing efforts

DragonForce distinguishes itself among emerging ransomware groups through its heavy emphasis on public relations and coalition branding, frequently issuing high-profile statements and partnership claims on criminal forums. In September 2025, it announced on RAMP a supposed “coalition” with Qilin and LockBit, presented as a unified affiliate initiative.

However, these declarations appear largely symbolic, with no verified evidence of shared infrastructure or joint operations. The announcements likely serve to attract affiliates and project influence within a fragmented RaaS market. This reflects DragonForce’s broader strategy to maintain visibility and credibility in an increasingly competitive underground ecosystem.

Figure 11 – DragonForce announcing updates and coalition with LockBit and Qilin.

DragonForce roughly tripled its monthly victim count since the shutdown of RansomHub and claimed 56 victims in Q3 2025. This is still fewer than Qilin and Akira but shows steady growth. DragonForce continues to actively recruit affiliates and promote new features in its RaaS program and recently announced on RAMP a data-driven extortion service that offers affiliates tailored analysis of stolen data to maximize ransom leverage.

Figure 12 – DragonForce’s “data audit” services.

Under this model, an affiliate that accessed a large dataset (typically over 300 GB) from a company with annual revenues above US $15 million can submit it for analysis and maximize the extortion impact. In a recent showcased example, DragonForce reviewed stolen files from a gold mining company and highlighted the most valuable commercial and financial information, accompanied by a customized extortion letter.

Figure 13 – DragonForce Audit example.

Qilin – The Dominant RaaS Operation of 2025

Qilin remains the most active ransomware group in 2025, increasing its monthly victim rate to an average of 75 victims in Q3, up from 36 in Q1.

Although the group presents itself as ideologically motivated, its operations appear entirely financially driven. In a June 2024 interview published on its WikiLeaksV2 blog, Qilin’s operators described themselves as “idealists who love our country.” This statement, however, contrasts sharply with the group’s broad and opportunistic targeting across sectors and geographies.

Figure 14 – Qilin interview from June 2024 on their official blog.

In a separate interview on SuspectFile, a Qilin affiliate characterized the program as profit-focused and flexible, with affiliates responsible for intrusion and exfiltration while Qilin manages infrastructure, leak-site operations, and negotiations. Reported affiliate share ranges between 80% and 85%, which is among the highest in the market. This has attracted numerous operators previously active under RansomHub and BianLian.

Qilin’s open affiliate framework accommodates actors with diverse motivations and capabilities. In one recent case, the group’s data-leak site briefly listed Israel’s Shamir Medical Center among its victims.

Figure 15 – Shamir Medical Center announcement on Qilin’s DLS.

According to Israeli researcher Erez Dassa, the responsible affiliate was likely an Iranian-linked threat actor. Dassa reported that following direct communication, Qilin’s administrators agreed that public association with terrorism or politically motivated activity could expose the group to additional pressure and subsequently removed the listing.

Figure 16- Erez Dassa’s Telegram post explaining the Shamir incident.

This incident illustrates the range of motivations operating within large RaaS ecosystems. While affiliates enjoy broad autonomy, Qilin demonstrates a degree of central oversight and reputational management, removing politically sensitive cases that may jeopardize its long-term operations. The group continues to balance open recruitment and strategic control, maintaining its position as a leading and resilient RaaS brand.

Geographic Distribution of Victims – Q3 2025

The geographic distribution of ransomware victims in Q3 2025 continues to follow established trends in the global ransomware ecosystem. The United States accounted for roughly half of all reported cases, reaffirming its position as the primary target for financially motivated threat actors. Most publicly listed victims remain concentrated in Western, developed economies, where organizations are perceived as having greater financial resources and a higher likelihood of paying ransom.

Figure 14- Publicly Claimed Victims by Countries, Q3 2025.

An unusual concentration of attacks in South Korea elevated the country to seventh place on the list of most affected nations this quarter. This spike is linked almost entirely to Qilin, which listed 30 South Korean victims, 28 of them between August and September 2025. Of these, 23 belonged to the financial services sector. While the cause of this focused campaign remains uncertain, the Korea Herald newspaper attributed it to a compromised cloud server operated by an IT contractor serving multiple mid-sized private equity funds.

Safepay maintained a strong focus on Germany and the United Kingdom, with each accounting for approximately 10% of its total victims in Q3. Together with INC Ransom, which also recorded 11 British victims, these two groups were the most active in the UK, followed closely by Qilin with 10.

Twenty percent of DragonForce’s victims were based in Germany, making it the most active ransomware group in the country.

INC Ransom reported a notably high proportion of Canadian victims (8%), the largest share of any group targeting Canadian organizations this quarter.

Figure 15- Canadian Victims by Actor, Q3 2025.

Ransomware Attacks by Industry – Q3 2025 Analysis

The industry distribution of ransomware victims in Q3 2025 shows a consistent cross-sector impact, largely unchanged from previous quarters. Profit-oriented organizations with high downtime costs, such as industrial manufacturing, and those holding sensitive or regulated data, such as business services, remained the most targeted sectors, each representing approximately 10% of all extortion attempts.

Healthcare and medical organizations continue to face steady targeting due to the critical nature of their operations and the sensitivity of stored information. Interestingly, threat actors view these victims as high-risk targets because of the attention such incidents attract from law enforcement. In Q2 2025, healthcare accounted for approximately 8% of all publicly listed victims, a figure that remained stable through Q3.

While some groups, such as Play Ransomware, appear to enforce an internal policy against attacking healthcare institutions, others such as Qilin, INC Ransomware, and KillSec continue to list healthcare providers among their victims, disregarding the informal ethical boundaries observed by larger RaaS brands.

Figure 16 – Ransomware Victims by Industry, Q3 2025.

Conclusion

The ransomware ecosystem in Q3 2025 remains highly active and structurally fragmented, while at the same time increasingly adaptive. The dissolution of major RaaS programs did not reduce the overall volume of attacks but instead redistributed the load among numerous smaller and more agile actors. Groups such as Qilin and DragonForce have capitalized on this trend, expanding through aggressive affiliate recruitment and service innovation. LockBit’s re-emergence signals a potential trend toward re-centralization around trusted brands.
Despite periodic law-enforcement disruptions, the fundamental dynamics of the ransomware market, using affiliate-driven operations, data extortion models, and cross-platform tooling, remain intact. Continued monitoring of affiliate migration patterns and emerging extortion techniques are essential to understanding how this decentralized ecosystem evolves through the remainder of 2025.

The post The State of Ransomware – Q3 2025 appeared first on Check Point Research.