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Signature Healthcare was forced to cancel some services, and pharmacies are unable to fill prescriptions due to the hacker attack.
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90% of attacks start with phishing. For CISOs, the real pain begins when the SOC cannot quickly tell whether a suspicious alert is just noise or the start of credential theft, account compromise, malware delivery, or wider business disruption.
Modern phishing campaigns are designed to create exactly that uncertainty. QR codes, redirect chains, CAPTCHAs, phishing kits, and AI-generated lures can all hide the real objective until late in the attack flow.
So what does phishing detection that actually works look like for a modern SOC or MSSP? Let’s find out.
Phishing is still one of the most common ways attackers get into organizations, but the threat no longer follows a simple pattern. Modern phishing campaigns are built to hide their real intent, delay validation, and make investigation harder for already overloaded security teams.
What makes today’s phishing especially disruptive is the mix of techniques now used in a single campaign. Security teams are no longer dealing with one suspicious email and one malicious link. They are dealing with layered attack flows that may include:
This combination puts much more pressure on SOC workflows. The challenge is understanding what actually happens next and doing it fast enough to reduce business risk.
The numbers reflect this shift. 20% of phishing campaigns hide links in QR codes, while Tycoon2FA attacks increased by 25% between Q1 and Q3 2025. Gartner also found that 62% of companies experienced a deepfake attack in 2025. Together, these trends show that phishing is more adaptive, more evasive, and more difficult to investigate quickly.

For SOC teams, this creates a dangerous workflow gap. An alert may show that something looks suspicious, but it often does not reveal whether credentials are being harvested, whether MFA is being bypassed, whether malware is delivered after the phishing stage, or how far the attack could spread if it succeeds. That lack of visibility is where delays begin.
When visibility breaks down, the workflow usually breaks down with it:
To make phishing detection work, CISOs need an approach that helps the SOC spot threats sooner, understand their impact earlier, and contain them before they escalate.
The first step is making sure the SOC can see phishing activity early enough to act on it. If malicious domains, URLs, or campaign indicators surface too late, the team starts every investigation from behind.
Strong monitoring is not just about collecting more alerts. It is about improving what the SOC sees first and giving teams a better chance to catch phishing before it spreads further. The more current and relevant the intelligence is, the easier it becomes to recognize real threats early and prioritize them correctly.
This is where the quality and scale of threat data make a real difference. ANY.RUN’s phishing intelligence is built on first-hand investigations of active campaigns observed across 15,000 organizations and used by more than 600,000 security professionals worldwide. That gives teams access to fresh phishing indicators grounded in real attack activity, not just static or generic reputation data.

With this kind of monitoring in place, SOC teams can:
A stronger monitoring layer gives the SOC a much better starting point. And when phishing is detected earlier, every step that follows becomes more effective.
Early detection is only the starting point. Once a phishing alert reaches the SOC, the next challenge is figuring out what the attack is actually doing and whether it creates real business risk.
This is where triage often slows down. A suspicious URL or attachment may trigger an alert, but that alone does not show whether the campaign leads to credential theft, MFA bypass, malware delivery, or a broader account takeover attempt. Without that visibility, teams spend more time validating the threat, confidence in verdicts drops, and more cases are escalated than necessary.
Strong phishing triage should help teams quickly answer a few critical questions:
ANY.RUN helps close this gap with Interactive Sandbox analysis that exposes the full phishing chain in a safe environment. Teams can detonate suspicious URLs and files, follow redirects, open attachments, scan QR codes, and inspect CAPTCHA-protected flows to see how the attack behaves in practice.
Instead of relying on assumptions, they can validate the threat based on what actually happens. Analysts can also interact with the environment at any time, which makes it easier to investigate suspicious behavior manually when a deeper look is needed.
See how a real quishing attack can be analyzed inside ANY.RUN’s Interactive Sandbox in seconds:
This process becomes even faster with Automated Interactivity. By imitating analyst behavior inside the sandbox, it can interact with phishing pages automatically, uncover hidden links behind QR codes, solve CAPTCHAs, and continue the analysis flow without waiting for manual input. That helps teams move through evasive phishing stages faster and reach the real malicious behavior sooner.
Check sandbox analysis with Automated Interactivity

Stronger triage reduces uncertainty, cuts wasted effort and helps teams reach conclusions faster. That means fewer unnecessary escalations, quicker containment, and less chance for phishing incidents to grow into broader operational or financial impact.
Phishing detection does not end when the SOC confirms that something looks suspicious. The next challenge is turning that analysis into fast, confident response.
This is where many workflows still slow down. Even after a phishing attack has been investigated, teams often need to manually collect indicators, document what happened, map behavior to known techniques, and prepare findings for escalation or response. That extra effort creates delays at exactly the moment when speed matters most.
A strong response workflow should give teams what they need to act without friction:
ANY.RUN helps speed up this stage by turning phishing analysis into decision-ready outputs. Teams can see how the attack unfolds across redirects, phishing pages, credential theft attempts, and payload delivery, often reaching a verdict within the first 60 seconds. Clear verdicts, extracted IOCs, mapped TTPs, visual behavior details, and auto-generated reports make incidents easier to understand and faster to contain.

For CISOs, the real benefit is a faster path from investigation to containment. It helps teams contain phishing incidents sooner, make more consistent decisions under pressure, and reduce the time attackers have to turn a phishing attempt into credential theft, fraud, or wider business disruption.
When SOC teams improve monitoring, sharpen triage, and speed up response, phishing becomes much harder to turn into a larger incident. Stronger phishing detection helps teams identify suspicious activity sooner, understand it more quickly, and act with greater confidence when time matters most.

This approach drives measurable improvements across day-to-day SOC operations:
The value goes beyond the numbers. Better phishing detection helps reduce alert fatigue by making suspicious activity easier to assess. It also helps Tier 1 handle more cases with confidence instead of pushing unclear investigations further down the workflow.
Key Outcomes for CISOs:Phishing is often the first step in account compromise, fraud, malware delivery, and wider business disruption. When SOC teams can detect it earlier and respond faster, the organization is in a much stronger position to stop the attack before the damage spreads.
ANY.RUN, a leading provider of interactive malware analysis and threat intelligence solutions, helps organizations detect, investigate, and respond to modern phishing attacks with greater speed and clarity.
By combining Interactive Sandbox, Threat Intelligence Lookup, and Threat Intelligence Feeds, ANY.RUN gives SOC and MSSP teams the tools to spot phishing activity sooner, investigate threats more effectively, and respond with structured findings. Its approach helps security teams expose full attack chains, investigate evasive phishing techniques, and make more confident decisions under pressure.
Trusted by more than 15,000 organizations and 600,000 security professionals worldwide, including 74% of Fortune 100 companies, ANY.RUN is built to support modern security operations with faster threat visibility, stronger investigation workflows, and more informed response. The company is SOC 2 Type II certified, reflecting its focus on strong security controls and customer data protection.
Integrate ANY.RUN’s solution for Tier 1/2/3 in your organization →
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Cybersecurity has always been a race, but it is no longer a fair one. Attackers now operate at machine speed, orchestrating campaigns that evolve in seconds, while many defense teams still rely on workflows measured in hours or days. This widening gap has forced a fundamental shift in thinking. The conversation is no longer about faster response alone; it is about anticipation, autonomy, and intelligent coordination.
Cybersecurity AI innovation built on agentic AI architecture is the new shift everyone is talking about. These systems are not passive tools waiting for instructions; they actively investigate, reason, and act. What distinguishes this evolution is the emergence of dual-brain design, a concept that blends real-time decision-making with long-term contextual understanding.
Traditional systems struggle because they attempt to process everything, real-time signals and historical context, within a single framework. Dual-brain architecture breaks this limitation by dividing responsibilities into two complementary layers.
The first layer, often described as neural memory, operates like a continuously evolving knowledge graph. It maps relationships across attacker behaviors, infrastructure patterns, and indicators of compromise. This is where neural memory threat intelligence becomes critical. Instead of storing static data, it builds a living model of how threats behave over time, adapting as new intelligence flows in.
The second layer focuses on unstructured information. Security data rarely arrives neatly packaged; it exists in fragmented reports, dark web discussions, and analyst notes. This layer transforms raw, ambiguous inputs into semantic meaning. It doesn’t just match patterns; it interprets intent.
Together, these layers create a system capable of both immediate reaction and informed reasoning. One “brain” reacts in real time; the other provides depth and memory. The result is a more balanced and capable AI cybersecurity architecture that can connect weak signals long before they become visible threats.
One of the most persistent failures in cybersecurity operations is an alert overload. Analysts are inundated with notifications, many of which lack context or urgency. Critical threats often hide in plain sight, buried under noise.
Dual-brain systems address this by shifting the focus from alerts to outcomes. Instead of generating isolated warnings, they construct a coherent narrative around a threat. Signals from endpoints, cloud systems, and external intelligence sources are correlated into a single, actionable story.
This is where autonomous AI security becomes transformative. The system doesn’t stop detecting; it investigates, validates, and responds. Compromised systems can be isolated, malicious domains blocked, and policies enforced automatically. What once required hours of manual effort can now happen in seconds, with minimal human intervention.
A clear example of this cybersecurity ai innovation in action can be seen in Cyble Blaze AI, a platform designed to operationalize agentic ai architecture at scale. Its implementation of dual-brain design brings together real-time detection and long-term contextual reasoning in a way that mirrors how experienced analysts think, only at machine speed.
Cyble Blaze AI uses a neural memory layer to continuously map relationships between threat actors, attack techniques, and infrastructure patterns. This intelligence base allows it to connect early indicators, such as leaked credentials or exploit chatter, with internal vulnerabilities. Complementing this is a vector-based processing layer that interprets unstructured data, enabling deeper contextual understanding across sources like dark web forums and fragmented threat reports.
What sets the platform apart is its ability to act on this intelligence autonomously. Built on a distributed agentic ai architecture, Cyble Blaze AI deploys specialized agents that monitor endpoints, cloud environments, and external threat landscapes simultaneously. These agents collaborate in real time, sharing insights and triggering coordinated responses across domains.
The platform’s predictive capabilities are particularly notable. By analyzing more than 350 billion threat data points, it identifies patterns that signal where attacks are likely to emerge. In many cases, it can forecast risks up to six months in advance, turning neural memory threat intelligence into a forward-looking defense mechanism rather than a retrospective tool.
The real power of this approach lies in its structure. Rather than relying on a monolithic system, modern platforms use a distributed agentic ai architecture composed of specialized agents.
Each agent has a defined role. Some continuously scan for anomalies across endpoints. Others focus on cloud environments or SaaS ecosystems. Response agents execute containment and remediation actions. What makes this effective is not just specialization, but coordination.
When one agent detects a signal, it is immediately shared across the system. A suspicious login identified in a cloud environment can trigger endpoint containment actions without delay. This real-time collaboration enables detection, analysis, and response to occur in under two minutes in many scenarios.
This level of orchestration marks a clear departure from traditional tools. It reflects a broader shift toward autonomous ai security, where systems operate with a high degree of independence while maintaining precision.
Perhaps the most significant advancement in this cybersecurity ai innovation is its predictive capability. By analyzing vast datasets, often exceeding 350 billion threat data points, these systems identify patterns that indicate where future attacks are likely to emerge.
This is not guesswork. It is a large-scale correlation across historical attacks, newly disclosed vulnerabilities, and global threat activity. Early indicators, such as leaked credentials or exploit discussions on underground forums, are linked to an organization’s environment.
Through neural memory threat intelligence, the system recognizes trajectories. It can forecast risks up to six months in advance, giving organizations a critical window to act before an attack materializes.
This fundamentally changes the role of cybersecurity. Defense is no longer reactive; it becomes anticipatory.
Dual-brain architecture redefines cybersecurity by shifting the goal from reacting to threats to preventing them altogether. By combining agentic ai architecture, predictive analytics, and neural memory threat intelligence, platforms like Cyble Blaze AI enable autonomous ai security that anticipates attack paths, reduces exposure, and neutralizes risks before they escalate.
This marks a fundamental evolution in AI cybersecurity architecture, where speed and context work together to deliver predictive, outcome-driven defense. To see how this cybersecurity AI innovation operates in practice, organizations can request a personalized demo for Cyble Blaze AI and explore its capabilities firsthand.
The post Dual-Brain Architecture: The Cybersecurity AI Innovation That Changes Everything appeared first on Cyble.
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The APT28 threat group exploited vulnerable TP-Link and MikroTik routers to conduct adversary-in-the-middle (AitM) attacks.
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Cisco Talos observed a spear-phishing attack delivering LucidRook, a newly identified stager that targeted a Taiwanese NGO in October 2025. The metadata in the email suggests that it was delivered via authorized mail infrastructure, which implies potential misuse of legitimate sending capabilities.
The email contained a shortened URL that leads to the download of a password protected and encrypted RAR archive. The decryption password was included in the email body. Based on this email and the collected samples, Talos observed two distinct infection chains originating from the delivered archives.
In the infection chain, the threat actor deployed a dropper that opens the decoy documents included in the bundle. One example decoy file is a letter issued by the Taiwanese government to universities in Taiwan. This document is a formal directive reminding national universities that teachers with administrative roles are legally required to obtain prior approval and file attendance records before traveling to China. An official version of this document can be found on the Taiwanese government website.

Talos identified two infection chains used to deploy LucidRook. Both were multi-stage and began with either an LNK or an EXE launcher. The LNK infection chain uses an initial dropper Talos tracks as LucidPawn.

The LNK-based infection chain was observed in both the sample targeting Taiwanese NGOs (which were distributed via spear-phishing emails) and the sample we suspect targeted Taiwanese universities. Both samples were delivered as an archive, containing an LNK file with a document file with substituted PDF file icon, as well as a hidden directory in the folder, as shown in Figure 3.

The hidden directory contains four layers of nested folders designed to evade analysis. The fourth-level directory contains the LucidPawn dropper sample (DismCore.dll), a legitimate EXE file (install.exe), and a decoy file. An example folder structure is shown in Figure 4.

When the user clicks the LNK file, it executes the PowerShell testing framework script C:Program FilesWindowsPowerShellModulesPester3.4.0Build.bat, passing the path to binaries located in the hidden directory in order to launch the embedded malware. This is a known technique that leverages living-off-the-land binaries and scripts (LOLBAS) to evade detection.

The PowerShell process executes the following command:

The index.exe file is a legitimate Windows binary associated with the Deployment Image Servicing and Management (DISM) framework. It is abused as a loader to sideload LucidPawn via DLL search order hijacking.
The LucidPawn dropper embeds two AES-encrypted binaries: a legitimate DISM executable and the LucidRook stager. Upon execution, both binaries are decrypted and written to %APPDATA%LocalMicrosoftWindowsApps, with the DISM executable renamed to msedge.exe to impersonate the Microsoft Edge browser and the LucidRook stager written as DismCore.dll. Persistence is established via a LNK file in the Startup folder that launches msedge.exe. After dropping the binaries, LucidPawn launches the DISM executable to sideload the LucidRook stager.
The LucidPawn dropper also handles decoy documents by locating files with specific document extensions (.pdf, .docx, .doc, .xlsx) in the working directory, copying them to the first layer directory, deleting the original lure LNK file, and opening the decoy using Microsoft Edge to distract the victim.
The second infection chain leverages only a malicious EXE written in the .NET framework without the LucidPawn dropper.

Talos observed the EXE-based infection chain in samples uploaded to public malware repositories in December 2025. The samples were distributed as password protected 7-Zip archives named “Cleanup(密碼:33665512).7z”. Based on the Traditional Chinese language used in the archive filename, the language shown in the malicious dropper, and the geographic context of the sample upload locations, we assess with moderate to high confidence that the campaign was intended to target Taiwanese entities.
The 7-Zip archive contains a single executable file named Cleanup.exe. The extracted binary masquerades as Trend Micro™ Worry-Free™ Business Security Services, using a forged application name and icon to impersonate a legitimate security product. In addition, the binary contains a compilation timestamp that is clearly falsified (2065-01-12 14:12:28 UTC).

The executable is a simple dropper written with the .NET framework. It embeds three binary files as Base64-encoded data within its code and, upon execution, decodes and drops these files into the C:ProgramData directory. The dropped files include a legitimate DISM executable, the LucidRook stager, and a LNK file placed in the Startup folder to establish persistence.

After execution, the program displays a decoy message box claiming that the cleanup process has completed.

LucidRook is a sophisticated 64-bit Windows DLL stager consisting of a Lua interpreter, embedded Rust-compiled libraries, and Lua bytecode payload. The DLL embeds a Lua 5.4.8 interpreter and retrieves a staged payload (in our sample named archive1.zip) from its C2 over FTP. After unpacking and validating the downloaded stage, the implant loads and executes the resulting Lua bytecode on the compromised host. Embedding the Lua interpreter effectively turns the native DLL into a stable execution platform while allowing the threat actor to update or tailor behavior for each target or campaigns by updating the Lua bytecode payload with a lighter and more flexible development process. This approach also improves operational security, since the Lua stage can be hosted only briefly and removed from C2 after delivery, and it can hinder post-incident reconstruction when defenders recover only the loader without the externally delivered Lua payload.
Due to the embedded Lua interpreter and stripped Rust-compiled components, the DLL is complex to reverse engineer. The binary is approximately 1.6MB in size and contains over 3,800 functions, reflecting the amount of runtime and library code bundled into a single module. Execution is initiated via the DllGetClassObject export; however, the sample implements no COM functionality and uses the export solely as an entry point.
Upon execution, the malware’s core workflow is twofold. First, it performs host reconnaissance, collecting system information that is encrypted, packaged, and exfiltrated to the C2 infrastructure. It then retrieves an encrypted, staged Lua bytecode payload from the C2 server, which is subsequently decrypted and executed on the compromised host.
LucidRook embeds a Lua 5.4.8 interpreter directly inside the DLL and uses it to execute a downloaded Lua bytecode stage. Before handing the stage to the VM, the loader verifies that the decrypted blob begins with the standard Lua bytecode magic (x1bLua), indicating the payload is a compiled Lua chunk rather than plaintext script.

The Lua runtime is also wrapped with additional controls. Notably, the malware implements a non-standard “safe mode” that disables package.loadlib (as shown by the unique error string “package.loadlibis disabled in safe mode”), which prevents Lua payloads from loading arbitrary external DLL-based modules via the standard require/loader pathway. Additionally, in the library initialization flow observed, the malware opens common standard libraries (e.g., io, os, string, math, package) but does not open the debug library, which would normally provide powerful introspection primitives; this omission is consistent with an anti-analysis hardening choice.

The LucidRook samples employ a sophisticated string obfuscation scheme. The obfuscation was applied to almost all the embedded strings including file extensions, internal identifiers, and C2 addresses. This transformation increases the difficulty of analysis and detection.
The deobfuscation follows a structured two-stage runtime process:
0x00) and a single-byte mask read from a parallel lookup table: Plaintext = Ciphertext ^ (Seed | Mask)The use of a parallel lookup table for masks significantly complicates the creation of automated “unpacking” scripts, as the relationship between the encrypted string and its corresponding mask is obscured by the flattened control flow.


The malware collects several system information including user account name, computer name, driver information, user profile directory, installed applications, running process, and so on. The collected information is stored into three files (named 1.bin, 2.bin, 3.bin) with two layers of encryptions: RSA and a password-encrypted ZIP archive. The BIN files are encrypted with an embedded RSA public key (DER hash ab72813444207dba5429cf498c6ffbc69e1bd665d8007561d0973246fa7f8175) and then compressed into a ZIP file encrypted with password !,OO5*+ZEYORE%&.K1PQHxiODU^RA046. With these encryptions in place, the exfiltrated data can only be decrypted by the threat actor. The decrypted RSA public key used to encrypt exfiltrated data is:
-----BEGIN RSA PUBLIC KEY----- MIIBCgKCAQEA3YeM0FbZO8QB3/ctZd2+oS8weSUwmgp33c5lVJ8InJx5yJJnXF+8 qLL+nzwcItVQyAQbZBymN9ueIgkNRBQuRJgZOxLHG2cbNIWXMImKb5zkkyIUfCz1 hLprvBu4i2IIeWTFyTLfIpwZ/rUn+lARRmIeWTmJezOaSh5QvVaF6Oqk5qoTXk9A MivxKnfFiMhlBh3/V6S4+gTzqy7IwgSuPv8IL6n5LF+N8DmIvAVCck1e2KIYMu54 UT7ef16N60LVksADJsnk+E5CSOeD4FzSTjS9G9c3sZFP/7r7xAbr5CbKvaBvJ+49 7OlzJjaq1H+M7aOAPKaf/hyewEHIr+W1EQIDAQAB -----END RSA PUBLIC KEY-----
The encrypted data is archived into a file named archive4.zip and uploaded to the C2 FTP server using authenticated credentials obfuscated and embedded in the stager.
The LucidRook stager communicates with the abused/compromised FTP servers to not only upload the collected system information but also to download and execute Lua bytecode payload to achieve remote code execution.
FTP servers with publicly exposed credentials
LucidRook uses plaintext FTP for both staging and exfiltration. In the observed captures, the implant authenticates with embedded credentials, switches to binary mode (TYPE I), enters passive mode (PASV), and uploads the exfiltrated information in an archive named archive4.zip via STOR before closing the session. It then establishes a second FTP session and attempts to retrieve archive1.zip (payload) via RETR.

The LucidRook samples connect to C2 infrastructure that appears to abuse FTP servers with exposed credentials to retrieve staged payloads. Talos identified two such C2 servers, both located in Taiwan and operated by printing companies. Initially, it was unclear why the threat actor selected this infrastructure; however, further investigation revealed that both companies publicly listed FTP credentials on their official websites as part of a “file uploading service”. We observed that this practice is common among local printing companies and effectively creates a pool of publicly accessible, low‑cost infrastructure that can be repurposed by threat actors as low-cost C2 staging servers.
Besides what we previously mentioned about the encryption for the exfiltrated data, the threat actor also employed stealthy protection for the downloaded payload. The LucidRook sample Talos obtained (edb25fed9df8e9a517188f609b9d1a030682c701c01c0d1b5ce79cba9f7ac809) uses the password ?.aX$p8dpiP$+4a$x?=0LC=M>^>f6N]a to decrypt the archive when it’s protected and requires that an index.bin file be found within the ZIP archive. After decryption, it uses a different RSA private key (DER hash 7e851b73bd59088d60101109c9ebf7ef300971090c991b57393e4c793f5e2d33) embedded and encrypted inside the malware to decrypt the payload. The corresponding public key (DER hash a42ad963c53f2e0794e7cd0c3632cc75b98f131c3ffceb8f2f740241c097214a) for this private key is:
-----BEGIN PUBLIC KEY----- MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEArQ9deG1+FiOgxT2eX78n 3Ni/PmrV/V6iuf+bc+ii+9wD6Pyc7QyicaZODr2YlKifwJabJuDsIcANRIQGBLf2 8j0yG3x25rP4XTnavTyPB6s+fJgNebmB9Hhgx3AY25ufJvNAelnmXnPn/xp6tZ/V kup72tiwKWeBVJOZYW3qYno4n5hffdNqTFIgUZDDLhqa+nT1gD6LZ6W/BidIM70O gn2h8ppc8aKc893FkfvNYwhgubiDFv9rgvSVvxt0uTVERtBsCyAScD1MMvswEyK6 LrgnyTz7KwOv5wyPfE3BPs8lpMQIyi/jcIIroyk9uLarfV/XIbgTOqEYf5/9bDSs iQIDAQAB -----END PUBLIC KEY-----
During investigation, Talos obtained a payload from a private source which matched the index.bin file structure. However, the password from the LucidRook sample we got was not able to decrypt the archive. We also obtained another version of the payload from the FTP C2 server, but this payload includes four files that does not match the version of LucidRook sample we analyzed as shown in Figure 16.

Based on this information, we suspect that the threat actor is generating different payloads using different sets of passwords for different targets, even though they share the same C2 server. The files inside the payload also suggest it potentially leverages different modules for different capabilities for the stager.
The LucidPawn dropper shares some similarity with LucidRook, including the same COM DLL masquerade technique, obfuscation scheme, and Rust-compiled code.
Upon execution, the LucidPawn dropper sends a DNS request to a domain “D.2fcc7078.digimg[.]store”. The domain “digimg[.]store” redirects to “dnslog[.]ink”, a public Chinese Out-of-band Application Security Testing (OAST) service. It is widely used by security researchers, penetration testers, and threat actors to verify network connectivity and vulnerability exploitation. By using this service, LucidRookoperators receive confirmation once the exploitation succeeds without setting up their own infrastructure. It is worth noting that the same service domain has been leveraged in other targeted campaigns; however, because the service is publicly accessible and can be used by any threat actor, Talos avoids making attribution based solely on this linkage.
LucidPawn implements a geo-targeting anti-analysis execution gate by querying the host’s Windows UI language via the GetUserDefaultUILanguage() API. Execution continues only when the system UI language matches Traditional Chinese environments associated with Taiwan.
The implementation compares a masked LANGID against 0x0404 (zh-TW). The mask and 0xF7FF clears bit 0x0800, causing only 0x0404 (zh-TW) and 0x0C04 (zh-HK) to normalize to the same value and satisfy the check. As a result, the sample exits early on most analysis sandboxes, which commonly use 0x0409 (en-US). This control reduces exposure by limiting execution to the intended victim geography and suppressing behavior in common analyst environments.

While hunting for additional LucidPawn samples, we identified a variant of LucidPawn (d8bc6047fb3fd4f47b15b4058fa482690b5b72a5e3b3d324c21d7da4435c9964). This sample shares the same geo-targeting anti-analysis logic observed in other samples used to deliver LucidRook. Compared with the LucidPawn samples associated with LucidRook delivery, however, this variant omits the callback to the out-of-band interactive service domain and functions solely as a dropper, deploying the reconnaissance tool LucidKnight (aa7a3e8b59b5495f6eebc19f0654b93bb01fd2fa2932458179a8ae85fb4b8ec1) after execution.
Like other malware in the Lucid family, LucidKnight is a 64-bit Windows DLL that contains embedded Rust-compiled components to implement various functions. The malware also uses a string obfuscation scheme similar to those observed in LucidPawn and LucidRook to conceal its C2 configuration.
Upon execution, LucidKnight collects system information including the computer name, OS version, processor architecture, CPU usage, running processes, and installed software. The collected data are written to four TXT files, encrypted with an embedded RSA public key, and packaged into a password-protected ZIP archive named archive.zip using the password xZh>1<{Km1YD3[V>x]X>=1u(Da)Y=N>u. The embedded RSA public key (DER hash 852a80470536cb1fdab1a04d831923616bf00c77320a6b4656e80fc3cc722a66) is shown below:
-----BEGIN RSA PUBLIC KEY----- MIIBCgKCAQEAuvXyx+rPGjS/bI6cvl8LIVVatwD6JU19EvJPlBWlmPqVm/se+3QS 9av+X8PFgwoGXJZTEanAY4JhOMXKYSbErwrLktbEY2tFi7w3/WyPPcB6/I6zD2yU Mqcoqy1Z3+4CsLz4D/LZtOst4alSGOgTDeKtrWKHCyigFvndfds4pdCy78KBRtQb kV3UUlKQZm/37tP0CPXkKwxQ1n/+DTh265gRaVrhr4+VUagNmYta1faMLsvM8O3F Lu2tQiOxeSZC21z6V3kcifYiBLT0khx11JqD3jTfA41OcngZfwWYHbitDBZF7rpL 26ZSitNxMAq1O6DrXzI5wdVn0fZgSXNEbwIDAQAB -----END RSA PUBLIC KEY-----
Unlike LucidRook, which uploads collected system information to a compromised FTP server, LucidKnight exfiltrates reconnaissance data via email using the embedded Rust lettre crate, which provides SMTP message creation and delivery functionality.
Specifically, the malware constructs an email with the Traditional Chinese subject “運動資訊平台” (“Sports Information Platform”) and includes the collected data as a MIME attachment. It then resolves “smtp.gmail.com”, authenticates to the Gmail account “fexopuboriw972@gmail.com” with an embedded application key, and sends the data to the temporary email address “crimsonanabel@powerscrews.com”. The following email shows an example of the content crafted by LucidKnight:
From: fexopuboriw972@gmail.com To: crimsonanabel@powerscrews.com Subject: =?utf-8?b?6YGL5YuV6LOH6KiK5bmz5Y+w?= MIME-Version: 1.0 Date: Tue, 17 Feb 2026 02:05:49 +0000 Content-Type: multipart/mixed; boundary="vlOcEyPfxrLCR89C5RuARsViLsqzv1brB2u8YvNd" --vlOcEyPfxrLCR89C5RuARsViLsqzv1brB2u8YvNd Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: base64 5oKo6KqN54K65Y+w54Gj55uu5YmN5Zyo6Jed5paH5rC457qM55m85bGV55qE5pS/562W5LiK5pyJ 5ZOq5Lqb5YW36auU55qE5oiQ5Yqf5qGI5L6L5oiW5YC85b6X5pS56YCy55qE5Zyw5pa577yf --vlOcEyPfxrLCR89C5RuARsViLsqzv1brB2u8YvNd Content-Type: application/zip Content-Disposition: attachment; filename="archive.zip" Content-Transfer-Encoding: base64 UEsDBDMAAQBjALgQUVwEOkfvkhkAAHEZAAAFAAsAMS50eHQBmQcAAQBBRQMIAEF/fb/F6o3HptX3 (redacted)

The discovery of LucidKnight suggests that the actor maintains a modular toolkit and may select components based on the operational context of each target, rather than deploying a fixed infection chain. LucidKnight may be used independently when lightweight reconnaissance is sufficient, or as a precursor to assess targets before committing the more complex LucidRook stager.
Based on the tactics, techniques, and procedures (TTPs) and the level of engineering investment observed across these infection chains, we assess with medium confidence that this activity reflects a targeted intrusion rather than broad, opportunistic malware distribution. Delivery via spearphishing, combined with LucidRook’s sophisticated design, suggests a sophisticated threat actor prioritizing flexibility, stealth, and victim-specific tasking.
Although Talos has not yet found a decryptable Lua bytecode payload executed by LucidRook, we are publishing these findings to make early detection possible and encourage community sharing, with the goal of uncovering additional indicators that may facilitate stronger clustering and attribution in the future.
The following ClamAV signature detects and blocks this threat:
The following SNORT® rules cover this threat:
IOCs for this research can also be found at our GitHub repository here.
d49761cdbea170dd17255a958214db392dc7621198f95d5eb5749859c603100a (malicious 7z)
adf676107a6c2354d1a484c2a08c36c33d276e355a65f77770ae1ae7b7c36143 (malicious archive)
b480092d8e5f7ca6aebdeaae676ea09281d07fc8ccf2318da2fa1c01471b818d (Forged EXE dropper that drops LucidRook)
c2d983d3812b5b6d592b149d627b118db2debd33069efe4de4e57306ba42b5dc (Forged EXE dropper that drops LucidRook)
6aba7b5a9b4f7ad4203f26f3fb539911369aeef502d43af23aa3646d91280ad9 (LucidPawn, DismCore.dll)
bdc5417ffba758b6d0a359b252ba047b59aacf1d217a8b664554256b5adb071d (LucidPawn dropper, DismCore.dll)
f279e462253f130878ffac820f5a0f9ac92dd14ad2f1e4bd21062bab7b99b839 (malicious LNK)
166791aac8b056af8029ab6bdeec5a2626ca3f3961fdf0337d24451cfccfc05d (malicious LNK)
11ae897d79548b6b44da75f7ab335a0585f47886ce22b371f6d340968dbed9ae (LucidRook stager, DismCore.dll)
edb25fed9df8e9a517188f609b9d1a030682c701c01c0d1b5ce79cba9f7ac809 (LucidRook stager, DismCore.dll)
0305e89110744077d8db8618827351a03bce5b11ef5815a72c64eea009304a34 (LucidRook stager, DismCore.dll)
d8bc6047fb3fd4f47b15b4058fa482690b5b72a5e3b3d324c21d7da4435c9964 (LucidPawn dropper dropping LucidKnight)
aa7a3e8b59b5495f6eebc19f0654b93bb01fd2fa2932458179a8ae85fb4b8ec1 (LucidKnight, DismCore.dll)
fd11f419e4ac992e89cca48369e7d774b7b2e0d28d0b6a34f7ee0bc1d943c056 (archive1.zip download from C2)
1.34.253[.]131 (abused FTP server)
59.124.71[.]242 (abused FTP server)
D.2fcc7078.digimg[.]store (DNS beaconing domain)
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