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February 25, 2025

Chinese APT Target Royal Thai Police in Malware Campaign

Cado Security Labs (now part of Darktrace) identified a malware campaign targeting the Royal Thai Police, attributed to Chinese APT group Mustang Panda. The campaign uses a disguised LNK file and PDF decoy to deliver the Yokai backdoor.
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Tara Gould
Malware Research Lead
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Feb 2025

Introduction

Researchers from Cado Security Labs (now part of Darktrace) have identified a malware campaign targeting the Royal Thai Police. The campaign uses seemingly legitimate documents with FBI content to deliver a shortcut file that eventually results in Yokai backdoor being executed and persisting on the victim's system. The activity observed in this campaign through this research is consistent with the Chinese APT group Mustang Panda.

shortcut file
Figure 1: shortcut file delivered

Technical analysis

The initial file is a rar archive named ด่วนมาก เชิญเข้าร่วมโครงการความร่วมมือฝึกอบรมหลักสูตร FBI.rar (English: Very urgent, please join the cooperation project to train the FBI course.rar). While the initial access is unknown, it is highly likely to have been delivered via phishing email. Inside the rar file is a LNK (shortcut) file ด่วนมาก เชิญเข้าร่วมโครงการความร่วมมือฝึกอบรมหลักสูตร FBI.docx.lnk, disguised PDF file and folder named $Recycle.bin.

Inside LNK file
Figure 2: Inside the rar file

The shortcut file executes ftp.exe (File Transfer Protocol), which then processes the commands inside the disguised PDF file as an FTP script. FTP scripts are automated scripts that execute a sequence of FTP commands. 

C:\\Windows\\System32\\ftp.exe -s:"แบบตอบรับ.pdf",File size: 58880 File attribute flags: 0x00000020 Drive type: 3 Drive serial number: 0x444b74c2 Volume label:  Local path: C:\\Windows\\System32\\ftp.exe cmd arguments: -s:"แบบตอบรับ.pdf" Relative path: ..\\Windows\\System32\\ftp.exe Icon location: .\\file.docx Link target: <My Computer> C:\\Windows\\System32\\C:\Windows\System32\ftp.exe 

แบบตอบรับ.pdf (english: Response form.pdf) is a fake PDF file containing Windows commands that are executed by cmd.exe. The PDF does not need to be opened by the victim, however if they do the document looks like a response form. 

Response form pdf
Figure 3: แบบตอบรับ.pdf (English: Response form.pdf)
Commands embedded inside fake PDF file
Figure 4: Commands embedded inside the fake PDF file

These commands move the docx file from the extracted $Recycle.bin folder to the main folder replacing the LNK with the decoy docx file. The “PDF” file in the extracted $Recycle.bin folder is moved to c:\programdata\PrnInstallerNew.exe and executed. 

 Inside $Recycle.bin folder
Figure 5: Inside $Recycle.bin folder
Decoy docx file
Figure 6: Decoy docx file ด่วนมาก เชิญเข้าร่วมโครงการความร่วมมือฝึกอบรมหลักสูตร FBI.docx (English: Very urgent, please join the cooperative training project for the FBI course.docx)

The decoy document replaces the shortcut file after it removes itself to remove traces of the infection. The document is not malicious.

File: PrnInstallerNew.exe

MD5: 571c2e8cfcd1669cc1e196a3f8200c4e

PrnInstallerNew.exe is a 32-bit executable that is a trojanized version of  PDF-XChange Driver Installer, a PDF printing software. The malware dynamically resolves calls through GetProcAddress(), storing them in a struct, to evade detection. Malware often avoids hardcoding API function names by constructing them dynamically at runtime, making detection by security tools more difficult. Instead of directly referencing functions like send(), the malware stores individual characters in an array and assembles the function name letter by letter before resolving it with GetProcAddress(). This technique helps bypass security tools, as they scan for known API names within a binary. Once the function name is constructed, it is passed to GetProcAddress(), which retrieves the function's memory address, allowing the malware to execute it indirectly without exposing API calls in their import tables. To enable persistence, the binary adds itself as a registry key “MYAccUsrSysCmd_9EBC4579851B72EE312C449C” in HKEY_CurrentUser/Software/Windows/CurrentVersion/Run; which will cause the malware to execute when the user logs in. 

Registry key added
Figure 7: Registry key added

Additionally, a mutex “MutexHelloWorldSysCmd007” is created, presumably to check for an already running instance. 

Mutex created
Figure 8: Mutex created

After dynamically resolving ws_32.dll, the Windows library for sockets, the malware connects to the IP 154[.]90[.]47[.]77 over TCP Port 443.

C2 image
Figure 9

As observed with Yokai backdoor, the hostname is sent to the C2 which will return commands after the validation is satisfied. 

Attribution 

The targeting of the Thai police appears to have been part of a greater campaign targeting Thai officials in the last months of last year. However, targeting of the Thai government is not new as groups, such as Chinese APT groups Mustang Panda and CerenaKeeper have been targeting Thailand for years. [1]

Mustang Panda are a China based APT group who have been active since at least 2014 and tend to target governments and NGOs in Asia, Europe and the United States for espionage. Recent Mustang Panda campaigns have used similar lures against governments, with similar techniques with decoy documents and shortcut files. While not observed in this campaign, Mustang Panda frequently uses DLL Sideloading to execute malicious payloads under legitimate processes, as observed in Netskope’s research. Instead of DLL Sideloading, this version instead has trojanized a legitimate application. Interestingly one of the reported binaries by Netskope contains code overlap with WispRider, a self-propagating USB malware used by Mustang Panda.

Malicious WispRider image
Figure 10

Key takeaways

The persistent targeting of Thailand by Chinese APT groups highlights the landscape of cyber espionage in Southeast Asia. As geopolitical tensions and economic competition intensify, Thailand remains a critical focal point for cyber operations aimed at intelligence gathering, political influence, and economic advantage. To mitigate these threats, organizations and government agencies must prioritize robust cybersecurity measures, threat intelligence sharing, and regional cooperation. 

IOCs

B73f59eb689214267ae2b39bd52c33c6  ด่วนมาก เชิญเข้าร่วมโครงการความร่วมมือฝึกอบรมหลักสูตร FBI.rar  

0b88f13e40218fcbc9ce6e1079d45169  ด่วนมาก เชิญเข้าร่วมโครงการความร่วมมือฝึกอบรมหลักสูตร FBI.docx   

87393d765abd8255b1d2da2d8dc2bf7f  ด่วนมาก เชิญเข้าร่วมโครงการความร่วมมือฝึกอบรมหลักสูตร FBI.docx.lnk  

571c2e8cfcd1669cc1e196a3f8200c4e  PrnInstallernew.exe  

154[.]90[.]47[.]77  C2

MITRE ATTACK

T1574.002  Hijack Execution Flow: DLL Side-Loading  

T1071.001  Application Layer Protocol: Web Protocols  

T1059.003  Command and Scripting Interpreter: Windows Command Shell  

T1547.001  Boot or Logon Autostart Execution: Registry Run Keys / Startup Folder  

T1113  File and Directory Discovery: File and Directory Discovery  

T1027  Obfuscated Files or Information  

T1036  Masquerading  

T1560.001  Archive Collected Data: Archive via Utility  

T1027.007  Dynamic API Resolution

References

[1] https://www.cyfirma.com/research/apt-profile-mustang-panda/

Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Tara Gould
Malware Research Lead

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July 24, 2026

Darktrace / EMAIL Expands Behavioral Defense Across Email and Collaboration Workflows

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Email and collaboration tools do more than carry messages. They are where organizations approve payments, share sensitive data, reset credentials, and make thousands of everyday decisions. Increasingly, they are interfaces through which humans direct AI agents in their daily activity. Email, Slack and Teams are high volume, rich with sensitive data, and an easy place to hide malicious activity.

The opportunity isn’t lost on bad actors. Darktrace / EMAIL detected more than 32 million high-confidence phishing emails globally in 2025, and 70% of those messages passed DMARC authentication.  Phishing is increasingly difficult to detect and familiar trust signals alone are not enough. People and security teams need to understand how a message fits the normal behavior of the sender, recipient, and organization. They also need to correlate activity across platforms to spot threats that span multiple channels.

To effectively secure against today’s evolved threats, security teams need to act at two levels: they need to help each employee make a safer decision ‘in the moment’, and they need to understand the wider patterns that may expose the business to risk.

Darktrace is introducing four new capabilities in Darktrace / EMAIL to address both challenges. The new features explain suspicious content more clearly to end users, strengthen the capabilities of Darktrace / Adaptive Human Defense with richer guidance, let organizations define their own patterns for detecting sensitive data in messages, and give security teams a process-level view of risk across email and collaboration workflows.

Darktrace / EMAIL Inbox Analysis highlights risky content within your emails

A warning is more useful when it explains what the user should look at. To help do that, we’ve expanded Darktrace / EMAIL’s Inbox Analysis Add-In to highlight potentially dangerous content within the body of emails that Darktrace / EMAIL flags as potentially suspicious or high risk.  

The add-in can highlight language designed to create urgency, financial references, requests for payment, suspicious links, and content that is unusual for the sender. Each highlighted element includes a pop up that explains why it may be suspicious. Instead of asking an employee to accept a verdict without context, the analysis helps them examine the message and make a more informed decision.

Enhanced Just-In-Time Training Banners in Darktrace / Adaptive Human Defense

Enhanced Just-In-Time Training Banners build on the same principle. The banners now include a contextual header, actionable advice, and specific detection context. This gives employees more useful guidance at the point of risk without adding unnecessary information or cognitive load.

Together, the capabilities help turn a warning into a short learning moment. Employees can see what looks unusual, understand what action to take, and build their judgment.

Custom Sensitive Data Detection in Darktrace / EMAIL - Data Loss Prevention

Sensitive data is different for every business. Standard categories such as payment card details or government identifiers matter, but organizations also have their own customer codes, project names, research formats, account structures, and internal identifiers.

Custom Sensitive Data Detection in Darktrace / EMAIL - Data Loss Prevention allows administrators to write custom expressions for the data their organization needs to protect. Matched content can trigger existing model actions and data loss prevention (DLP) workflows, extending Darktrace's DLP capabilities.

This extends data loss detection beyond a fixed library of common data types. Security teams can apply controls to information that is sensitive in the context of their own organization and adapt those controls as the business changes.

Introducing Email and Collaboration Workflow Risk Posture Dashboards

Some of the most important risks are not isolated events. They are repeated ways of working that create an opening for error, misuse, or attack. For example, a payment request may be one suspicious message, but a recurring approval workflow that relies on weak verification is a business process risk.

The new Email and Collaboration Workflow Risk Posture Dashboard analyzes email and collaboration data across Email, Microsoft Teams, Slack and Zoom to provide a process-level view of risk in the organization. These may include financial authorization workflows, sensitive data sharing patterns, and activity that could expose credentials.

The dashboard brings these patterns into a view and provides actionable recommendations. This helps security teams determine where to investigate or strengthen controls, where ownership needs to be clarified, and where the business may need to change a risky process. It gives CISOs a clearer view of how human and communication risk is embedded in everyday operations, not only where individual alerts occur.

Behavior connects the individual decision to the wider risk

These capabilities build on Darktrace’s unique behavioral approach to security. We use Adaptive AI to learn how people and AI normally behave within an organization, creating the context needed to recognize when activity changes.

Within the Darktrace Behavioral Defense Platform, Darktrace / EMAIL helps protect people against phishing, account takeover, data exfiltration, and human risk across email and collaboration tools. The new capabilities extend that protection in both directions. They give employees clearer context for the decision in front of them, while giving security leaders a broader view of the workflows and behavior that create risk across the organization.

The result is not simply more alerts. It is a better understanding of why something is risky, what action to take, and where the organization can reduce risk before a familiar process becomes an easy route for an attacker.

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About the author
Carlos Gray
Senior Product Marketing Manager, Email

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July 24, 2026

When Guardrails Break: Why Securing AI Requires Behavioral Detection and Autonomous Containment

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Bottom line up front: Governance, guardrails, identity controls, and secure development are necessary to secure AI, but they are not sufficient. AI systems are probabilistic, adaptive, and non-deterministic. Therefore, organizations need two critical layers of security:

  1. Behavioral-based detection that can identify when AI begins to act outside its intended purpose; and  
  2. Surgical, explainable autonomous containment that can stop risky activity before it causes material damage.  

That capability depends on multiple specialized AI models working together, not one LLM making every decision.

Organizations are embedding AI into development, business operations, and security workflows faster than most security programs can adapt. The risk is no longer limited to the model. It extends across prompts, data, identities, agents, memory, APIs, tools, permissions, and the trust relationships connecting them.

In my recent blog, Securing AI: Analysis of the Complete Security Stack with Governance and Controls, I outlined a defense-in-depth strategy spanning governance, identity, data security, secure development, runtime detection, autonomous containment, and recovery. The most urgent requirement across that architecture is the ability to understand how AI behaves in practice and contain it when that behavior becomes risky.  

Why non-deterministic systems require behavioral-based detection

Traditional controls remain foundational. Organizations need least privilege, strong identity controls, secure-by-design architecture, data governance, AI inventories, guardrails, testing, and clear boundaries on autonomy.

But deterministic controls, which assume predictable and repeatable behavior, cannot fully secure non-deterministic systems, where the same input may not always produce the same outcome.

AI agents can interpret the same instruction differently, chain individually authorized actions into an unsafe outcome, or pursue a legitimate goal through a method the organization did not anticipate. One of the most recent examples of this is the incident that OpenAI and Hugging Face jointly disclosed, where an autonomous agent escaped its intended testing boundaries and compromised Hugging Face infrastructure.  

An agent may have permission to access data and invoke a tool, but that does not mean every use of that access is appropriate. It is not enough to know whether an action is allowed. Organizations need to know whether it makes sense.

  • Is this normal for this agent?  
  • Is it acting within its intended purpose?  
  • Is it accessing unusual data, invoking an unexpected tool, or beginning to drift?  
  • Do a series of ordinary-looking actions become risky when viewed together?

Behavioral-based detection specific to an environment or organization with an understanding of context and risk enables provides the needed detection engineering for AI systems. It learns normal activity across people, systems, data, devices, and AI agents, then identifies deviations and evaluates their risk, intent, and context. This enables detection of misuse, abuse, compromise, manipulation, and unintended behavior even when no known attack signature or explicit policy violation exists.

Why accuracy is the foundation for SOC optimization

AI will only improve the SOC if it produces accurate, explainable, and actionable outcomes.

If analysts must manually validate every AI-generated finding because they cannot understand the evidence or confidence behind it, automation has not reduced workload. It has moved the workload. False positives increase fatigue. False negatives cause the most risk and damage to organizations. Inaccurate autonomous actions can disrupt critical operations.

Accuracy is therefore more than a model-performance metric. It is the prerequisite for analyst trust, SOC optimization, and safe autonomous response.

That accuracy is unlikely to come from one model.

Generative AI is valuable for natural-language analysis, summarization, and human interaction. But an LLM should not be the sole analytical engine for behavioral-based detection, investigation, risk assessment, and containment. Interpretability and consistency are required for high-consequence security decisions.

A stronger architecture uses multiple specialized AI systems collaboratively:  

  • Behavioral models can establish normal activity.  
  • Unsupervised learning can identify novel anomalies.  
  • Graph analysis can evaluate relationships among agents, identities, systems, and tools.  
  • Other models can correlate events, investigate competing hypotheses, and assess risk.  
  • Semantic models can analyze language where behavior-based language analysis is needed but this can be used in tandem with vector embeddings, graph neural networks, and a variety of other AI systems.

Each model contributes a different analytical perspective. Their outputs can corroborate one another, improving accuracy and creating a more reliable basis for response. The objective is not one model operating as an oracle. It is layered, adaptive intelligence designed to produce decisions the SOC can understand and trust.

Autonomous containment is required to secure autonomous systems

Many SOCs remain hesitant to trust LLM-based agents with autonomous containment. That concern is reasonable. A poorly selected response can isolate the wrong asset, stop a critical workflow, block a legitimate identity, or create more operational damage than the original incident.

But relying exclusively on human response is also not viable.

AI systems can operate at machine speed. They can expose sensitive data, execute workflows, modify records, call tools, or propagate actions across connected systems before an analyst can investigate and intervene. The behavior may be unintentional, the result of an agent optimizing toward a goal, or caused by misuse, compromise, prompt injection, or offensive AI.

Intent affects the investigation. It does not change the need to stop the damage.

Organizations need autonomous response, but it must be surgical and explainable. The objective is not to shut down an entire agent, user, application, or business process whenever an anomaly occurs. It is to interrupt the specific risky behavior: block an unusual connection, constrain a tool call, stop an abnormal data transfer, or temporarily limit an agent when it is performing anomalous, risky activity.  

That buys humans time. It stops the spread, limits damage, and allows the SOC to investigate without unnecessarily disrupting the business.

Layered, Adaptive AI provides a path forward

Darktrace has spent more than a decade researching and operationalizing layered, behavioral, Adaptive AI that learns a specific organization rather than relying only on historic attacks or predefined signatures.

The approach is designed to understand normal behavior, identify anomalous activity, assess its risk, correlate related events, autonomously investigate, and, when necessary, apply targeted containment while normal operations continue.

That sequence matters. Autonomous response cannot simply be added to the end of an LLM workflow. Trusted containment depends on broad visibility, continuous behavioral understanding, multiple analytical techniques, risk and context evaluation, autonomous investigation, explainability, and precise response actions.

This represents a more responsible model for security autonomy: not automation for its own sake, but controlled autonomy built to improve security outcomes and protect business operations.

Security must enable AI adoption

The answer for security teams is not to block AI. Organizations are adopting it to improve productivity, accelerate development, and create new business value.

But innovation without behavioral detection and autonomous containment is not sustainable.

Organizations should continue investing in governance, identity, least privilege, data security, secure MLOps, guardrails, testing, evaluation, validation, verification, kill switches, rollback, and forensic readiness. At the same time, they cannot wait for every governance program to mature before addressing runtime risk.

Behavioral-based detection and autonomous containment provide an immediate layer of resilience. They allow organizations to detect exploitation and risky AI behavior they did not anticipate, contain it at machine speed, and preserve human control over broader remediation.

The future of AI security will not be defined by a single model making every decision. It will be defined by multiple specialized AI systems working collaboratively, with sufficient accuracy, transparency, and context to support trusted autonomous action.

Surgical, explainable autonomous containment is no longer a future capability. It is a requirement for scaling AI securely today.

Learn how to build a defense-in-depth strategy for securing AI at scale in our talk at Black Hat on August 5 at 3:15 PM.  

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Nicole Carignan
SVP, Security & AI Strategy, Field CISO
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