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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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25
Feb 2025

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 system. The activity observed in this campaign through this research is consistent with the Chinese APT group Mustang Panda.

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.

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. 

A close up of a cardAI-generated content may be incorrect.

แบบตอบรับ.pdf (english: Response form.pdf)

A screen shot of a computerAI-generated content may be incorrect.

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

A screenshot of a computerAI-generated content may be incorrect.

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

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

A close up of a logoAI-generated content may be incorrect.

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. Using the connect(

A computer screen shot of a codeAI-generated content may be incorrect.

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 Pandacampaigns [2], 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.

A screenshot of a reportAI-generated content may be incorrect.

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/

[2] https://medium.com/@FatzQatz/unveiling-the-mustang-panda-operation-attack-on-thai-parliament-member-ac197a1ad8fa

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 6, 2026

NIST Just Proved It: AI Security Can’t Be Solved With Rules

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Static AI guardrails are inherently limited

As organizations adopt generative AI, many still assume that the right set of guardrails will be enough. The problem is you can’t anticipate every way these systems might be misused, abused or attacked. What NIST has done is put a mathematical foundation under that intuition.

In recent research building on Gödel’s incompleteness theorems, which showed that any system built on a fixed set of rules will always have gaps, NIST demonstrates that there is no finite set of guardrails that can be universally robust against adversarial prompts. In plain terms, if your defense is based on a fixed set of rules, there will always be inputs that bypass them. Not because the rules are badly written, but because the problem space is bigger than static rules can ever cover.

This is not new in cybersecurity - detection rules have always had to live with this trade-off. What is different with GenAI is the scale and shape of that problem. These systems are built on human language, and human language is not bounded. It is fluid, contextual and deliberately ambiguous. The number of ways intent can be hidden is effectively limitless. You are not defending against a defined protocol or a fixed exploit chain. You are defending against the entire expressive capacity of people.

So attempting to create a complete set of rules is the wrong starting point. It assumes the problem can be deterministically described. NIST’s work shows that it cannot. Organizations still need a way to manage AI risk, but the traditional approach of defining allowed and disallowed patterns is always going to lag behind what is actually happening. The same input can be benign in one context and risky in another, and static rules struggle to capture that distinction.

The question then is what fills that gap?

AI security must shift from rules to behavior

What's required is a shift in what you are trying to understand. Rules try to describe what should and shouldn't happen. Behavior shows you what is happening. Or to put it another way, if inputs are unbounded and adversaries adapt, the only stable signal is behavior.

In a GenAI context, that means analyzing how an AI model is being used, how prompts evolve over time, how outputs are shaped, and where AI agent interactions start to drift from what is expected. It means moving from static definitions of bad to a more dynamic understanding of intent.

Instead of trying to predict every bad prompt, you focus on identifying when behavior starts to move outside expected norms. Instead of asking whether a single input matches a rule, you ask whether the overall pattern of activity makes sense for the system and how it’s being used.

Guardrails remain important but they are only one layer

This does not eliminate the need for guardrails. They still play a role. But they will never address the entire problem space and are simply one part of your defense in depth approach.

NIST’s proof is useful because it makes this explicit. It removes the assumption that with enough effort, a complete rule set is achievable. It isn’t.

Once you accept that, the shift becomes unavoidable. This is no longer a problem of writing better rules, but of understanding behavior in a space where the possible inputs are effectively unbounded.

For security leaders, that changes the nature of the problem. It is less about defining what should be allowed, and more about recognizing when something is no longer consistent with expected behavior.

That does not remove the need for guardrails, but it does change their role. They set boundaries, but they do not define understanding. The gap between the two is where risk now sits.

In the end, this is what “can’t be solved with rules” really means. Rules will always leave gaps, and those gaps are not theoretical. They show up in how systems actually behave Not what we expect them to do, or what we intended them to do, but what they are doing in practice. That is where the signal is, and increasingly, that is where the security problem sits.

References:

https://www.nist.gov/news-events/news/2026/06/nist-mathematical-proof-supports-transition-continuous-monitor-and-update

https://ieeexplore.ieee.org/document/11475847

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About the author
Andrew Hollister
Principal Solutions Engineer, Cyber Technician

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July 1, 2026

5 Ways AI is changing traditional security models according to modern CISOs

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The Reality of Securing AI in Motion

Traditional security tools were built for environments defined by fixed rules and predictable workflows. But AI behavior is non-deterministic. The same prompt can produce different outcomes, and risk often emerges gradually as AI behavior adapts, and permissions drift over time. This creates a constantly shifting environment where security teams are working to define control in a system that resists stability. “In AI security, yesterday's priorities can become tomorrow's blind spots. The landscape shifts that fast,” warned the SVP and Head of Technology and Cybersecurity of a real estate investment trust. Conventional approaches, which rely on establishing and maintaining a steady baseline, struggle to keep up with that level of change.

At the same time, AI adoption is accelerating across organizations, often faster than security teams can implement the controls needed to manage it. “The car is being built while it’s already on the road,” explained the CISO of a global private fund administrator. “The threats we're securing against today won't be the threats we're facing tomorrow. What kept us up three months ago looks nothing like what we're dealing with today.”

As businesses move quickly to unlock value from AI, security teams are left closing gaps in real time, while also facing adversaries who are using AI to make their attacks more scalable, adaptive, and difficult to detect. In this recent roundtable discussion of CISOs and security leaders, five themes emerged around AI cyber risk.  

1. AI agents with human access but no human judgment

In Darktrace’s 2026 State of AI Cybersecurity report, 96% of the surveyed security professionals agree that AI significantly improves the speed and efficiency with which they work. Yet, 92% admitted that they’re concerned with the security implications of the use of AI agents across their workforce.

AI agents now operate with human-level permissions across systems, acting at machine speed, orchestrating actions across platforms, and making decisions without the judgment or caution a person would apply. Unlike human users, they cannot be expected to pause and question whether a given action is appropriate.

Their identities are also difficult to inventory, govern, and audit. As agents become easier to deploy than legacy IT systems ever were, organizations are quickly losing track of what is running, what it has access to, and what it is doing. This creates a growing class of highly privileged, autonomous actors operating without the visibility or oversight that traditional identity and access controls were designed to provide.“While AI adoption is critical to running a modern business, AI alone can’t solve all our cybersecurity challenges,” said a global financial sector CISO. “We still need think critically and use human judgement. Those are two things AI can’t do.”

This lack of human judgment becomes especially risky as new architectures, such as Model Context Protocol (MCP), can expand how agents connect to data, tools, and external systems. By design, MCP enables agents to dynamically discover and interact with new resources, increasing flexibility but also introducing new pathways for unintended access, data exposure, or abuse if not properly governed.

The CISO of a fund administrator highlighted one emerging vector as an example: rogue MCP servers. “Our developers want to move quickly and bring value to the business, but technologies like these can unintentionally expose sensitive data in ways that would never have happened before.”

2. Increased digital complexity and expanded attack surface

AI activity rarely stays contained. A single prompt can trigger a chain of actions across networks, email, cloud infrastructure, SaaS platforms, endpoints, identity systems, and development environments, spanning systems that were never designed to be secured as a single, connected flow. This expands both the scale and complexity of what security teams need to monitor and defend.

Yet no single control has visibility across that entire chain. “You can’t defend effectively what you can’t see,” cautioned the private fund administrator CISO. As AI-driven activity moves fluidly across environments, gaps in coverage become inevitable, creating blind spots that attackers can exploit.

Threat actors are already capitalizing on this lack of visibility. “Threat actors have advanced their use of generative AI to launch more convincing phishing campaigns, automate social engineering, and scale attacks with greater precision down to the individual level,” said the SVP of Technology and Cybersecurity for the real estate investment trust. What was once manual and targeted can now be automated and personalized at scale, making attacks harder to detect and easier to execute.

At the same time, the pace of exploitation is accelerating. As a global CISO operating across 40+ countries described it: “Zero-day vulnerabilities are no longer zero day; it’s minus one day. By the time you get to it and address it, it’s already a problem.” By the time risk is identified, it has often already been realized.

The result is a rapidly expanding and increasingly interconnected attack surface that challenges security teams to maintain visibility, context, and control across AI-driven activity.

3. Shadow AI is already everywhere

76% of organizations now cite shadow AI as a problem, one that is spreading through organizations in ways that are hard to track and even harder to control.

Employees are experimenting with publicly available Gen AI tools. Teams are spinning up low-code automations on their own. SaaS providers are quietly embedding AI into existing products. Developers are plugging AI services directly into workflows, often without pausing to consider what that exposure means.

The result is a lack of visibility into:

  • What AI tools are being used
  • What data those tools can access
  • Where prompts and outputs are going
  • Which AI agents are interacting with enterprise systems

The SVP of Cybersecurity at a real estate investment trust described the shift: “Before, I was worried about someone sending data erroneously to their personal email. Now we have all these agents online that people are utilizing, and we’re looking at those vectors as well.” For security teams, this means operating without a complete view of how AI is being used, what it can access, and where risk may already be emerging.

4. Built-in guardrails are not enough

Organizations often assume that native AI guardrails or provider-level controls are sufficient to manage AI risk. But securing AI requires ongoing visibility, oversight, and governance, not just controls configured at deployment. "It’s a misconception that adopting AI is going to solve all your problems,” warns a global financial services CISO.

Security leaders are increasingly recognizing the limitations of these controls as:

  • Fragmented and difficult to enforce consistently across multiple AI systems, workflows, and environments
  • Ambiguous in terms of accountability due to shared responsibility for AI governance between IT, security, developers, business teams, and third-party providers
  • Limited in end-to-end oversight, leaving gaps that stretch from the initial prompt all the way through to the downstream impact of an agent's actions

Securing AI demands more than simple prompt filtering or static policy enforcement. It requires understanding intent, behavior, and context across both human and AI activity.

The next phase of cybersecurity: securing AI

To safely and responsibly adopt AI at scale, organizations need a new operational model for cybersecurity that’s capable of:

• Understanding AI behavior

• Identifying risk in real time

• Maintaining governance without slowing innovation

The CSO of a $10 billion municipal utility organization described the challenge with precision: “We have to move at the speed of innovation and risk, because both are accelerating faster than ever.”

Embrace AI with confidence with Darktrace / SECURE AI

Darktrace has introduced Darktrace / SECURE AI™, a new product within the Darktrace ActiveAI Security Platform™  ,designed to provide enterprise-wide security for AI by applying industry leading behavioral analysis to how prompts, agents, and AI systems are used.

Darktrace / SECURE AITM delivers real-time visibility and control across Enterprise and SaaS GenAI prompts, AI agent identities, development and production environments, and Shadow AI - detecting even subtle misuse, misconfiguration, and drift that traditional, rule-based controls simply do not understand. By interpreting context and intent across humans and machines, Darktrace enables organizations to adopt AI at scale without introducing unmanaged risk

What makes this possible is Darktrace’s decade-long maturity and expertise in behavioral understanding and AI-native cybersecurity. Achieved with Self-Learning AI that has been proven across more than 10,000 organizations, Darktrace understands what “normal” looks like for a business, across its users, systems, and now AI, so that meaningful deviations can be detected and acted on before they become incidents.

With one CISO describing Darktrace’s Self-Learning AI as “a leap forward compared to other tools” and another as a “force multiplier,” the technology can interpret ambiguous interactions, understand how access accumulates over time, and recognize when behavior, human or machine, begins to drift.

“Strategically, we’re looking to gain more visibility into how AI is operating across the environment and achieve greater control over what AI should be allowed to access and do,” shared the CISO at a private fund administrator.  

“What I’ve seen from Darktrace / SECURE AI is extremely promising. I have tremendous confidence in Darktrace’s vision for where this is headed and its ability to execute on this new solution.”

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