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November 6, 2022

Behind Yanluowang: Unveiling Cyber Threat Tactics

Discover the latest insights into the Yanluowang leak organization, uncovering its members and tactics.
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
Taisiia Garkava
Security Analyst
Written by
Dillon Ashmore
Security and Research
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06
Nov 2022

Background of Yanluowang

Yanluowang ransomware, also known as Dryxiphia, was first spotted in October 2021 by Symantec’s Threat Hunter Team. However, it has been operational since August 2021, when a threat actor used it to attack U.S. corporations. Said attack shared similar TTPs with ransomware Thieflock, designed by Fivehands ransomware gangs. This connection alluded to a possible link between the two through the presence or influence of an affiliate. The group has been known for successfully ransoming organisations globally, particularly those in the financial, manufacturing, IT services, consultancy, and engineering sectors.

Yanluowang attacks typically begin with initial reconnaissance, followed by credential harvesting and data exfiltration before finally encrypting the victim’s files. Once deployed on compromised networks, Yanluowang halts hypervisor virtual machines, all running processes and encrypts files using the “.yanluowang” extension. A file with name README.txt, containing a ransom note is also dropped. The note also warns victims against contacting law enforcement, recovery companies or attempting to decrypt the files themselves. Failure to follow this advice would result in distributed denial of service attacks against a victim, its employees and business partners. Followed by another attack, a few weeks later, in which all the victim’s files would be deleted.

The group’s name “Yanluowang” was inspired by the Chinese mythological figure Yanluowang, suggesting the group’s possible Chinese origin. However, the recent leak of chat logs belonging to the group, revealed those involved in the organisation spoke Russian. 

 Leak of Yanluowang’s chat logs

 On the 31st of October, a Twitter user named @yanluowangleaks shared the matrix chat and server leaks of the Yanluowang ransomware gang, alongside the builder and decryption source. In total, six files contained internal conversations between the group’s members. From the analysis of these chats, at least eighteen people have been involved in Yanluowang operations.

Twitter account where the leaks and decryption source were shared
Figure 1: Twitter account where the leaks and decryption source were shared

Potential members: ‘@killanas', '@saint', '@stealer', '@djonny', '@calls', '@felix', '@win32', '@nets', '@seeyousoon', '@shoker', '@ddos', '@gykko', '@loader1', '@guki', '@shiwa', '@zztop', '@al', '@coder1'

Most active members: ‘@saint’, ‘@killanas’, ‘@guki’, ‘@felix’, ‘@stealer’. 

To make the most sense out of the data that we analyzed, we combined the findings into two categories: tactics and organization.

Tactics 

From the leaked chat logs, several insights into the group’s operational security and TTPs were gained. Firstly, members were not aware of each other’s offline identities. Secondly, discussions surrounding security precautions for moving finances were discussed by members @killanas and @felix. The two exchanged recommendations on reliable currency exchange platforms as well as which ones to avoid that were known to leak data to law enforcement. The members also expressed paranoia over being caught with substantial amounts of money and therefore took precautions such as withdrawing smaller amounts of cash or using QR codes for withdrawals.

Additionally, the chat logs exposed the TTPs of Yanluowang. Exchanges between the group’s members @stealer, @calls and @saint, explored the possibilities of conducting attacks against critical infrastructure. One of these members, @call, was also quick to emphasise that Yanluowang would not target the critical infrastructure of former Soviet countries. Beyond targets, the chat logs also highlighted Yanluowang’s use of the ransomware, PayloadBIN but also that attacks that involved it may potentially have been misattributed to another ransomware actor, Evil Corp.

Further insight surrounding Yanluowang’s source code was also gained as it was revealed that it had been previously published on XSS.is as a downloadable file. The conversations surrounding this revealed that two members, @killanas and @saint, suspected @stealer was responsible for the leak. This suspicion was supported by @saint, defending another member whom he had known for eight years. It was later revealed that the code had been shared after a request to purchase it was made by a Chinese national. @saint also used their personal connections to have the download link removed from XSS.is. These connections indicate that some members of Yanluowang are well embedded in the ransomware and wider cybercrime community.

Another insight gained from the leaked chat logs was an expression by @saint in support of Ukraine, stating, “We stand with Ukraine” on the negotiation page of Yanluowang’s website. This action reflects a similar trend observed among threat actors where they have taken sides in the Russia-Ukraine conflict.

Regarding Yanluowang’s engagement with other groups, it was found that a former member of Conti had joined the group. This inference was made by @saint when a conversation regarding the Conti leak revolved around the possible identification of the now Yanluowang member @guki, in the Conti files. It was also commented that Conti was losing a considerable number of its members who were then looking for new work. Conversations about other ransomware groups were had with the mentioning of the REVIL group by @saint, specifically stating that five arrested members of the gang were former classmates. He backed his statement by attaching the article about it, to which @djonny replies that those are indeed REVIL members and that he knows it from his sources.

Organization 

When going through the chat logs, several observations were made that can offer some insights into the group's organizational structure. In one of the leaked files, user @saint was the one to publish the requirements for the group's ".onion" website and was also observed instructing other users on the tasks they had to complete. Based on this, @saint could be considered the leader of the group. Additionally, there was evidence indicating that a few users could be in their 30s or 40s, while most participants are in their 20s.

More details regarding Yanluowang's organizational structure were discussed deeper into the leak. The examples indicate various sub-groups within the Yanlouwang group and that a specific person coordinates each group. From the logs, there is a high probability that @killanas is the leader of the development team and has several people working under him. It is also possible that @stealer is on the same level as @killanas and is potentially the supervisor of another team within the group. This was corroborated when @stealer expressed concerns about the absence of certain group members on several occasions. There is also evidence showing that he was one of three people with access to the source code of the group. 

Role delineation within the group was also quite clear, with each user having specific tasks: DDoS (distributed denial of service) attacks, social engineering, victim negotiations, pentesting or development, to mention a few. When it came to recruiting new members, mostly pentesters, Yanluowang would recruit through XSS.is and Exploit.in forums.

Underground analysis and members’ identification 

From the leaked chat logs, several “.onion” URLs were extracted; however, upon further investigation, each site had been taken offline and removed from the TOR hashring. This suggests that Yanluowang may have halted all operations. One of the users on XSS.is posted a picture showing that the Yanluowang onion website was hacked, stating, “CHECKMATE!! YANLUOWANG CHATS HACKED @YANLUOWANGLEAKS TIME’S UP!!”.

Figure 2: The screenshot of Yanluowang website on Tor (currently offline)

After learning that Yanluowang used Russian Web Forums, we did an additional search to see what we could find about the group and the mentioned nicknames. 

By searching through XSS.Is we managed to identify the user registered as @yanluowang. The date of the registration on the forum dates to 15 March 2022. Curiously, at the time of analysis, we noticed the user was online. There were in total 20 messages posted by @yanluowang, with a few publications indicating the group is looking for new pentesters.

Figure 3: The screenshot of Yanluowang profile on XSS.is 

Figure 4: The screenshot of Yanluowang posts about pentester recruitment on XSS.is 

While going through the messages, it was noticed the reaction posted by another user identified as @Sa1ntJohn, which could be the gang member @saint.

Figure 5: The screenshot of Sa1ntJohn’s profile on XSS.is

Looking further, we identified that user @Ekranoplan published three links to the website doxbin.com containing information about three potential members of the YanLuoWang gang: @killanas/coder, @hardbass and @Joe/Uncle. The profile information was published by the user @Xander2727.

Figure 6: The screenshot of Yanlouwang member-profile leak on XSS.is
Figure 7: The screenshot of @hardbass Yanlouwang member profile leak
Figure 8: The screenshot of @killanas/coder Yanlouwang member profile leak.

If the provided information is correct, two group members are Russian and in their 30s, while another member is Ukrainian and in his 20s. One of the members, @killanas, who was also referenced in chat logs, is identified as the lead developer of the Yanluowang group; giving the interpretation of the chat leaks a high-level of confidence. Another two members, who were not referenced in the logs, took roles as Cracked Software/Malware provider and English translator/Victim Negotiator.

Implications for the wider ransomware landscape

To conclude with the potential implications of this leak, we have corroborated the evidence gathered throughout this investigation and employed contrarian analytical techniques. The ascertained implications that follow our mainline judgement, supporting evidence and our current analytical view on the matter can be categorized into three key components of this leak:

Impact on the ransomware landscape

The leak of Yanluowang’s chat logs has several implications for the broader ransomware landscape. This leak, much like the Conti leak in March, spells the end for Yanluowang operations for the time being, given how much of the group’s inner workings it has exposed. This could have an adverse effect. While Yanluowang did not control as large of a share of the ransomware market as Conti did, their downfall will undoubtedly create a vacuum space for established groups for their market share. The latter being a consequence of the release of their source code and build tools. 

Source code

The release of Yanluowang’s source code has several outcomes. If the recipients have no malintent, it could aid in reverse engineering the ransomware, like how a decryption tool for Yanluowng was released earlier this year. An alternative scenario is that the publication of the source code will increase the reach and deployment of this ransomware in the future, in adapted or modified versions by other threat actors. Reusing leaked material is notorious among ransomware actors, as seen in the past, when Babuk’s source code was leaked and led to the development of several variants based on this leak, including Rook and Pandora. This could also make it harder to attribute attacks to one specific group.

Members

The migration of unexposed Yanluowang members to other ransomware gangs could further add to the proliferation of ransomware groups. Such forms of spreading ransomware have been documented in the past when former Conti members repurposed their tactics to join efforts with an initial access broker, UAC-0098. Yet, the absence of evidence from members expressing and/or acting upon this claim requires further investigation and analysis. However, as there is no evidence of absence – this implication is based on the previously observed behavior from members of other ransomware gangs.

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
Taisiia Garkava
Security Analyst
Written by
Dillon Ashmore
Security and Research

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November 25, 2025

UK Cyber Security & Resilience Bill: What Organizations Need to Know

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Why the Bill has been introduced

The UK’s cyber threat landscape has evolved dramatically since the 2018 NIS regime was introduced. Incidents such as the Synnovis attack against hospitals and the British Library ransomware attack show how quickly operational risk can become public harm. In this context, the UK Department for Science, Innovation and Technology estimates that cyber-attacks cost UK businesses around £14.7 billion each year.

At the same time, the widespread adoption of AI has expanded organisations’ attack surfaces and empowered threat actors to launch more effective and sophisticated activities, including crafting convincing phishing campaigns, exploiting vulnerabilities and initiating ransomware attacks at unprecedented speed and scale.  

The CSRB responds to these challenges by widening who is regulated, accelerating incident reporting and tightening supply chain accountability, while enabling rapid updates that keep pace with technology and emerging risks.

Key provisions of the Cyber Security and Resilience Bill

A wider set of organisations in scope

The Bill significantly broadens the range of organisations regulated under the NIS framework.

  • Managed service providers (MSPs) - medium and large MSPs, including MSSPs, managed SOCs, SIEM providers and similar services,will now fall under NIS obligations due to their systemic importance and privileged access to client systems. The Information Commissioner’s Office (ICO) will act as the regulator. Government analysis anticipates that a further 900 to 1,100 MSPs will be in scope.
  • Data infrastructure is now recognised as essential to the functioning of the economy and public services. Medium and large data centres, as well as enterprise facilities meeting specified thresholds, will be required to implement appropriate and proportionate measures to manage cyber risk. Oversight will be shared between DSIT and Ofcom, with Ofcom serving as the operational regulator.
  • Organisations that manage electrical loads for smart appliances, such as those supporting EV charging during peak times, are now within scope.

These additions sit alongside existing NIS-regulated sectors such as transport, energy, water, health, digital infrastructure, and certain digital services (including online marketplaces, search engines, and cloud computing).

Stronger supply chain requirements

Under the CSRB, regulators can now designate third-party suppliers as ‘designated critical suppliers’ (DCS) when certain threshold criteria are met and where disruption could have significant knock-on effects. Designated suppliers will be subject to the same security and incident-reporting obligations as Operators of Essential Services (OES) and Relevant Digital Service Providers (RDSPs).

Government will scope the supply chain duties for OES and RDSPs via secondary legislation, following consultation. infrastructure incidents where a single supplier’s compromise caused widespread disruption.

Faster incident reporting

Sector-specific regulators, 12 in total, will be responsible for implementing the CSRB, allowing for more effective and consistent reporting. In addition, the CSRB introduces a two-stage reporting process and expands incident reporting criteria. Regulated entities must submit an initial notification within 24 hours of becoming aware of a significant incident, followed by an incident report within 72 hours. Incident reporting criteria are also broadened to capture incidents beyond those which actually resulted in an interruption, ensuring earlier visibility for regulators and the National Cyber Security Centre (NCSC). The importance of information sharing across agencies, law enforcement and regulators is also facilitated by the CSRB.

The reforms also require data centres and managed service providers to notify affected customers where they are likely to have been impacted by a cyber incident.

An agile regulatory framework

To keep pace with technological change, the CSRB will enable the Secretary of State to update elements of the framework via secondary legislation. Supporting materials such as the NCSC Cyber Assessment Framework (CAF) are to be "put on a stronger footing” allowing for requirements to be more easily followed, managed and updated. Regulators will also now be able to recover full costs associated with NIS duties meaning they are better resourced to carry out their associated responsibilities.

Relevant Managed Service Providers must identify and take appropriate and proportionate measures to manage risks to the systems they rely on for providing services within the UK. Importantly, these measures must, having regard to the state of the art, ensure a level of security appropriate to the risk posed, and prevent or minimise the impact of incidents.

The Secretary of State will also be empowered to issue a Statement of Strategic Priorities, setting cross-regime outcomes to drive consistency across the 12 competent authorities responsible for implementation.

Penalties

The enforcement framework will be strengthened, with maximum fines aligned with comparable regimes such as the GDPR, which incorporate maximums tied to turnover. Under the CSRB, maximum penalties for more serious breaches could be up to £17 million or 4% of global turnover, whichever is higher.

Next steps

The Bill is expected to progress through Parliament over the course of 2025 and early 2026, with Royal Assent anticipated in 2026. Once enacted, most operational measures will not take immediate effect. Instead, Government will bring key components into force through secondary legislation following further consultation, providing regulators and industry with time to adjust practices and prepare for compliance.

Anticipated timeline

  • 2025-2026: Parliamentary scrutiny and passage;
  • 2026: Royal Assent;  
  • 2026 consultation: DSIT intends to consult on detailed implementation;
  • From 2026 onwards: Phased implementation via secondary legislation, following further consultation led by DSIT.

How Darktrace can help

The CSRB represents a step change in how the UK approaches digital risk, shifting the focus from compliance to resilience.

Darktrace can help organisations operationalise this shift by using AI to detect, investigate and respond to emerging threats at machine speed, before they escalate into incidents requiring regulatory notification. Proactive tools which can be included in the Darktrace platform allow security teams to stress-test defences, map supply chain exposure and rehearse recovery scenarios, directly supporting the CSRB’s focus on resilience, transparency and rapid response. If an incident does occur, Darktrace’s autonomous agent, Cyber AI Analyst, can accelerate investigations and provide a view of every stage of the attack chain, supporting timely reporting.  

Darktrace’s AI can provide organisations with a vital lens into both internal and external cyber risk. By continuously learning patterns of behaviour across interconnected systems, Darktrace can flag potential compromise or disruption to detect supply chain risk before it impacts your organisation.

In a landscape where compliance and resilience go hand in hand, Darktrace can equip organisations to stay ahead of both evolving threats and evolving regulatory requirements.

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November 20, 2025

Managing OT Remote Access with Zero Trust Control & AI Driven Detection

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The shift toward IT-OT convergence

Recently, industrial environments have become more connected and dependent on external collaboration. As a result, truly air-gapped OT systems have become less of a reality, especially when working with OEM-managed assets, legacy equipment requiring remote diagnostics, or third-party integrators who routinely connect in.

This convergence, whether it’s driven by digital transformation mandates or operational efficiency goals, are making OT environments more connected, more automated, and more intertwined with IT systems. While this convergence opens new possibilities, it also exposes the environment to risks that traditional OT architectures were never designed to withstand.

The modernization gap and why visibility alone isn’t enough

The push toward modernization has introduced new technology into industrial environments, creating convergence between IT and OT environments, and resulting in a lack of visibility. However, regaining that visibility is just a starting point. Visibility only tells you what is connected, not how access should be governed. And this is where the divide between IT and OT becomes unavoidable.

Security strategies that work well in IT often fall short in OT, where even small missteps can lead to environmental risk, safety incidents, or costly disruptions. Add in mounting regulatory pressure to enforce secure access, enforce segmentation, and demonstrate accountability, and it becomes clear: visibility alone is no longer sufficient. What industrial environments need now is precision. They need control. And they need to implement both without interrupting operations. All this requires identity-based access controls, real-time session oversight, and continuous behavioral detection.

The risk of unmonitored remote access

This risk becomes most evident during critical moments, such as when an OEM needs urgent access to troubleshoot a malfunctioning asset.

Under that time pressure, access is often provisioned quickly with minimal verification, bypassing established processes. Once inside, there’s little to no real-time oversight of user actions whether they’re executing commands, changing configurations, or moving laterally across the network. These actions typically go unlogged or unnoticed until something breaks. At that point, teams are stuck piecing together fragmented logs or post-incident forensics, with no clear line of accountability.  

In environments where uptime is critical and safety is non-negotiable, this level of uncertainty simply isn’t sustainable.

The visibility gap: Who’s doing what, and when?

The fundamental issue we encounter is the disconnect between who has access and what they are doing with it.  

Traditional access management tools may validate credentials and restrict entry points, but they rarely provide real-time visibility into in-session activity. Even fewer can distinguish between expected vendor behavior and subtle signs of compromise, misuse or misconfiguration.  

As a result, OT and security teams are often left blind to the most critical part of the puzzle, intent and behavior.

Closing the gaps with zero trust controls and AI‑driven detection

Managing remote access in OT is no longer just about granting a connection, it’s about enforcing strict access parameters while continuously monitoring for abnormal behavior. This requires a two-pronged approach: precision access control, and intelligent, real-time detection.

Zero Trust access controls provide the foundation. By enforcing identity-based, just-in-time permissions, OT environments can ensure that vendors and remote users only access the systems they’re explicitly authorized to interact with, and only for the time they need. These controls should be granular enough to limit access down to specific devices, commands, or functions. By applying these principles consistently across the Purdue Model, organizations can eliminate reliance on catch-all VPN tunnels, jump servers, and brittle firewall exceptions that expose the environment to excess risk.

Access control is only one part of the equation

Darktrace / OT complements zero trust controls with continuous, AI-driven behavioral detection. Rather than relying on static rules or pre-defined signatures, Darktrace uses Self-Learning AI to build a live, evolving understanding of what’s “normal” in the environment, across every device, protocol, and user. This enables real-time detection of subtle misconfigurations, credential misuse, or lateral movement as they happen, not after the fact.

By correlating user identity and session activity with behavioral analytics, Darktrace gives organizations the full picture: who accessed which system, what actions they performed, how those actions compared to historical norms, and whether any deviations occurred. It eliminates guesswork around remote access sessions and replaces it with clear, contextual insight.

Importantly, Darktrace distinguishes between operational noise and true cyber-relevant anomalies. Unlike other tools that lump everything, from CVE alerts to routine activity, into a single stream, Darktrace separates legitimate remote access behavior from potential misuse or abuse. This means organizations can both audit access from a compliance standpoint and be confident that if a session is ever exploited, the misuse will be surfaced as a high-fidelity, cyber-relevant alert. This approach serves as a compensating control, ensuring that even if access is overextended or misused, the behavior is still visible and actionable.

If a session deviates from learned baselines, such as an unusual command sequence, new lateral movement path, or activity outside of scheduled hours, Darktrace can flag it immediately. These insights can be used to trigger manual investigation or automated enforcement actions, such as access revocation or session isolation, depending on policy.

This layered approach enables real-time decision-making, supports uninterrupted operations, and delivers complete accountability for all remote activity, without slowing down critical work or disrupting industrial workflows.

Where Zero Trust Access Meets AI‑Driven Oversight:

  • Granular Access Enforcement: Role-based, just-in-time access that aligns with Zero Trust principles and meets compliance expectations.
  • Context-Enriched Threat Detection: Self-Learning AI detects anomalous OT behavior in real time and ties threats to access events and user activity.
  • Automated Session Oversight: Behavioral anomalies can trigger alerting or automated controls, reducing time-to-contain while preserving uptime.
  • Full Visibility Across Purdue Layers: Correlated data connects remote access events with device-level behavior, spanning IT and OT layers.
  • Scalable, Passive Monitoring: Passive behavioral learning enables coverage across legacy systems and air-gapped environments, no signatures, agents, or intrusive scans required.

Complete security without compromise

We no longer have to choose between operational agility and security control, or between visibility and simplicity. A Zero Trust approach, reinforced by real-time AI detection, enables secure remote access that is both permission-aware and behavior-aware, tailored to the realities of industrial operations and scalable across diverse environments.

Because when it comes to protecting critical infrastructure, access without detection is a risk and detection without access control is incomplete.

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About the author
Pallavi Singh
Product Marketing Manager, OT Security & Compliance
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