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Inside the Yanluowang leak: Organization, members, and tactics

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06
Nov 2022
06
Nov 2022
YanLuoWang ransomware was first used to attack a handful of US corporations in August 2021. Since then, the group have successfully ransomed organizations across the world, with global software giant Cisco among its victims. This blog post reveals Darktrace analysts' research into the organization’s structure and tactics.

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.
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Taisiia Garkava
Security Analyst
Dillon Ashmore
Security and Research
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Blog

Email

How Empowering End Users can Improve Your Email Security and Decrease the Burden on the SOC

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08
May 2024

Why do we pay attention to the end user?

Every email security solution filters inbound mail, then typically hands over false positives and false negatives to the security team for manual triage. A crucial problem with this lifecycle is that it ignores the inevitability of end users being at the front line of any organization. Employees may receive point in time security awareness training, but it is rarely engaging or contextualized to their reality. While an employee may report a suspicious-looking email to the security team, they will rarely get to understand the outcome or impact of that decision. This means that the quality of reporting never improves, so the burden on the security team of triaging these emails – of which 90% are falsely reported – persists and grows with the business over time.

At Darktrace, we recognize that employees will always be on the front line of email security. That’s why we aim to improve end-user reporting from the ground up, reducing the overall number of emails needing triage and saving security team resource.

How does Darktrace improve the quality of end-user reporting?

Darktrace prioritizes improving users’ security awareness to increase the quality of end-user reporting from day one. We train users and optimize their experience, which in turn provides better detection. 

That starts with training and security awareness. Traditionally, organizations oblige employees to attend point-in-time training sessions which interrupt their daily work schedules. With Darktrace/Email, if a message contains some potentially suspicious markers but is most likely safe, Darktrace takes a specific action to neutralize the risky components and presents it to the user with a simple narrative explaining why certain elements have been held back. The user can then decide whether to report this email to the security team. 

AI shares its analysis in context and in real time at the moment a user is questioning an email
Figure 1: AI shares its analysis in context and in real time at the moment a user is questioning an email

The AI narrative gives the user context for why their specific email may carry risk, putting their security awareness training into practice. This creates an element of trust with the security solution, rather than viewing it as outside of daily workflows. Users may also receive a daily or weekly digest of their held emails and make a decision on whether to release or report them.  

Whatever the user’s existing workflow is for reporting emails, Darktrace/Email can integrate with it and improve its quality. Our add-in for Outlook gives users a fully optimized experience, allowing them to engage with the narratives for each email, as well as non-productive mail management. However, if teams want to integrate Darktrace into an existing workflow, it can analyze emails reported to an internal SOC mailbox, the native email provider’s 'Report Phish’ button, or the ‘Knowbe4’ button.

By empowering the user with contextual feedback on each unique email, we foster employee engagement and elevate both reporting quality and security awareness. In fact, 60% fewer benign emails are reported because of the extra context supplied by Darktrace to end users. The eventual report is then fed back to the detection algorithm, improving future decision-making.  

Reducing the amount of emails that reach the SOC

Out of the higher-quality emails that do end up being reported by users, the next step is to reduce the amount of emails that reach the SOC.   

Once a user reports an email, Darktrace will independently determine if the mail should be automatically remediated based on second level triage. Darktrace/Email’s Mailbox Security Assistant automates secondary triage by combining additional behavioral signals and the most advanced link analysis engine we have ever built. It detects 70% more sophisticated malicious phishing links by looking at an additional twenty times more context than at the primary analysis stage, revealing the hidden intent within interactive and dynamic webpages. This directly alleviates the burden of manual triage for security analysts.

Following this secondary triage the emails that are deemed worthy of security team attention are then passed over, resulting in a lower quantity and higher quality of emails for SOC manual triage.

Centralizing and speeding analysis for investigations

For those emails that are received by the SOC, Darktrace also helps to improve triage time for manual remediation.  

AI-generated narratives and automated remediation actions empower teams to fast-track manual triage and remediation, while still providing security analysts with the necessary depth. With live inbox view, security teams gain access to a centralized platform that combines intuitive search capabilities, Cyber AI Analyst reports, and mobile application access. With all security workflows consolidated within a unified interface, users can analyze and take remediation actions without the need to navigate multiple tools, such as e-discovery platforms – eliminating console hopping and accelerating incident response.

Our customers tell us that our AI allows them to go in-depth quickly for investigations, versus other solutions that only provide a high-level view.

Cyber AI Analyst provides a simple language narrative for each reported email, allowing teams to quickly understand why it may be suspicious
Figure 2: Cyber AI Analyst provides a simple language narrative for each reported email, allowing teams to quickly understand why it may be suspicious

Conclusion

Unlike our competitors, we believe that improving the quality of users’ experience is not only a nice-to-have, but a fundamental means for improving security. Any modern solution should consider end users as a key source of information as well as an opportunity for defense. Darktrace does both – optimizing the user experience as well as our AI learning from the user to augment detection.  

The benefits of empowering users are ultimately felt by the security team, who benefit from improved detection, a reduction in manual triage of benign emails, and faster investigation workflows.

Augmented end user reporting is just one of a range of features new to Darktrace/Email. Check out the latest Innovations to Darktrace/Email in our recent blog.

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About the author
Carlos Gray
Product Manager

Blog

Inside the SOC

Detecting Attacks Across Email, SaaS, and Network Environments with Darktrace’s AI Platform Approach

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30
Apr 2024

The State of AI in Cybersecurity

In a recent survey outlined in Darktrace’s State of AI Cyber Security whitepaper, 95% of cyber security professionals agree that AI-powered security solutions will improve their organization’s detection of cyber-threats [1]. Crucially, a combination of multiple AI methods is the most effective to improve cybersecurity; improving threat detection, accelerating threat investigation and response, and providing visibility across an organization’s digital environment.

In March 2024, Darktrace’s AI-led security platform was able to detect suspicious activity affecting a customer’s email, Software-as-a-Service (SaaS), and network environments, whilst its applied supervised learning capability, Cyber AI Analyst, autonomously correlated and connected all of these events together in one single incident, explained concisely using natural language processing.

Attack Overview

Following an initial email attack vector, an attacker logged into a compromised SaaS user account from the Netherlands, changed inbox rules, and leveraged the account to send thousands of phishing emails to internal and external users. Internal users fell victim to the emails by clicking on contained suspicious links that redirected them to newly registered suspicious domains hosted on same IP address as the hijacked SaaS account login. This activity triggered multiple alerts in Darktrace DETECT™ on both the network and SaaS side, all of which were correlated into one Cyber AI Analyst incident.

In this instance, Darktrace RESPOND™ was not active on any of the customer’s environments, meaning the compromise was able to escalate until their security team acted on the alerts raised by DETECT. Had RESPOND been enabled at the time of the attack, it would have been able to apply swift actions to contain the attack by blocking connections to suspicious endpoints on the network side and disabling users deviating from their normal behavior on the customer’s SaaS environment.

Nevertheless, thanks to DETECT and Cyber AI Analyst, Darktrace was able to provide comprehensive visibility across the customer’s three digital estate environments, decreasing both investigation and response time which enabled them to quickly enact remediation during the attack. This highlights the crucial role that Darktrace’s combined AI approach can play in anomaly detection cyber defense

Attack Details & Darktrace Coverage

Attack timeline

1. Email: the initial attack vector  

The initial attack vector was likely email, as on March 18, 2024, Darktrace observed a user device making several connections to the email provider “zixmail[.]net”, shortly before it connected to the first suspicious domain. Darktrace/Email identified multiple unusual inbound emails from an unknown sender that contained a suspicious link. Darktrace recognized these emails as potentially malicious and locked the link, ensuring that recipients could not directly click it.

Suspected initial compromise email from an unknown sender, containing a suspicious link, which was locked by Darktrace/Email.
Figure 1: Suspected initial compromise email from an unknown sender, containing a suspicious link, which was locked by Darktrace/Email.

2. Escalation to Network

Later that day, despite Darktrace/Email having locked the link in the suspicious email, the user proceeded to click on it and was directed to a suspicious external location, namely “rz8js7sjbef[.]latovafineart[.]life”, which triggered the Darktrace/Network DETECT model “Suspicious Domain”. Darktrace/Email was able to identify that this domain had only been registered 4 days before this activity and was hosted on an IP address based in the Netherlands, 193.222.96[.]9.

3. SaaS Account Hijack

Just one minute later, Darktrace/Apps observed the user’s Microsoft 365 account logging into the network from the same IP address. Darktrace understood that this represented unusual SaaS activity for this user, who had only previously logged into the customer’s SaaS environment from the US, triggering the “Unusual External Source for SaaS Credential Use” model.

4. SaaS Account Updates

A day later, Darktrace identified an unusual administrative change on the user’s Microsoft 365 account. After logging into the account, the threat actor was observed setting up a new multi-factor authentication (MFA) method on Microsoft Authenticator, namely requiring a 6-digit code to authenticate. Darktrace understood that this authentication method was different to the methods previously used on this account; this, coupled with the unusual login location, triggered the “Unusual Login and Account Update” DETECT model.

5. Obfuscation Email Rule

On March 20, Darktrace detected the threat actor creating a new email rule, named “…”, on the affected account. Attackers are typically known to use ambiguous or obscure names when creating new email rules in order to evade the detection of security teams and endpoints users.

The parameters for the email rule were:

“AlwaysDeleteOutlookRulesBlob: False, Force: False, MoveToFolder: RSS Feeds, Name: ..., MarkAsRead: True, StopProcessingRules: True.”

This rule was seemingly created with the intention of obfuscating the sending of malicious emails, as the rule would move sent emails to the "RSS Feeds” folder, a commonly used tactic by attackers as the folder is often left unchecked by endpoint users. Interestingly, Darktrace identified that, despite the initial unusual login coming from the Netherlands, the email rule was created from a different destination IP, indicating that the attacker was using a Virtual Private Network (VPN) after gaining a foothold in the network.

Hijacked SaaS account making an anomalous login from the unusual Netherlands-based IP, before creating a new email rule.
Figure 2: Hijacked SaaS account making an anomalous login from the unusual Netherlands-based IP, before creating a new email rule.

6. Outbound Phishing Emails Sent

Later that day, the attacker was observed using the compromised customer account to send out numerous phishing emails to both internal and external recipients. Darktrace/Email detected a significant spike in inbound emails on the compromised account, with the account receiving bounce back emails or replies in response to the phishing emails. Darktrace further identified that the phishing emails contained a malicious DocSend link hidden behind the text “Click Here”, falsely claiming to be a link to the presentation platform Prezi.

Figure 3: Darktrace/Email detected that the DocSend link displayed via text “Click Here”, was embedded in a Prezi link.
Figure 3: Darktrace/Email detected that the DocSend link displayed via text “Click Here”, was embedded in a Prezi link.

7. Suspicious Domains and Redirects

After the phishing emails were sent, multiple other internal users accessed the DocSend link, which directed them to another suspicious domain, “thecalebgroup[.]top”, which had been registered on the same day and was hosted on the aforementioned Netherlands-based IP, 193.222.96[.]91. At the time of the attack, this domain had not been reported by any open-source intelligence (OSINT), but it has since been flagged as malicious by multiple vendors [2].

External Sites Summary showing the suspicious domain that had never previously been seen on the network. A total of 11 “Suspicious Domain” models were triggered in response to this activity.
Figure 4: External Sites Summary showing the suspicious domain that had never previously been seen on the network. A total of 11 “Suspicious Domain” models were triggered in response to this activity.  

8. Cyber AI Analyst’s Investigation

As this attack was unfolding, Darktrace’s Cyber AI Analyst was able to autonomously investigate the events, correlating them into one wider incident and continually adding a total of 14 new events to the incident as more users fell victim to the phishing links.

Cyber AI Analyst successfully weaved together the initial suspicious domain accessed in the initial email attack vector (Figure 5), the hijack of the SaaS account from the Netherlands IP (Figure 6), and the connection to the suspicious redirect link (Figure 7). Cyber AI Analyst was also able to uncover other related activity that took place at the time, including a potential attempt to exfiltrate data out of the customer’s network.

By autonomously analyzing the thousands of connections taking place on a network at any given time, Darktrace’s Cyber AI Analyst is able to detect seemingly separate anomalous events and link them together in one incident. This not only provides organizations with full visibility over potential compromises on their networks, but also saves their security teams precious time ensuring they can quickly scope out the ongoing incident and begin remediation.

Figure 5: Cyber AI Analyst correlated the attack’s sequence, starting with the initial suspicious domain accessed in the initial email attack vector.
Figure 5: Cyber AI Analyst correlated the attack’s sequence, starting with the initial suspicious domain accessed in the initial email attack vector.
Figure 6: As the attack progressed, Cyber AI Analyst correlated and appended additional events to the same incident, including the SaaS account hijack from the Netherlands-based IP.
Figure 6: As the attack progressed, Cyber AI Analyst correlated and appended additional events to the same incident, including the SaaS account hijack from the Netherlands-based IP.
Cyber AI Analyst correlated and appended additional events to the same incident, including additional users connecting to the suspicious redirect link following the outbound phishing emails being sent.
Figure 7: Cyber AI Analyst correlated and appended additional events to the same incident, including additional users connecting to the suspicious redirect link following the outbound phishing emails being sent.

Conclusion

In this scenario, Darktrace demonstrated its ability to detect and correlate suspicious activities across three critical areas of a customer’s digital environment: email, SaaS, and network.

It is essential that cyber defenders not only adopt AI but use a combination of AI technology capable of learning and understanding the context of an organization’s entire digital infrastructure. Darktrace’s anomaly-based approach to threat detection allows it to identify subtle deviations from the expected behavior in network devices and SaaS users, indicating potential compromise. Meanwhile, Cyber AI Analyst dynamically correlates related events during an ongoing attack, providing organizations and their security teams with the information needed to respond and remediate effectively.

Credit to Zoe Tilsiter, Analyst Consulting Lead (EMEA), Brianna Leddy, Director of Analysis

Appendices

References

[1] https://darktrace.com/state-of-ai-cyber-security

[2] https://www.virustotal.com/gui/domain/thecalebgroup.top

Darktrace DETECT Model Coverage

SaaS Models

- SaaS / Access / Unusual External Source for SaaS Credential Use

- SaaS / Compromise / Unusual Login and Account Update

- SaaS / Compliance / Anomalous New Email Rule

- SaaS / Compromise / Unusual Login and New Email Rule

Network Models

- Device / Suspicious Domain

- Multiple Device Correlations / Multiple Devices Breaching Same Model

Cyber AI Analyst Incidents

- Possible Hijack of Office365 Account

- Possible SSL Command and Control

Indicators of Compromise (IoCs)

IoC – Type – Description

193.222.96[.]91 – IP – Unusual Login Source

thecalebgroup[.]top – Domain – Possible C2 Endpoint

rz8js7sjbef[.]latovafineart[.]life – Domain – Possible C2 Endpoint

https://docsend[.]com/view/vcdmsmjcskw69jh9 - Domain - Phishing Link

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About the author
Zoe Tilsiter
Cyber Analyst
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