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May 25, 2022

Uncovering the Sysrv-Hello Crypto-Jacking Bonet

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25
May 2022
Discover the cyber kill chain of a Sysrv-hello botnet infection in France and gain insights into the latest TTPs of the botnet in March and April 2022.

In recent years, the prevalence of crypto-jacking botnets has risen in tandem with the popularity and value of cryptocurrencies. Increasingly crypto-mining malware programs are distributed by botnets as they allow threat actors to harness the cumulative processing power of a large number of machines (discussed in our other Darktrace blogs.1 2 One of these botnets is Sysrv-hello, which in addition to crypto-mining, propagates aggressively across the Internet in a worm-like manner by trolling for Remote Code Execution (RCE) vulnerabilities and SSH worming from the compromised victim devices. This all has the purpose of expanding the botnet.

First identified in December 2020, Sysrv-hello’s operators constantly update and change the bots’ behavior to evolve and stay ahead of security researchers and law enforcement. As such, infected systems can easily go unnoticed by both users and organizations. This blog examines the cyber kill chain sequence of a Sysrv-hello botnet infection detected at the network level by Darktrace DETECT/Network, as well as the botnet’s tactics, techniques, and procedures (TTPs) in March and April 2022.

Figure 1: Timeline of the attack

Delivery and exploitation

The organization, which was trialing Darktrace, had deployed the technology on March 2, 2022. On the very same day, the initial host infection was seen through the download of a first-stage PowerShell loader script from a rare external endpoint by a device in the internal network. Although initial exploitation of the device happened prior to the installation and was not observed, this botnet is known to target RCE vulnerabilities in various applications such as MySQL, Tomcat, PHPUnit, Apache Solar, Confluence, Laravel, JBoss, Jira, Sonatype, Oracle WebLogic and Apache Struts to gain initial access to internal systems.3 Recent iterations have also been reported to have been deployed via drive-by-downloads from an empty HTML iframe pointing to a malicious executable that downloads to the device from a user visiting a compromised website.4

Initial intrusion

The Sysrv-hello botnet is distributed for both Linux and Windows environments, with the corresponding compatible script pulled based on the architecture of the system. In this incident, the Windows version was observed.

On March 2, 2022 at 15:15:28 UTC, the device made a successful HTTP GET request to a malicious IP address5 that had a rarity score of 100% in the network. It subsequently downloaded a malicious PowerShell script named ‘ldr.ps1'6 onto the system. The associated IP address ‘194.145.227[.]21’ belongs to ‘ASN AS48693 Rices Privately owned enterprise’ and had been identified as a Sysrv-hello botnet command and control (C2) server in April the previous year. 3

Looking at the URI ‘/ldr.ps1?b0f895_admin:admin_81.255.222.82:8443_https’, it appears some form of query was being executed onto the object. The question mark ‘?’ in this URI is used to delimit the boundary between the URI of the queryable object and the set of strings used to express a query onto that object. Conventionally, we see the set of strings contains a list of key/value pairs with equal signs ‘=’, which are separated by the ampersand symbol ‘&’ between each of those parameters (e.g. www.youtube[.]com/watch?v=RdcCjDS0s6s&ab_channel=SANSCyberDefense), though the exact structure of the query string is not standardized and different servers may parse it differently. Instead, this case saw a set of strings with the hexadecimal color code #b0f895 (a light shade of green), admin username and password login credentials, and the IP address ‘81.255.222[.]82’ being applied during the object query (via HTTPS protocol on port 8443). In recent months this French IP has also had reports of abuse from the OSINT community.7

On March 2, 2022 at 15:15:33 UTC, the PowerShell loader script further downloaded second-stage executables named ‘sys.exe’ and ‘xmrig.2 sver.8 9 These have been identified as the worm and cryptocurrency miner payloads respectively.

Establish foothold

On March 2, 2022 at 17:46:55 UTC, after the downloads of the worm and cryptocurrency miner payloads, the device initiated multiple SSL connections in a regular, automated manner to Pastebin – a text storage website. This technique was used as a vector to download/upload data and drop further malicious scripts onto the host. OSINT sources suggest the JA3 client SSL fingerprint (05af1f5ca1b87cc9cc9b25185115607d) is associated with PowerShell usage, corroborating with the observation that further tooling was initiated by the PowerShell script ‘ldr.ps1’.

Continual Pastebin C2 connections were still being made by the device almost two months since the initiation of such connections. These Pastebin C2 connections point to new tactics and techniques employed by Sysrv-hello — reports earlier than May do not appear to mention any usage of the file storage site. These new TTPs serve two purposes: defense evasion using a web service/protocol and persistence. Persistence was likely achieved through scheduling daemons downloaded from this web service and shellcode executions at set intervals to kill off other malware processes, as similarly seen in other botnets.10 Recent reports have seen other malware programs also switch to Pastebin C2 tunnels to deliver subsequent payloads, scrapping the need for traditional C2 servers and evading detection.11

Figure 2: A section of the constant SSL connections that the device was still making to ‘pastebin[.]com’ even in the month of April, which resembles beaconing scheduled activity

Throughout the months of March and April, suspicious SSL connections were made from a second potentially compromised device in the internal network to the infected breach device. The suspicious French IP address ‘81.255.222[.]82’ previously seen in the URI object query was revealed as the value of the Server Name Indicator (SNI) in these SSL connections where, typically, a hostname or domain name is indicated.

After an initial compromise, attackers usually aim to gain long-term remote shell access to continue the attack. As the breach device does not have a public IP address and is most certainly behind a firewall, for it to be directly accessible from the Internet a reverse shell would need to be established. Outgoing connections often succeed because firewalls generally filter only incoming traffic. Darktrace observed the device making continuous outgoing connections to an external host listening on an unusual port, 8443, indicating the presence of a reverse shell for pivoting and remote administration.

Figure 3: SSL connections to server name ‘81.255.222[.]8’ at end of March and start of April

Accomplish mission

On March 4, 2022 at 15:07:04 UTC, the device made a total of 16,029 failed connections to a large volume of external endpoints on the same port (8080). This behavior is consistent with address scanning. From the country codes, it appears that public IP addresses for various countries around the world were contacted (at least 99 unique addresses), with the US being the most targeted.

From 19:44:36 UTC onwards, the device performed cryptocurrency mining using the Minergate mining pool protocol to generate profits for the attacker. A login credential called ‘x’ was observed in the Minergate connections to ‘194.145.227[.]21’ via port 5443. JSON-RPC methods of ‘login’ and ‘submit’ were seen from the connection originator (the infected breach device) and ‘job’ was seen from the connection responder (the C2 server). A high volume of connections using the JSON-RPC application protocol to ‘pool-fr.supportxmr[.]com’ were also made on port 80.

When the botnet was first discovered in December 2020, mining pools MineXMR and F2Pool were used. In February 2021, MineXMR was removed and in March 2021, Nanopool mining pool was added,12 before switching to the present SupportXMR and Minergate mining pools. Threat actors utilize such proxy pools to help hide the actual crypto wallet address where the contributions are made by the crypto-mining activity. From April onwards, the device appears to download the ‘xmrig.exe’ executable from a rare IP address ‘61.103.177[.]229’ in Korea every few days – likely in an attempt to establish persistency and ensure the cryptocurrency mining payload continues to exist on the compromised system for continued mining.

On March 9, 2022 from 18:16:20 UTC onwards, trolling for various RCE vulnerabilities (including but not limited to these four) was observed over HTTP connections to public IP addresses:

  1. Through March, the device made around 5,417 HTTP POSTs with the URI ‘/vendor/phpunit/phpunit/src/Util/PHP/eval-stdin.php’ to at least 99 unique public IPs. This appears to be related to CVE-2017-9841, which in PHPUnit allows remote attackers to execute arbitrary PHP code via HTTP POST data beginning with a ‘13 PHPUnit is a common testing framework for PHP, used for performing unit tests during application development. It is used by a variety of popular Content Management Systems (CMS) such as WordPress, Drupal and Prestashop. This CVE has been called “one of the most exploitable CVEs of 2019,” with around seven million attack attempts being observed that year.14 This framework is not designed to be exposed on the critical paths serving web pages and should not be reachable by external HTTP requests. Looking at the status messages of the HTTP POSTs in this incident, some ‘Found’ and ‘OK’ messages were seen, suggesting the vulnerable path could be accessible on some of those endpoints.

Figure 4: PCAP of CVE-2017-9841 vulnerability trolling

Figure 5: The CVE-2017-9841 vulnerable path appears to be reachable on some endpoints

  1. Through March, the device also made around 5,500 HTTP POSTs with the URI ‘/_ignition/execute-solution’ to at least 99 unique public IPs. This appears related to CVE-2021-3129, which allows unauthenticated remote attackers to execute arbitrary code using debug mode with Laravel, a PHP web application framework in versions prior to 8.4.2.15 The POST request below makes the variable ‘username’ optional, and the ‘viewFile’ parameter is empty, as a test to see if the endpoint is vulnerable.16

Figure 6: PCAP of CVE-2021-3129 vulnerability trolling

  1. The device made approximately a further 252 HTTP GETs with URIs containing ‘invokefunction&function’ to another minimum of 99 unique public IPs. This appears related to a RCE vulnerability in ThinkPHP, an open-source web framework.17

Figure 7: Some of the URIs associated with ThinkPHP RCE vulnerability

  1. A HTTP header related to a RCE vulnerability for the Jakarta Multipart parser used by Apache struts2 in CVE-2017-563818 was also seen during the connection attempts. In this case the payload used a custom Content-Type header.

Figure 8: PCAP of CVE-2017-5638 vulnerability trolling

Two widely used methods of SSH authentication are public key authentication and password authentication. After gaining a foothold in the network, previous reports3 19 have mentioned that Sysrv-hello harvests private SSH keys from the compromised device, along with identifying known devices. Being a known device means the system can communicate with the other system without any further authentication checks after the initial key exchange. This technique was likely performed in conjunction with password brute-force attacks against the known devices. Starting from March 9, 2022 at 20:31:25 UTC, Darktrace observed the device making a large number of SSH connections and login failures to public IP ranges. For example, between 00:05:41 UTC on March 26 and 05:00:02 UTC on April 14, around 83,389 SSH connection attempts were made to 31 unique public IPs.

Figure 9: Darktrace’s Threat Visualizer shows large spikes in SSH connections by the breach device

Figure 10: Beaconing SSH connections to a single external endpoint, indicating a potential brute-force attack

Darktrace coverage

Cyber AI Analyst was able to connect the events and present them in a digestible, chronological order for the organization. In the aftermath of any security incidents, this is a convenient way for security users to conduct assisted investigations and reduce the workload on human analysts. However, it is good to note that this activity was also easily observed in real time from the model section on the Threat Visualizer due to the large number of escalating model breaches.

Figure 11: Cyber AI Analyst consolidating the events in the month of March into a summary

Figure 12: Cyber AI Analyst shows the progression of the attack through the month of March

As this incident occurred during a trial, Darktrace RESPOND was enabled in passive mode – with a valid license to display the actions that it would have taken, but with no active control performed. In this instance, no Antigena models breached for the initial compromised device as it was not tagged to be eligible for Antigena actions. Nonetheless, Darktrace was able to provide visibility into these anomalous connections.

Had Antigena been deployed in active mode, and the breach device appropriately tagged with Antigena All or Antigena External Threat, Darktrace would have been able to respond and neutralize different stages of the attack through network inhibitors Block Matching Connections and Enforce Group Pattern of Life, and relevant Antigena models such as Antigena Suspicious File Block, Antigena Suspicious File Pattern of Life Block, Antigena Pastebin Block and Antigena Crypto Currency Mining Block. The first of these inhibitors, Block Matching Connections, will block the specific connection and all future connections that matches the same criteria (e.g. all future outbound HTTP connections from the breach device to destination port 80) for a set period of time. Enforce Group Pattern of Life allows a device to only make connections and data transfers that it or any of its peer group typically make.

Conclusion

Resource hijacking results in unauthorized consumption of system resources and monetary loss for affected organizations. Compromised devices can potentially be rented out to other threat actors and botnet operators could switch from conducting crypto-mining to other more destructive illicit activities (e.g. DDoS or dropping of ransomware) whilst changing their TTPs in the future. Defenders are constantly playing catch-up to this continual evolution, and retrospective rules and signatures solutions or threat intelligence that relies on humans to spot future threats will not be able to keep up.

In this case, it appears the botnet operator has added an object query in the URL of the initial PowerShell loader script download, added Pastebin C2 for evasion and persistence, and utilized new cryptocurrency mining pools. Despite this, Darktrace’s Self-Learning AI was able to identify the threats the moment attackers changed their approach, detecting every step of the attack in the network without relying on known indicators of threat.

Appendix

Darktrace model detections

  • Anomalous File / Script from Rare Location
  • Anomalous File / EXE from Rare External Location
  • Compromise / Agent Beacon (Medium Period)
  • Compromise / Slow Beaconing Activity To External Rare
  • Compromise / Beaconing Activity To External Rare
  • Device / External Address Scan
  • Compromise / Crypto Currency Mining Activity
  • Compromise / High Priority Crypto Currency Mining
  • Compromise / High Volume of Connections with Beacon Score
  • Compromise / SSL Beaconing to Rare Destination
  • Anomalous Connection / Multiple HTTP POSTs to Rare Hostname
  • Device / Large Number of Model Breaches
  • Anomalous Connection / Multiple Failed Connections to Rare Endpoint
  • Anomalous Connection / SSH Brute Force
  • Compromise / SSH Beacon
  • Compliance / SSH to Rare External AWS
  • Compromise / High Frequency SSH Beacon
  • Compliance / SSH to Rare External Destination
  • Device / Multiple C2 Model Breaches
  • Anomalous Connection / POST to PHP on New External Host

MITRE ATT&CK techniques observed:

IoCs

Thanks to Victoria Baldie and Yung Ju Chua for their contributions.

Footnotes

1. https://www.darktrace.com/en/blog/crypto-botnets-moving-laterally

2. https://www.darktrace.com/en/blog/how-ai-uncovered-outlaws-secret-crypto-mining-operation

3. https://www.lacework.com/blog/sysrv-hello-expands-infrastructure

4. https://www.riskiq.com/blog/external-threat-management/sysrv-hello-cryptojacking-botnet

5. https://www.virustotal.com/gui/ip-address/194.145.227.21

6. https://www.virustotal.com/gui/url/c586845daa2aec275453659f287dcb302921321e04cb476b0d98d731d57c4e83?nocache=1

7. https://www.abuseipdb.com/check/81.255.222.82

8. https://www.virustotal.com/gui/file/586e271b5095068484446ee222a4bb0f885987a0b77e59eb24511f6d4a774c30

9. https://www.virustotal.com/gui/file/f5bef6ace91110289a2977cfc9f4dbec1e32fecdbe77326e8efe7b353c58e639

10. https://www.ironnet.com/blog/continued-exploitation-of-cve-2021-26084

11. https://www.zdnet.com/article/njrat-trojan-operators-are-now-using-pastebin-as-alternative-to-central-command-server

12. https://blogs.juniper.net/en-us/threat-research/sysrv-botnet-expands-and-gains-persistence

13. https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2017-9841

14. https://www.imperva.com/blog/the-resurrection-of-phpunit-rce-vulnerability

15. https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-3129

16. https://isc.sans.edu/forums/diary/Laravel+v842+exploit+attempts+for+CVE20213129+debug+mode+Remote+code+execution/27758

17. https://securitynews.sonicwall.com/xmlpost/thinkphp-remote-code-execution-rce-bug-is-actively-being-exploited

18. https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2017-5638

19. https://sysdig.com/blog/crypto-sysrv-hello-wordpress

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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Shuh Chin Goh
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November 19, 2024

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Darktrace Leading the Future of Network Detection and Response with Recognition from KuppingerCole

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KuppingerCole has recognized Darktrace as an overall Leader, Product Leader, Market Leader and Innovation Leader in the KuppingerCole Leadership Compass: Network Detection and Response (2024).

With the perimeter all but dissolved, Network Detection and Response (NDR) tools are quickly becoming a critical component of the security stack, as the main tool to span the modern network. NDRs connect on-premises infrastructure to cloud, remote workers, identities, SaaS applications, and IoT/OT – something not available to EDR that requires agents and isolates visibility to individual devices.

KuppingerCole Analysts AG designated Darktrace an ‘Overall Leader’ position because of our continual innovation around user-led security. Self-Learning AI together with automated triage through Cyber AI Analyst and real-time autonomous response actions have been instrumental to security teams in stopping potential threats before they become a breach. With this time saved, Darktrace is leading beyond reactive security to truly harden a network, allowing the team to spend more time in preventive security measures.

Network Detection and Response protects where others fail to reach

NDR solutions operate at the network level, deploying inside or parallel to your network to ingest raw traffic via virtual or physical sensors. This gives them unprecedented potential to identify anomalies and possible breaches in any network - far beyond simple on-prem, into dynamic virtual environments, cloud or hybrid networks, cloud applications, and even remote devices accessing the corporate network via ZTNA or VPN.

Rather than looking at processes level data, NDR can detect the lateral movement of an adversary across multiple assets by analyzing network traffic patterns which endpoint solutions may not be able to identify [1]. In the face of a growing, complex environment, organizations large and small, will benefit from using NDRs either in conjunction, or as the foundation for, their Extended Detection and Response (XDR) for a unified view that improves their overall threat detection, ease of investigation and faster response times.

Today's NDR solutions are expected to include advanced ML and artificial intelligence (AI) algorithms [1]

Traditional IDS & IPS systems are labor intensive, requiring continuous rule creation, outdated signature maintenance, and manual monitoring for false positives or incorrect actions. This is no longer viable against a higher volume and changing landscape, making NDR the natural network tool to level against these evolutions. The role of AI in NDRs is designed to meet this challenge, “to reduce both the labor need for analysis and false positives, as well as add value by improving anomaly detection and overall security posture” .

Celebrating success in leadership and innovation

Darktrace is proud to have been recognized as an NDR “Overall Leader” in KuppingerCole Analyst AG’s Leadership Compass. The report gave further recognition to Darktrace as a ‘Product Leader”, “Innovation Leader” and “Market Leader”.

Maximum scores were received for core product categories, in addition to market presence and financial strength. Particular attention was directed to our innovation. This year has seen several NDR updates via Darktrace’s ActiveAI Security Platform version 6.2 which has enhanced investigation workflows and provided new AI transparency within the toolset.

Positive scores were also received for Darktrace’s deployment ecosystem and surrounding support, minimizing the need for extraneous integrations through a unique platform architecture that connects with over 90 other vendors.

High Scores received in Darktrace’s KuppingerCole Spider Chart across Core NDR capability areas
Figure 1: High Scores received in Darktrace’s KuppingerCole Spider Chart across Core NDR capability areas

Darktrace’s pioneering AI approach sets it apart

Darktrace / NETWORK’s approach is fundamentally different to other NDRs. Continual anomaly-based detection (our Self-Learning AI), understands what is normal across each of your network entities, and then examines deviations from these behaviors rather than needing to apply static rules or ML to adversary techniques. As a result, Darktrace / NETWORK can focus on surfacing the novel threats that cannot be anticipated, whilst our proactive solutions expose gaps that can be exploited and reduce the risk of known threats.    

Across the millions of possible network events that may occur, Darktrace’s Cyber AI Analyst reduces that manual workload for SOC teams by presenting only what is most important in complete collated incidents. This accelerates SOC Level 2 analyses of incidents by 10x2, giving time back, first for any necessary response and then for preventive workflows.

Finally, when incidents begin to escalate, Darktrace can natively (or via third-party) autonomously respond and take precise actions based on a contextual understanding of both the affected assets and incident in question so that threats can be disarmed without impacting wider operations.

Within the KuppingerCole report, several standout strengths were listed:

  • Cyber AI Analyst was celebrated as a core differentiator, enhancing both visibility and investigation into critical network issues and allowing a faster response.
  • Darktrace / NETWORK was singled for its user benefits. Both a clear interface for analysts with advanced filtering and analytical tools, and efficient role-based access control (RBAC) and configuration options for administrators.
  • At the product level, Darktrace was recognized for complete network traffic analysis (NTA) capabilities allowing extensive analysis into components like application use/type, fingerprinting, source/destination communication, in addition to comprehensive protocol support across a range of network device types from IT, OT, IoT and mobiles and detailed MITRE ATT&CK mapping.
  • Finally, at the heart of it, Darktrace’s innovation was highlighted in relation to its intrinsic Self Learning AI, utilizing multiple layers of deep learning, neural networks, LLMs, NLP, Generative AI and more to understand network activity and filter it for what’s critical on an individual customer level.

Going beyond reactive security

Darktrace’s visibility and AI-enabled detection, investigation and response enable security teams to focus on hardening gaps in their network through contextual relevance & priority. Darktrace / NETWORK explicitly gives time back to security teams allowing them to focus on the bigger strategic and governance workflows that sometimes get overlooked. This is enabled through proactive solutions intrinsically connected to our NDR:

  • Darktrace / Proactive Exposure Management, which looks beyond just CVE risks to instead discover, prioritize and validate risks by business impact and how to mobilize against them early, to reduce the number of real threats security teams face.
  • Darktrace / Incident Readiness & Recovery, a solution rather than service-based approach to incident response (IR) that lets teams respond in the best way to each incident and proactively test their familiarity and effectiveness of IR workflows with sophisticated incident simulations involving their own analysts and assets.

Together, these solutions allow Darktrace / NETWORK to go beyond the traditional NDR and shift teams to a more hardened and proactive state.

Putting customers first

Customers continue to sit at the forefront of Darktrace R&D, with their emerging needs and pain points being the direct inspiration for our continued innovation.

This year Darktrace / NETWORK has protected thousands of customers against the latest attacks, from data exfil and destruction, to unapproved privilege escalation and ransomware including strains like Medusa, Qilin and AlphV BlackCat.

In each instance, Darktrace / NETWORK was able to provide a holistic lens of the anomalies present in their traffic, collated those that were important, and either responded or gave teams the ability to take targeted actions against their threats – even when adversaries pivoted. In one example of a Gootloader compromise, Darktrace ensured a SOC went from detection to recovery within 5 days, 92.8% faster than the average containment time of 69 days.

Results like these, focused on user-led security, have secured Darktrace’s position within the latest NDR Leadership Compass.

To find out more about what makes Darktrace / NETWORK special, read the full KuppingerCole report.

References

[1] Osman Celik, KuppingerCole Leadership Compass:Network Detection and Response (2024)

[2] Darktrace's AI Analyst customer fleet data

[3] https://www.ibm.com/reports/data-breach

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Gabriel Few-Wiegratz
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November 1, 2024

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Inside the SOC

Phishing and Persistence: Darktrace’s Role in Defending Against a Sophisticated Account Takeover

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The exploitation of SaaS platforms

As businesses continue to grow and evolve, the need for sharing ideas through productivity and cloud Software-as-a-Service (SaaS) platforms is becoming increasingly crucial. However, these platforms have also become prime targets for cyber attackers.

Threat actors often exploit these widely-used services to gain unauthorized access, steal sensitive information, and disrupt business operations. The growing reliance on SaaS platforms makes them attractive entry points for cybercriminals, who use sophisticated techniques such as phishing, social engineering, and malware to compromise these systems.

Services like Microsoft 365 are regularly targeted by threat actors looking for an entry point into an organization’s environment to carry out malicious activities. Securing these platforms is crucial to protect business data and ensure operational continuity.

Darktrace / EMAIL detection of the phishing attack

In a recent case, Darktrace observed a customer in the manufacturing sector receiving a phishing email that led to a threat actor logging in and creating an email rule. Threat actors often create email rules to move emails to their inbox, avoiding detection. Additionally, Darktrace detected a spoofed domain registered by the threat actor. Despite already having access to the customer’s SaaS account, the actor seemingly registered this domain to maintain persistence on the network, allowing them to communicate with the spoofed domain and conduct further malicious activity.

Darktrace / EMAIL can help prevent compromises like this one by blocking suspicious emails as soon as they are identified. Darktrace’s AI-driven email detection and response recognizes anomalies that might indicate phishing attempts and applies mitigative actions autonomously to prevent the escalation of an attack.

Unfortunately, in this case, Darktrace was not configured in Autonomous Response mode at the time of the attack, meaning actions had to be manually applied by the customer’s security team. Had it been fully enabled, it would have held the emails, preventing them from reaching the intended recipient and stopping the attack at its inception.

However, Darktrace’s Managed Threat Detection alerted the Security Operations Center (SOC) team to the compromise, enabling them to thoroughly investigate the incident and notify the customer before further damage could occur.

The Managed Threat Detection service continuously monitors customer networks for suspicious activities that may indicate an emerging threat. When such activities are detected, alerts are sent to Darktrace’s expert Cyber Analysts for triage, significantly speeding up the remediation process.

Attack Overview

On May 2, 2024, Darktrace detected a threat actor targeting a customer in the manufacturing sector then an unusual login to their SaaS environment was observed prior to the creation of a new email rule.

Darktrace immediately identified the login as suspicious due to the rarity of the source IP (31.222.254[.]27) and ASN, coupled with the absence of multi-factor authentication (MFA), which was typically required for this account.

The new email rule was intended to mark emails as read and moved to the ‘Conversation History’ folder for inbound emails from a specific domain. The rule was named “….,,,”, likely the attacker attempting to setup their new rule with an unnoteworthy name to ensure it would not be noticed by the account’s legitimate owner. Likewise, by moving emails from a specific domain to ‘Conversation History’, a folder that is rarely used by most users, any phishing emails sent by that domain would remain undetected by the user.

Darktrace’s detection of the unusual SaaS login and subsequent creation of the new email rule “….,,,”.
Figure 1: Darktrace’s detection of the unusual SaaS login and subsequent creation of the new email rule “….,,,”.

The domain in question was identified as being newly registered and an example of a typosquat domain. Typosquatting involves registering new domains with intentional misspelling designed to convince users to visit fake, and often malicious, websites. This technique is often used in phishing campaigns to create a sense of legitimacy and trust and deceive users into providing sensitive information. In this case, the suspicious domain closely resembled several of the customer’s internal domains, indicating an attempt to impersonate the organization’s legitimate internal sites to gain the target’s trust. Furthermore, the creation of this lookalike domain suggests that the attack was highly targeted at this specific customer.

Interestingly, the threat actor registered this spoofed domain despite already having account access. This was likely intended to ensure persistence on the network without having to launch additional phishing attacks. Such use of spoofed domain could allow an attacker to maintain a foothold in their target network and escalate their malicious activities without having to regain access to the account. This persistence can be used for various purposes, including data exfiltration, spreading malware, or launching further attacks.

Following this, Darktrace detected a highly anomalous email being sent to the customer’s account from the same location as the initial unsual SaaS login. Darktrace’s anomaly-based detection is able to identify threats that human security teams and traditional signature-based methods might miss. By analyzing the expected behavior of network users, Darktrace can recognize the subtle deviations from the norm that may indicate malicious activity. Unfortunately, in this instance, without Darktrace’s Autonomous Response capability enabled, the phishing email was able to successfully reach the recipient. While Darktrace / EMAIL did suggest that the email should be held from the recipients inbox, the customer was required to manually approve it.

Despite this, the Darktrace SOC team were still able to support the customer as they were subscribed to the Managed Threat Detection service. Following the detection of the highlight anomalous activity surrounding this compromise, namely the unusual SaaS login followed by a new email rule, an alert was sent to the Darktrace SOC for immediate triage, who then contacted the customer directly urging immediate action.

Conclusion

This case underscores the need to secure SaaS platforms like Microsoft 365 against sophisticated cyber threats. As businesses increasingly rely on these platforms, they become prime targets for attackers seeking unauthorized access and disruption.

Darktrace’s anomaly-based detection and response capabilities are crucial in identifying and mitigating such threats. In this instance, Darktrace detected a phishing email that led to a threat actor logging in and creating a suspicious email rule. The actor also registered a spoofed domain to maintain persistence on the network.

Darktrace / EMAIL, with its AI-driven detection and analysis, can block suspicious emails before they reach the intended recipient, preventing attacks at their inception. Meanwhile, Darktrace’s SOC team promptly investigated the activity and alerted the customer to the compromise, enabling them to take immediate action to remediate the issue and prevent any further damage.

Credit to Vivek Rajan (Cyber Security Analyst) and Ryan Traill (Threat Content Lead).

Appendices

Darktrace Model Detections

  • SaaS / Access / Unusual External Source for SaaS Credential Use
  • SaaS / Compromise / Login From Rare Endpoint While User Is Active
  • SaaS / Resource / Unusual Access to Delegated Resource by Non Owner
  • SaaS / Email Nexus / Unusual Login Location Following Sender Spoof
  • Compliance / Anomalous New Email Rule
  • SaaS / Compromise / Unusual Login and New Email Rule

Indicators of Compromise (IoCs)

IoC - Type - Description + Confidence

31.222.254[.]27 – IP -  Suspicious Login Endpoint

MITRE ATT&CK Mapping

Tactic – Technqiue – Sub-technique of (if applicable)

Cloud Accounts - DEFENSE EVASION, PERSISTENCE, PRIVILEGE ESCALATION, INITIAL ACCESS - T1078.004 - T1078

Cloud Service Dashboard – DISCOVERY - T1538

Compromise Accounts - RESOURCE DEVELOPMENT - T1586

Steal Web Session Cookie - CREDENTIAL ACCESS - T1539

Outlook Rules – PERSISTENCE - T1137.005 - T1137

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About the author
Vivek Rajan
Cyber Analyst
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