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Dem Ransomware-as-a-Service-Geschäftsmodell von REvil einen Schritt voraus sein

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13
Februar 2022
13
Februar 2022
In diesem Blog werden die Auswirkungen der jüngsten Verhaftungen im Zusammenhang mit der cyberkriminellen Gruppe REvil im breiteren Kontext des Ransomware-as-a-Service-Geschäftsmodells bewertet. Dabei wird eine reale Ransomware-Kampagne von REvil untersucht, die von der Darktrace KI entdeckt wurde.

REvil, auch bekannt als Sodinokibi, ist eine Ransomware-as-a-Service (RaaS)-Bande, die für einen der größten Ransomware-Angriffe der Geschichte verantwortlich ist. Am 14. Januar 2022 gab Russland bekannt, dass es 14 Mitglieder der kriminellen Bande verhaftet hat. Dieser Schritt erfolgte auf Ersuchen der US-Behörden, die gemeinsam mit internationalen Partnern hart gegen die Hacker vorgegangen sind. Im vergangenen Jahr wurden mehrere aufsehenerregende Angriffe der REvil-Gruppe zugeschrieben, darunter die Ransomware von JBS und die Vorfälle in der Lieferkette von Kaseya.

Die Verhaftungen sind sicherlich ein Sieg für die westlichen Strafverfolgungsbehörden und folgen auf die Ankündigung von Europol im November, dass in den vorangegangenen Monaten sieben REvil-Mitglieder verhaftet worden waren. Die Frage ist: Inwieweit werden diese Verhaftungen die Machenschaften der Kriminellen stören, und für wie lange?

Erste Hinweise von Sicherheitsforschern bei ReversingLabs deuten darauf hin, dass die REvil-Aktivität nicht beeinträchtigt wurde. Die Statistiken über REvil-Implantate sind zwei Wochen nach den russischen Verhaftungen unverändert und deuten eher auf einen leichten Anstieg hin.

Diese anhaltende Aktivität lässt auf eines von zwei Szenarien schließen:

  • Die zahlreichen Verhaftungen betrafen nur die "Mittelsmänner" in der Hierarchie der kriminellen Organisation
  • Das Ransomware-as-a-Service-Modell von REvil ist widerstandsfähig genug, um Störungen durch Strafverfolgungsbehörden zu überstehen

Beide Szenarien sind besorgniserregend für diejenigen, die Ransomware-Banden zum Opfer fallen könnten. Die Realität ist wahrscheinlich eine viel komplexere Mischung aus diesen und anderen Faktoren. Das Durchgreifen gegen Ransomware ist längst überfällig, aber der Kampf wird wahrscheinlich sehr langwierig sein. Die Strafverfolgungsbehörden müssen das Geschäftsmodell so weit unterbinden, dass es nicht mehr rentabel ist, im Ransomware-Geschäft mitzumischen. Dies wird wahrscheinlich Monate oder sogar Jahre in Anspruch nehmen.

Die Bekämpfung von Ransomware spielt sich auf der größten Bühne ab. Welchen Trost können Sicherheitsteams aus den jüngsten Ereignissen ziehen, wenn es überhaupt einen gibt?

Dem sich entwickelnden RaaS-Modell mit KI immer einen Schritt voraus

Ein gemeinsamer Bericht über Ransomware, der kürzlich vom FBI, CISA, NCSC, ACSC und der NSA veröffentlicht wurde, zeigt die wichtigsten Trends des vergangenen Jahres auf:

  • RaaS hat sich zunehmend professionalisiert, Geschäftsmodelle und Prozesse sind inzwischen gut etabliert.
  • Das Geschäftsmodell erschwert die Zuordnung, da es komplexe Netzwerke von Entwicklern, Partnern und Freiberuflern gibt.
  • Ransomware-Gruppen tauschen Informationen über ihre Opfer untereinander aus, wodurch die Bedrohung für Unternehmen noch vielfältiger wird.

Zusammenfassend zeigt der Bericht, wie Ransomware-Banden immer anpassungsfähiger werden, wenn es darum geht, die Strafverfolgung zu umgehen und den Gewinn aus Lösegeldzahlungen zu maximieren. Mehrere Gruppen sind verschwunden oder haben sich zurückgezogen, nur um unter einem anderen Namen und mit einer leicht veränderten Strategie wieder aufzutauchen. Die Taktiken, Techniken und Verfahren (TTPs) unterscheiden sich von Opfer zu Opfer, was vor allem daran liegt, dass die Angriffe von verschiedenen Ransomware-Betreibern und angeschlossenen Unternehmen durchgeführt werden.

Dies ist beunruhigend für die Strafverfolgungsbehörden, die versuchen, gegen die Personen vorzugehen, die hinter diesen Angriffen stecken. Wenn eine RaaS-Gruppe wie REvil aus einem amorphen und sich ständig verändernden Netz von Partnern besteht, ist die Verhaftung einzelner Personen ein ständiges Hinterherlaufen und wird die Gruppe als Ganzes wahrscheinlich nicht zu Fall bringen.

Der gleiche Kampf spielt sich auch auf der Ebene einzelner Angriffskampagnen ab. Sicherheitstools, die sich auf die Merkmale früherer Bedrohungen konzentrieren, befinden sich ebenfalls in einer ständigen Aufholjagd: Bis ein einzelner Angriff erkannt, mit Fingerabdrücken versehen und für das nächste Mal gespeichert ist, haben sich die Angreifer und ihre Techniken bereits weiterentwickelt.

Aber es gibt noch eine andere Möglichkeit für Verteidiger, die zunehmend auf selbstlernende KI setzen, um Angreifern einen Schritt voraus zu sein. Indem sie Ihre digitale Umgebung erlernt und subtile Abweichungen identifiziert, die auf einen Angriff hindeuten, kann diese Technologie neuartige Angriffe bereits beim ersten Auftreten erkennen und darauf reagieren. Unten sehen Sie ein Beispiel dafür, wie die selbstlernende KI einen Angriff von REvil ohne Regeln oder Signaturen erkannt hat.

REvil Angriffe finden

Im Sommer 2021 startete ein REvil-Tochterunternehmen einen Angriff auf eine Organisation des Gesundheits- und Sozialwesens. Ein Sektor, in dem die Zahl der Cyberangriffe seit Beginn der weltweiten Pandemie stark zugenommen hat. Der Angriff wurde zwar von der KI ohne Verwendung von Regeln oder Signaturen erkannt, aber das Sicherheitsteam überwachte Darktrace zu diesem Zeitpunkt nicht. Da die autonome Reaktionentgegen aller Warnungen nicht live geschalten war, konnte der Angriff fortgesetzt werden.

Nachdem sich der Angreifer über den Laptop eines Remote-Mitarbeiters Zugang zum Netzwerk verschafft hatte, konnte er eine legitime Remote-Desktop-Verbindung (RDP) zu einem Jump-Server des Unternehmens missbrauchen, um weitere Anmeldedaten abzufangen.

Sobald der Angreifer über weitere Anmeldeinformationen verfügte, stellte er über RDP eine Verbindung zu mehreren internen Geräten her, darunter auch zu einem zweiten Jump-Server. Die Datenexfiltration begann von dem ursprünglich kompromittierten Server über den RDP-Port 3389.

Zwei Wochen später identifizierte der Angreifer die Kronjuwelen der Organisation, die auf einem dritten Server gespeichert waren, und versuchte, die Command-and-Control-Kommunikation (C2) zu initiieren. Der Server stellte eine Reihe ungewöhnlicher externer Verbindungen her, darunter Versuche, sich mit einer seltenen Domain zu verbinden, die dem Aktivitätsmuster ähnelte, das mit der früheren Kaseya-Ransomware-Kampagne von REvil verbunden war.

Darktrace for Endpoint, das auf Remote-Benutzergeräten ausgeführt wurde, sorgte für zusätzliche Transparenz und ermöglichte es dem Sicherheitsteam, das ursprünglich gefährdete Benutzergerät zu ermitteln. Wäre Antigena auf dem Endpunkt aktiv gewesen, hätte es eingegriffen, um diese ungewöhnliche Aktivität zu stoppen, indem es die spezifischen ungewöhnlichen Verbindungen blockiert hätte. Der Angriff wäre eingedemämmt worden, ohne den regulären Geschäftsbetrieb zu beeinträchtigen.

Verknüpfung der Punkte eines Low-and-Slow-Angriffs

Die Gesamtverweildauer der Angreifer betrug 22 Tage. Sie waren geduldig und führten ihre Aktionen in Schüben durch. Oftmals lagen Tage dazwischen. Dieses Verhaltensmuster ist für Ransomware-Angriffe nicht ungewöhnlich, insbesondere für solche, die das RaaS-Modell verwenden, bei dem jeder Schritt von verschiedenen Bandenmitgliedern oder verbundenen Unternehmen ausgeführt werden kann.

Der Darktrace Cyber AI Analyst war in der Lage, den gesamten Lebenszyklus des Angriffs über mehrere Wochen in Echtzeit zu verfolgen und die einzelnen Phasen des Angriffs zu einem kohärenten Sicherheitsvorfall zusammenzufügen.

Abbildung 1: Der Cyber AI Analyst zeigt die komplette Angriffskette auf

Neuer Name, gleiches Spiel

Bei diesem Angriff handelt es sich um einen weiteren Fall von Bedrohungsakteuren, die sehr gezielt vorgehen: Sie nutzen legitime Programme und Prozesse, die bereits in der Umgebung verwendet wurden, um bösartige Aktivitäten durchzuführen. Dies kann mit herkömmlichen Tools, die auf statischen Anwendungsfällen basieren und eine legitime RDP-Sitzung nicht von einer bösartigen unterscheiden können, sehr schwer zu erkennen sein.

Da cyberkriminelle Gruppen wie REvil weiterhin den Bemühungen der Strafverfolgungsbehörden trotzen, müssen Unternehmen mit KI-Technologie, die ihre Umgebung erlernt, aufrüsten. Autonomous Response wird bereits von Tausenden von Unternehmen in allen Bereichen der digitalen Infrastruktur eingesetzt - von E-Mail- und Cloud-Diensten bis hin zu Endgeräten, um Ransomware-Angriffe frühzeitig zu stoppen, bevor eine Verschlüsselung erfolgt.

Wir danken der Analystin Petal Beharry von Darktrace für ihren Einblick in die oben genannte Bedrohungslage.

Technische Einzelheiten

Darktrace Modell-Erkennungen:

  • Device / RDP Scan
  • Device / Bruteforce Activity
  • Compliance / Outbound Remote Desktop
  • Anomalous Connection / Upload via Remote Desktop
  • Anomalous Connection / Download and Upload
  • Anomalous Connection / Uncommon 1 GiB Outbound
  • Anomalous Connection / Active Remote Desktop Tunnel
  • Device / New or Uncommon SMB Named Pipe
  • Device / Large Number of Connections to New Endpoints

EINBLICKE IN DAS SOC-Team
Darktrace Cyber-Analysten sind erstklassige Experten für Threat Intelligence, Threat Hunting und Incident Response. Sie bieten Tausenden von Darktrace Kunden auf der ganzen Welt rund um die Uhr SOC-Support. Einblicke in das SOC-Team wird ausschließlich von diesen Experten verfasst und bietet Analysen von Cyber-Vorfällen und Bedrohungstrends, die auf praktischen Erfahrungen in diesem Bereich basieren.
AUTOR
ÜBER DEN AUTOR
Oakley Cox
Analyst Technical Director, APAC

Oakley is a technical expert with 5 years’ experience as a Cyber Analyst. After leading a team of Cyber Analysts at the Cambridge headquarters, he relocated to New Zealand and now oversees the defense of critical infrastructure and industrial control systems across the APAC region. His research into cyber-physical security has been published by Cyber Security journals and CISA. Oakley is GIAC certified in Response and Industrial Defense (GRID), and has a Doctorate (PhD) from the University of Oxford.

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Quasar Remote Access Tool: When a Legitimate Admin Tool Falls into the Wrong Hands

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23
Feb 2024

The threat of interoperability

As the “as-a-Service” market continues to grow, indicators of compromise (IoCs) and malicious infrastructure are often interchanged and shared between multiple malware strains and attackers. This presents organizations and their security teams with a new threat: interoperability.

Interoperable threats not only enable malicious actors to achieve their objectives more easily by leveraging existing infrastructure and tools to launch new attacks, but the lack of clear attribution often complicates identification for security teams and incident responders, making it challenging to mitigate and contain the threat.

One such threat observed across the Darktrace customer base in late 2023 was Quasar, a legitimate remote administration tool that has becoming increasingly popular for opportunistic attackers in recent years. Working in tandem, the anomaly-based detection of Darktrace DETECT™ and the autonomous response capabilities of Darktrace RESPOND™ ensured that affected customers were promptly made aware of any suspicious activity on the attacks were contained at the earliest possible stage.

What is Quasar?

Quasar is an open-source remote administration tool designed for legitimate use; however, it has evolved to become a popular tool used by threat actors due to its wide array of capabilities.  

How does Quasar work?

For instance, Quasar can perform keylogging, take screenshots, establish a reverse proxy, and download and upload files on a target device [1].  A report released towards the end of 2023 put Quasar back on threat researchers’ radars as it disclosed the new observation of dynamic-link library (DLL) sideloading being used by malicious versions of this tool to evade detection [1].  DLL sideloading involves configuring legitimate Windows software to run a malicious file rather than the legitimate file it usually calls on as the software loads.  The evolving techniques employed by threat actors using Quasar highlights defenders’ need for anomaly-based detections that do not rely on pre-existing knowledge of attacker techniques, and can identify and alert for unusual behavior, even if it is performed by a legitimate application.

Although Quasar has been used by advanced persistent threat (APT) groups for global espionage operations [2], Darktrace observed the common usage of default configurations for Quasar, which appeared to use shared malicious infrastructure, and occurred alongside other non-compliant activity such as BitTorrent use and cryptocurrency mining.  

Quasar Attack Overview and Darktrace Coverage

Between September and October 2023, Darktrace detected multiple cases of malicious Quasar activity across several customers, suggesting probable campaign activity.  

Quasar infections can be difficult to detect using traditional network or host-based tools due to the use of stealthy techniques such as DLL side-loading and encrypted SSL connections for command-and control (C2) communication, that traditional security tools may not be able to identify.  The wide array of capabilities Quasar possesses also suggests that attacks using this tool may not necessarily be modelled against a linear kill chain. Despite this, the anomaly-based detection of Darktrace DETECT allowed it to identify IoCs related to Quasar at multiple stages of the kill chain.

Quasar Initial Infection

During the initial infection stage of a Quasar compromise observed on the network of one customer, Darktrace detected a device downloading several suspicious DLL and executable (.exe) files from multiple rare external sources using the Xmlst user agent, including the executable ‘Eppzjtedzmk[.]exe’.  Analyzing this file using open-source intelligence (OSINT) suggests this is a Quasar payload, potentially indicating this represented the initial infection through DLL sideloading [3].

Interestingly, the Xmlst user agent used to download the Quasar payload has also been associated with Raccoon Stealer, an information-stealing malware that also acts as a dropper for other malware strains [4][5]. The co-occurrence of different malware components is increasingly common across the threat landscape as MaaS operating models increases in popularity, allowing attackers to employ cross-functional components from different strains.

Figure 1: Cyber AI Analyst Incident summarizing the multiple different downloads in one related incident, with technical details for the Quasar payload included. The incident event for Suspicious File Download is also linked to Possible HTTP Command and Control, suggesting escalation of activity following the initial infection.  

Quasar Establishing C2 Communication

During this phase, devices on multiple customer networks were identified making unusual external connections to the IP 193.142.146[.]212, which was not commonly seen in their networks. Darktrace analyzed the meta-properties of these SSL connections without needing to decrypt the content, to alert the usage of an unusual port not typically associated with the SSL protocol, 4782, and the usage of self-signed certificates.  Self-signed certificates do not provide any trust value and are commonly used in malware communications and ill-reputed web servers.  

Further analysis into these alerts using OSINT indicated that 193.142.146[.]212 is a Quasar C2 server and 4782 is the default port used by Quasar [6][7].  Expanding on the self-signed certificate within the Darktrace UI (see Figure 3) reveals a certificate subject and issuer of “CN=Quasar Server CA”, which is also the default self-signed certificate compiled by Quasar [6].

Figure 2: Cyber AI Analyst Incident summarizing the repeated external connections to a rare external IP that was later associated with Quasar.
Figure 3: Device Event Log of the affected device, showing Darktrace’s analysis of the SSL Certificate associated with SSL connections to 193.142.146[.]212.

A number of insights can be drawn from analysis of the Quasar C2 endpoints detected by Darktrace across multiple affected networks, suggesting a level of interoperability in the tooling used by different threat actors. In one instance, Darktrace detected a device beaconing to the endpoint ‘bittorrents[.]duckdns[.]org’ using the aforementioned “CN=Quasar Server CA” certificate. DuckDNS is a dynamic DNS service that could be abused by attackers to redirect users from their intended endpoint to malicious infrastructure, and may be shared or reused in multiple different attacks.

Figure 4: A device’s Model Event Log, showing the Quasar Server CA SSL certificate used in connections to 41.233.139[.]145 on port 5, which resolves via passive replication to ‘bittorrents[.]duckdns[.]org’.  

The sharing of malicious infrastructure among threat actors is also evident as several OSINT sources have also associated the Quasar IP 193.142.146[.]212, detected in this campaign, with different threat types.

While 193.142.146[.]212:4782 is known to be associated with Quasar, 193.142.146[.]212:8808 and 193.142.146[.]212:6606 have been associated with AsyncRAT [11], and the same IP on port 8848 has been associated with RedLineStealer [12].  Aside from the relative ease of using already developed tooling, threat actors may prefer to use open-source malware in order to avoid attribution, making the true identity of the threat actor unclear to incident responders [1][13].  

Quasar Executing Objectives

On multiple customer deployments affected by Quasar, Darktrace detected devices using BitTorrent and performing cryptocurrency mining. While these non-compliant, and potentially malicious, activities are not necessarily specific IoCs for Quasar, they do suggest that affected devices may have had greater attack surfaces than others.

For instance, one affected device was observed initiating connections to 162.19.139[.]184, a known Minergate cryptomining endpoint, and ‘zayprostofyrim[.]zapto[.]org’, a dynamic DNS endpoint linked to the Quasar Botnet by multiple OSINT vendors [9].

Figure 5: A Darktrace DETECT Event Log showing simultaneous connections to a Quasar endpoint and a cryptomining endpoint 162.19.139[.]184.

Not only does cryptocurrency mining use a significant amount of processing power, potentially disrupting an organization’s business operations and racking up high energy bills, but the software used for this mining is often written to a poor standard, thus increasing the attack surfaces of devices using them. In this instance, Quasar may have been introduced as a secondary payload from a user or attacker-initiated download of cryptocurrency mining malware.

Similarly, it is not uncommon for malicious actors to attach malware to torrented files and there were a number of examples of Darktrace detect identifying non-compliant activity, like BitTorrent connections, overlapping with connections to external locations associated with Quasar. It is therefore important for organizations to establish and enforce technical and policy controls for acceptable use on corporate devices, particularly when remote working introduces new risks.  

Figure 6: A device’s Event Log filtered by Model Breaches, showing a device connecting to BitTorrent shortly before making new or repeated connections to unusual endpoints, which were subsequently associated to Quasar.

In some cases observed by Darktrace, devices affected by Quasar were also being used to perform data exfiltration. Analysis of a period of unusual external connections to the aforementioned Quasar C2 botnet server, ‘zayprostofyrim[.]zapto[.]org’, revealed a small data upload, which may have represented the exfiltration of some data to attacker infrastructure.

Darktrace’s Autonomous Response to Quasar Attacks

On customer networks that had Darktrace RESPOND™ enabled in autonomous response mode, the threat of Quasar was mitigated and contained as soon as it was identified by DETECT. If RESPOND is not configured to respond autonomously, these actions would instead be advisory, pending manual application by the customer’s security team.

For example, following the detection of devices downloading malicious DLL and executable files, Darktrace RESPOND advised the customer to block specific connections to the relevant IP addresses and ports. However, as the device was seen attempting to download further files from other locations, RESPOND also suggested enforced a ‘pattern of life’ on the device, meaning it was only permitted to make connections that were part its normal behavior. By imposing a pattern of life, Darktrace RESPOND ensures that a device cannot perform suspicious behavior, while not disrupting any legitimate business activity.

Had RESPOND been configured to act autonomously, these mitigative actions would have been applied without any input from the customer’s security team and the Quasar compromise would have been contained in the first instance.

Figure 7: The advisory actions Darktrace RESPOND initiated to block specific connections to a malicious IP and to enforce the device’s normal patterns of life in response to the different anomalies detected on the device.

In another case, one customer affected by Quasar did have enabled RESPOND to take autonomous action, whilst also integrating it with a firewall. Here, following the detection of a device connecting to a known Quasar IP address, RESPOND initially blocked it from making connections to the IP via the customer’s firewall. However, as the device continued to perform suspicious activity after this, RESPOND escalated its response by blocking all outgoing connections from the device, effectively preventing any C2 activity or downloads.

Figure 8: RESPOND actions triggered to action via integrated firewall and TCP Resets.

Schlussfolgerung

When faced with a threat like Quasar that utilizes the infrastructure and tools of both legitimate services and other malicious malware variants, it is essential for security teams to move beyond relying on existing knowledge of attack techniques when safeguarding their network. It is no longer enough for organizations to rely on past attacks to defend against the attacks of tomorrow.

Crucially, Darktrace’s unique approach to threat detection focusses on the anomaly, rather than relying on a static list of IoCs or "known bads” based on outdated threat intelligence. In the case of Quasar, alternative or future strains of the malware that utilize different IoCs and TTPs would still be identified by Darktrace as anomalous and immediately alerted.

By learning the ‘normal’ for devices on a customer’s network, Darktrace DETECT can recognize the subtle deviations in a device’s behavior that could indicate an ongoing compromise. Darktrace RESPOND is subsequently able to follow this up with swift and targeted actions to contain the attack and prevent it from escalating further.

Credit to Nicole Wong, Cyber Analyst, Vivek Rajan Cyber Analyst

Appendices

Darktrace DETECT Model Breaches

  • Anomalous Connection / Multiple Failed Connections to Rare Endpoint
  • Anomalous Connection / Anomalous SSL without SNI to New External
  • Anomalous Connection / Application Protocol on Uncommon Port
  • Anomalous Connection / Rare External SSL Self-Signed
  • Compromise / New or Repeated to Unusual SSL Port
  • Compromise / Beaconing Activity To External Rare
  • Compromise / High Volume of Connections with Beacon Score
  • Compromise / Large Number of Suspicious Failed Connections
  • Unusual Activity / Unusual External Activity

List of IoCs

IP:Port

193.142.146[.]212:4782 -Quasar C2 IP and default port

77.34.128[.]25: 8080 - Quasar C2 IP

Domain

zayprostofyrim[.]zapto[.]org - Quasar C2 Botnet Endpoint

bittorrents[.]duckdns[.]org - Possible Quasar C2 endpoint

Certificate

CN=Quasar Server CA - Default certificate used by Quasar

Executable

Eppzjtedzmk[.]exe - Quasar executable

IP Address

95.214.24[.]244 - Quasar C2 IP

162.19.139[.]184 - Cryptocurrency Miner IP

41.233.139[.]145[VR1] [NW2] - Possible Quasar C2 IP

MITRE ATT&CK Mapping

Command and Control

T1090.002: External Proxy

T1071.001: Web Protocols

T1571: Non-Standard Port

T1001: Data Obfuscation

T1573: Encrypted Channel

T1071: Application Layer Protocol

Resource Development

T1584: Compromise Infrastructure

References

[1] https://thehackernews.com/2023/10/quasar-rat-leverages-dll-side-loading.html

[2] https://symantec-enterprise-blogs.security.com/blogs/threat-intelligence/cicada-apt10-japan-espionage

[3]https://www.virustotal.com/gui/file/bd275a1f97d1691e394d81dd402c11aaa88cc8e723df7a6aaf57791fa6a6cdfa/community

[4] https://twitter.com/g0njxa/status/1691826188581298389

[5] https://www.linkedin.com/posts/grjk83_raccoon-stealer-announce-return-after-hiatus-activity-7097906612580802560-1aj9

[6] https://community.netwitness.com/t5/netwitness-community-blog/using-rsa-netwitness-to-detect-quasarrat/ba-p/518952

[7] https://www.cisa.gov/news-events/analysis-reports/ar18-352a

[8]https://any.run/report/6cf1314c130a41c977aafce4585a144762d3fb65f8fe493e836796b989b002cb/7ac94b56-7551-4434-8e4f-c928c57327ff

[9] https://threatfox.abuse.ch/ioc/891454/

[10] https://www.virustotal.com/gui/ip-address/41.233.139.145/relations

[11] https://raw.githubusercontent.com/stamparm/maltrail/master/trails/static/malware/asyncrat.txt

[12] https://sslbl.abuse.ch/ssl-certificates/signature/RedLineStealer/

[13] https://www.botconf.eu/botconf-presentation-or-article/hunting-the-quasar-family-how-to-hunt-a-malware-family/

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Nicole Wong
Cyber Security Analyst

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Attack Trends: VIP Impersonation Across the Business Hierarchy

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22
Feb 2024

What is VIP impersonation?

VIP impersonation involves a threat actor impersonating a trusted, prominent figure at an organization in an attempt to solicit sensitive information from an employee.

VIP impersonation is a high-priority issue for security teams, but it can be difficult to assess the exact risks, and whether those are more critical than other types of compromise. Looking across a range of Darktrace/Email™ customer deployments, this blog explores the patterns of individuals targeted for impersonation and evaluates if these target priorities correspond with security teams' focus on protecting attack pathways to critical assets.

How do security teams stop VIP Impersonation?

Protecting VIP entities within an organization has long been a traditional focus for security teams. The assumption is that VIPs, due to their prominence, possess the greatest access to critical assets, making them prime targets for cyber threats.  

Email remains the predominant vector for attacks, with over 90% of breaches originating from malicious emails. However, the dynamics of email-based attacks are shifting, as the widespread use of generative AI is lowering the barrier to entry by allowing adversaries to create hyper-realistic emails with minimal errors.

Given these developments, it's worth asking the question – which entities (VIP/non-VIP) are most targeted by threat actors via email? And, more importantly – which entities (VIP/non-VIP) are more valuable if they are successfully compromised?

There are two types of VIPs:  

1. When referring to emails and phishing, VIPs are the users in an organization who are well known publicly.  

2. When referring to attack paths, VIPs are users in an organization that are known publicly and have access to highly privileged assets.  

Not every prominent user has access to critical assets, and not every user that has access to critical assets is prominent.  

Darktrace analysis of VIP impersonation

We analyzed patterns of attack pathways and phishing attempts across 20 customer deployments from a large, randomized pool encompassing a diverse range of organizations.  

Understanding Attack Pathways

Our observations revealed that 57% of low-difficulty attack paths originated from VIP entities, while 43% of observed low-difficulty attack paths towards critical assets or entities began through non-VIP users. This means that targeting VIPs is not the only way attackers can reach critical assets, and that non-VIP users must be considered as well.  

While the sample size prevents us from establishing statistical significance across all customers, the randomized selection lends credence to the generalizability of these findings to other environments.

Phishing Attempts  

On average, 1.35% of total emails sent to these customers exhibited significantly malicious properties associated with phishing or some form of impersonation. Strikingly, nearly half of these malicious emails (49.6%) were directed towards VIPs, while the rest were sent to non-VIPs. This near-equal split is worth noting, as attack paths show that non-VIPs also serve as potential entry points for targeting critical assets.  

Darktrace/Email UI
Figure 1: A phishing email actioned by Darktrace, sent to multiple VIP and non-VIP entities

For example, a recent phishing campaign targeted multiple customers across deployments, with five out of 13 emails specifically aimed at VIP users. Darktrace/Email actioned the malicious emails by double locking the links, holding the messages, and stripping the attachments.

Given that non-VIP users receive nearly half of the phishing or impersonation emails, it underscores the critical importance for security teams to recognize their blind spots in protecting critical assets. Overlooking the potential threat originating from non-VIP entities could lead to severe consequences. For instance, if a non-VIP user falls victim to a phishing attack or gets compromised, their credentials could be exploited to move laterally within the organization, potentially reaching critical assets.

This highlights the necessity for a sophisticated security tool that can identify targeted users, without the need for extensive customization and regardless of VIP status. By deploying a solution capable of promptly responding to email threats – including solicitation, phishing attempts, and impersonation – regardless of the status of the targeted user, security teams can significantly enhance their defense postures.

Darktrace vs Traditional Email Detection Methods

Traditional rules and signatures-based detection mechanisms fall short in identifying the evolving threats we’ve observed, due to their reliance on knowledge of past attacks to categorize emails.

Secure Email Gateway (SEG) or Integrated Cloud Email Security (ICES) tools categorize emails based on previous or known attacks, operating on a known-good or known-bad model. Even if tools use AI to automate this process, the approach is still fundamentally looking to the past and therefore vulnerable to unknown and zero-day threats.  

Darktrace uses AI to understand each unique organization and how its email environment interoperates with each user and device on the network. Consequently, it is able to identify the subtle deviations from normal behavior that qualify as suspicious. This approach goes beyond simplistic categorizations, considering factors such as the sender’s history and recipient’s exposure score.  

This nuanced analysis enables Darktrace to differentiate between genuine communications and malicious impersonation attempts. It automatically understands who is a VIP, without the need for manual input, and will action more strongly on incoming malicious emails  based on a user’s status.

Email does determine who is a VIP, without a need of manual input, and will action more strongly on incoming malicious emails.

Darktrace/Email also feeds into Darktrace’s preventative security tools, giving the interconnected AI engines further context for assessing the high-value targets and pathways to vital internal systems and assets that start via the inbox.

Leveraging AI for Enhanced Protection Across the Enterprise  

The efficacy of AI-driven security solutions lies in their ability to make informed decisions and recommendations based on real-time business data. By leveraging this data, AI driven solutions can identify exploitable attack pathways and an organizations most critical assets. Darktrace uniquely uses several forms of AI to equip security teams with the insights needed to make informed decisions about which pathways to secure, reducing human bias around the importance of protecting VIPs.

With the emergence of tools like AutoGPT, identifying potential targets for phishing attacks has become increasingly simplified. However, the real challenge lies in gaining a comprehensive understanding of all possible and low-difficulty attack paths leading to critical assets and identities within the organization.

At the same time, organizations need email tools that can leverage the understanding of users to prevent email threats from succeeding in the first instance. For every email and user, Darktrace/Email takes into consideration changes in behavior from the sender, recipient, content, and language, and many other factors.

Integrating Darktrace/Email with Darktrace’s attack path modeling capabilities enables comprehensive threat contextualization and facilitates a deeper understanding of attack pathways. This holistic approach ensures that all potential vulnerabilities, irrespective of the user's status, are addressed, strengthening the overall security posture.  

Schlussfolgerung

Contrary to conventional wisdom, our analysis suggests that the distinction between VIPs and non-VIPs in terms of susceptibility to impersonation and low-difficulty attack paths is not as pronounced as presumed. Therefore, security teams must adopt a proactive stance in safeguarding all pathways, rather than solely focusing on VIPs.  

Attack path modeling enhances Darktrace/Email's capabilities by providing crucial metrics on potential impact, damage, exposure, and weakness, enabling more targeted and effective threat mitigation strategies. For example, stronger email actions can be enforced for users who are known to have a high potential impact in case of compromise. 

In an era where cyber threats continue to evolve in complexity, an adaptive and non-siloed approach to securing inboxes, high-priority individuals, and critical assets is indispensable.  

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About the author
Kendra Gonzalez Duran
Director of Technology Innovation

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