Comment l'IA défend les infrastructures critiques contre les ransomwares

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12
May 2021
12
May 2021
À la suite de la cyberattaque de Colonial Pipeline, ce blog aborde les nombreuses menaces qui pèsent sur les infrastructures critiques et explique comment la cyberintelligence a permis de déjouer une attaque par ransomware similaire de type "double extorsion" contre un fournisseur de services publics d'électricité.

Modern Threats to OT Environments

At the 2021 RSA cyber security conference, US Secretary of Homeland Security Alejandro Mayorkas made an era-defining statement regarding the cyber security landscape: “Let me be clear: ransomware now poses a national security threat.”

Last weekend, Mayorkas’ words rang true. A ransomware attack on the Colonial Pipeline – responsible for nearly half of the US East Coast’s diesel, gasoline, and jet fuel – resulted in the shutdown of a critical fuel network supplying a number of Eastern states.

The fallout from the attack demonstrated how widespread and damaging the consequences of ransomware can be. Against critical infrastructure and utilities, cyber-attacks have the potential to disrupt supplies, harm the environment, and even threaten human lives.

Though full details remain to be confirmed, the attack is reported to have been conducted by an affiliate of the cyber-criminal group called DarkSide, and likely leveraged common remote desktop tools. Remote access has been enabled as an exploitable vulnerability within critical infrastructure by the shift to remote work that many organizations made last year, including those with Industrial Control Systems (ICS) and Operational Technology (OT).

The rise of industrial ransomware

Ransomware against industrial environments is on the rise, with a reported 500% increase since 2018. Oftentimes, these threats leverage the convergence of IT and OT systems, first targeting IT before pivoting to OT. This was seen with the EKANS ransomware that included ICS processes in its ‘kill list’, as well as the Cring ransomware that compromised ICS after first exploiting a vulnerability in a virtual private network (VPN).

It remains to be seen whether the initial attack vector in the Colonial Pipeline compromise exploited a technical vulnerability, compromised credentials, or a targeted spear phishing campaign. It has been reported that the attack first impacted IT systems, and that Colonial then shut down OT operations as a safety precaution. Colonial confirms that the ransomware “temporarily halted all pipeline operations and affected some of our IT systems,” showing that, ultimately, both OT and IT were affected. This is a great example of how many OT systems depend on IT, such that an IT cyber-attack has the ability to take down OT and ICS processes.

In addition to locking down systems, the threat actors also stole 100GB of sensitive data from Colonial. This kind of double extortion attack — in which data is exfiltrated before files are encrypted — has unfortunately become the norm rather than the exception, with over 70% of ransomware attacks involving exfiltration. Some ransomware gangs have even announced that they are dropping encryption altogether in favor of data theft and extortion methods.

Earlier this year, Darktrace defended against a double extortion ransomware attack waged against a critical infrastructure organization, which also leveraged common remote access tools. This blog will outline the threat find in depth, showing how Darktrace’s self-learning AI responded autonomously to an attack strikingly similar to the Colonial Pipeline incident.

Darktrace threat find

Ransomware against electric utilities equipment supplier

In an attack against a North American equipment supplier for electrical utilities earlier this year, Darktrace/OT demonstrated its ability to protect critical infrastructure against double extortion ransomware that targeted organizations with ICS and OT.

The ransomware attack initially targeted IT systems, and, thanks to self-learning Cyber AI, was stopped before it could spill over into OT and disrupt operations.

The attacker first compromised an internal server in order to exfiltrate data and deploy ransomware over the course of 12 hours. The short amount of time between initial compromise and deployment is unusual, as ransomware threat actors often wait several days to spread stealthily as far across the cyber ecosystem as possible before striking.

Figure 1: A timeline of the attack

How did the attack bypass the rest of the security stack?

The attacker leveraged ‘Living off the Land’ techniques to blend into the business’ normal ‘patterns of life’, using a compromised admin credential and a remote management tool approved by the organization, in its attempts to remain undetected.

Darktrace commonly sees the abuse of legitimate remote management software in attackers’ arsenal of techniques, tactics, and procedures (TTPs). Remote access is also becoming an increasingly common vector of attack in ICS attacks in particular. For example, in the cyber-incident at the Florida water treatment facility last February, attackers exploited a remote management tool in attempts to manipulate the treatment process.

The specific strain of ransomware deployed by this attacker also successfully evaded detection by anti-virus by using a unique file extension when encrypting files. These forms of ‘signatureless’ ransomware easily slip past legacy approaches to security that rely on rules, signatures, threat feeds, and lists of documented Common Vulnerabilities and Exposures (CVEs), as these are methods that can only detect previously documented threats.

La seule façon de détecter des menaces inédites telles que les ransomwares sans signature est d'utiliser une technologie qui détecte les comportements anormaux, plutôt que de s'appuyer sur des listes de "méchants connus". Cela est possible grâce à une technologie d'auto-apprentissage, qui repère les écarts les plus subtils par rapport aux "schémas de vie" normaux de tous les appareils, utilisateurs et connexions entre eux.

Darktrace insights

Initial compromise and establishing foothold

Despite the abuse of a legitimate tool and the absence of known signatures, Darktrace/OT was able to use a holistic understanding of normal activity to detect the malicious activity at multiple points in the attack lifecycle.

The first clear sign of an emerging threat that was alerted by Darktrace was the unusual use of a privileged credential. The device also served an unusual remote desktop protocol (RDP) connection from a Veeam server shortly before the incident, indicating that the attacker may have moved laterally from elsewhere in the network.

Three minutes later, the device initiated a remote management session which lasted 21 hours. This allowed the attacker to move throughout the broader cyber ecosystem while remaining undetected by traditional defences. Darktrace, however, was able to detect unusual remote management usage as another early warning indicative of an attack.

Double threat part one: Data exfiltration

One hour after the initial compromise, Darktrace detected unusual volumes of data being sent to a 100% rare cloud storage solution, pCloud. The outbound data was encrypted using SSL, but Darktrace created multiple alerts relating to large internal downloads and external uploads that were a significant deviation from the device’s normal ‘pattern of life’.

The device continued to exfiltrate data for nine hours. Analysis of the files downloaded by the device, which were transferred using the unencrypted SMB protocol, suggests that they were sensitive in nature. Fortunately, Darktrace was able to pinpoint the specific files that were exfiltrated so that the customer could immediately evaluate the potential implications of the compromise.

Double threat part two: File encryption

A short time later, at 01:49 local time, the compromised device began encrypting files in a SharePoint back-up share drive. Over the next three and a half hours, the device encrypted over 13,000 files on at least 20 SMB shares. In total, Darktrace produced 23 alerts for the device in question, which amounted to 48% of all the alerts produced in the corresponding 24-hour period.

Darktrace’s Cyber AI Analyst then automatically launched an investigation, identifying the internal data transfers and the file encryption over SMB. From this, it was able to present incident reports that connected the dots among these disparate anomalies, piecing them together into a coherent security narrative. This put the security team in a position to immediately take remediating action.

If the customer had been using Darktrace’s autonomous response technology, there is no doubt the activity would have been halted before significant volumes of data could have been exfiltrated or files encrypted. Fortunately, after seeing both the alerts and Cyber AI Analyst reports, the customer was able to use Darktrace’s ‘Ask the Expert’ (ATE) service for incident response to mitigate the impact of the attack and assist with disaster recovery.

Figure 2: AI Analyst Incident reporting an unusual reprogram command using the MODBUS protocol. The incident includes a plain English summary, relevant technical information, and the investigation process used by the AI.  

Detecting the threat before it could disrupt critical infrastructure

The targeted supplier was overseeing OT and had close ties to critical infrastructure. By facilitating the early-stage response, Darktrace prevented the ransomware from spreading further onto the factory floor. Crucially, Darktrace also minimized operational disruption, helping to avoid the domino effect which the attack could have had, affecting not only the supplier itself, but also the electric utilities that this supplier supports.

As both the recent Colonial Pipeline incident and the above threat find reveal, ransomware is a pressing concern for organizations overseeing industrial operations across all forms of critical infrastructure, from pipelines to the power grid and its suppliers. With self-learning AI, these attack vectors can be dealt with before the damage is done through real-time threat detection, autonomous investigations, and — if activated — targeted machine-speed response.

Looking forward: Using Self-Learning AI to protect critical infrastructure across the board

In late April, the Biden administration announced an ambitious effort to “safeguard US critical infrastructure from persistent and sophisticated threats.” The Department of Energy’s (DOE) 100-day plan specifically seeks technologies “that will provide cyber visibility, detection, and response capabilities for industrial control systems of electric utilities.”

The Biden administration’s cyber sprint clearly calls for a technology that protects critical energy infrastructure, rather than merely best practice measures and regulations. As seen in the above threat find, Darktrace AI is a powerful technology that leverages unsupervised machine learning to autonomously safeguard critical infrastructure and its suppliers with machine speed and precision.

Darktrace enhances detection, mitigation, and forensic capabilities to detect  sophisticated and novel attacks, along with insider threats and pre-existing infections, using Self-Learning Cyber AI, without rules, signatures, or lists of CVEs. Incident investigations provided in real time by Cyber AI Analyst jumpstart remediation with actionable insights, containing emerging attacks at their early stages, before they escalate into crisis.

Enable near real-time situational awareness and response capabilities

Darktrace immediately understands, identifies, and investigates all anomalous activity in ICS/OT networks, whether human or machine driven. Additionally, Darktrace actions targeted response where appropriate to neutralize threats, either actively or in human confirmation mode. Because Self-learning AI adapts alongside evolutions in the ecosystem, organizations benefit from real-time awareness with no tuning or human input necessary

Deploy technologies to increase visibility of threats in ICS and OT systems

Darktrace contextualizes security events, adapts to novel techniques, and translates findings into a security narrative that can be actioned by humans in minutes. Delivering a unified view across IT and OT systems.

Darktrace detects, investigates, and responds to threats at higher Purdue levels and in IT systems before they ‘spill over’ into OT. ‘Plug and play’ deployment seamlessly integrates with technological architecture, presenting 3D network topology with granular visibility into all users, devices, and subnets.

Darktrace's asset identification continuously catalogues all ICS/OT devices and identifies and investigates all threatening activity indicative of emerging attacks – be it ICS ransomware, APTs, zero-day exploits, insider threats, pre-existing infections, DDoS, crypto-mining, misconfigurations, or never-before-seen attacks.

Thanks to Darktrace analyst Oakley Cox for his insights on the above threat find.

Darktrace model detections:

  • Initial compromise:
  • User / New Admin Credential on Client
  • Data exfiltration:
  • Anomalous Connection / Uncommon 1 GiB Outbound
  • Anomalous Connection / Low and Slow Exfiltration
  • Device / Anomalous SMB Followed by Multiple Model Breaches
  • Anomalous Connection / Download and Upload
  • File encryption:
  • Compromise / Ransomware / Suspicious SMB Activity
  • Anomalous Connection / SMB Enumeration
  • Device / Anomalous RDP Followed by Multiple Model Breaches
  • Anomalous File / Internal / Additional Extension Appended to SMB File
  • Anomalous Connection / Sustained MIME Type Conversion
  • Anomalous Connection / Suspicious Read Write Ratio
  • Device / Multiple Lateral Movement Model Breaches

DANS LE SOC
Darktrace sont des experts de classe mondiale en matière de renseignement sur les menaces, de chasse aux menaces et de réponse aux incidents. Ils fournissent une assistance SOC 24 heures sur 24 et 7 jours sur 7 à des milliers de clients Darktrace dans le monde entier. Inside the SOC est exclusivement rédigé par ces experts et fournit une analyse des cyberincidents et des tendances en matière de menaces, basée sur une expérience réelle sur le terrain.
AUTEUR
à propos de l'auteur
David Masson
Directeur de la sécurité d'entreprise

David Masson is Darktrace’s Director of Enterprise Security, and has over two decades of experience working in fast moving security and intelligence environments in the UK, Canada and worldwide. With skills developed in the civilian, military and diplomatic worlds, he has been influential in the efficient and effective resolution of various unique national security issues. David is an operational solutions expert and has a solid reputation across the UK and Canada for delivery tailored to customer needs. At Darktrace, David advises strategic customers across North America and is also a regular contributor to major international and national media outlets in Canada where he is based. He holds a master’s degree from Edinburgh University.

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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.

Conclusion

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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About the author
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.  

Conclusion

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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