Find High-Impact Attack Paths with Darktrace / Proactive Exposure Management
22
Feb 2023
Understand high-impact attack paths with Darktrace / Proactive Exposure Management. Learn from detailed use cases and improve your cybersecurity measures effectively.
What are the people, process and technology assets that would do the most harm, if compromised by an attacker?
Attack path modeling provides a detailed map of all the roads that lead to an organization's crown jewels, prioritized in order of likelihood and potential impact. CISO's are increasingly looking to this kind of solution to complement their security stack because it highlights risks that are specific to this organization's structure, as well as potentially unexpected relationships between devices or users that would prove catastrophic if they were exploited.
What makes Darktrace's Attack Path Modeling solution stand out?
Data sources are varied and information from the entire digital estate is considered
Modeling is real-time and continuously re-evaluated
Output does not require expert technical knowledge to be leveraged
Valuable as a standalone for vulnerability prioritization
As a component of the Cyber AI Loop, the solution provides immediate value by feeding back into DETECT and RESPOND (e.g. tag critical assets for detection) but also provides long term systemic improvements as outcomes are followed up.
Thinking like an attacker
In 2023, CISOs will move beyond just insurance and checkbox compliance, as underwriters include more and more exclusions for certain types of cyber-attacks and the limits of compliance ticking the protection box rather than bolstering operational assurance become more apparent. They will push their teams to opt for more proactive cyber security measures to maximize ROI in the face of budget cuts, shifting investment into tools and capabilities that continuously improve their cyber resilience and demonstrate cyber risk reduction.
While red teams can provide insight into where effort and resource should be most immediately applied, the exercises themselves are often costly, non-exhaustive and infrequently run.
Hackers are constantly seeking pathways, preferably those of least resistance, to compromise a system by exploiting its vulnerabilities. Attack path modeling enables security teams to look at their environment from the perspective of the attacker. In turn, this helps them eliminate attack paths progressively, reducing the options an attacker would have, should they breach the walls.
A deeper dive into Attack Path Modeling
An attack path is a visual representation of the path that an attacker takes to exploit a weakness in the system. It highlights the series of steps (attack vectors) that a threat actor might take from one of the doors into the organization (attack surface) to access valuable assets.
It is typically unusual for an attacker to have a boulevard straight down to the crown jewels. They will most likely leverage a couple of loopholes, unexpected relationships and blind spots in the security stack to piece together a path to these confidential assets. Attack path modeling can help to highlight the attack vectors that connect, to form this path to compromise.
Figure 1: The Darktrace / Proactive Exposure Management user interface.
How to model attack paths
Darktrace's proprietary Self-Learning AI models relationships, and graph theory is incorporated to understand the importance of users, documents and relationships between these.
Darktrace's Attack Path Modeling component identifies target nodes (users, accounts, devices), it then calculates the shortest paths to these target nodes and weights the results according to the likelihood of this attack path and the damage caused if the target asset was compromised. This is exactly what an attacker would do when planning an attack, albeit with a significant advantage to Darktrace's AI Engine, which has access to more information than the attacker. For the first time, defenders have the upper hand against attackers.
Avoiding siloed efforts
According to a Gartner survey, 75% of organizations are looking at consolidating security tools, not primarily because of cost, but because it helps drive cyber risk reduction. Ensuring that security efforts are part of a wider security ecosystem, rather than siloed efforts, is crucial to maximize the return on these investments.
Darktrace / Proactive Exposure Management integrates with Darktrace's detection and response to ensure that the organization's security posture is hardened, even if the team doesn't have time to eliminate the attack path.
Defensive superiority is key, and Attack Path Modeling is one way to help security teams gain back an advantage. Find out how you can test it in your own environment.
Attack Path Modeling is an objective, however, and there are a few important questions to consider when assessing the different methods of creating these models.
Are we considering all the relevant data when building my attack paths map?
Consider the case where one of your marketing executives has a close friendship with someone in your development team. How do you model that into your attack paths cartography? Attack paths encompass the full digital estate, so the attack path modeling solution should consider information from various parts, internal and external. This may include data from the Email environment, the Network, Endpoints, SaaS & Cloud, Active Directory, Vulnerability Scanners, etc.
Cross-data analysis is the only way to understand holistic attack paths.
Are we looking at the most up to date map of attack paths?
Relationships between users, devices and other sensitive assets can evolve on a daily basis, this implies attack paths evolve on a daily basis. Ensuring that the methods or solutions used update their understanding continuously and in real-time is vital if security teams want the most up to date understanding of their organization's risk posture.
To improve our security posture, how do we know which attack paths to start with?
One thing is to map the sum-total of attack paths, another is to prioritize them. Attack path modeling gives you the map but adding a risk-assessment (explored in more depth below) layer on top is how you prioritize. This is where graph theory can be very useful to identify choke points that you may want to strengthen.
Does this output yield actionable insights?
The prime objective of this solution is not simply to provide an assessment of cyber risk posture, but rather to help drive security efforts in the right direction. To that end, the output needs to be accessible to team members that may not have expert cyber skills. Lowering barriers to entry with usable insights and mitigation advice is key to successfully improve the organization's security posture.
Assessing risk to prioritize attack paths
Darktrace Attack Path Modeling (APM) is a risk-based approach to assessing cyber-attack pathways, thinking like an attacker, and probing the path of least resistance. 'Risk' in this case is defined as the product of two factors: Probability and Impact. By using this information to categorize possible attack paths in the risk matrix below, Darktrace's APM can prioritize attack paths to ensure security team efforts are spent on controlling for the most relevant risks for their organization.
Figure 2: Risk matrix for attack path prioritization
A: Defining Probability
There are two types of probability to consider:
The likelihood of one particular door being chosen by an attacker to infiltrate the organization (among the assets at the attack surface - this could be an internet-facing server, an inbox, a SaaS/Cloud account, etc). And,
The likelihood of one particular node (defined as a device or user account) being compromised next, via lateral movement.
Figure 3: Simplified example of calculating probability of lateral movement from a compromised agent to one of two servers
B Defining Impact
Impact refers to the overall impact of an asset being compromised and unusable. In the case of an asset (e.g.: a key server), the bigger the disruption if this asset goes down, the higher the impact score. If considering a particular document, restricted access and sensitivity score of users accessing it are some of the variables used to estimate impact.
Figure 4: Diagram showing a simplified example of mapping access volume and sensitivity to estimate document value.
Both variables are calculated by the AI autonomously, without requiring human input. Security teams can of course reinforce the AI's understanding of the organization with their business expertise (by tagging additional sensitive devices for example).
A more in-depth description of how impact is propagated to identify key servers or sensitive documents, as well as other components that comprise the Darktrace Attack Path Modeling module can be found in this white paper.
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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.
Author
Elliot Stocker
Product SME
After 2 years in a commercial role helping to deploy Darktrace across a broad range of digital environments, Elliot currently occupies the role of Product Subject Matter Expert, where he helps to articulate the value of Darktrace’s technology to customers around the world. Elliot holds a Masters degree in Data Science and Machine Learning, using this knowledge to communicate concepts around machine learning and AI in an accessible way to different audiences.
From Royal to BlackSuit: Understanding the Tactics and Impact of a Sophisticated Ransomware Strain
What is BlackSuit Ransomware?
Since late 2023, Darktrace has detected BlackSuit ransomware infiltrating multiple customer networks in the US. This ransomware has targeted a wide range of industries, including arts, entertainment, real estate, public administration, defense, and social security.
Emerging in May 2023, BlackSuit is believed to be a spinoff of Royal ransomware due to similarities in code and Conti, and most likely consists of Russian and Eastern European hackers [1]. Recorded Future reported that the ransomware had affected 95 organizations worldwide, though the actual number is likely much higher [2]. While BlackSuit does not appear to focus on any particular sector, it has targeted multiple organizations in the healthcare, eduction, IT, government, retail and manufacturing industries [3]. Employing double extortion tactics, BlackSuit not only encrypts files but also steals sensitive data to leverage ransom payments.
BlackSuit has demanded over USD 500 million in ransoms, with the highest individual demand reaching USD 60 million [4]. Notable targets include CDK Global, Japanese media conglomerate Kadokawa, multiple educational institutions, Octapharma Plasma, and the government of Brazil [5][6][7][8].
Darktrace’s Coverage of BlackSuit Ransomware Attack
Case 1, November 2023
The earliest attack on a Darktrace customer by BlackSuit was detected at the start of November 2023. The unusual network activity began on a weekend—a time commonly chosen by ransomware groups to increase their chances of success, as many security teams operate with reduced staff. Darktrace identified indicators of the attackers’ presence on the network for almost two weeks, during which a total of 15 devices exhibited suspicious behavior.
The attack commenced with unusual internal SMB (Server Message Block) connections using a compromised service account. An internal device uploaded an executable (zzza.exe) to a domain controller (DC) and shortly after, wrote a script (socks5.ps1) to another device. According to a Cybersecurity Advisory from the CISA (Cybersecurity and Infrastructure Security Agency, US), the script file was a PowerShell reverse proxy [9].
Approximately an hour and a half later, the device to which the script was written exhibited uncommon WMI (Windows Management Instrumentation) activity. Two hours after receiving the executable file, the DC was observed making an outgoing NTLM request, using PowerShell to remotely execute commands, distributing differently named executable files (<PART OF THE CUSTOMER’S NAME>.exe), and controlling services on other devices.
Eighteen hours after the start of the unusual activity, Darktrace detected another device making repeated connections to “mystuff.bublup[.]com”, which the aforementioned CISA Advisory identifies as a domain used by BlackSuit for data exfiltration [9].
About ten minutes after the suspicious executables were distributed across the network, and less than 24 hours after the start of the unusual activity, file encryption began. A total of ten devices were seen appending the “.blacksuit” extension to files saved on other devices using SMB, as well as writing ransom notes (readme.blacksuit.txt). The file encryption lasted less than 20 minutes.
Figure 1: An example of the contents of a BlackSuit ransom note being written over SMB.
During this compromise, external connections to endpoints related to ConnectWise’s ScreenConnect remote management tool were also seen from multiple servers, suggesting that the tool was likely being abused for command-and-control (C2) activity. Darktrace identified anomalous connectivity associated with ScreenConnect was seen up to 11 days after the start of the attack.
10 days after the start of the compromise, an account belonging to a manager was detected adding “.blacksuit” extensions to the customer’s Software-a-Service (SaaS) resources while connecting from 173.251.109[.]106. Six minutes after file encryption began, Darktrace flagged the unusual activity and recommended a block. However, since Autonomous Response mode was not enabled, the customer’s security team needed to manually confirm the action. Consequently, suspicious activity continued for about a week after the initial encryption. This included disabling authentication on the account and an unusual Teams session initiated from the suspicious external endpoint 216.151.180[.]147.
Case 2, February 2024
Another BlackSuit compromise occurred at the start of February 2024, when Darktrace identified approximately 50 devices exhibiting ransomware-related activity in another US customer’s environment. Further investigation revealed that a significant number of additional devices had also been compromised. These devices were outside Darktrace’s purview to the customer’s specific deployment configuration. The threat actors managed to exfiltrate around 4 TB of data.
Initial access to the network was gained via a virtual private network (VPN) compromise in January 2024, when suspicious connections from a Romanian IP address were detected. According to CISA, the BlackSuit group often utilizes the services of initial access brokers (IAB)—actors who specialize in infiltrating networks, such as through VPNs, and then selling that unauthorized access to other threat actors [9]. Other initial access vectors include phishing emails, RDP (Remote Desktop Protocol) compromise, and exploitation of vulnerable public-facing applications.
Similar to the first case, the file encryption began at the end of the working week. During this phase of the attack, affected devices were observed encrypting files on other internal devices using two compromised administrator accounts. The encryption activity lasted for approximately six and a half hours. Multiple alerts were sent to the customer from Darktrace’s Security Operations Centre (SOC) team, who began reviewing the activity within four minutes of the start of the file encryption.
Figure 2: Darktrace’s Cyber AI Analyst clustering together multiple events related to unusual activity on the network, including file encryption over SMB by BlackSuit.
Figure 3: A spike in model alerts on the day when file encryption by BlackSuit was observed in the network.
In this case, the threat actor utilized SystemBC proxy malware for command and control (C2). A domain controller (DC) was seen connecting to 137.220.61[.]94 on the same day the file encryption took place. The DC was also observed connecting to a ProxyScrape domain around the same time, which is related to the SOCKS5 protocol used by SystemBC. During this compromise, RDP, SSH, and SMB were used for lateral movement within the network.
Figure 4: A Cyber AI Analyst investigation alerting to a device on the VPN subnet making suspicious internal SSH connections due to malicious actors moving laterally within the network.
Signs of threat actors potentially being on the network were observed as early as two days prior to the file encryption. This included unusual internal network scanning via multiple protocols (ICMP, SMB, RDP, etc.), credential brute-forcing, SMB access failures, and anonymous SMBv1 sessions. These activities were traced to IP addresses belonging to two desktop devices in the VPN subnet associated with two regular employee user accounts. Threat actors were seemingly able to exploit at least one of these accounts due to LDAP legacy policies being in place on the customer’s environment.
Figure 5: A Cyber AI Analyst incident summary alerting to a device on the VPN subnet conducting internal reconnaissance.
Figure 6: Examples of the proposed Darktrace Autonomous Response actions on the day BlackSuit initiated file encryption.
Case 3, August 2024
The most recently observed BlackSuit compromise occurred in August 2024, when a device was observed attempting to brute-force the credentials of an IT administrator. This activity continued for 11 days.
Once the admin’s account was successfully compromised, network scanning, unusual WMI, and SAMR (Security Account Manager Remote protocol) activity followed. A spike in the use of this account was detected on a Sunday—once again, the attackers seemingly targeting the weekend—when the account was used by nearly 50 different devices.
The compromised admin’s account was exploited for data gathering via SMB, resulting in the movement of 200 GB of data between internal devices in preparation for exfiltration. The files were then archived using the naming convention “*.part<number>.rar”.
Around the same time, Darktrace observed data transfers from 19 internal devices to “bublup-media-production.s3.amazonaws[.]com,” totaling just over 200 GB—the same volume of data gathered internally. Connections to other Bublup domains were also detected. The internal data download and external data transfer activity took approximately 8-9 hours.
Unfortunately, Darktrace was not configured in Autonomous Response mode at the time of the attack, meaning any mitigative actions to stop the data gathering or exfiltration required human confirmation.
Figure 7: One of the compromised devices was seen sending 80 GB of data to bublup-media-production.s3.amazonaws[.]com within a span of 4 hours.
Once the information was stolen, the threat actor moved on to the final stage of the attack—file encryption. Five internal devices, using either the compromised admin account or connecting via anonymous SMBv1 sessions, were seen encrypting files and writing ransom notes to five other devices on the network. The attempts at file encryption continued for around two hours, but Darktrace’s Autonomous Response capability was able to block the activity and prevent the attack from escalating.
Conclusion
The persistent and evolving threat posed by ransomware like BlackSuit underscores the critical importance of robust cybersecurity measures across all sectors. Since its emergence in 2023, BlackSuit has demonstrated a sophisticated approach to infiltrating networks, leveraging double extortion tactics, and demanding substantial ransoms. The cases highlighted above illustrate the varied methods and persistence of BlackSuit attackers, from exploiting VPN vulnerabilities to abusing remote management tools and targeting off-hours to maximize impact.
Although many similar connection patterns, such as the abuse of Bublup services for data exfiltration or the use of SOCKS5 proxies for C2, were observed during cases investigated by Darktrace, BlackSuit actors are highly sophisticated and tailors their attacks to each target organization. The consequences of a successful attack can be highly disruptive, and remediation efforts can be time-consuming and costly. This includes taking the entire network offline while responding to the incident, restoring encrypted files from backups (if available), dealing with damage to the organization’s reputation, and potential lawsuits.
These BlackSuit ransomware incidents emphasize the need for continuous vigilance, timely updates to security protocols, and the adoption of autonomous response technologies to swiftly counteract such attacks. As ransomware tactics continue to evolve, organizations must remain agile and informed to protect their critical assets and data. By learning from these incidents and enhancing their cybersecurity frameworks, organizations can better defend against the relentless threat of ransomware and ensure the resilience of their operations in an increasingly digital world.
Credit to Signe Zaharka (Principal Cyber Analyst) and Adam Potter (Senior Cyber Analyst)
Darktrace’s First 6: Half-Year Threat Report 2024 highlights the latest attack trends and key threats observed by the Darktrace Threat Research team in the first six months of 2024.
Focuses on anomaly detection and behavioral analysis to identify threats
Maps mitigated cases to known, publicly attributed threats for deeper context
Offers guidance on improving security posture to defend against persistent threats
Appendices
Darktrace Model Detections
Anomalous Connection / Data Sent to Rare Domain
Anomalous Connection / High Volume of New or Uncommon Service Control
Anomalous Connection / New or Uncommon Service Control
Anomalous Connection / Rare WinRM Outgoing
Anomalous Connection / SMB Enumeration
Anomalous Connection / Suspicious Activity On High Risk Device
Anomalous Connection / Suspicious Read Write Ratio
Anomalous Connection / Suspicious Read Write Ratio and Unusual SMB
Anomalous Connection / Sustained MIME Type Conversion
.blacksuit - File extension – When encrypting the files, this extension is appended to the filename – High
readme.blacksuit.txt – ransom note - A file demanding cryptocurrency payment in exchange for decrypting the victim's files and not leaking the stolen data – High
mystuff.bublup[.]com, bublup-media-production.s3.amazonaws[.]com – data exfiltration domains related to an organization and project management app that has document sharing functionality – High
137.220.61[.]94:4001 – SystemBC C2 related IP address (this tool is often used by other ransomware groups as well) - Medium
173.251.109[.]106 – IP address seen during a SaaS BlackSuit compromise (during file encryption) – Medium
216.151.180[.]147 – IP address seen during a SaaS BlackSuit compromise (during an unusual Teams session) - Medium
MITRE ATT&CK Mapping
Tactic - Technqiue
Account Manipulation - PERSISTENCE - T1098
Alarm Suppression - INHIBIT RESPONSE FUNCTION - T0878
Application Layer Protocol - COMMAND AND CONTROL - T1071
Automated Collection - COLLECTION - T1119
Block Command Message - INHIBIT RESPONSE FUNCTION - T0803
Block Reporting Message - INHIBIT RESPONSE FUNCTION - T0804
Onomastics Gymnastics: How Darktrace Detects Spoofing and Business Email Compromise in Multi-Name Users
Note: For privacy reasons, actual surnames and email addresses observed in these incidents below have been replaced with fictitious placeholder names, using the common Spanish names “Fulano” and “Mengano”.
Naming conventions
Modeling names and their variants of members of an organization is a critical component to properly detect if those same names and variants are being spoofed by malicious actors. For many predominantly English-speaking organizations, these variants can largely be captured by variants of a person’s given name (e.g. James-Jimmy-Jim) and a consistent, singular surname or family name (e.g. Smith). Naming conventions, however, are far from universal. This piece will review how Darktrace / EMAIL manages the common naming conventions of much of the Spanish-speaking world, and can use its modeling to create high-fidelity detections of multiple types of spoofing attempts.
A brief summary of the common convention across Spain and much of Spanish-speaking America: most people are given one or two given names (e.g. Roberto, Juan, María, Natalia), and their surnames are the first surname of their father, followed by the first surname of their mother. While there are various exceptions to this norm, the below graphic Wikipedia [1][2] highlights the general rule.
Figure 1: Example Spanish naming convention for father “José García Torres” and mother “María Acosta Gómez” for child “Pablo García Acosta”. If shortened to one surname, the convention holds the child would be referred to as “Pablo García” [1].
Detection of improper name usage
Implicit in the above comment that shortening to one surname follows the convention of using the first surname, shortening to the second surname is often a tell-tale sign of someone unfamiliar with the person or their broader culture. This can be a useful corroborating feature in detecting a spoof attempt – analogous to a spelling error.
In the case of a Spanish customer, this misuse of name shortening contributed to the detection of a spoof attempt trying to solicit a response by impersonating an internal user forwarding information about ‘Data Protection’.
Figure 2: The Cyber AI Analyst summary of the Darktrace / EMAIL detections shows the use of the Gmail sender impersonating Isabel Maria Fulano Mengano, but incorrectly uses the second surname Mengano.
While the limited communication history from the sender and the nature of the text content already marks the mail as suspicious, Darktrace / EMAIL notes the personal name used in the email is similar to a high-value user (‘whale’ to use the terminology of spearphishing). The additional context provided by the detection of the attempted spoof prompted more severe actioning of this email, leading to a ‘Hold’ action instead of a less-severe ‘Unspoof’ action via a banner on the email.
Figure 3: The content summary of the sender showing the ‘Personal’ field of the email being ‘Isabel Mengano’, breaking from the standard name-shortening convention. The additional metrics identify features that might be anomalous about the sender.
Malicious email properly using both surnames
Misusing the name-shortening convention is not the only way that Darktrace / EMAIL can detect spoofing attempts. In the case of another Spanish customer, Darktrace observed a whale impersonation being sent to 230 users with solicitation content, but no links or attachments. Although the name was modeled internally in the “Surname, Given-name” format, Darktrace identified the spoofing attempt targeting a high-value user and took action, blocking the series of emails from reaching end-user inboxes to prevent unsuspecting users from responding.
Figure 4: Cyber AI Analyst summary of a suspicious email. The personal field is visible as ‘juan fulano mengano’, which is consistent with the reverse-order modelled user ‘fulano mengano, juan’. The subject line ‘Urgent Request’ sent to 230 users gives an intuitive indicator of the emails potentially being part of a malicious solicitation campaign.
In Summary: A case of onomastics gymnastics
The variety in valid usage of human language can be a barrier to evaluating when a given text is benign or malicious. Despite this, Darktrace / EMAIL is designed to manage this variety, as exemplified by the detections of two spoofing attempts seen against organizations using the distinct Spanish-speaking world’s common naming convention. The scope of this design as seen in this onomastic context, extends to a wide range of detections surrounding emails and their behavioral anomalies.
Credit to Roberto Romeu (Principal Cyber Analyst), Justin Torres (Senior Cyber Analyst) and Natalia Sánchez Rocafort (Senior Analyst Consultant).
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![One of the compromised devices was seen sending 80 GB of data to bublup-media-production.s3.amazonaws[.]com within a span of 4 hours.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/673513f1135d0e009caf256f_6735136a725738f4afe4de2b_Fig%25207.png)
