The latest escalation in the cyber arms race sees attackers choosing stealth over speed and cunning over chaos.
Introduction
The speed of today’s most advanced threats can be devastating. In the few minutes it takes a security analyst to step away from her screen to grab a coffee, ransomware can take down thousands of computers before human teams or traditional tools have the chance to respond. And while big, fast threats are more likely to grab the headlines, cyber-attacks which do the opposite can be just as dangerous. The latest escalation in the cyber arms race sees attackers choosing stealth over speed and cunning over chaos.
As defenders work to rapidly deploy new security and detection technologies, malware authors have been similarly innovative, working to find a means of evading them. New ‘low and slow’ attacks are able to bypass traditional security tools because each individual action compiling the larger threat is too small to detect. These attacks are designed to operate over a longer period of time – and by minimizing disruption to any data transfer or connectivity levels, they blend into legitimate traffic.
For advanced and well-resourced actors like nation states in search of valuable intellectual property or sensitive political records, subtle and prolonged exposure to the systems they attack is a significant benefit. When it comes to the most sophisticated threats, slow and steady really can win the race.
Nevertheless, detection of low and slow attacks is possible with advanced machine learning techniques. To do so, contextual knowledge is critical; by modeling the subtle and unique ‘patterns of life’ of every user, device, and the network as a whole, AI-powered defenses are, for the first time, winning this battle.
This blog explores how attackers use low and slow techniques during multiple stages of the kill chain to achieve their eventual goal. We examine three real-world case studies, drawn from over 7,000 deployments of the Enterprise Immune System, to demonstrate how cyber AI detects low and slow reconnaissance, data exfiltration, and command-and-control activity.
Low and slow reconnaissance
By monitoring the behavioral pattern of devices and users, Darktrace AI is able to learn an evolving profile for expected activity. Armed with this understanding of ‘normal’ for the network, it can then identify significant anomalies indicative of a threat. It does all this without relying on training sets of historical data, enabling the technology to spot threats that other tools miss.
On the network of a European financial services firm, Darktrace discovered a server conducting port scans of various internal computers. This type of network scanning is regularly performed for legitimate testing purposes by administrative devices, but it is also a tactic for attackers to identify vulnerabilities and points of compromise – an early stage of an attack.
Over a duration of 7 days, the server made around 214,000 failed connections to 276 unique devices. However, only a small number of ports were targeted per day. The attack was sequential, but slow over time. Measured in one day, the level of disturbance was minimal enough to evade all rules-based defenses. Nevertheless, by learning ‘self’ across the entire digital business over time, cyber AI can detect even the subtlest deviation from ‘normal’ relative to the individual device, user, or network. Darktrace recognized the longer pattern of network scanning and alerted the customer immediately.
Advanced search view showing regular connections to closed ports over the scanning period.
Low and slow data exfiltration
At an industrial manufacturing company, a desktop was identified establishing over 2,000 connections to a rare host over a 7-day period. During this time, a total of 9.15GB of data was transferred externally. No single connection transmitted more than a few MB of data – an amount which, if viewed in isolation, would not be cause for concern. However, the destination for these connections was 100% rare for the network and maintained that level of rarity for the entire period of exfiltration. This not only flagged the activity as initially suspicious, but also prevented it from being absorbed into legitimate traffic. Combined with the accumulated volume of data leaving the network, Darktrace AI identified this as significant deviation in the device’s behavior, indicating a threat in progress.
Steady exfiltration of data over a 7-day period.
A series of model breaches (orange circles) occurring throughout the period of steady external data exfiltration (blue line).
Low and slow command and control
Darktrace is extremely successful in finding malware infections before they appear on open-source threat lists, a crucial ability when stopping the most serious, never-before-seen threats. This is achieved in large part by detecting beaconing patterns rather than relying on signatures. Beaconing occurs when a malicious program attempts to establish contact with its online infrastructure. Similar to network scanning, it creates a surge in outgoing connections.
Darktrace was deployed in a corporate network where a device was found making connections at steady intervals to a malicious browser extension. The average rate of connection was 11 connections every 4 hours – a low activity level which could easily have blended into legitimate internet traffic. Having identified the regularity of these connections, Darktrace’s AI assigned a high beaconing score, which indicated that they were likely initiated by an automated process. If we include the fact that the destination was rare, it became clear that this was caused by a malicious background program that was running unbeknownst to the user.
As cyber security advances, attackers will develop increasingly sophisticated methods to operate under the radar. Traditional cyber security tools which work in binary ways based on historical data – either the upload exceeded a predefined limit or not – cannot keep up. This new era will see AI proven crucial because of its ability to learn a constantly-evolving ‘pattern of life’ for a network over the duration of its deployment. This allows Darktrace AI to effectively locate the disturbances in connectivity levels – no matter how small – that have been caused by malicious or non-compliant activity. Fundamentally, this enables Darktrace to discover in-progress attacks and then autonomously respond, neutralizing them before they become a crisis.
High-profile, fast-moving attacks like NotPetya and WannaCry have encouraged some organizations to focus on preventing certain types of threat, at the expense of others – and hackers are catching on. By leveraging powerful AI, Darktrace empowers customers to prevent not just the fastest-moving attacks, but also the slowest and subtlest.
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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.
With the perimeter all but dissolved, Network Detection and Response (NDR) tools are quickly becoming a critical component of the security stack, as the main tool to span the modern network. NDRs connect on-premises infrastructure to cloud, remote workers, identities, SaaS applications, and IoT/OT – something not available to EDR that requires agents and isolates visibility to individual devices.
KuppingerCole Analysts AG designated Darktrace an ‘Overall Leader’ position because of our continual innovation around user-led security. Self-Learning AI together with automated triage through Cyber AI Analyst and real-time autonomous response actions have been instrumental to security teams in stopping potential threats before they become a breach. With this time saved, Darktrace is leading beyond reactive security to truly harden a network, allowing the team to spend more time in preventive security measures.
Network Detection and Response protects where others fail to reach
NDR solutions operate at the network level, deploying inside or parallel to your network to ingest raw traffic via virtual or physical sensors. This gives them unprecedented potential to identify anomalies and possible breaches in any network - far beyond simple on-prem, into dynamic virtual environments, cloud or hybrid networks, cloud applications, and even remote devices accessing the corporate network via ZTNA or VPN.
Rather than looking at processes level data, NDR can detect the lateral movement of an adversary across multiple assets by analyzing network traffic patterns which endpoint solutions may not be able to identify [1]. In the face of a growing, complex environment, organizations large and small, will benefit from using NDRs either in conjunction, or as the foundation for, their Extended Detection and Response (XDR) for a unified view that improves their overall threat detection, ease of investigation and faster response times.
Today's NDR solutions are expected to include advanced ML and artificial intelligence (AI) algorithms [1]
Traditional IDS & IPS systems are labor intensive, requiring continuous rule creation, outdated signature maintenance, and manual monitoring for false positives or incorrect actions. This is no longer viable against a higher volume and changing landscape, making NDR the natural network tool to level against these evolutions. The role of AI in NDRs is designed to meet this challenge, “to reduce both the labor need for analysis and false positives, as well as add value by improving anomaly detection and overall security posture” .
Celebrating success in leadership and innovation
Darktrace is proud to have been recognized as an NDR “Overall Leader” in KuppingerCole Analyst AG’s Leadership Compass. The report gave further recognition to Darktrace as a ‘Product Leader”, “Innovation Leader” and “Market Leader”.
Maximum scores were received for core product categories, in addition to market presence and financial strength. Particular attention was directed to our innovation. This year has seen several NDR updates via Darktrace’s ActiveAI Security Platform version 6.2 which has enhanced investigation workflows and provided new AI transparency within the toolset.
Positive scores were also received for Darktrace’s deployment ecosystem and surrounding support, minimizing the need for extraneous integrations through a unique platform architecture that connects with over 90 other vendors.
Figure 1: High Scores received in Darktrace’s KuppingerCole Spider Chart across Core NDR capability areas
Darktrace’s pioneering AI approach sets it apart
Darktrace / NETWORK’s approach is fundamentally different to other NDRs. Continual anomaly-based detection (our Self-Learning AI), understands what is normal across each of your network entities, and then examines deviations from these behaviors rather than needing to apply static rules or ML to adversary techniques. As a result, Darktrace / NETWORK can focus on surfacing the novel threats that cannot be anticipated, whilst our proactive solutions expose gaps that can be exploited and reduce the risk of known threats.
Across the millions of possible network events that may occur, Darktrace’s Cyber AI Analyst reduces that manual workload for SOC teams by presenting only what is most important in complete collated incidents. This accelerates SOC Level 2 analyses of incidents by 10x2, giving time back, first for any necessary response and then for preventive workflows.
Finally, when incidents begin to escalate, Darktrace can natively (or via third-party) autonomously respond and take precise actions based on a contextual understanding of both the affected assets and incident in question so that threats can be disarmed without impacting wider operations.
Within the KuppingerCole report, several standout strengths were listed:
Cyber AI Analyst was celebrated as a core differentiator, enhancing both visibility and investigation into critical network issues and allowing a faster response.
Darktrace / NETWORK was singled for its user benefits. Both a clear interface for analysts with advanced filtering and analytical tools, and efficient role-based access control (RBAC) and configuration options for administrators.
At the product level, Darktrace was recognized for complete network traffic analysis (NTA) capabilities allowing extensive analysis into components like application use/type, fingerprinting, source/destination communication, in addition to comprehensive protocol support across a range of network device types from IT, OT, IoT and mobiles and detailed MITRE ATT&CK mapping.
Finally, at the heart of it, Darktrace’s innovation was highlighted in relation to its intrinsic Self Learning AI, utilizing multiple layers of deep learning, neural networks, LLMs, NLP, Generative AI and more to understand network activity and filter it for what’s critical on an individual customer level.
Going beyond reactive security
Darktrace’s visibility and AI-enabled detection, investigation and response enable security teams to focus on hardening gaps in their network through contextual relevance & priority. Darktrace / NETWORK explicitly gives time back to security teams allowing them to focus on the bigger strategic and governance workflows that sometimes get overlooked. This is enabled through proactive solutions intrinsically connected to our NDR:
Darktrace / Proactive Exposure Management, which looks beyond just CVE risks to instead discover, prioritize and validate risks by business impact and how to mobilize against them early, to reduce the number of real threats security teams face.
Darktrace / Incident Readiness & Recovery, a solution rather than service-based approach to incident response (IR) that lets teams respond in the best way to each incident and proactively test their familiarity and effectiveness of IR workflows with sophisticated incident simulations involving their own analysts and assets.
Together, these solutions allow Darktrace / NETWORK to go beyond the traditional NDR and shift teams to a more hardened and proactive state.
Putting customers first
Customers continue to sit at the forefront of Darktrace R&D, with their emerging needs and pain points being the direct inspiration for our continued innovation.
This year Darktrace / NETWORK has protected thousands of customers against the latest attacks, from data exfil and destruction, to unapproved privilege escalation and ransomware including strains like Medusa, Qilin and AlphV BlackCat.
In each instance, Darktrace / NETWORK was able to provide a holistic lens of the anomalies present in their traffic, collated those that were important, and either responded or gave teams the ability to take targeted actions against their threats – even when adversaries pivoted. In one example of a Gootloader compromise, Darktrace ensured a SOC went from detection to recovery within 5 days, 92.8% faster than the average containment time of 69 days.
Results like these, focused on user-led security, have secured Darktrace’s position within the latest NDR Leadership Compass.
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, education, 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
![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)
