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January 4, 2023

BlackMatter's Smash-and-Grab Ransom Attack Incident Analysis

Stay informed on cybersecurity trends! Read about a BlackMatters ransom attack incident and Darktrace's analysis on how RESPOND could have stopped the attack.
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.
Written by
The Darktrace Analyst Team
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04
Jan 2023

Only a few years ago, popular reporting announced that the days of smash-and-grab attacks were over and that a new breed of hackers were taking over with subtler, ‘low-and-slow’ tactics [1]. Although these have undoubtedly appeared, smash-and-grab have quickly become overlooked – perhaps with worrying consequences. Last year, Google saw repeated phishing campaigns using cookie theft malware and most recently, reports of hacktivists using similar techniques have been identified during the 2022 Ukraine Conflict [2 & 3]. Where did their inspiration come from? For larger APT groups such as BlackMatter, which first appeared in the summer of 2021, smash-and-grabs never went out of fashion.

This blog dissects a BlackMatter ransomware attack that hit an organization trialing Darktrace back in 2021. The case reveals what can happen when a security team does not react to high-priority alerts. 

When entire ransomware attacks can be carried out over the course of just 48 hours, there is a high risk to relying on security teams to react to detection notifications and prevent damage before the threat escalates. Although there has been hesitancy in its uptake [4], this blog also demonstrates the need for automated response solutions like Darktrace RESPOND.

The Name Game: Untangling BlackMatter, REvil, and DarkSide

Despite being a short-lived criminal organization on the surface [5], a number of parallels have now been drawn between the TTPs (Tactics, Techniques and Procedures) of the newer BlackMatter group and those of the retired REvil and DarkSide organizations [6]. 

Prior to their retirement, DarkSide and REvil were perhaps the biggest names in cyber-crime, responsible for two of last year’s most devastating ransomware attacks. Less than two weeks after the Colonial Pipeline attack, DarkSide announced it was shutting down its operation [7]. Meanwhile the FBI shutdown REvil in January 2022 after its devastating Fourth of July Kaseya attacks and a failed return in September [8]. It is now suspected that members from one or both went on to form BlackMatter.

This rebranding strategy parallels the smash-and-grab attacks these groups now increasingly employ: they make their money, and a lot of noise, and when they’re found out, they disappear before organizations or governments can pull together their threat intelligence and organize an effective response. When they return days, weeks or months later, they do so having implemented enough small changes to render themselves and their attacks unrecognizable. That is how DarkSide can become BlackMatter, and how its attacks can slip through security systems trained on previously encountered threats. 

Attack Details

In September 2021 Darktrace was monitoring a US marketing agency which became the victim of a double extortion ransomware attack that bore hallmarks of a BlackMatter operation. This began when a single domain-authenticated device joined the company’s network. This was likely a pre-infected company device being reconnected after some time offline. 

Only 15 minutes after joining, the device began SMB and ICMP scanning activities towards over 1000 different internal IPs. There was also a large spike of requests for Epmapper, which suggested an intent for RPC-based lateral movement. Although one credential was particularly prominent, multiple were used including labelled admin credentials. Given it’s unexpected nature, this recon quickly triggered a chain of DETECT/Network model breaches which ensured that Darktrace’s SOC were alerted via the Proactive Threat Notification service. Whilst SOC analysts began to triage the activity, the organization failed to act on any of the alerts they received, leaving the detected threat to take root within their digital environment. 

Shortly after, a series of C2 beaconing occurred towards an endpoint associated with Cobalt Strike [9]. This was accompanied by a range of anomalous WMI bind requests to svcctl, SecAddr and further RPC connections. These allowed the initial compromised device to quickly infect 11 other devices. With continued scanning over the next day, valuable data was soon identified. Across several transfers, 230GB of internal data was then exfiltrated from four file servers via SSH port 22. This data was then made unusable to the organization through encryption occurring via SMB Writes and Moves/Renames with the randomly generated extension ‘.qHefKSmfd’. Finally a ransom note titled ‘qHefKSmfd.README.txt’ was dropped.

This ransom note was appended with the BlackMatter ASCII logo:

Figure 1- The ASCII logo which accompanied BlackMatter’s ransom note

Although Darktrace DETECT and Cyber AI Analyst continued to provide live alerting, the actor successfully accomplished their mission.  

There are numerous reasons that an organization may fail to organize a response to a threat, (including resource shortages, out of hours attacks, and groups that simply move too fast). Without Darktrace’s RESPOND capabilities enabled, the threat actors could proceed this attack without obstacles. 

Figure 2- Cyber AI Analyst breaks down the stages of the attack [Note: this screenshot is from V5 of DETECT/Network] 

How would the attack have unfolded with RESPOND?

Armed with Darktrace’s evolving knowledge of ‘self’ for the customer’s unique digital environment, RESPOND would have activated within seconds of the first network scan, which was recognized as highly anomalous. The standard action taken here would usually involve enforcing the standard ‘pattern of life’ for the compromised device over a set time period in order to halt the anomaly while allowing the business to continue operating as normal.

RESPOND constantly re-evaluates threats as attacks unfold. Had the first stage still been successful, it would have continued to take targeted action at each corresponding stage of this attack. RESPOND models would have alerted to block the external connections to C2 servers over port 443, the outbound exfil attempts and crucially the SMB write activity over port 445 related to encryption.

As DETECT and RESPOND feed into one another, Darktrace would have continued to assess its actions as BlackMatter pivoted tactics. These actions buy back critical time for security teams that may not be in operation over the weekend, and stun the attacker into place without applying overly aggressive responses that create more problems than they solve.

Ultimately although this incident did not resolve autonomously, in response to the ransom event, Darktrace offered to enable RESPOND and set it in active mode for ransomware indicators across all client and server devices. This ensured an event like this would not occur again. 

Why does RESPOND work?

Response solutions must be accurate enough to fire only when there is a genuine threat, configurable enough to let the user stay in the driver’s seat, and intelligent enough to know the right action to take to contain only the malicious activity- without disrupting normal business operations. 

This is only possible if you can establish what ‘normal’ is for any one organization. And this is how Darktrace’s RESPOND product family ensures its actions are targeted and proportionate. By feeding off DETECT alerting which highlights subtle or large deviations across the network, cloud and SaaS, RESPOND can provide a measured response to the potential threat. This includes actions such as:

  • Enforcing the device’s ‘pattern of life’ for a given length of time 
  • Enforcing the ‘group pattern of life’ (stopping a device from doing anything its peers haven’t done in the past)
  • Blocking connections of a certain type to a certain destination
  • Logging out of a cloud account 
  • ‘Smart quarantining’ an endpoint device- maintaining access to VPNs and company’s AV solution

Conclusion 

In its report on BlackMatter [10], CISA recommended that organizations invest in network monitoring tools with the capacity to investigate anomalous activity. Picking up on unusual behavior rather than predetermined rules and signatures is an important step in fighting back against new threats. As this particular story shows, however, detection alone is not always enough. Turning on RESPOND, which takes immediate and precise action to contain threats, regardless of when and where they come in, is the best way to counter smash-and-grab attacks and protect organizations’ digital assets. There is little doubt that the threat actors behind BlackMatter will or have already returned with new names and strategies- but organizations with RESPOND will be ready for them.

Appendices

Darktrace Model Detections (in order of breach)

Those with the ‘PTN’ prefix were alerted directly to Darktrace’s 24/7 SOC team.

  • Device / ICMP Address Scan
  • Device / Suspicious SMB Scanning Activity
  • (PTN) Device / Suspicious Network Scan Activity
  • Anomalous Connection / SMB Enumeration
  • Device / Possible RPC Lateral Movement
  • Device / Active Directory Reconnaissance
  • Unusual Activity / Possible RPC Recon Activity
  • Device / Possible SMB/NTLM Reconnaissance
  • Compliance / Default Credential Usage
  • Device / New or Unusual Remote Command Execution
  • Anomalous Connection / New or Uncommon Service Control
  • Device / New or Uncommon SMB Named Pipe
  • Device / SMB Session Bruteforce
  • Device / New or Uncommon WMI Activity
  • (PTN) Device / Multiple Lateral Movement Model Breaches
  • Compromise / Sustained SSL or HTTP Increase
  • Compromise / SSL or HTTP Beacon
  • Compromise / Sustained TCP Beaconing Activity To Rare Endpoint
  • Device / Anomalous SMB Followed By Multiple Model Breaches
  • Device / Anomalous RDP Followed By Multiple Model Breaches
  • Anomalous Server Activity / Rare External from Server
  • Anomalous Connection / Anomalous SSL without SNI to New External
  • Anomalous Connection / Rare External SSL Self-Signed
  • Device / Long Agent Connection to New Endpoint
  • Compliance / SMB Drive Write
  • Anomalous Connection / Unusual Admin SMB Session
  • Anomalous Connection / High Volume of New or Uncommon Service Control
  • Anomalous Connection / Unusual Admin RDP Session
  • Device / Suspicious File Writes to Multiple Hidden SMB Shares
  • Anomalous Connection / Multiple Connections to New External TCP Port
  • Compliance / SSH to Rare External Destination
  • Anomalous Connection / Uncommon 1 GiB Outbound
  • Anomalous Connection / Data Sent to Rare Domain
  • Anomalous Connection / Download and Upload
  • (PTN) Unusual Activity / Enhanced Unusual External Data Transfer
  • Anomalous File / Internal / Additional Extension Appended to SMB File
  • (PTN) Compromise / Ransomware / Suspicious SMB Activity

List of IOCs 

Reference List 

[1] https://www.designnews.com/industrial-machinery/new-age-hackers-are-ditching-smash-and-grab-techniques 

[2] https://cybernews.com/cyber-war/how-do-smash-and-grab-cyberattacks-help-ukraine-in-waging-war/

[3] https://blog.google/threat-analysis-group/phishing-campaign-targets-youtube-creators-cookie-theft-malware/

[4] https://www.ukcybersecuritycouncil.org.uk/news-insights/articles/the-benefits-of-automation-to-cyber-security/

[5] https://techcrunch.com/2021/11/03/blackmatter-ransomware-shut-down/ 

[6] https://www.trellix.com/en-us/about/newsroom/stories/research/blackmatter-ransomware-analysis-the-dark-side-returns.html

[7] https://www.nytimes.com/2021/05/14/business/darkside-pipeline-hack.html

[8] https://techcrunch.com/2022/01/14/fsb-revil-ransomware/ 

[9] https://www.virustotal.com/gui/domain/georgiaonsale.com/community

[10] https://www.cisa.gov/uscert/ncas/alerts/aa21-291a

Credit to: Andras Balogh, SOC Analyst and Gabriel Few-Wiegratz, Threat Intelligence Content Production Lead

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.
Written by
The Darktrace Analyst Team

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May 8, 2025

Anomaly-based threat hunting: Darktrace's approach in action

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What is threat hunting?

Threat hunting in cybersecurity involves proactively and iteratively searching through networks and datasets to detect threats that evade existing automated security solutions. It is an important component of a strong cybersecurity posture.

There are several frameworks that Darktrace analysts use to guide how threat hunting is carried out, some of which are:

  • MITRE Attack
  • Tactics, Techniques, Procedures (TTPs)
  • Diamond Model for Intrusion Analysis
  • Adversary, Infrastructure, Victims, Capabilities
  • Threat Hunt Model – Six Steps
  • Purpose, Scope, Equip, Plan, Execute, Feedback
  • Pyramid of Pain

These frameworks are important in baselining how to run a threat hunt. There are also a combination of different methods that allow defenders diversity– regardless of whether it is a proactive or reactive threat hunt. Some of these are:

  • Hypothesis-based threat hunting
  • Analytics-driven threat hunting
  • Automated/machine learning hunting
  • Indicator of Compromise (IoC) hunting
  • Victim-based threat hunting

Threat hunting with Darktrace

At its core, Darktrace relies on anomaly-based detection methods. It combines various machine learning types that allows it to characterize what constitutes ‘normal’, based on the analysis of many different measures of a device or actor’s behavior. Those types of learning are then curated into what are called models.

Darktrace models leverage anomaly detection and integrate outputs from Darktrace Deep Packet Inspection, telemetry inputs, and additional modules, creating tailored activity detection.

This dynamic understanding allows Darktrace to identify, with a high degree of precision, events or behaviors that are both anomalous and unlikely to be benign.  On top of machine learning models for detection, there is also the ability to change and create models showcasing the tool’s diversity. The Model Editor allows security teams to specify values, priorities, thresholds, and actions they want to detect. That means a team can create custom detection models based on specific use cases or business requirements. Teams can also increase the priority of existing detections based on their own risk assessments to their environment.

This level of dexterity is particularly useful when conducting a threat hunt. As described above, and in previous ‘Inside the SOC’ blogs such a threat hunt can be on a specific threat actor, specific sector, or a  hypothesis-based threat hunt combined with ‘experimenting’ with some of Darktrace’s models.

Conducting a threat hunt in the energy sector with experimental models

In Darktrace’s recent Threat Research report “AI & Cybersecurity: The state of cyber in UK and US energy sectors” Darktrace’s Threat Research team crafted hypothesis-driven threat hunts, building experimental models and investigating existing models to test them and detect malicious activity across Darktrace customers in the energy sector.

For one of the hunts, which hypothesised utilization of PerfectData software and multi-factor authentication (MFA) bypass to compromise user accounts and destruct data, an experimental model was created to detect a Software-as-a-Service (SaaS) user performing activity relating to 'PerfectData Software’, known to allow a threat actor to exfiltrate whole mailboxes as a PST file. Experimental model alerts caused by this anomalous activity were analyzed, in conjunction with existing SaaS and email-related models that would indicate a multi-stage attack in line with the hypothesis.

Whilst hunting, Darktrace researchers found multiple model alerts for this experimental model associated with PerfectData software usage, within energy sector customers, including an oil and gas investment company, as well as other sectors. Upon further investigation, it was also found that in June 2024, a malicious actor had targeted a renewable energy infrastructure provider via a PerfectData Software attack and demonstrated intent to conduct an Operational Technology (OT) attack.

The actor logged into Azure AD from a rare US IP address. They then granted Consent to ‘eM Client’ from the same IP. Shortly after, the actor granted ‘AddServicePrincipal’ via Azure to PerfectData Software. Two days later, the actor created a  new email rule from a London IP to move emails to an RSS Feed Folder, stop processing rules, and mark emails as read. They then accessed mail items in the “\Sent” folder from a malicious IP belonging to anonymization network,  Private Internet Access Virtual Private Network (PIA VPN) [1]. The actor then conducted mass email deletions, deleting multiple instances of emails with subject “[Name] shared "[Company Name] Proposal" With You” from the  “\Sent folder”. The emails’ subject suggests the email likely contains a link to file storage for phishing purposes. The mass deletion likely represented an attempt to obfuscate a potential outbound phishing email campaign.

The Darktrace Model Alert that triggered for the mass deletes of the likely phishing email containing a file storage link.
Figure 1: The Darktrace Model Alert that triggered for the mass deletes of the likely phishing email containing a file storage link.

A month later, the same user was observed downloading mass mLog CSV files related to proprietary and Operational Technology information. In September, three months after the initial attack, another mass download of operational files occurred by this actor, pertaining to operating instructions and measurements, The observed patience and specific file downloads seemingly demonstrated an intent to conduct or research possible OT attack vectors. An attack on OT could have significant impacts including operational downtime, reputational damage, and harm to everyday operations. Darktrace alerted the impacted customer once findings were verified, and subsequent actions were taken by the internal security team to prevent further malicious activity.

Conclusion

Harnessing the power of different tools in a security stack is a key element to cyber defense. The above hypothesis-based threat hunt and custom demonstrated intent to conduct an experimental model creation demonstrates different threat hunting approaches, how Darktrace’s approach can be operationalized, and that proactive threat hunting can be a valuable complement to traditional security controls and is essential for organizations facing increasingly complex threat landscapes.

Credit to Nathaniel Jones (VP, Security & AI Strategy, Field CISO at Darktrace) and Zoe Tilsiter (EMEA Consultancy Lead)

References

  1. https://spur.us/context/191.96.106.219

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About the author
Nathaniel Jones
VP, Security & AI Strategy, Field CISO

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May 6, 2025

Combatting the Top Three Sources of Risk in the Cloud

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With cloud computing, organizations are storing data like intellectual property, trade secrets, Personally Identifiable Information (PII), proprietary code and statistics, and other sensitive information in the cloud. If this data were to be accessed by malicious actors, it could incur financial loss, reputational damage, legal liabilities, and business disruption.

Last year data breaches in solely public cloud deployments were the most expensive type of data breach, with an average of $5.17 million USD, a 13.1% increase from the year before.

So, as cloud usage continues to grow, the teams in charge of protecting these deployments must understand the associated cybersecurity risks.

What are cloud risks?

Cloud threats come in many forms, with one of the key types consisting of cloud risks. These arise from challenges in implementing and maintaining cloud infrastructure, which can expose the organization to potential damage, loss, and attacks.

There are three major types of cloud risks:

1. Misconfigurations

As organizations struggle with complex cloud environments, misconfiguration is one of the leading causes of cloud security incidents. These risks occur when cloud settings leave gaps between cloud security solutions and expose data and services to unauthorized access. If discovered by a threat actor, a misconfiguration can be exploited to allow infiltration, lateral movement, escalation, and damage.

With the scale and dynamism of cloud infrastructure and the complexity of hybrid and multi-cloud deployments, security teams face a major challenge in exerting the required visibility and control to identify misconfigurations before they are exploited.

Common causes of misconfiguration come from skill shortages, outdated practices, and manual workflows. For example, potential misconfigurations can occur around firewall zones, isolated file systems, and mount systems, which all require specialized skill to set up and diligent monitoring to maintain

2. Identity and Access Management (IAM) failures

IAM has only increased in importance with the rise of cloud computing and remote working. It allows security teams to control which users can and cannot access sensitive data, applications, and other resources.

Cybersecurity professionals ranked IAM skills as the second most important security skill to have, just behind general cloud and application security.

There are four parts to IAM: authentication, authorization, administration, and auditing and reporting. Within these, there are a lot of subcomponents as well, including but not limited to Single Sign-On (SSO), Two-Factor Authentication (2FA), Multi-Factor Authentication (MFA), and Role-Based Access Control (RBAC).

Security teams are faced with the challenge of allowing enough access for employees, contractors, vendors, and partners to complete their jobs while restricting enough to maintain security. They may struggle to track what users are doing across the cloud, apps, and on-premises servers.

When IAM is misconfigured, it increases the attack surface and can leave accounts with access to resources they do not need to perform their intended roles. This type of risk creates the possibility for threat actors or compromised accounts to gain access to sensitive company data and escalate privileges in cloud environments. It can also allow malicious insiders and users who accidentally violate data protection regulations to cause greater damage.

3. Cross-domain threats

The complexity of hybrid and cloud environments can be exploited by attacks that cross multiple domains, such as traditional network environments, identity systems, SaaS platforms, and cloud environments. These attacks are difficult to detect and mitigate, especially when a security posture is siloed or fragmented.  

Some attack types inherently involve multiple domains, like lateral movement and supply chain attacks, which target both on-premises and cloud networks.  

Challenges in securing against cross-domain threats often come from a lack of unified visibility. If a security team does not have unified visibility across the organization’s domains, gaps between various infrastructures and the teams that manage them can leave organizations vulnerable.

Adopting AI cybersecurity tools to reduce cloud risk

For security teams to defend against misconfigurations, IAM failures, and insecure APIs, they require a combination of enhanced visibility into cloud assets and architectures, better automation, and more advanced analytics. These capabilities can be achieved with AI-powered cybersecurity tools.

Such tools use AI and automation to help teams maintain a clear view of all their assets and activities and consistently enforce security policies.

Darktrace / CLOUD is a Cloud Detection and Response (CDR) solution that makes cloud security accessible to all security teams and SOCs by using AI to identify and correct misconfigurations and other cloud risks in public, hybrid, and multi-cloud environments.

It provides real-time, dynamic architectural modeling, which gives SecOps and DevOps teams a unified view of cloud infrastructures to enhance collaboration and reveal possible misconfigurations and other cloud risks. It continuously evaluates architecture changes and monitors real-time activity, providing audit-ready traceability and proactive risk management.

Real-time visibility into cloud assets and architectures built from network, configuration, and identity and access roles. In this unified view, Darktrace / CLOUD reveals possible misconfigurations and risk paths.
Figure 1: Real-time visibility into cloud assets and architectures built from network, configuration, and identity and access roles. In this unified view, Darktrace / CLOUD reveals possible misconfigurations and risk paths.

Darktrace / CLOUD also offers attack path modeling for the cloud. It can identify exposed assets and highlight internal attack paths to get a dynamic view of the riskiest paths across cloud environments, network environments, and between – enabling security teams to prioritize based on unique business risk and address gaps to prevent future attacks.  

Darktrace’s Self-Learning AI ensures continuous cloud resilience, helping teams move from reactive to proactive defense.

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About the author
Pallavi Singh
Product Marketing Manager, OT Security & Compliance
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