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Attacks are Getting Personal, but Cyber Security is Not

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13
Dec 2023
13
Dec 2023
Generative AI and other open-source tools are allowing threat actors to launch targeted 'one-on-one' attacks at scale. Security tools that apply AI in the wrong way won't see new and targeted attacks coming - but Self-Learning AI that trains itself on your data can. This blog compares cyber security AI approaches and methods.

Cyber-attacks are getting personal. The usual opportunistic “spray and pray” attacks that reach many would-be targets at once are still present, but as cyber defence has advanced, today’s more sophisticated campaigns take precise aim at a particular company.

Threat actors willingly put in extra time and effort to realize a bigger payday at the end of it, but developments in the tools they have at their disposal are also making targeted, personal attacks easier.

CAPTCHA-breaking AI techniques like computer vision and convolutional neural networks can be used to gather information on an organization’s attack surface, and Generative AI is able to perform OSINT collection on a specific target, or targets, within an organization. Once inside, attackers can further leverage AI to automatically tweak attacks and create novel, highly targeted threats that elude defenses.

A new white paper, The CISO’s Guide to Cyber AI, explains how CISOs and their teams can make smarter use of defensive AI and machine learning (ML) to protect today’s digital environments from these and more advanced novel threats.

Today’s threats don’t necessarily resemble past attacks  

Darktrace analytics pointed to a sharp rise in novel cyber-attacks earlier this year. Generative AI and large language model (LLM) tools continue to lower the barrier to entry for threat actors, making it easier than ever to build smarter, faster, more targeted attacks.

But while attacks are getting personal, security tools that apply AI in the wrong way won’t see these attacks coming.

Here’s why: most cyber security tools and platforms rely on a combination of supervised machine learning, deep learning, and transformers to train and inform their systems. This entails shipping your company’s data out to a large data lake housed somewhere in the cloud where it gets blended with attack data from thousands of other organizations. The resulting homogenized data set gets used to train AI systems — yours and everyone else’s — to recognize patterns of attack based on previously encountered threats.

At its conception, this was a reasonably smart way of approaching cyber security. For a long time, the assumption that today’s threats will resemble yesterday’s attacks was a valid one. But in an age where the commoditization of cyber-crime has lowered the bar-to-entry for attackers, and where Generative AI and other open-source tools are enabling personalized attacks at scale, this is no longer the case.

Darktrace has seen evidence this year of a marked rise in more sophisticated attack techniques. Between May and July this year, our Cyber AI Research Centre observed that multistage payload attacks, in which a malicious email encourages the recipient to follow a series of steps before delivering a payload or attempting to harvest sensitive information, have increased by an average of 59% across Darktrace customers. Some of this will be QR code phishing, the latest trend in attack tactics, others will include automation. The speed of these types of attacks will likely rise as greater automation and AI are adopted and applied by attackers.

This ‘historical’ approach is not able to identify threats that haven’t been seen before: attacks that use new malware, novel social engineering, and those that are targeted to your organization. There are no indicators of compromise (IoCs) to teach your system to recognize these kinds of attacks.

IoC-based defenses won’t necessarily spot strange and unusual activity by an authorized user, device, or known IP address until threat actors tip their hand — and by then it’s too late. Looking for repeat patterns works well for detecting threats that resemble past attacks, but this increasingly won’t be the case. The only way to spot unique and novel threats is to build cyber security that’s tailored to you, and that requires a whole new approach.

Smarter use of AI levels the playing field

Security teams and adversaries continue to innovate to gain the upper-hand, and the advantage of time.

Since AI equips even novice cyber criminals to mount sophisticated attacks, AI must evolve to do three things:

  • Understand and continue to learn what “normal” looks like for your unique digital environment
  • Detect and alert on any anomalous behavior the instant it occurs
  • Initiate a targeted response to contain threats and give your analysts more time, without disrupting the flow of business

Darktrace uses Self-Learning AI to understand what constitutes ‘normal’ for everyone and everything in your business, including cloud resources, identities, email accounts, endpoint devices, and even OT controllers. As the name suggests, Self-Learning AI trains itself, developing and maintaining deep understanding of ‘patterns of life’ for your business environment. Used in combination with other AI methods such as LLMs, generative AI, and supervised ML, Self-Learning AI identifies novel cyber-threats most static (backward-looking) tools miss.

The technology learns ‘on the job’ and from scratch, without relying on historical data or a massive upfront effort by your team to train the system. Probabilistic mathematics revise assumptions about behavior on a constant basis so the system keeps itself up-to-date without repeat efforts by your team.

The result is that areas of risk, as well as real-time emerging attacks, are brought to the surface – regardless of whether those attacks have been seen before in the wild.

Surgical attacks warrant surgical response

Supervised ML continues to serve a purpose, but the dawning age of novel and AI-led attacks favors a more proactive approach to securing the cloud. Tools must take greater responsibility for their own education and greater initiative via autonomous response.

What some solutions call response ultimately amounts to sending alerts and opening tickets that create more needless work for analysts. Other tools claim to automate response, but either take very limited actions like automating the process of ticket creation, or overly ambitious steps like quarantining entire systems.

Darktrace’s dynamic understanding of your environment enables a truly autonomous and precise cloud-native response. Its understanding of ‘normal’ for every user and device allows it to enforce ‘normal’ – cutting out only the malicious activity, while allowing normal business to continue functioning.

How this response will take place will depend on where Darktrace is deployed in your environment. In the network, it might mean blocking specific, anomalous connections over a certain port. In the cloud, it could mean detaching EC2 instances and applying security groups to contain only assets at risk. In email, this could be locking links or flattening attachments.

Get personal with ‘One on One’ Security

The widespread accessibility of generative AI has altered the threat landscape permanently, allowing cyber-criminals to deploy unique and personalized attacks at scale and at machine speed. In the near future, we can expect to see more novel and sophisticated phishing attacks, new automated creation of malicious code, sustained attack campaigns targeting an individual or company, and even deep fakes designed to elicit human trust.

To meet the needs of today and tomorrow, cyber security needs to leverage AI deeply and intelligently – not just using it to automate outdated historical approaches, or bolting generative AI onto existing products to keep up with the latest trend. Since 2013 Darktrace has been using AI in a fundamentally unique way: a system that learns your unique organization and understands what’s normal at a granular level. Only with this personalized understanding can you be confident in your ability as an organization to identify and shut down novel threats on the first encounter.

This form of personalized, ‘One on One’ security is a no longer a ‘nice to have’ for defenders. ‘Spray and pray’ tactics will continue to exist, but the attacks most likely to slip through the net and cause you damage are the sophisticated, the personal, and the never-before-seen. That’s what Self-Learning AI was built for – learning your business to deliver personalized cyber security, meeting every attack one-on-one.

The CISO’s Guide to Cyber AI overviews the differences between common AI approaches in cyber security and offers a high-level checklist for choosing the ideal solution for stopping attacks — including new novel threats.  To learn more about making the smartest use of AI to stop novel and targeted cloud attacks, download the guide today.

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Os analistas cibernéticos da Darktrace são especialistas de classe mundial em inteligência de ameaças, caça de ameaças e resposta a incidentes, e fornecem suporte 24/7 SOC a milhares de Darktrace clientes em todo o mundo. Dentro do SOC é de autoria exclusiva desses especialistas, fornecendo análises de incidentes cibernéticos e tendências de ameaças, com base na experiência do mundo real na área.
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Email

Beyond DMARC: Navigating the Gaps in Email Security

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29
Feb 2024

Email threat landscape  

Email has consistently ranked among the most targeted attack vectors, given its ubiquity and criticality to business operations. From September to December 2023, 10.4 million phishing emails were detected across Darktrace’s customer fleet demonstrating the frequency of attempted email-based attacks.

Businesses are searching for ways to harden their email security posture alongside email providers who are aiming to reduce malicious emails traversing their infrastructure, affecting their clients. Domain-based Message Authentication (DMARC) is a useful industry-wide protocol organizations can leverage to move towards these goals.  

What is DMARC?

DMARC is an email authentication protocol designed to enhance the security of email communication.

Major email service providers Google and Yahoo recently made the protocol mandatory for bulk senders in an effort to make inboxes safer worldwide. The new requirements demonstrate an increasing need for a standardized solution as misconfigured or nonexistent authentication systems continue to allow threat actors to evade detection and leverage the legitimate reputation of third parties.  

DMARC is a powerful tool that allows email administrators to confidently identify and stop certain spoofed emails; however, more organizations must implement the standard for it to reach its full potential. The success and effectiveness of DMARC is dependent on broad adoption of the standard – by organizations of all sizes.  

How does DMARC work?

DMARC builds on two key authentication technologies, Sender Policy Framework (SPF) and DomainKeys Identified Mail (DKIM) and helps to significantly improve their ability to prevent domain spoofing. SPF verifies that a sender’s IP address is authorized to send emails on behalf of a particular domain and DKIM ensures integrity of email content by providing a verifiable digital signature.  

DMARC adds to this by allowing domain owners to publish policies that set expectations for how SPF and DKIM verification checks relate to email addresses presented to users and whose authenticity the receiving mail server is looking to establish.  

These policies work in tandem to help authenticate email senders by verifying the emails are from the domain they say they are, working to prevent domain spoofing attacks. Key benefits of DMARC include:

  1. Phishing protection DMARC protects against direct domain spoofing in which a threat actor impersonates a legitimate domain, a common phishing technique threat actors use to trick employees to obtain sensitive information such as privileged credentials, bank information, etc.  
  2. Improving brand reputation: As DMARC helps to prevent impersonation of domains, it stands to maintain and increase an organization’s brand reputation. Additionally, as organizational reputation improves, so will the deliverability of emails.
  3. Increased visibility: DMARC provides enhanced visibility into email communication channels, including reports of all emails sent on behalf of your domain. This allows security teams to identify shadow-IT and any unauthorized parties using their domain.

Understanding DMARC’s Limitations

DMARC is often positioned as a way for organizations to ‘solve’ their email security problems, however, 65% of the phishing emails observed by Darktrace successfully passed DMARC verification, indicating that a significant number of threat actors are capable of manipulating email security and authentication systems in their exploits. While DMARC is a valuable tool in the fight against email-based attacks, the evolving threat landscape demands a closer look at its limitations.  

As threat actors continue to innovate, improving their stealth and evasion tactics, the number of attacks with valid DMARC authentication will only continue to increase in volume and sophistication. These can include:

  1. Phishing attacks that leverage non-spoofed domains: DMARC allows an organization to protect the domains that they own, preventing threat actors from being able to send phishing emails from their domains. However, threat actors will often create and use ‘look-a-like’ domains that closely resemble an organization’s domain to dupe users. 3% of the phishing emails identified by Darktrace utilized newly created domains, demonstrating shifting tactics.  
  2. Email Account Takeovers: If a threat actor gains access to a user’s email account through other social engineering means such as credential stuffing, they can then send phishing emails from the legitimate domain to pursue further attacks. Even though these emails are malicious, DMARC would not identify them as such because they are coming from an authorized domain or sender.  

Organizations must also ensure their inbound analysis of emails is not skewed by successful DMARC authentication. Security teams cannot inherently trust emails that pass DMARC, because the source cannot always be legitimized, like in the event of an account takeover. If a threat actor gains access to an authenticated email account, emails sent by the threat actor from that account will pass DMARC – however the contents of that email may be malicious. Sender behavior must be continuously evaluated and vetted in real time as past communication history and validated DMARC cannot be solely relied upon amid an ever-changing threat landscape.  

Security teams should lean on other security measures, such as anomaly detection tools that can identify suspicious emails without relying on historical attack rules and static data. While DMARC is not a silver bullet for email security, it is nevertheless foundational in helping organizations protect their brand identity and must be viewed as an essential layer in an organization's overall cyber security strategy.  

Implementing DMARC

Despite the criticality of DMARC for preserving brand reputation and trust, adoption of the standard has been inconsistent. DMARC can be complex to implement with many organizations lacking the time required to understand and successfully implement the standard. Because of this, DMARC set-up is often outsourced, giving security and infrastructure teams little to no visibility into or control of the process.  

Implementation of DMARC is only the start of this process, as DMARC reports must be consistently monitored to ensure organizations have visibility into who is sending mail from their domain, the volume of mail being sent and whether the mail is passing authentication protocols. This process can be time consuming for security teams who are already faced with mounting responsibilities, tight budgets, and personnel shortages. These complexities unfortunately delay organizations from using DMARC – especially as many today still view it as a ‘nice to have’ rather than an essential.  

With the potential complexities of the DMARC implementation process, there are many ways security and infrastructure teams can still successfully roll out the standard. Initial implementation should start with monitoring, policy adjustment and then enforcement. As business changes over time, DMARC should be reviewed regularly to ensure ongoing protection and maintain domain reputation.

The Future of Email Security

As email-based attacks continue to rise, the industry must recognize the importance of driving adoption of foundational email authentication protocols. To do this, a new and innovative approach to DMARC is needed. DMARC products must evolve to better support organizations throughout the ongoing DMARC monitoring process, rather than just initial implementation. These products must also be able to share intelligence across an organization’s security stack, extending beyond email security tools. Integration across these products and tools will help organizations optimize their posture, ensuring deep understanding of their domain and increased visibility across the entire enterprise.

DMARC is critical in protecting brand identity and mitigating exact-domain based attacks. However, organizations must understand DMARC’s unique benefits and limitations to ensure their inboxes are fully protected. In today’s evolving threat landscape, organizations require a robust, multi-layered approach to stop email threats – in inbound mail and beyond. Email threats have evolved – its time security does too.

Join Darktrace on 9 April for a virtual event to explore the latest innovations needed to get ahead of the rapidly evolving threat landscape. Register today to hear more about our latest innovations coming to Darktrace’s offerings. For additional insights check out Darktrace’s 2023 End of Year Threat Report.

Credit to Carlos Gray and Stephen Pickman for their contribution to this blog

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Carlos Gray
Product Manager

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Dentro do SOC

No Bad Luck for Darktrace: Combatting ALPHV BlackCat Ransomware

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29
Feb 2024

As-a-Service malware trending

Throughout the course of 2023, “as-a-Service” strains of malware remained the most consistently observed threat type to affect Darktrace customers, mirroring their overall prominence across the cyber threat landscape. With this trend expected to continue throughout 2024, organizations and their security teams should be prepared to defend their network against increasingly versatile and tailorable malware-as-a-service (MaaS) and ransomware-as-a-service (RaaS) strains [1].

What is ALPHV ransomware?

The ALPHV ransomware, also known as ‘BlackCat’ or ‘Noberus’, is one example of a RaaS strain that has been prominent across the threat landscape over the last few years.

ALPHV is a ransomware strain coded in the Rust programming language. The ransomware is sold as part of the RaaS economy [2], with samples of the ransomware being provided and sold by a criminal group (the RaaS ‘operator’) to other cybercriminals (the RaaS ‘affiliates’) who then gain entry to organizations' networks with the intention of detonating the ransomware and demanding ransom payments.

ALPHV was likely first used in the wild back in November 2021 [3]. Since then, it has become one of the most prolific ransomware strains, with the Federal Bureau of Investigation (FBI) reporting nearly USD 300 million in ALPHV ransom payments as of September 2023 [4].

In December 2023, the FBI and the US Department of Justice announced a successful disruption campaign against the ALPHV group, which included a takedown of the their data leak site, and the release of a decryption tool for the ransomware strain [5], and in February 2024, the US Department of State announced  a reward of up to USD 10 million for information leading to the identification or location of anyone occupying a key leadership position in the group operating the ALPHV ransomware strain [6].

The disruption campaign against the ransomware group appeared to have been successful, as evidenced by the recent, significant decline in ALPHV attacks, however, it would not be surprising for the group to simply return with new branding, in a similar vein to its apparent predecessors, DarkSide and BlackMatter [7].

How does ALPHV ransomware work?

ALPHV affiliates have been known to employ a variety of methods to progress towards their objective of detonating ALPHV ransomware [4]. In the latter half of 2023, ALPHV affiliates were observed using malicious advertising (i.e, malvertising) to deliver a Python-based backdoor-dropper known as 'Nitrogen' to users' devices [8][12]. These malvertising operations consisted in affiliates setting up malicious search engine adverts for tools such as WinSCP and AnyDesk.

Users' interactions with these adverts led them to sites resembling legitimate software distribution sites. Users' attempts to download software from these spoofed sites resulted in the delivery of a backdoor-dropping malware sample dubbed 'Nitrogen' to their devices. Nitrogen has been observed dropping a variety of command-and-control (C2) implants onto users' devices, including Cobalt Strike Beacon and Sliver C2. ALPHV affiliates often used the backdoor access afforded to them by these C2 implants to conduct reconnaissance and move laterally, in preparation for detonating ALPHV ransomware payloads.

Darktrace Detection of ALPHV Ransomware

During October 2023, Darktrace observed several cases of ALPHV affiliates attempting to infiltrate organizations' networks via the use of malvertising to socially engineer users into downloading and installing Nitrogen from impersonation websites such as 'wireshhark[.]com' and wìnscp[.]net (i.e, xn--wnscp-tsa[.]net).

While the attackers managed to bypass traditional security measures and evade detection by using a device from the customer’s IT team to perform its malicious activity, Darktrace DETECT™ swiftly identified the subtle indicators of compromise (IoCs) in the first instance. This swift detection of ALPHV, along with Cyber AI Analyst™ autonomously investigating the wide array of post-compromise activity, provided the customer with full visibility over the attack enabling them to promptly initiate their remediation and recovery efforts.

Unfortunately, in this incident, Darktrace RESPOND™ was not fully deployed within their environment, hindering its ability to autonomously counter emerging threats. Had RESPOND been fully operational here, it would have effectively contained the attack in its early stages, avoiding the eventual detonation of the ALPHV ransomware.

Figure 1: Timeline of the ALPHV ransomware attack.

In mid-October, a member of the IT team at a US-based Darktrace customer attempted to install the network traffic analysis software, Wireshark, onto their desktop. Due to the customer’s configuration, Darktrace's visibility over this device was limited to its internal traffic, despite this it was still able to identify and alert for a string of suspicious activity conducted by the device.

Initially, Darktrace observed the device making type A DNS requests for 'wiki.wireshark[.]org' immediately before making type A DNS requests for the domain names 'www.googleadservices[.]com', 'allpcsoftware[.]com', and 'wireshhark[.]com' (note the two 'h's). This pattern of activity indicates that the device’s user was redirected to the website, wireshhark[.]com, as a result of the user's interaction with a sponsored Google Search result pointing to allpcsoftware[.]com.

At the time of analysis, navigating to wireshhark[.]com directly from the browser search bar led to a YouTube video of Rick Astley's song "Never Gonna Give You Up". This suggests that the website, wireshhark[.]com, had been configured to redirect users to this video unless they had arrived at the website via the relevant sponsored Google Search result [8].

Although it was not possible to confirm this with certainty, it is highly likely that users who visited the website via the appropriate sponsored Google Search result were led to a fake website (wireshhark[.]com) posing as the legitimate website, wireshark[.]com. It seems that the actors who set up this fake version of wireshark[.]com were inspired by the well-known bait-and-switch technique known as 'rickrolling', where users are presented with a desirable lure (typically a hyperlink of some kind) which unexpectedly leads them to a music video of Rick Astley's "Never Gonna Give You Up".

After being redirected to wireshhark[.]com, the user unintentionally installed a malware sample which dropped what appears to be Cobalt Strike onto their device. The presence of Cobalt Strike on the user's desktop was evidenced by the subsequent type A DNS requests which the device made for the domain name 'pse[.]ac'. These DNS requests were responded to with the likely Cobalt Strike C2 server address, 194.169.175[.]132. Given that Darktrace only had visibility over the device’s internal traffic, it did not observe any C2 connections to this Cobalt Strike endpoint. However, the desktop's subsequent behavior suggests that a malicious actor had gained 'hands-on-keyboard' control of the device via an established C2 channel.

Figure 2: Advanced Search data showing an customer device being tricked into visiting the fake website, wireshhark[.]com.

Since the malicious actor had gained control of an IT member's device, they were able to abuse the privileged account credentials to spread Python payloads across the network via SMB and the Windows Management Instrumentation (WMI) service. The actor was also seen distributing the Windows Sys-Internals tool, PsExec, likely in an attempt to facilitate their lateral movement efforts. It was normal for this IT member's desktop to distribute files across the network via SMB, which meant that this malicious SMB activity was not, at first glance, out of place.

Figure 3: Advanced Search data showing that it was normal for the IT member's device to distribute files over SMB.

However, Darktrace DETECT recognized that the significant spike in file writes being performed here was suspicious, even though, on the surface, it seemed ‘normal’ for the device. Furthermore, Darktrace identified that the executable files being distributed were attempting to masquerade as a different file type, potentially in an attempt to evade the detection of traditional security tools.

Figure 4: Event Log data showing several Model Breaches being created in response to the IT member's DEVICE's SMB writes of Python-based executables.

An addition to DETECT’s identification of this unusual activity, Darktrace’s Cyber AI Analyst launched an autonomous investigation into the ongoing compromise and was able to link the SMB writes and the sharing of the executable Python payloads, viewing the connections as one lateral movement incident rather than a string of isolated events. After completing its investigation, Cyber AI Analyst was able to provide a detailed summary of events on one pane of glass, ensuring the customer could identify the affected device and begin their remediation.

Figure 5: Cyber AI Analyst investigation summary highlighting the IT member's desktop’s lateral movement activities.

C2 Activity

The Python payloads distributed by the IT member’s device were likely related to the Nitrogen malware, as evidenced by the payloads’ names and by the network behaviours which they engendered.  

Figure 6: Advanced Search data showing the affected device reaching out to the C2 endpoint, pse[.]ac, and then distributing Python-based executable files to an internal domain controller.

The internal devices to which these Nitrogen payloads were distributed immediately went on to contact C2 infrastructure associated with Cobalt Strike. These C2 connections were made over SSL on ports 443 and 8443.  Darktrace identified the attacker moving laterally to an internal SQL server and an internal domain controller.

Figure 7: Advanced Search data showing an internal SQL server contacting the Cobalt Strike C2 endpoint, 194.180.48[.]169, after receiving Python payloads from the IT member’s device.
Figure 8: Event Log data showing several DETECT model breaches triggering in response to an internal SQL server’s C2 connections to 194.180.48[.]169.

Once more, Cyber AI Analyst launched its own investigation into this activity and was able to successfully identify a series of separate SSL connections, linking them together into one wider C2 incident.

Figure 9: Cyber AI Analyst investigation summary highlighting C2 connections from the SQL server.

Darktrace observed the attacker using their 'hands-on-keyboard' access to these systems to elevate their privileges, conduct network reconnaissance (primarily port scanning), spread Python payloads further across the network, exfiltrate data from the domain controller and transfer a payload from GitHub to the domain controller.

Figure 10: Cyber AI Analyst investigation summary an IP address scan carried out by an internal domain controller.
Figure 12: Event Log data showing an internal domain controller contacting GitHub around the time that it was in communication with the C2 endpoint, 194.180.48[.]169.
Figure 13: Event Log data showing a DETECT model breach being created in response to an internal domain controller's large data upload to the C2 endpoint, 194.180.48[.]169.

After conducting extensive reconnaissance and lateral movement activities, the attacker was observed detonating ransomware with the organization's VMware environment, resulting in the successful encryption of the customer’s VMware vCenter server and VMware virtual machines. In this case, the attacker took around 24 hours to progress from initial access to ransomware detonation.  

If the targeted organization had been signed up for Darktrace's Proactive Threat Notification (PTN) service, they would have been promptly notified of these suspicious activities by the Darktrace Security Operations Center (SOC) in the first instance, allowing them to quickly identify affected devices and quarantine them before the compromise could escalate.

Additionally, given the quantity of high-severe alerts that triggered in response to this attack, Darktrace RESPOND would, under normal circumstances, have inhibited the attacker's activities as soon as they were identified by DETECT. However, due to RESPOND not being configured to act on server devices within the customer’s network, the attacker was able to seamlessly move laterally through the organization's server environment and eventually detonate the ALPHV ransomware.

Nevertheless, Darktrace was able to successfully weave together multiple Cyber AI Analyst incidents which it generated into a thread representing the chain of behavior that made up this attack. The thread of Incident Events created by Cyber AI Analyst provided a substantial account of the attack and the steps involved in it, which significantly facilitated the customer’s post-incident investigation efforts.  

Figure 14: Darktrace's AI Analyst weaved together 33 of the Incident Events it created together into a thread representing the attacker’s chain of behavior.

Conclusão

It is expected for malicious cyber actors to revise and upgrade their methods to evade organizations’ improving security measures. The continued improvement of email security tools, for example, has likely created a need for attackers to develop new means of Initial Access, such as the use of Microsoft Teams-based malware delivery.

This fast-paced ALPHV ransomware attack serves as a further illustration of this trend, with the actor behind the attack using malvertising to convince an unsuspecting user to download the Python-based malware, Nitrogen, from a fake Wireshark site. Unbeknownst to the user, this stealthy malware dropped a C2 implant onto the user’s device, giving the malicious actor the ‘hands-on-keyboard’ access they needed to move laterally, conduct network reconnaissance, and ultimately detonate ALPHV ransomware.

Despite the non-traditional initial access methods used by this ransomware actor, Darktrace DETECT was still able to identify the unusual patterns of network traffic caused by the attacker’s post-compromise activities. The large volume of alerts created by Darktrace DETECT were autonomously investigated by Darktrace’s Cyber AI Analyst, which was able to weave together related activities of different devices into a comprehensive timeline of the attacker’s operation. Given the volume of DETECT alerts created in response to this ALPHV attack, it is expected that Darktrace RESPOND would have autonomously inhibited the attacker’s operation had the capability been appropriately configured.

As the first post-compromise activities Darktrace observed in this ALPHV attack were seemingly performed by a member of the customer’s IT team, it may have looked normal to a human or traditional signature and rules-based security tools. To Darktrace’s Self-Learning AI, however, the observed activities represented subtle deviations from the device’s normal pattern of life. This attack, and Darktrace’s detection of it, is therefore a prime illustration of the value that Self-Learning AI can bring to the task of detecting anomalies within organizations’ digital estates.

Credit to Sam Lister, Senior Cyber Analyst, Emma Foulger, Principal Cyber Analyst

Appendices

Darktrace DETECT Model Breaches

- Compliance / SMB Drive Write

- Compliance / High Priority Compliance Model Breach

- Anomalous File / Internal / Masqueraded Executable SMB Write

- Device / New or Uncommon WMI Activity

- Anomalous Connection / New or Uncommon Service Control

- Anomalous Connection / High Volume of New or Uncommon Service Control

- Device / New or Uncommon SMB Named Pipe

- Device / Multiple Lateral Movement Model Breaches

- Device / Large Number of Model Breaches  

- SMB Writes of Suspicious Files (Cyber AI Analyst)

- Suspicious Remote WMI Activity (Cyber AI Analyst)

- Suspicious DCE-RPC Activity (Cyber AI Analyst)

- Compromise / Connection to Suspicious SSL Server

- Compromise / High Volume of Connections with Beacon Score

- Anomalous Connection / Suspicious Self-Signed SSL

- Anomalous Connection / Anomalous SSL without SNI to New External

- Compromise / Suspicious TLS Beaconing To Rare External

- Compromise / Beacon to Young Endpoint

- Compromise / SSL or HTTP Beacon

- Compromise / Agent Beacon to New Endpoint

- Device / Long Agent Connection to New Endpoint

- Compromise / SSL Beaconing to Rare Destination

- Compromise / Large Number of Suspicious Successful Connections

- Compromise / Slow Beaconing Activity To External Rare

- Anomalous Server Activity / Outgoing from Server

- Device / Multiple C2 Model Breaches

- Possible SSL Command and Control (Cyber AI Analyst)

- Unusual Repeated Connections (Cyber AI Analyst)

- Device / ICMP Address Scan

- Device / RDP Scan

- Device / Network Scan

- Device / Suspicious Network Scan Activity

- Scanning of Multiple Devices (Cyber AI Analyst)

- ICMP Address Scan (Cyber AI Analyst)

- Device / Anomalous Github Download

- Unusual Activity / Unusual External Data Transfer

- Device / Initial Breach Chain Compromise

MITRE ATT&CK Mapping

Resource Development techniques:

- Acquire Infrastructure: Malvertising (T1583.008)

Initial Access techniques:

- Drive-by Compromise (T1189)

Execution techniques:

- User Execution: Malicious File (T1204.002)

- System Services: Service Execution (T1569.002)

- Windows Management Instrumentation (T1047)

Defence Evasion techniques:

- Masquerading: Match Legitimate Name or Location (T1036.005)

Discovery techniques:

- Remote System Discovery (T1018)

- Network Service Discovery (T1046)

Lateral Movement techniques:

- Remote Services: SMB/Windows Admin Shares

- Lateral Tool Transfer (T1570)

Command and Control techniques:

- Application Layer Protocol: Web Protocols (T1071.001)

- Encrypted Channel: Asymmetric Cryptography (T1573.002)

- Non-Standard Port (T1571)

- Ingress Tool Channel (T1105)

Exfiltration techniques:

- Exfiltration Over C2 Channel (T1041)

Impact techniques:

- Data Encrypted for Impact (T1486)

List of Indicators of Compromise

- allpcsoftware[.]com

- wireshhark[.]com

- pse[.]ac • 194.169.175[.]132

- 194.180.48[.]169

- 193.42.33[.]14

- 141.98.6[.]195

References  

[1] https://darktrace.com/threat-report-2023

[2] https://www.microsoft.com/en-us/security/blog/2022/05/09/ransomware-as-a-service-understanding-the-cybercrime-gig-economy-and-how-to-protect-yourself/

[3] https://www.bleepingcomputer.com/news/security/alphv-blackcat-this-years-most-sophisticated-ransomware/

[4] https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-353a

[5] https://www.justice.gov/opa/pr/justice-department-disrupts-prolific-alphvblackcat-ransomware-variant

[6] https://www.state.gov/u-s-department-of-state-announces-reward-offers-for-criminal-associates-of-the-alphv-blackcat-ransomware-variant/

[7] https://www.bleepingcomputer.com/news/security/blackcat-alphv-ransomware-linked-to-blackmatter-darkside-gangs/

[8] https://www.trendmicro.com/en_us/research/23/f/malvertising-used-as-entry-vector-for-blackcat-actors-also-lever.html

[9] https://news.sophos.com/en-us/2023/07/26/into-the-tank-with-nitrogen/

[10] https://www.esentire.com/blog/persistent-connection-established-nitrogen-campaign-leverages-dll-side-loading-technique-for-c2-communication

[11] https://www.esentire.com/blog/nitrogen-campaign-2-0-reloads-with-enhanced-capabilities-leading-to-alphv-blackcat-ransomware

[12] https://www.esentire.com/blog/the-notorious-alphv-blackcat-ransomware-gang-is-attacking-corporations-and-public-entities-using-google-ads-laced-with-malware-warns-esentire

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Sam Lister
SOC Analyst

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