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Kill Chain Insights: Detecting AutoIT Malware Compromise

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18
Oct 2022
18
Oct 2022
AutoIt can be exploited. Learn how Darktrace detected and stopped an AutoIt malware in the cyber kill chain. Enhance cyber security with Darktrace's expertise.

Introduction 

Good defence is like an onion, it has layers. Each part of a security implementation should have checks built in so that if one wall is breached, there are further contingencies. Security aficionados call this ‘defence in depth’, a military concept introduced to the cyber-sphere in 2009 [1]. Since then, it has remained a central tenet when designing secure systems, digital or otherwise [2]. Despite this, the attacker’s advantage is ever-present with continued development of malware and zero-day exploits. No matter how many layers a security platform has, how can organisations be expected to protect against a threat they do not know or even understand? 

Take the case of one Darktrace customer, a government-contracted manufacturing company located in the Americas. This company possesses a modern OT and IT network comprised of several thousand devices. They have dozens of servers, a few of which host Microsoft Exchange. Every week, these few mail servers receive hundreds of malicious payloads which will ultimately attempt to make their way into over a thousand different inboxes while dodging different security gateways. Had the RESPOND portion of Darktrace for Email been properly enabled, this is where the story would have ended. However, in June 2022 an employee made an instinctual decision that could have potentially cost the company its time, money, and reputation as a government contractor. Their crime: opening an unknown html file attached to a compelling phishing email. 

Following this misstep, a download was initiated which resulted in compromise of the system via vulnerable Microsoft admin tools from endpoints largely unknown to conventional OSINT sources. Using these tools, further malicious connectivity was accomplished before finally petering out. Fortunately, their existing Microsoft security gateway was up to date on the command and control (C2) domains observed in this breach and refused the connections.

Darktrace detected this activity at every turn, from the initial email to the download and subsequent attempted C2. Cyber AI Analyst stitched the events together for easy understanding and detected Indicators of Compromise (IOCs) that were not yet flagged in the greater intelligence community and, critically, did this all at machine speed. 

So how did the attacker evade action for so long? The answer is product misconfiguration - they did not refine their ‘layers’.  

Attack Details

On the night of June 8th an employee received a malicious email. Darktrace detected that this email contained a html attachment which itself contained links to endpoints 100% rare to the network. This email also originated from a never-before-seen sender. Although it would usually have been withheld based on these factors, the customer’s Darktrace/Email deployment was set to Advisory Mode meaning it continued through to the inbox. Late the next day, this user opened the attachment which then routed them to the 100% rare endpoint ‘xberxkiw[.]club’, a probable landing page for malware that did not register on OSINT available at the time.

Figure 1- Popular OSINT VirusTotal showing zero hits against the rare endpoint 

Only seconds after reaching the endpoint, Darktrace detected the Microsoft BITS user agent reaching out to another 100% rare endpoint ‘yrioer[.]mikigertxyss[.]com’, which generated a DETECT/Network model breach, ‘Unusual BITS Activity’. This was immediately suspicious since BITS is a deprecated and insecure windows admin tool which has been known to facilitate the movement of malicious payloads into and around a network. Upon successfully establishing a connection, the affected device began downloading a self-professed .zip file. However, Darktrace detected this file to be an extension-swapped .exe file. A PCAP of this activity can be seen below in Figure 2.

Figure 2- PCAP highlighting BITs service connections and false .zip (.exe) download

This activity also triggered a correlating breach of the ‘Masqueraded File Transfer’ model and pushed a high-fidelity alert to the Darktrace Proactive Threat Notification (PTN) service. This ensured both Darktrace and the customer’s SOC team were alerted to the anomalous activity.

At this stage the local SOC were likely beginning their triage. However further connections were being made to extend the compromise on the employee’s device and the network. The file they downloaded was later revealed to be ‘AutoIT3.exe’, a default filename given to any AutoIt script. AutoIt scripts do have legitimate use cases but are often associated with malicious activity for their ability to interact with the Windows GUI and bypass client protections. After opening, these scripts would launch on the host device and probe for other weaknesses. In this case, the script may have attempted to hunt passwords/default credentials, scan the local directory for common sensitive files, or scout local antivirus software on the device. It would then share any information gathered via established C2 channels.  

After the successful download of this mismatched MIME type, the device began attempting to further establish C2 to the endpoint ‘dirirxhitoq[.]kialsoyert[.]tk’. Even though OSINT still did not flag this endpoint, Darktrace detected this outreach as suspicious and initiated its first Cyber AI Analyst investigation into the beaconing activity. Following the sixth connection made to this endpoint on the 10th of June, the infected device breached C2 models, such as ‘Agent Beacon (Long Period)’ and ‘HTTP Beaconing to Rare Destination’. 

As the beaconing continued, it was clear that internal reconnaissance from AutoIt was not widely achieved, although similar IOCs could be detected on at least two other internal devices. This may represent other users opening the same malicious email, or successful lateral movement and infection propagation from the initial user/device. However comparatively, these devices did not experience the same level of infection as the first employee’s machine and never downloaded any malicious executables. AutoIt has a history of being used to deliver information stealers, which suggests a possible motivation had wider network compromise been successful [3].

Thankfully, after the 10th of June no further exploitation was observed. This was likely due to the combined awareness and action brought by the PTN alerting, static security gateways and action from the local security team. The company were protected thanks to defence in depth.  

Darktrace Coverage

Despite this, the role of Darktrace itself cannot be understated. Darktrace/Email was integral to the early detection process and provided insight into the vector and delivery methods used by this attacker. Post-compromise, Darktrace/Network also observed the full range of suspicious activity brought about by this incursion. In particular, the AI analyst feature played a major role in reducing the time for the SOC team to triage by detecting and flagging key information regarding some of the earliest IOCs.

Figure 3- Sample information pulled by AI analyst about one of the involved endpoints

Alongside the early detection, there were several instances where RESPOND/Network would have intervened however autonomous actions were limited to a small test group and not enabled widely throughout the customer’s deployment. As such, this activity continued unimpeded- a weak layer. Figure 4 highlights the first Darktrace RESPOND action which would have been taken.

Figure 4- Upon detecting the download of a mismatched mime from a rare endpoint, Darktrace RESPOND would have blocked all connections to the rare endpoint on the relevant port in a targeted manner

This Darktrace RESPOND action provides a precise and limited response by blocking the anomalous file download. However, after continued anomalous activity, RESPOND would have strengthened its posture and enforced stronger curbs across the wider anomalous activity. This stronger enforcement is a measure designed to relegate a device to its established norm. The breach which would generate this response can be seen below:

Figure 5- After a prolonged period of anomalous activity, Darktrace RESPOND would have stepped in to enforce the typical pattern of life observed on this device

Although Darktrace RESPOND was not fully enabled, this company had an extra layer of security in the PTN service, which alerted them just minutes after the initial file download was detected, alongside details relevant to the investigation. This ensured both Darktrace analysts and their own could review the activity and begin to isolate and remediate the threat. 

Concluding Insights

Thankfully, with multiple layers in their security, the customer managed to escape this incident largely unscathed. Quick and comprehensive email and network detection, customer alerting and local gateway blocking C2 connections ensured that the infection did not have leeway to propagate laterally throughout the network. However, even though this infection did not lead to catastrophe, the fact that it happened in the first place should be a learning point. 

Had RESPOND/Email been properly configured, this threat would have been stopped before reaching its intended recipients, removing the need to rely on end-users as a security measure. Furthermore, had RESPOND/Network been utilized beyond a limited test group, this activity would have been blocked at every other step of the network-level kill chain. From the anomalous MIME download to the establishment of C2, Darktrace RESPOND would have been able to effectively isolate and quarantine this activity to the host device, without any reliance on slow-to-update OSINT sources. RESPOND allows for the automation of time-sensitive security decisions and adds a powerful layer of defence that conventional security solutions cannot provide. Although it can be difficult to relinquish human ownership of these decisions, doing so is necessary to prevent unknown attackers from infiltrating using unknown vectors to achieve unknown ends.  

In conclusion, this incident demonstrates an effective case study around detecting a threat with novel IOCs. However, it is also a reminder that a company’s security makeup can always be improved. Overall, when building security layers in a company’s ‘onion’, it is great to have the best tools, but it is even greater to use them in the best way. Only with continued refining can organisations guarantee defence in depth. 

Thanks to Connor Mooney and Stefan Rowe for their contributions.

Appendices

Darktrace Model Detections

·      Anomalous File / EXE from Rare External Location 

·      Compromise / Agent Beacon (Long Period) 

·      Compromise / HTTP Beaconing to Rare Destination 

·      Device / Large Number of Model Breaches 

·      Device / Suspicious Domain 

·      Device / Unusual BITS Activity 

·      Enhanced Monitoring: Anomalous File / Masqueraded File Transfer 

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.
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Joel Davidson
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Elevating Network Security: Confronting Trust, Ransomware, & Novel Attacks

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21
Jun 2024

Understanding the Network Security Market

Old tools blind to new threats

With the rise of GenAI and novel attacks, organizations can no longer rely solely on traditional network security solutions that depend on historical attack data, such as signatures and detection rules, to identify threats. However, in many cases network security vendors and traditional solutions like IDS/IPS focus on detecting known attacks using historical data. What happens is organizations are left vulnerable to unknown and novel threats, as these approaches only detect known malicious behavior and cannot keep up with unknown threats or zero-day attacks.

Advanced threats

Darktrace's End of Year Threat Report for 2023 highlights significant changes in the cyber threat landscape, particularly due to advancements in technology such as generative AI. The report notes a substantial increase in sophisticated attacks, including those utilizing generative AI, which have made it more challenging for traditional security measures to keep up. The report also details the rise of multi-functional malware, like Black Basta ransomware, which not only encrypts data for ransom but also spreads other types of malware such as the Qbot banking trojan. These complex attacks are increasingly being deployed by advanced cybercriminal groups, underscoring the need for organizations to adopt advanced security measures that can detect and respond to novel threats in real-time.

Defenders need a solution that can level the playing field, especially when they are operating with limited resources and getting overloaded with endless alerts. Most network security tools on the market have a siloed approach and do not integrate with the rest of an organization’s digital estate, but attackers don’t operate in a single domain.

Disparate workforce

With so many organizations continuing to support a remote or hybrid working environment, the need to secure devices that are outside the corporate network or off-VPN is increasingly important. While endpoint protection or endpoint detection and response (EDR) tools are a fundamental part of any security stack, it’s not possible to install an agent on every device, which can leave blind spots in an organization’s attack surface. Managing trust and access policies is also necessary to protect identities, however this comes with its own set of challenges in terms of implementation and minimizing business disruption.

This blog will dive into these challenges and show examples of how Darktrace has helped mitigate risk and stop novel and never-before-seen threats.

Network Security Challenge 1: Managing trust

What is trust in cybersecurity?

Trust in cybersecurity means that an entity can be relied upon. This can involve a person, organization, or system to be authorized or authenticated by proving their identity is legitimate and can be trusted to have access to the network or sensitive information.

Why is trust important in cybersecurity?

Granting access and privileges to your workforce and select affiliates has profound implications for cybersecurity, brand reputation, regulatory compliance, and financial liability. In a traditional network security model, traffic gets divided into two categories — trusted and untrusted — with some entities and segments of the network deemed more creditable than others.

How do you manage trust in cybersecurity?

Zero trust is too little, but any is too much.

Modern network security challenges point to an urgent need for organizations to review and update their approaches to managing trust. External pressure to adopt zero trust security postures literally suggests trusting no one, but that impedes your freedom
to do business. IT leaders need a proven but practical process for deciding who should be allowed to use your network and how.

Questions to ask in updating Trusted User policies include:

  • What process should you follow to place trust in third
    parties and applications?
  • Do you subject trusted entities to testing and other due
    diligence first?
  • How often do you review this process — and trusted
    relationships themselves — after making initial decisions?
  • How do you tell when trusted users should no longer be
    trusted?

Once trust has been established, security teams need new and better ways to autonomously verify that those transacting within your network are indeed those trusted users that they claim to be, taking only the authorized actions you’ve allowed them to take.

Exploiting trust in the network

Insider threats have a major head start. The opposite of attacks launched by nameless, faceless strangers, insider threats originate through parties once deemed trustworthy. That might mean a current or former member of your workforce or a partner, vendor, investor, or service provider authorized by IT to access corporate systems and data. Threats also arise when a “pawn” gets unwittingly tricked into disclosing credentials or downloading malware.

Common motives for insider attacks include revenge, stealing or leaking sensitive data, taking down IT systems, stealing assets or IP, compromising your organization’s credibility, and simply harassing your workforce. Put simply, rules and signatures based security solutions won’t flag insider threats because an insider does not immediately present themselves as an intruder. Insider threats can only be stopped by an evolving understanding of ‘normal’ for every user that immediately alerts your team when trusted users do something strange.

“By 2026, 10% of large enterprises will have a comprehensive, mature and measurable zero-trust program in place, up from less than 1% today.” [1]

Use Case: Darktrace spots an insider threat

Darktrace/OT detected a subtle deviation from normal behavior when a reprogram command was sent by an engineering workstation to a PLC controlling a pump, an action an insider threat with legitimized access to OT systems would take to alter the physical process without any malware involved. In this instance, AI Analyst, Darktrace’s investigation tool that triages events to reveal the full security incident, detected the event as unusual based on multiple metrics including the source of the command, the destination device, the time of the activity, and the command itself.  

As a result, AI Analyst created a complete security incident, with a natural language summary, the technical details of the activity, and an investigation process explaining how it came to its conclusion. By leveraging Explainable AI, a security team can quickly triage and escalate Darktrace incidents in real time before it becomes disruptive, and even when performed by a trusted insider.

Read more about insider threats here

Network Security Challenge 2: Stopping Ransomware at every stage    

What is Ransomware?

Ransomware is a type of malware that encrypts valuable files on a victim’s device, denying the account holder access, and demanding money in exchange for the encryption key. Ransomware has been increasingly difficult to deal with, especially with ransom payments being made in crypto currency which is untraceable. Ransomware can enter a system by clicking a link dangerous or downloading malicious files.

Avoiding ransomware attacks ranks at the top of most CISOs’ and risk managers’ priority lists, and with good reason. Extortion was involved in 25% of all breaches in 2022, with front-page attacks wreaking havoc across healthcare, gas pipelines, food processing plants, and other global supply chains. [2]

What else is new?

The availability of “DIY” toolkits and subscription-based ransom- ware-as-a-service (RaaS) on the dark web equips novice threat actors to launch highly sophisticated attacks at machine speed. For less than $500, virtually anyone can acquire and tweak RaaS offerings such as Philadelphia that come with accessible customer interfaces, reviews, discounts, and feature updates — all the signature features of commercial SaaS offerings.                  

Darktrace Cyber AI breaks the ransomware cycle

The preeminence of ransomware keeps security teams on high alert for indicators of attack but hypervigilance — and too many tools churning out too many alerts — quickly exhausts analysts’ bandwidth. To reverse this trend, AI needs to help prioritize and resolve versus merely detect risk.

Darktrace uses AI to recognize and contextualize possible signs of ransomware attacks as they appear in your network and across multiple domains. Viewing behaviors in the context of your organization’s normal ‘pattern of life’ updates and enhances detection that watches for a repeat of previous techniques.

Darktrace's AI brings the added advantage of continuously analyzing behavior in your environment at machine speed.

Darktrace AI also performs Autonomous Response, shutting down attacks at every stage of the ransomware cycle, including the first telltale signs of exfiltration and encryption of data for extortion purposes.

Use Case: Stopping Hive Ransomware attack

Hive is distributed via a RaaS model where its developers update and maintain the code, in return for a percentage of the eventual ransom payment, while users (or affiliates) are given the tools to carry out attacks using a highly sophisticated and complex malware they would otherwise be unable to use.

In early 2022, Darktrace/Network identified several instances of Hive ransomware on the networks of multiple customers. Using its anomaly-based detection, Darktrace was able to successfully detect the attacks and multiple stages of the kill chain, including command and control (C2) activity, lateral movement, data exfiltration, and ultimately data encryption and the writing of ransom notes.

Darktrace’s AI understands customer networks and learns the expected patterns of behavior across an organization’s digital estate. Using its anomaly-based detection Darktrace is able to identify emerging threats through the detection of unusual or unexpected behavior, without relying on rules and signatures, or known IoCs.

Read the full story here

Network Security Challenge 3: Spotting Novel Attacks

You can’t predict tomorrow’s weather by reading yesterday’s forecast, yet that’s essentially what happens when network security tools only look for known attacks.

What are novel attacks?

“Novel attacks” include unknown or previously unseen exploits such as zero-days, or new variations of known threats that evade existing detection rules.

Depending on how threats get executed, the term “novel” can refer to brand new tactics, techniques, and procedures (TTPs), or to subtle new twists on perennial threats like DoS, DDoS, and Domain Name Server (DNS) attacks.

Old tools may be blind to new threats

Stopping novel threats is less about deciding whom to trust than it is about learning to spot something brand new. As we’ve seen with ransomware, the growing “aaS” attack market creates a profound paradigm shift by allowing non-technical perpetrators to tweak, customize, and coin never-before-seen threats that elude traditional network, email, VPN, and cloud security.

Tools based on traditional rules and signatures lack a frame of reference. This is where AI’s ability to spot and analyze abnormalities in the context of normal patterns of life comes into play.                        

Darktrace AI spots what other tools miss                                      

Instead of training in cloud data lakes that pool data from unrelated attacks worldwide, Darktrace AI learns about your unique environment from your environment. By flagging and analyzing everything unusual — instead of only known signs of compromise — Darktrace’s Self-Learning AI keeps security stacks from missing less obvious but potentially more dangerous events.

The real challenge here is achieving faster “time to meaning” and contextualizing behavior that might — or might not — be part of a novel attack. Darktrace/Network does not require a “patient zero” to identify a novel attack, or one exploiting a zero-day vulnerability.

Use Case: Stopping Novel Ransomware Attack

In late May 2023, Darktrace observed multiple instances of Akira ransomware affecting networks across its customer base. Thanks to its anomaly-based approach to threat detection Darktrace successfully identified the novel ransomware attacks and provided full visibility over the cyber kill chain, from the initial compromise to the eventual file encryptions and ransom notes. Darktrace identified Akira ransomware on multiple customer networks, even when threat actors were utilizing seemingly legitimate services (or spoofed versions of them) to carry out malicious activity. While this may have gone unnoticed by traditional security tools, Darktrace’s anomaly-based detection enabled it to recognize malicious activity for what it was. In cases where Darktrace’s autonomous response was enabled these attacks were mitigated in their early stages, thus minimizing any disruption or damage to customer networks.

Read the full story here

References

[1] Gartner, “Gartner Unveils Top Eight Cybersecurity Predictions for 2023-2024,” 28 March 2023.                    

[2] TechTarget, “Ransomware trends, statistics and facts in 2023,” Sean Michael Kerner, 26 January 2023.

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About the author
Mikey Anderson
Product Manager, Network Detection & Response

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Inside the SOC

The Price of Admission: Countering Stolen Credentials with Darktrace

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03
Jun 2024

Using leaked credentials to gain unauthorized access

Dark web marketplaces selling sensitive data have increased accessibility for malicious actors, similar to Ransomware-as-a-Service (RaaS), lowering the barrier to entry usually associated with malicious activity. By utilizing leaked credentials, malicious actors can easily gain unauthorized access to accounts and systems which they can leverage to carry out malicious activities like data exfiltration or malware deployment.

Usage of leaked credentials by malicious actors is a persistent concern for both organizations and security providers. Google Cloud’s ‘H1 2024 Threat Horizons Report’ details that initial access seen in 2.9% of cloud compromises observed on Google Cloud resulted from leaked credential usage [1], with the ‘IBM X-Force Threat Intelligence Index 2024’ reporting 71% year-on-year increase in cyber-attacks which utilize stolen or compromised credentials [2].

Darktrace coverage of leaked credentials

In early 2024, one Darktrace customer was compromised by a malicious actor after their internal credentials had been leaked on the dark web. Subsequent attack phases were detected by Darktrace/Network and the customer was alerted to the suspicious activity via the Proactive Threat Notification (PTN) service, following an investigation by Darktrace’s Security Operation Center (SOC).

Darktrace detected a device on the network of a customer in the US carrying out a string of anomalous activity indicative of network compromise. The device was observed using a new service account to authenticate to a Virtual Private Network (VPN) server, before proceeding to perform a range of suspicious activity including internal reconnaissance and lateral movement.

Malicious actors seemingly gained access to a previously unused service account for which they were able to set up multi-factor authentication (MFA) to access the VPN. As this MFA setup was made possible by the configuration of the customer’s managed service provider (MSP), the initial access phase of the attack fell outside of Darktrace’s purview.

Unfortunately for the customer in this case, Darktrace RESPOND™ was not enabled on the network at the time of the attack. Had RESPOND been active, it would have been able to autonomously act against the malicious activity by disabling users, strategically blocking suspicious connections and limiting devices to their expected patterns of activity.

Attack timeline of leaked credentials spotted by darktrace

Network Scanning Activity

On February 22, 2024, Darktrace detected the affected device performing activity indicative of network scanning, namely initiating connections on multiple ports, including ports 80, 161 389 and 445, to other internal devices. While many of these internal connection attempts were unsuccessful, some successful connections were observed.

Devices on a network can gather information about other internal devices by performing network scanning activity. Defensive scanning can be used to support network security, allowing internal security teams to discover vulnerabilities and potential entry points that require their attention, however attackers are also able to take advantage of such information, such as open ports and services available on internal devices, with offensive scanning.

Brute Force Login Attempts

Darktrace proceeded to identify the malicious actor attempting to access a previously unused service account for which they were able to successfully establish MFA to access the organization’s VPN. As the customer’s third-party MSP had been configured to allow all users to login to the organization’s VPN using MFA, this login was successful. Moreover, the service account had never previously been used and MFA and never been established, allowing the attacker to leverage it for their own nefarious means.

Darktrace/Network identified the attacker attempting to authenticate over the Kerberos protocol using a total of 30 different usernames, of which two were observed successfully authenticating. There was a total of 6 successful Kerberos logins identified from two different credentials.  Darktrace also observed over 100 successful NTLM attempts from the same device for multiple usernames including “Administrator” and “mail”. These credentials were later confirmed by the customer to have been stolen and leaked on the dark web.

Advanced Search query results showing the usernames that successfully authenticated via NTLM.
Figure 1: Advanced Search query results showing the usernames that successfully authenticated via NTLM.

Even though MFA requirements had been satisfied when the threat actor accessed the organization’s VPN, Darktrace recognized that this activity represented a deviation from its previously learned behavior.

Malicious actors frequently attempt to gain unauthorized access to accounts and internal systems by performing login attempts using multiple possible usernames and passwords. This type of brute-force activity is typically accomplished using computational power via the use of software or scripts to attempt different username/password combinations until one is successful.

By purchasing stolen credentials from dark web marketplaces, attackers are able to significantly increase the success rate of brute-force attacks and, if they do gain access, they can easily act on their objectives, be that exfiltrating sensitive data or moving through their target networks to further the compromise.

Share Enumeration

Around 30 minutes after the initial network scanning activity, the compromised device was observed performing SMB enumeration using one of the aforementioned accounts. Darktrace understood that this activity was suspicious as the device had never previously been used to perform SMB activity and had not been tagged as a security device.

Darktrace/Network identifying the suspicious SMB enumeration performed by the compromised device.
Figure 2: Darktrace/Network identifying the suspicious SMB enumeration performed by the compromised device.

Such enumeration can be used by malicious actors to gain insights into the structures and configurations of a target device, view permissions associated with shared resources, and also view general identifying information about the system.

Darktrace further identified that the device connected to the named pipe “srvsvc”. By enumerating over srvsvc, a threat actor is able to request a list of all available SMB shares on a destination device, enabling further data gathering as part of network reconnaissance. Srvsvc also provides access to remote procedure call (RPC) for various services on a destination device.

At this stage, a Darktrace/Network Enhanced Monitoring model was triggered for lateral movement activity taking place on the customer’s network. As this particular customer was subscribed to the PTN service, the Enhanced Monitoring model alert was promptly triaged and investigated by the Darktrace SOC. The customer was alerted to the emerging activity and given full details of the incident and the SOC team’s investigation.

Attack and Reconnaissance Tool Usage

A few minutes later, Darktrace observed the device making a connection with a user agent associated with the Nmap network scanning tool, “Mozilla/5.0 (compatible; Nmap Scripting Engine; https://nmap.org/book/nse[.]html)”. While these tools are often used legitimately by an organization’s security team, they can also be used maliciously by attackers to exploit vulnerabilities that attackers may have unearthed during earlier reconnaissance activity.

As such services are often seen as normal network traffic, attackers can often use them to bypass traditional security measures. Darktrace’s Self-Learning AI, however, was able to recognize that the affected device was not a security device and therefore not expected to carry out such activity, even if it was using a legitimate Nmap service.

Darktrace/Network identifying the compromised device using the Nmap scanning tool.
Figure 3: Darktrace/Network identifying the compromised device using the Nmap scanning tool.

Further Lateral Movement

Following this suspicious Nmap usage, Darktrace observed a range of additional anomalous SMB activity from the aforementioned compromised account. The affected device attempted to establish almost 900 SMB sessions, as well as performing 65 unusual file reads from 29 different internal devices and over 300 file deletes for the file “delete.me” from over 100 devices using multiple paths, including ADMIN$, C$, print$.

Darktrace also observed the device making several DCE-RPC connections associated with Active Directory Domain enumeration, including DRSCrackNames and DRSGetNCChanges; a total of more than 1000 successful DCE-RPC connection were observed to a domain controller.

As this customer did not have Darktrace/Network's autonomous response deployed on their network, the above detailed lateral movement and network reconnaissance activity was allowed to progress unfettered, until Darktrace’s SOC alerted the customer’s security team to take urgent action. The customer also received follow-up support through Darktrace’s Ask the Expert (ATE) service, allowing them to contact the analyst team directly for further details and support on the incident.

Thanks to this early detection, the customer was able to quickly identify and disable affected user accounts, effectively halting the attack and preventing further escalation.

Conclusions

Given the increasing trend of ransomware attackers exfiltrating sensitive data for double extortion and the rise of information stealers, stolen credentials are commonplace across dark web marketplaces. Malicious actors can exploit these leaked credentials to drastically lower the barrier to entry associated with brute-forcing access to their target networks.

While implementing well-configured MFA and enforcing regular password changes can help protect organizations, these measures alone may not be enough to fully negate the advantage attackers gain with stolen credentials.

In this instance, an attacker used leaked credentials to compromise an unused service account, allowing them to establish MFA and access the customer’s VPN. While this tactic may have allowed the attacker to evade human security teams and traditional security tools, Darktrace’s AI detected the unusual use of the account, indicating a potential compromise despite the organization’s MFA requirements being met. This underscores the importance of adopting an intelligent decision maker, like Darktrace, that is able to identify and respond to anomalies beyond standard protective measures.

Credit to Charlotte Thompson, Cyber Security Analyst, Ryan Traill, Threat Content Lead

Appendices

Darktrace DETECT Model Coverage

-       Device / Suspicious SMB Scanning Activity (Model Alert)

-       Device / ICMP Address Scan (Model Alert)

-       Device / Network Scan (Model Alert)

-       Device / Suspicious LDAP Search Operation (Model Alert)

-       User / Kerberos Username Brute Force (Model Alert)

-       Device / Large Number of Model Breaches (Model Alert)

-       Anomalous Connection / SMB Enumeration (Model Alert)

-       Device / Multiple Lateral Movement Model Breaches (Enhanced Monitoring Model Alert)

-       Device / Possible SMB/NTLM Reconnaissance (Model Alert)

-       Anomalous Connection / Possible Share Enumeration Activity (Model Alert)

-       Device / Attack and Recon Tools (Model Alert)

MITRE ATT&CK Mapping

Tactic – Technique - Code

INITIAL ACCESS - Hardware Additions     -T1200

DISCOVERY - Network Service Scanning -T1046

DISCOVERY - Remote System Discovery - T1018

DISCOVERY - Domain Trust Discovery      - T1482

DISCOVERY - File and Directory Discovery - T1083

DISCOVERY - Network Share Discovery - T1135

RECONNAISSANCE - Scanning IP Blocks - T1595.001

RECONNAISSANCE - Vulnerability Scanning - T1595.002

RECONNAISSANCE - Client Configurations - T1592.004

RECONNAISSANCE - IP Addresses - T1590.005

CREDENTIAL ACCESS - Brute Force - T1110

LATERAL MOVEMENT - Exploitation of Remote Services -T1210

References

  1. 2024 Google Cloud Threat Horizons Report
    https://services.google.com/fh/files/misc/threat_horizons_report_h12024.pdf
  2. IBM X-Force Threat Intelligence Index 2024
    https://www.ibm.com/reports/threat-intelligence
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About the author
Charlotte Thompson
Cyber Analyst
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