January 6, 2021

Darktrace Insights On SolarWinds Hack

Learn how Darktrace analyzed the SolarWinds hack without signatures. Understand the techniques used to identify and mitigate this major cyber threat.

Written by

Max Heinemeyer

Global Field CISO

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

Max Heinemeyer

Global Field CISO

Jan 2021

For a high-level explanation of the SolarWinds hack, watch our video below.

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The SUNBURST malware attacks against SolarWinds have heightened companies’ concerns about the risk to their digital environments. Malware installed during software updates in March 2020 has allowed advanced attackers to gain unauthorized access to files that may include customer data and intellectual property.

Darktrace does not use SolarWinds, and its operations remain unaffected by this breach. However, SolarWinds is an IT discovery tool that is used by a significant number of Darktrace customers. In what follows, we explore a set of Darktrace detections that highlight and alert security teams to the types of behaviors related to this breach.

This is not an example of a SolarWinds compromise, but examples of anomalous behaviors we can expect to see from this type of breach. These examples stress the value of self-learning Cyber AI capable of understanding the evolving normal ‘patterns of life’ within an enterprise – as opposed to a signature-based approach that looks at historical data to predict today’s threat.

As Darktrace detects device activity patterns rather than known malicious signatures, detecting use of these techniques will fall into the scope of Darktrace’s capabilities without further need for configuration. The technology automatically clusters devices into ‘peer groups’, allowing it to detect cases of an individual device behaving unusually. Using a self-learning approach is the best possible mechanism to catch an attacker who gains access into your systems using a degree of stealth so as to not trigger signature-based detection.

A number of these models may fire in combination with other models in order to make a strong detection over a time-series – and this is exactly where Darktrace’s autonomous incident triage capability, Cyber AI Analyst, plays a crucial role in investigating the alerts on behalf of security teams. Cyber AI Analyst saves critical time for security teams, and its results should be treated with a high priority during this period of vigilance.

How SolarWinds was detected with AI

We want to focus on the most sophisticated details of the hands-on intrusion that in many cases followed the initial automated attack. This post-exploitation part of the attack is much more varied and stealthy. These stages are also near-impossible to predict, as they are driven by the attacker’s intentions and goals for each individual victim at this stage – making the use of signatures, threat intelligence or static use cases virtually useless.

While the automated, initial malware execution is a critical initial step to understand, the behavior was pre-configured for the malware and included the download of further payloads and the connection to domain-generation-algorithm (DGA) based subdomains of avsvmcloud[.]com. These automated first stages of the attack have been sufficiently researched in depth by the community. This post is not aiming to add anything to these findings, but instead takes a look at the potential post-infection activities.

Malware / C2 domains

The threat-actor set the hostnames on their later-stage command and control (C2) infrastructure to match a legitimate hostname found within the victim’s environment. This allowed the adversary to blend into the environment, avoid suspicion, and evade detection. They further used C2 servers in geopolitical proximity to their victims, further circumventing static geo-based trusts lists. Darktrace is unaffected by this type of tradecraft as it does not have implicit, pre-defined trust of any geo-locations.

This would be very likely to trigger the following Darktrace Cyber AI models. The models were not specifically designed to detect SolarWinds modifications but have been in place for years – they are designed to detect the subtle but significant attacker activities occurring within an organization’s network.

Compromise / Agent Beacon to New Endpoint
Compromise / SSL Beaconing to New Endpoint
Compromise / HTTP Beaconing to New Endpoint*

*The implant uses SSL, but may be identified as HTTP if using a proxy.

Lateral movement using different credentials

Once the attacker gained access to the network with compromised credentials, they moved laterally using multiple different credentials. The credentials used for lateral movement were always different from those used for remote access.

This very likely would trigger the following Cyber AI models:

User / Multiple Uncommon New Credentials on Device

*Figure 1: Example breach event log showing anomalous (new) logins from a single device, with multiple user credentials*

User / New Admin Credentials on Client

*Figure 2: Example breach event log showing anomalous admin login*

Temporary file replacement and temporary task modification

The attacker used a temporary file replacement technique to remotely execute utilities: they replaced a legitimate utility with theirs, executed their payload, and then restored the legitimate original file. They similarly manipulated scheduled tasks by updating an existing legitimate task to execute their tools and then returned the scheduled task to its original configuration. They routinely removed their tools – including the removal of backdoors once legitimate remote access was achieved.

This would be very likely to trigger the following Cyber AI models:

Anomalous Connection / New or Uncommon Service Control

*Figure 3: Example breach showing uncommon service control*

Anomalous Connection / High Volume of New or Uncommon Service Control

*Figure 4: Example breach showing 10 uncommon service controls*

Device / AT Service Scheduled Task

*Figure 5: Breach event log shows new AT service scheduled task activity*

Device / Multiple RPC Requests for Unknown Services

*Figure 6: Breach shows multiple binds to unknown RPC services*

Device / Anomalous SMB Followed By Multiple Model Breaches

*Figure 7: Breach shows unusual SMB activity, combined with slow beaconing*

Device / Suspicious File Writes to Multiple Hidden SMB Shares

*Figure 8: Breach shows device writing .bat file to temp folder on another device*

Unusual Activity / Anomalous SMB to New or Unusual Locations

*Figure 9: Breach shows new access to SAMR, combined with SMB Reads and Kerberos login failures*

Unusual Activity / Sustained Anomalous SMB Activity

*Figure 10: Breach shows significant deviation in SMB activity from device*

SolarWinds breach remembered

By understanding where credentials are used and which devices talk to each other, Cyber AI has an unprecedented and dynamic understanding of business systems. This empowers it to alert security teams to enterprise changes that could indicate cyber risk in real time.

These alerts demonstrate how AI learns ‘normal’ for the unique digital environment surrounding it, and then alerts operators to deviations, including those that are directly relevant to the SUNBURST compromise. It further provides insights into how the attacker exploited those networks that did not have the appropriate visibility and detection capabilities.

On top of these alerts, Cyber AI Analyst will also be automatically correlating these detections over time to identify patterns, generating comprehensive and intuitive incident summaries and significantly reducing triage time. Reviewing Cyber AI Analyst alerts should be given high priority over the next several weeks.

Written by

Max Heinemeyer

Global Field CISO

Inside the SOC

Written by

Max Heinemeyer

Global Field CISO

The Darktrace Community

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July 1, 2025

Pre-CVE Threat Detection: 8 Examples Identifying Malicious Activity Prior to Public Disclosure of a Vulnerability

Can you detect cyber threats before the world knows about them?

Every year, tens of thousands of Common Vulnerabilities and Exposures (CVEs) are disclosed, over 40,000 in 2024 alone [1], and a predicted higher number for 2025 by the Forum for Incident Response and Security Teams (FIRST).

However, cybercriminals don't wait for disclosure. They exploit zero-days while defenders remain in the dark.

Traditional, signature-based tools struggle to detect these early-stage threats. That’s why anomaly detection is becoming essential for organizations seeking pre-CVE detection.

Understanding the gap between zero-day attacks and public CVE disclosure

When a vulnerability is discovered, the standard practice is to report it to the vendor or the responsible organization, allowing them to develop and distribute a patch or fix before the details are made public. This is known as responsible disclosure.

The gap between exploitation of a zero-day and the disclosure of the vulnerability can sometimes be considerable, and retroactively attempting to identify successful exploitation on your network can be challenging, particularly if taking a signature-based approach.

However, abnormal behaviors in networks or systems, such as unusual login patterns or data transfers, can indicate attempted cyber-attacks, insider threats, or compromised systems.

Detecting threats without relying on CVE disclosure

Since Darktrace does not rely on rules or signatures, it can detect malicious activity that is anomalous even without full context of the specific device or asset in question.

For example, during the Fortinet exploitation late last year, the Darktrace Threat Research team were investigating a different Fortinet vulnerability, namely CVE 2024-23113, for exploitation when Mandiant released a security advisory around CVE 2024-47575, which aligned closely with Darktrace’s findings.

Retrospective analysis like this is used by Darktrace’s threat researchers to better understand detections across the threat landscape and to add additional context.

Below are eight examples from the past year where Darktrace detected malicious activity days or even weeks before a vulnerability was publicly disclosed.

ten examples from the past year where Darktrace detected malicious activity days or even weeks before a vulnerability was publicly disclosed.

Trends in pre-cve exploitation

The attack vs. patch race

In many cases, the disclosure of an exploited vulnerability can be off the back of an incident response investigation related to a compromise by an advanced threat actor using a zero-day. Once the vulnerability is registered and publicly disclosed as having been exploited, it can kick off a race between the attacker and defender.

Skilled nation-state actors

Nation-state actors, highly skilled with significant resources, are known to use a range of capabilities to achieve their target, including zero-day use. Often, pre-CVE activity is “low and slow”, last for months with high operational security.

After CVE disclosure, the barriers to entry lower, allowing less skilled and less resourced attackers, like some ransomware gangs, to exploit the vulnerability and cause harm. This is why two distinct types of activity are often seen: pre and post disclosure of an exploited vulnerability.

Examples of exploitation

Darktrace saw this consistent story line play out during several of the Fortinet and PAN OS threat actor campaigns highlighted above last year, where nation-state actors were seen exploiting vulnerabilities first, followed by ransomware gangs impacting organizations [2].

The same applies with the recent SAP Netweaver exploitations being tied to a China based threat actor earlier this spring with subsequent ransomware incidents being observed [3].

You spotted the anomaly but did you stop the breach?

Anomaly-based detection offers the benefit of identifying malicious activity even before a CVE is disclosed; however, security teams still need to quickly contain and isolate the activity.

For example, during the Ivanti chaining exploitation in the early part of 2025, a customer had Darktrace’s Autonomous Response capability enabled on their network. As a result, Darktrace was able to contain the compromise and shut down any ongoing suspicious connectivity by blocking internal connections and enforcing a “pattern of life” on the affected device.

This pre-CVE detection and response by Darktrace occurred 11 days before any public disclosure, demonstrating the value of an anomaly-based approach.

In some cases, customers have even reported that Darktrace stopped malicious exploitation of devices several days before a public disclosure of a vulnerability.

For example, During the ConnectWise exploitation, a customer informed the team that Darktrace had detected malicious software being installed via remote access. Upon further investigation, four servers were found to be impacted, while Autonomous Response had blocked outbound connections and enforced patterns of life on impacted devices.

Conclusion

By continuously analyzing behavioral patterns, systems can spot unusual activities and patterns from users, systems, and networks to detect anomalies that could signify a security breach.

Through ongoing monitoring and learning from these behaviors, anomaly-based security systems can detect threats that traditional signature-based solutions might miss, while also providing detailed insights into threat tactics, techniques, and procedures (TTPs). This type of behavioral intelligence supports pre-CVE detection, allows for a more adaptive security posture, and enables systems to evolve with the ever-changing threat landscape.

Credit to Nathaniel Jones (VP, Security & AI Strategy, Field CISO), Emma Fougler (Global Threat Research Operations Lead), Ryan Traill (Analyst Content Lead)

References and further reading:

Realted Darktrace blogs:

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*Self-reported by customer, confirmed afterwards.

**Updated January 2024 blog now reflects current findings

About the author

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Network

June 27, 2025

Patch and Persist: Darktrace’s Detection of Blind Eagle (APT-C-36)

login on laptop dual factor authentication

What is Blind Eagle?

Since 2018, APT-C-36, also known as Blind Eagle, has been observed performing cyber-attacks targeting various sectors across multiple countries in Latin America, with a particular focus on Colombian organizations.

Blind Eagle characteristically targets government institutions, financial organizations, and critical infrastructure [1][2].

Attacks carried out by Blind Eagle actors typically start with a phishing email and the group have been observed utilizing various Remote Access Trojans (RAT) variants, which often have in-built methods for hiding command-and-control (C2) traffic from detection [3].

What we know about Blind Eagle from a recent campaign

Since November 2024, Blind Eagle actors have been conducting an ongoing campaign targeting Colombian organizations [1].

In this campaign, threat actors have been observed using phishing emails to deliver malicious URL links to targeted recipients, similar to the way threat actors have previously been observed exploiting CVE-2024-43451, a vulnerability in Microsoft Windows that allows the disclosure of a user’s NTLMv2 password hash upon minimal interaction with a malicious file [4].

Despite Microsoft patching this vulnerability in November 2024 [1][4], Blind Eagle actors have continued to exploit the minimal interaction mechanism, though no longer with the intent of harvesting NTLMv2 password hashes. Instead, phishing emails are sent to targets containing a malicious URL which, when clicked, initiates the download of a malicious file. This file is then triggered by minimal user interaction.

Clicking on the file triggers a WebDAV request, with a connection being made over HTTP port 80 using the user agent ‘Microsoft-WebDAV-MiniRedir/10.0.19044’. WebDAV is a transmission protocol which allows files or complete directories to be made available through the internet, and to be transmitted to devices [5]. The next stage payload is then downloaded via another WebDAV request and malware is executed on the target device.

Attackers are notified when a recipient downloads the malicious files they send, providing an insight into potential targets [1].

Darktrace’s coverage of Blind Eagle

In late February 2025, Darktrace observed activity assessed with medium confidence to be associated with Blind Eagle on the network of a customer in Colombia.

Within a period of just five hours, Darktrace / NETWORK detected a device being redirected through a rare external location, downloading multiple executable files, and ultimately exfiltrating data from the customer’s environment.

Since the customer did not have Darktrace’s Autonomous Response capability enabled on their network, no actions were taken to contain the compromise, allowing it to escalate until the customer’s security team responded to the alerts provided by Darktrace.

Darktrace observed a device on the customer’s network being directed over HTTP to a rare external IP, namely 62[.]60[.]226[.]112, which had never previously been seen in this customer’s environment and was geolocated in Germany. Multiple open-source intelligence (OSINT) providers have since linked this endpoint with phishing and malware campaigns [9].

The device then proceeded to download the executable file hxxp://62[.]60[.]226[.]112/file/3601_2042.exe.

Figure 1: Darktrace’s detection of the affected device connecting to an unusual location based in Germany.

Figure 2: Darktrace’s detection of the affected device downloading an executable file from the suspicious endpoint.

The device was then observed making unusual connections to the rare endpoint 21ene.ip-ddns[.]com and performing unusual external data activity.

This dynamic DNS endpoint allows a device to access an endpoint using a domain name in place of a changing IP address. Dynamic DNS services ensure the DNS record of a domain name is automatically updated when the IP address changes. As such, malicious actors can use these services and endpoints to dynamically establish connections to C2 infrastructure [6].

Further investigation into this dynamic endpoint using OSINT revealed multiple associations with previous likely Blind Eagle compromises, as well as Remcos malware, a RAT commonly deployed via phishing campaigns [7][8][10].

Figure 3: Darktrace’s detection of the affected device connecting to the suspicious dynamic DNS endpoint, 21ene.ip-ddns[.]com.

Shortly after this, Darktrace observed the user agent ‘Microsoft-WebDAV-MiniRedir/10.0.19045’, indicating usage of the aforementioned transmission protocol WebDAV. The device was subsequently observed connected to an endpoint associated with Github and downloading data, suggesting that the device was retrieving a malicious tool or payload. The device then began to communicate to the malicious endpoint diciembrenotasenclub[.]longmusic[.]com over the new TCP port 1512 [11].

Around this time, the device was also observed uploading data to the endpoints 21ene.ip-ddns[.]com and diciembrenotasenclub[.]longmusic[.]com, with transfers of 60 MiB and 5.6 MiB observed respectively.

Figure 4: UI graph showing external data transfer activity.

This chain of activity triggered an Enhanced Monitoring model alert in Darktrace / NETWORK. These high-priority model alerts are designed to trigger in response to higher fidelity indicators of compromise (IoCs), suggesting that a device is performing activity consistent with a compromise.

Figure 5: Darktrace’s detection of initial attack chain activity.

A second Enhanced Monitoring model was also triggered by this device following the download of the aforementioned executable file (hxxp://62[.]60[.]226[.]112/file/3601_2042.exe) and the observed increase in C2 activity.

Following this activity, Darktrace continued to observe the device beaconing to the 21ene.ip-ddns[.]com endpoint.

Darktrace’s Cyber AI Analyst was able to correlate each of the individual detections involved in this compromise, identifying them as part of a broader incident that encompassed C2 connectivity, suspicious downloads, and external data transfers.

Figure 6: Cyber AI Analyst’s investigation into the activity observed on the affected device.

Figure 7: Cyber AI Analyst’s detection of the affected device’s broader connectivity throughout the course of the attack.

As the affected customer did not have Darktrace’s Autonomous Response configured at the time, the attack was able to progress unabated. Had Darktrace been properly enabled, it would have been able to take a number of actions to halt the escalation of the attack.

For example, the unusual beaconing connections and the download of an unexpected file from an uncommon location would have been shut down by blocking the device from making external connections to the relevant destinations.

Conclusion

The persistence of Blind Eagle and ability to adapt its tactics, even after patches were released, and the speed at which the group were able to continue using pre-established TTPs highlights that timely vulnerability management and patch application, while essential, is not a standalone defense.

Organizations must adopt security solutions that use anomaly-based detection to identify emerging and adapting threats by recognizing deviations in user or device behavior that may indicate malicious activity. Complementing this with an autonomous decision maker that can identify, connect, and contain compromise-like activity is crucial for safeguarding organizational networks against constantly evolving and sophisticated threat actors.

Credit to Charlotte Thompson (Senior Cyber Analyst), Eugene Chua (Principal Cyber Analyst) and Ryan Traill (Analyst Content Lead)

Appendices

IoCs

IoC – Type - Confidence
Microsoft-WebDAV-MiniRedir/10.0.19045 – User Agent

62[.]60[.]226[.]112 – IP – Medium Confidence

hxxp://62[.]60[.]226[.]112/file/3601_2042.exe – Payload Download – Medium Confidence

21ene.ip-ddns[.]com – Dynamic DNS Endpoint – Medium Confidence

diciembrenotasenclub[.]longmusic[.]com - Hostname – Medium Confidence

Darktrace’s model alert coverage

Anomalous File / Suspicious HTTP Redirect
Anomalous File / EXE from Rare External Location
Anomalous File / Multiple EXE from Rare External Location
Anomalous Server Activity / Outgoing from Server
Unusual Activity / Unusual External Data to New Endpoint
Device / Anomalous Github Download
Anomalous Connection / Multiple Connections to New External TCP Port
Device / Initial Attack Chain Activity
Anomalous Server Activity / Rare External from Server
Compromise / Suspicious File and C2
Compromise / Fast Beaconing to DGA
Compromise / Large Number of Suspicious Failed Connections
Device / Large Number of Model Alert

Mitre Attack Mapping:

Tactic – Technique – Technique Name

Initial Access - T1189 – Drive-by Compromise
Initial Access - T1190 – Exploit Public-Facing Application
Initial Access ICS - T0862 – Supply Chain Compromise
Initial Access ICS - T0865 – Spearphishing Attachment
Initial Access ICS - T0817 - Drive-by Compromise
Resource Development - T1588.001 – Malware
Lateral Movement ICS - T0843 – Program Download
Command and Control - T1105 - Ingress Tool Transfer
Command and Control - T1095 – Non-Application Layer Protocol
Command and Control - T1571 – Non-Standard Port
Command and Control - T1568.002 – Domain Generation Algorithms
Command and Control ICS - T0869 – Standard Application Layer Protocol
Evasion ICS - T0849 – Masquerading
Exfiltration - T1041 – Exfiltration Over C2 Channel
Exfiltration - T1567.002 – Exfiltration to Cloud Storage