July 7, 2020

Cryptomining Campaigns & Technical Analysis of Vulnerability

Crypto-mining campaigns stood no chance against Darktrace's AI as it identified the threat in real time. Put your trust in Darktrace's assistance!

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

Jul 2020

Introduction

The speed with which attackers can weaponize vulnerabilities is steadily increasing. While technology is rapidly evolving and cyber-attacks are becoming more sophisticated, the advantages of exploiting software vulnerabilities over devising a more elaborate and lengthy attack plan have not been overlooked by hackers. These vulnerabilities are also a quick way to gain access into a businesses’ infrastructure. In recent years, attackers have found great benefit and substantial success through quickly weaponizing vulnerabilities in web-facing systems.

Just recently, critical vulnerabilities in Citrix Gateway resulted in a spate of activity targeting Darktrace customers, as reported earlier this year. Without an immediate patch released upon the public announcement of the discovered flaws in Citrix, exploits quickly followed. Similarly, in late April, SaltStack developers reported vulnerabilities in Salt, an open source framework used to monitor and update the state of servers in cloud environments and data centers.

The vulnerabilities found in Salt would allow hackers to bypass authentication and authorization controls and execute code in Salt master servers exposed to the internet. The Salt master is responsible for sending commands to Salt minions and can manage thousands of minions at once. Due to this structure, one exposed Salt master can lead to a compromise of all underlying minions.

On May 2, Darktrace detected successful crypto-miner infections across a number of its customers exploiting the CVE-2020-11651 and CVE-2020-11652 vulnerabilities in SaltStack server management software. In the same weekend, LineageOS — an Android mobile operating system – and Ghost — a blogging platform – both reported suffering a crypto-mining attack due to exposed, unpatched Salt servers. Most notable about these attacks was the sheer speed from a vulnerability being published to a widespread attack campaign.

Timeline

Figure 1: A timeline of events identified by Darktrace on May 3

Technical analysis

Initial compromise

Darktrace initially detected that a number of customer servers running SaltStack were making external connections to endpoints previously not seen on the network. The connections used the curl or wget utilities to download and execute a bash script, which would install a secondary-stage payload containing a cryptocurrency miner.

The systems were targeted directly utilizing 2020-11651 and CVE-2020-11652 vulnerabilities in the ZeroMQ protocol running on SaltStack. These vulnerabilities would allow direct remote code execution as root on the targeted systems, allowing the script to be downloaded and executed successfully with highest system privileges.

The downloader script is almost identical to the one utilized in March in H2Miner infections targeting exposed Docker APIs and Redis instances.

Before downloading the secondary stage payload, the script cleans the target system of a number of pre-existing infections and miners, as well as disabling a number of known security tools and software.

Figure 2: The downloader script

Following the initial clean up, the script would iterate through three functions to download the crypto-miner payload — salt-storer

SHA256 837d768875417578c0b1cab4bd0aa38146147799f643bb7b3c6c6d3d82d7aa2a

— from three different hard-coded servers. An MD5 check for the downloaded executable would be performed prior to execution. The below screenshot illustrates two out of the three downloader functions that would be invoked.

Figure 3: Two of the downloader functions

Second stage payload

Following the cryptographic checks, the downloaded ELF LSB executable kicks into action. No payload analysis was carried out, however it’s execution would result in a crypto-miner being installed and a C2 channel opened.

OSINT indicates that several new versions of the payload were observed carrying additional capabilities, including database dumping and advanced persistence methods. The variants detected by Darktrace’s AI included the more advanced “Version 5” payload purported to have worming capabilities, but in this case they were not observed directly.

Command and control

Upon the execution of an LSB executable, a plaintext HTTP C2 channel would be established, sending basic metadata about the infected host such as processor architecture, available resources, and whether root execution was achieved. This indicates that the C2 mechanisms were likely repurposed from other infections, as this particular infection would execute as root, making the respective component redundant.

Figure 4: A Command and control channel

The complete attack lifecycle was investigated and reported on by Darktrace’s Cyber AI Analyst, which automatically surfaced some crucial details regarding the C2 communication, including other servers that were seen making similar communication patterns, as seen in the bottom right below.

Figure 5: The Cyber AI Analyst automatically generating a natural-language summary of the overall security incident

Figure 6: Further information on the suspicious endpoints

Actions on target

Lastly, devices began mining for cryptocurrency. Cryptocurrency mining demands a substantial proportion of a device’s processing power, such as CPU and GPU, in order to calculate hashes. However, except for the occasional increase in CPU or RAM usage, it can go undetected for months as traditional security products do not normally detect its pattern of behavior as malicious.

Conclusion

Failing to patch vulnerabilities quickly and decisively can have serious consequences. Sometimes, however, the window of opportunity before an attack hits is too short for patching to be feasible. This example demonstrates how quickly unpatched vulnerabilities can be exploited following an initial public disclosure. And yet, even two months after SaltStack published the updates, many Salt servers remain unpatched and run the risk of becoming compromised.

In the case of Citrix, some exploits led to a ransomware attack. Darktrace’s AI-powered Immune System technology not only detected every stage of these ransomware attacks, but its autonomous response was able to halt any anomalous event and contain further damage.

Because new vulnerabilities are, by nature, unexpected, traditional security tools relying on rules and signatures don’t know to look for malicious activity that arises as a result. However, with its constantly evolving understanding of ‘normal’, Darktrace’s AI detects and investigates any unusual behavior, regardless of its origin or whether an attack has been seen before.

Crypto-mining is still favored among many threat actors due to its ability to generate profits, and a successfully infection can have a serious impact on the confidentiality and integrity of the corporate network. The need for Cyber AI that can detect new vulnerabilities and novel threats, and autonomously respond to stop an attack in its tracks, are critical to ensuring businesses remain secure in the face of cyber-criminals who are mobilizing to exploit vulnerabilities more quickly than ever.

IoCs:

IoCComment144.217.129[.]111Likely C2, URIs: /ms /h /s91.215.152[.]69Likely C2, URI: /h89.223.121[.]139Download of payload sa.sh217.12.210[.]192Download of payload sa.sh45.147.201[.]62Destination for crypto-mining217.12.210[.]245Download of payload salt_storer

Darktrace model breaches:

Device / Initial Breach Chain Compromise
Compromise / SSL or HTTP Beacon
Device / Large Number of Model Breaches
Anomalous Connection / New User Agent to IP Without Hostname
Anomalous File / Script from Rare External
Compromise / Beaconing Activity To External Rare
Anomalous Connection / Multiple Failed Connections to Rare Destination
Compromise / Sustained SSL or HTTP Increase
Compliance / Crypto Currency Mining Activity

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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)

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