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July 25, 2021

Detecting Lateral Movement in Crypto-Botnets

Explore how crypto botnets move laterally within networks and the implications for cybersecurity and threat detection.
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
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25
Jul 2021

Botnets have increasingly become the vehicle of choice to deliver crypto-mining malware. By infecting various corporate assets such as servers and IoT devices, cyber-criminals can use the collective processing power of hundreds – or thousands – of machines to mine cryptocurrency and spread to further devices.

This blog explores how an Internet-facing server was breached in a company in Singapore. The threat actors used the device to move laterally and deploy crypto-mining software. Within two days, several devices in the company had begun cryptocurrency mining.

Creating the botnet

Only a few days after Darktrace had been installed in a Proof of Value (POV) trial, it detected a server in the company downloading a malicious executable from a rare endpoint, 167.71.87[.]85.

Figure 1: Timeline of the attack.

The server was observed making HTTP connections to a range of rare external endpoints, without a user agent header. The main hostname was t[.]amynx[.]com, a domain on open-source intelligence (OSINT) associated with crypto-mining trojans.

The device initiated repeated external connections to a range of external IPs over the TCP port 445 (SMB). This was followed by an unusually large number of internal connection attempts to a wide range of devices, suggesting scanning activity.

Figure 2: Details for the TCP scanning activity in a similar incident — note the consolidation of six relevant events into one summary.

Growing the botnet

The malware began to move laterally from the initially infected server, predominantly by establishing chains of unsual RDP connections. Subsequently, the server started making external SMB and RPC connections to rare endpoints on the Internet, in an attempt to find further vulnerable hosts.

Other lateral movement activities included the repeated failing attempts to access multiple internal devices over the SMB file-sharing protocol, with a range of different usernames. This implies bruteforce network access, as the threat actor attempted to guess correct account details through trial and error.

Existing tools such as RDP and Windows Service Control reveal that the attacker was employing ‘Living off the Land’ techniques. This makes a system administrator’s job inherently harder, as they must distinguish the malicious use of built-in tools versus their legitimate application.

Crypto-mining begins

Finally, the compromised server completed the lateral movement by transferring suspicious executable files over SMB to multiple internal devices, with names that appear randomly generated (e.g. gMtWAvEc.exe, daSsZhPf.exe) to deploy crypto-mining malware using the Minergate protocol.

Minergate is a public mining pool utilized for several types of cryptocurrency including Bitcoin, Monero, Ethereum, Zcash, and Grin. In recent months, ransomware actors have begun shifting away from Bitcoin towards Monero and other more anonymous cryptocurrences – but crypto-miners have been using altcoins for years.

Figure 3: The graph shows a clear increase in model breaches on a similar device, which easily identifies the time frame for the compromise.

As this was part of a trial, Antigena – Darktrace’s Autonomous Response capability – was not in active mode and so could not take action to stop the initial vector of infection. However, the Antigena model “Antigena / Network / External Threat / Antigena Suspicious File Block” was breached on July 18 at 03:55:45. If active, Antigena would have instantly blocked connections to 167.71.87[.]85 on port 80 for two hours, allowing the security team enough time to remediate the breach.

Crypto-mining malware: All the rage

Crypto-mining attacks are extremely common. Although not as destructive as ransomware, they can have a serious impact on network latency and take a long time to detect and clean up. While the infection remains unnoticed, it provides a backdoor into the victim organization – and could switch from conducting crypto-mining to delivering ransomware at any moment. In this case, it is clear the attacker aimed to create maximum disruption by transferring malicious software with targets such as internal servers and domain controllers.

Darktrace detected every step of the attack without relying on known indicators of threat. Cyber AI Analyst automated the complete investigation process, saving the security team crucial time during the live incident.

Especially with the recent crackdowns on Bitcoin farms in China, underground botnets and cloud-based crypto-mining are likely to become more prominent. As we see more of these intrusions in the near future, AI-powered detection, investigation, and response, will prove critical in defending organizations of all sizes, at all times.

Learn more about crypto-mining malware

IoCs:

IoCComment167.71.87[.]85Malware Download — SHA1: 6a4c477ba19a7bb888540d02acdd9be0d5d3fd02VirusTotalt[.]amynx[.]comHTTP Command and Control – recently created domain with suspicious indicators on OSINT sites (associated with cryptomining trojans)AlienVaultVirusTotallplp[.]ackng[.]comCrypto Currency Mining Activity (Minergate)VirusTotalgMtWAvEc.exedaSsZhPf.exeyAElKPQi.exeExamples of malicious executables

Darktrace model breaches:

  • Antigena / Network / Insider Threat / Antigena Network Scan Block
  • Device / Suspicious Network Scan Activity
  • Device / Large Number of Model Breaches
  • Device / Multiple Lateral Movement Model Breaches (x2)
  • Unusual Activity / Successful Admin Bruteforce Activity
  • Anomalous Connection / SMB Enumeration
  • Antigena / Network / Significant Anomaly / Antigena Controlled and Model Breach (x2)
  • Antigena / Network / External Threat / Antigena Suspicious File Block
  • Compromise / Beacon to Young Endpoint (x4)
  • Device / Possible RPC Lateral Movement
  • Antigena / Network / Insider Threat / Antigena SMB Enumeration Block
  • Compromise / Beaconing Activity To External Rare (x5)
  • Anomalous Server Activity / Denial of Service Activity
  • Antigena / Network / External Threat / Antigena Suspicious Activity Block (x4)
  • Device / Large Number of Connections to New Endpoints
  • Device / Network Scan - Low Anomaly Score
  • Anomalous Connection / New or Uncommon Service Control (x3)
  • Device / New User Agent To Internal Server
  • Device / Anomalous RDP Followed By Multiple Model Breaches (x3)
  • Device / Anomalous SMB Followed By Multiple Model Breaches (x3)
  • Device / SMB Session Bruteforce (x2)
  • Device / Increased External Connectivity
  • Device / Network Scan
  • Compromise / High Volume of Connections with Beacon Score (x5)
  • Unusual Activity / Unusual External Activity (x3)
  • Anomalous Connection / Unusual Admin SMB Session
  • Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block
  • Compliance / SMB Drive Write (x3)
  • Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block (x14)
  • Compliance / Internet Facing RDP Server
  • Anomalous Connection / Multiple Failed Connections to Rare Endpoint (x5)
  • Compliance / Outbound RDP (x3)
  • Anomalous Server Activity / Rare External from Server (x5)
  • Compromise / Slow Beaconing Activity To External Rare (x8)
  • Anomalous Server Activity / Outgoing from Server (x2)
  • Device / New User Agent
  • Anomalous Connection / New Failed External Windows Connection (x5)
  • Compliance / External Windows Communications
  • Device / New Failed External Connections (x7)
  • Compliance / Crypto Currency Mining Activity (x9)
  • Compliance / Incoming Remote Desktop (x9)

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

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July 3, 2025

Top Eight Threats to SaaS Security and How to Combat Them

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The latest on the identity security landscape

Following the mass adoption of remote and hybrid working patterns, more critical data than ever resides in cloud applications – from Salesforce and Google Workspace, to Box, Dropbox, and Microsoft 365.

On average, a single organization uses 130 different Software-as-a-Service (SaaS) applications, and 45% of organizations reported experiencing a cybersecurity incident through a SaaS application in the last year.

As SaaS applications look set to remain an integral part of the digital estate, organizations are being forced to rethink how they protect their users and data in this area.

What is SaaS security?

SaaS security is the protection of cloud applications. It includes securing the apps themselves as well as the user identities that engage with them.

Below are the top eight threats that target SaaS security and user identities.

1.  Account Takeover (ATO)

Attackers gain unauthorized access to a user’s SaaS or cloud account by stealing credentials through phishing, brute-force attacks, or credential stuffing. Once inside, they can exfiltrate data, send malicious emails, or escalate privileges to maintain persistent access.

2. Privilege escalation

Cybercriminals exploit misconfigurations, weak access controls, or vulnerabilities to increase their access privileges within a SaaS or cloud environment. Gaining admin or superuser rights allows attackers to disable security settings, create new accounts, or move laterally across the organization.

3. Lateral movement

Once inside a network or SaaS platform, attackers move between accounts, applications, and cloud workloads to expand their foot- hold. Compromised OAuth tokens, session hijacking, or exploited API connections can enable adversaries to escalate access and exfiltrate sensitive data.

4. Multi-Factor Authentication (MFA) bypass and session hijacking

Threat actors bypass MFA through SIM swapping, push bombing, or exploiting session cookies. By stealing an active authentication session, they can access SaaS environments without needing the original credentials or MFA approval.

5. OAuth token abuse

Attackers exploit OAuth authentication mechanisms by stealing or abusing tokens that grant persistent access to SaaS applications. This allows them to maintain access even if the original user resets their password, making detection and mitigation difficult.

6. Insider threats

Malicious or negligent insiders misuse their legitimate access to SaaS applications or cloud platforms to leak data, alter configurations, or assist external attackers. Over-provisioned accounts and poor access control policies make it easier for insiders to exploit SaaS environments.

7. Application Programming Interface (API)-based attacks

SaaS applications rely on APIs for integration and automation, but attackers exploit insecure endpoints, excessive permissions, and unmonitored API calls to gain unauthorized access. API abuse can lead to data exfiltration, privilege escalation, and service disruption.

8. Business Email Compromise (BEC) via SaaS

Adversaries compromise SaaS-based email platforms (e.g., Microsoft 365 and Google Workspace) to send phishing emails, conduct invoice fraud, or steal sensitive communications. BEC attacks often involve financial fraud or data theft by impersonating executives or suppliers.

BEC heavily uses social engineering techniques, tailoring messages for a specific audience and context. And with the growing use of generative AI by threat actors, BEC is becoming even harder to detect. By adding ingenuity and machine speed, generative AI tools give threat actors the ability to create more personalized, targeted, and convincing attacks at scale.

Protecting against these SaaS threats

Traditionally, security leaders relied on tools that were focused on the attack, reliant on threat intelligence, and confined to a single area of the digital estate.

However, these tools have limitations, and often prove inadequate for contemporary situations, environments, and threats. For example, they may lack advanced threat detection, have limited visibility and scope, and struggle to integrate with other tools and infrastructure, especially cloud platforms.

AI-powered SaaS security stays ahead of the threat landscape

New, more effective approaches involve AI-powered defense solutions that understand the digital business, reveal subtle deviations that indicate cyber-threats, and action autonomous, targeted responses.

[related-resource]

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About the author
Carlos Gray
Senior Product Marketing Manager, Email

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July 2, 2025

Pre-CVE Threat Detection: 10 Examples Identifying Malicious Activity Prior to Public Disclosure of a Vulnerability

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Vulnerabilities are weaknesses in a system that can be exploited by malicious actors to gain unauthorized access or to disrupt normal operations. Common Vulnerabilities and Exposures (or CVEs) are a list of publicly disclosed cybersecurity vulnerabilities that can be tracked and mitigated by the security community.

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.

With a record-breaking 40,000 CVEs reported for 2024 and a predicted higher number for 2025 by the Forum for Incident Response and Security Teams (FIRST) [1], anomaly-detection is essential for identifying these potential risks. The gap between exploitation of a zero-day and 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.

Detecting threats without relying on CVE disclosure

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

Often, 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: attack vs patch.

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.

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].

Autonomous Response

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:

  1. https://www.first.org/blog/20250607-Vulnerability-Forecast-for-2025
  2. https://cloud.google.com/blog/topics/threat-intelligence/fortimanager-zero-day-exploitation-cve-2024-47575
  3. https://thehackernews.com/2025/05/china-linked-hackers-exploit-sap-and.html

Related Darktrace blogs:

*Self-reported by customer, confirmed afterwards.

**Updated January 2024 blog now reflects current findings

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