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February 24, 2021

LockBit Ransomware Analysis: Compromised Credentials

Darktrace examines how a LockBit ransomware attack that took place over just four hours was caused by one compromised credential. Read more here.
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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24
Feb 2021

Lockbit ransomware found

LockBit ransomware was recently identified by Darktrace's Cyber AI during a trial with a retail company in the US. After an initial foothold was established via a compromised administrative credential, internal reconnaissance, lateral movement, and encryption of files occurred simultaneously, allowing the ransomware to steamroll through the digital system in just a few hours.

This incident serves as the latest reminder that ransomware campaigns now move through organizations at a speed that far outpaces human responders, demonstrating the need for machine-speed Autonomous Response to contain the threat before damage is done.

Lockbit ransomware defined

First discovered in 2019, LockBit is a relatively new family of ransomware that quickly exploits commonly available protocols and tools like SMB and PowerShell. It was originally known as ‘ABCD’ due the filename extension of the encrypted files, before it started using the current .lockbit extension. Since those early beginnings, it has evolved into one of the most calamitous strains of malware to date, asking for an average ransom of around $40,000 per organization.

As cyber-criminals level up the speed and scale of their attacks, ransomware remains a critical concern for organizations across every industry. In the past 12 months, Darktrace has observed an increase of over 20% in ransomware incidents across its customer base. Attackers are constantly developing new threat variants targeting exploits, utilizing off-the-shelf tools, and profiting from the burgeoning Ransomware-as-a-Service (RaaS) business model.

How does LockBit work?

In a typical attack, a threat actor will spend days or weeks inside a system, manually screening for the best way to grind the victim’s business to a halt. This phase tends to expose multiple indicators of compromise such as command and control (C2) beaconing, which Darktrace AI identifies in real time.

LockBit, however, only requires the presence of a human for a number of hours, after which it propagates through a system and infects other hosts on its own, without the need for human oversight. Crucially, the malware performs reconnaissance and continues to spread during the encryption phase. This allows it to cause maximal damage faster than other manual approaches.

AI-powered defense is essential in fighting back against these machine-driven attacks, which have the capacity to spread at speed and scale, and often go undetected by signature-based security tools. Cyber AI augments human teams by not only detecting the subtle signs of a threat, but autonomously responding in seconds, quicker than any human can be expected to react.

Ransomware analysis: Breaking down a LockBit attack with AI

Figure 1: Timeline of attack on the infected host and the encryption host. The infected host was the device initially infected with LockBit, which then spread to the encryption host, the device which performed the encryption.

Initial compromise

The attack commenced when a cyber-criminal gained access to a single privileged credential – either through a brute-force attack on an externally facing device, as seen in previous LockBit ransomware attacks, or simply with a phishing email. With the use of this credential, the device was able to spread and encrypt files within hours of the initial infection.

Had the method of infiltration been via phishing attack, a route that has become increasingly popular in recent months, Darktrace/ EMAIL would have withheld the email and stripped the malicious payloads, and so prevented the attack from the outset.

Limiting permissions, the use of strong passwords, and multi-factor authentication (MFA), are critical in preventing the exploitation of standard network protocols in such attacks.

Internal reconnaissance

At 14:19 local time, the first of many WMI commands (ExecMethod) to multiple internal destinations was performed by an internal IP address over DCE-RPC. This series of commands occurred throughout the encryption process. Given these commands were unusual in the context of the normal ‘pattern of life’ for the organization, Darktrace DETECT alerted the security team to each of these connections.

Within three minutes, the device had started to write executable files over SMB to hidden shares on multiple destinations – many of which were the same. File writes to hidden shares are ordinarily restricted. However, the unauthorized use of an administrative credential granted these privileges. The executable files were written to the Windows / Temp directory. Filenames had a similar formatting: .*eck[0-9]?.exe

Darktrace identified each of these SMB writes as a potential threat, since such administrative activity was unexpected from the compromised device.

The WMI commands and executable file writes continued to be made to multiple destinations. In less than two hours, the ExecMethod command was delivered to a critical device – the ‘encryption host’ – shortly followed by an executable file write (eck3.exe) to its hidden c$ share.

LockBit’s script has the capability to check its current privileges and, if non-administrative, it attempts to bypass using Windows User Account Control (UAC). This particular host did provide the required privileges to the process. Once this device was infected, encryption began.

File encryption

Only one second after encryption had started, Darktrace alerted on the unusual file extension appendage in addition to the previous, high-fidelity alerts for earlier stages of the attack lifecycle.

A recovery file – ‘Restore-My-Files.txt’ – was identified by Darktrace one second after the first encryption event. 8,998 recovery files were written, one to each encrypted folder.

An example of Darktrace’s Threat Visualizer showcasing anomalous SMB connections, with model breaches represented by dots.
Figure 2: An example of Darktrace’s Threat Visualizer showcasing anomalous SMB connections, with model breaches represented by dots.

The encryption host was a critical device that regularly utilized SMB. Exploiting SMB is a popular tactic for cyber-criminals. Such tools are so frequently used that it is difficult for signature-based detection methods to identify quickly whether their activity is malicious or not. In this case, Darktrace’s ‘Unusual Activity’ score for the device was elevated within two seconds of the first encryption, indicating that the device was deviating from its usual pattern of behavior.

Throughout the encryption process, Darktrace also detected the device performing network reconnaissance, enumerating shares on 55 devices (via srvsvc) and scanning over 1,000 internal IP addresses on nine critical TCP ports.

During this time, ‘Patient Zero’ – the initially infected device – continued to write executable files to hidden file shares. LockBit was using the initial device to spread the malware across the digital estate, while the ‘encryption host’ performed reconnaissance and encrypted the files simultaneously.

Despite Cyber AI detecting the threat even before the encryption had begun, the security team did not have eyes on Darktrace at the time of the attack. The intrusion was thus allowed to continue and over 300,000 files were encrypted and appended with the .lockbit extension. Four servers and 15 desktop devices were affected, before the attack was stopped by the administrators.

The rise of ‘hit and run’ ransomware

While most ransomware resides inside an organization for days or weeks, LockBit’s self-governing nature allows the attacker to ‘hit and run’, deploying the ransomware with minimal interaction required after the initial intrusion. The ability to detect anomalous activity across the entire digital infrastructure in real time is therefore crucial in LockBit’s prevention.

WMI and SMB are relied upon by the vast majority of companies around the world, and yet they were utilized in this attack to propagate through the system and encrypt hundreds of thousands of files. The prevalence and volume of these connections make them near-impossible to monitor with humans or signature-based detection techniques alone.

Moreover, the uniqueness of every enterprise’s digital estate impedes signature-based detection from effectively alerting on internal connections and the volume of such connections. Darktrace, however, uses machine learning to understand the individual pattern of behavior for each device, in this case allowing it to highlight the unusual internal activity as it occurred.

The organization involved did not have Darktrace’s Autonomous Response technology configured in active mode. If enabled, i would have surgically blocked the initial WMI operations and SMB drive writes that triggered the attack whilst allowing the critical network devices to continue standard operations. Even if the foothold had been established, D would have enforced the ‘pattern of life’ of the encryption host, preventing the cascade of encryption over SMB. This demonstrates the importance of meeting machine-speed attacks with autonomous cyber security, which reacts in real time to sophisticated threats when human security teams cannot.

LockBit has the ability to encrypt thousands of files in just seconds, even when targeting well-prepared organizations. This type of ransomware, with built-in worm-like functionality, is expected to become increasingly common over 2021. Such attacks can move at a speed which no human security team alone can match. Darktrace’s approach, which uses unsupervised machine learning, can respond in seconds to these rapid attacks and shut them down in their earliest stages.

Thanks to Darktrace analyst Isabel Finn for her insights on the above threat find.

Darktrace model detections:

  • Device / New or Uncommon WMI Activity
  • Compliance / SMB Drive Write
  • Compromise / Ransomware / Suspicious SMB Activity
  • Compromise / Ransomware / Ransom or Offensive Words Written to SMB
  • Anomalous File / Internal / Additional Extension Appended to SMB File
  • Anomalous Connection / SMB Enumeration
  • Device / Network Scan – Low Anomaly Score
  • Anomalous Connection / Sustained MIME Type Conversion
  • Anomalous Connection / Suspicious Read Write Ratio
  • Unusual Activity / Sustained Anomalous SMB Activity
  • Device / Large Number of Model Breaches

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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Proactive Security

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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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About the author
Nathaniel Jones
VP, Security & AI Strategy, Field CISO
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