Blog
/
/
March 6, 2018

How Malware Abused Sixt.com and Breitling.com

See how Darktrace neutralized an advanced malware infection on a customer's devices by pinpointing the source of communication and anomalous behavior.
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
Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
06
Mar 2018

Introduction

Last month Darktrace identified an advanced malware infection on a customer’s device, which used a sophisticated Command & Control (C2) channel to communicate with the attacker. The attacker spent a lot of effort in engineering a C2 channel that was meant to stay covert for months.

The malware used changing domains generated by Domain Generation Algorithms (DGAs). It also sent HTTP POST requests to malicious IP addresses while using reputable domain names for the hostname of the HTTP requests in order to blend in with normal web browsing. The attacker effectively tried to make the C2 communication look like a user browsing the well-known car rental website sixt.com and the luxury watch manufacturer breitling.com. Without using blacklists or signatures, Darktrace instantly identified this anomalous behavior, and as a result, the security team immediately isolated the infected device.

Beaconing to DGA websites

A laptop appeared on the network and made anomalous HTTP requests. The initial HTTP requests were made to the DGA domain tequbvchrjar[.]com on IP address 66.220.23[.]114. Within the next two days, several hundred HTTP POST requests were made to either this domain or to jckdxdvvm[.]com or cqyegwug[.]com, all hosted on the IP 66.220.23[.]114. Darktrace identified this behavior as beaconing – repeated connections often used in C2 communication – to DGA-domains.

What made this even more suspicious is that the POST requests used 5 different Internet Explorer User Agents for the HTTP requests. This was unusual behavior for the laptop as Darktrace had previously only observed Google Chrome User Agents. Darktrace’s unsupervised machine learning identified the User Agents as new and in conjunction with the DGA-domains as unusual activity.

The beaconing followed a steady pattern during afternoon to evening hours when the laptop was being used. This is visualized in the following graph over several days:

Malicious beaconing to reputable domains

In addition to beaconing to the DGA-domains, the device made several hundred HTTP POST requests using the hostnames sixt.com and breitling.com. Both domains are rather well-known and no public record exists of these domains having been compromised. The HTTP POST requests were made without prior GET requests and continued for several days – this is highly unusual behavior and does not resemble a user browsing those websites.

Upon closer inspection it became clear that the malware used indeed the hostnames sixt.com and breitling.com for the HTTP requests – but it was sending the HTTP requests to IP addresses owned by the attacker, not to the IP addresses that sixt.com and breitling.com resolve to on non-infected devices.

The requests for sixt.com were sent to the IP 184.105.76[.]250 while the requests for breitling.com were sent to 64.71.188[.]178. These two IP addresses, as well as the IP address hosting the DGA-domains, were hosted in the same ASN, AS6939 Hurricane Electric, which made this behavior even more suspicious. It is unlikely that all domains would be hosted in the same ASN by chance.

The malware authors used the trick of beaconing to well-known hostnames to circumvent reputation-based security controls and domain-based filters such as domain-blacklists, and to divert attention from security analysts investigating the beaconing. After all, the behavior looked on the surface like a user was browsing rental cars and luxury watches.

Further rapid investigation

Darktrace quickly revealed more details about the C2 communication. All requests were made to suspiciously-looking PHP endpoints and returned HTTP status code 200, ‘OK’, in all cases. The following shows an example of requests to three domains.

Darktrace instantly alerted on this as anomalous behavior:

A PCAP was directly downloaded from the Darktrace interface to inspect the suspicious C2 traffic:

The actual POST data appears to be encoded. Using an encoded POST request and a Content-Type of ‘x-www-form-urlencoded’ is commonly seen in malware communication.

Actively developed malware strain

It appears that this malware strain is under active development.

Open source research suggests that malware that behaves similarly has been circulated at least since the end of 2016. Some sources have attributed the malware families Razy and Nymaim to the executables seen. However, little research on these strains exist and both malware strains are generic in nature. Below are two samples from 2016:

Sample 1: [reverse.it]
Sample 2: [hybrid-analysis.com]

These pieces of malware likely represent a prior version of the malware identified by Darktrace. The 2016 version also communicated with sixt.com and breitling.com, but also made HTTP requests to carvezine.com and sievecnda.com. No DGA domains were observed in the 2016 version.

The PHP endpoints in the URI have also changed. In the version from 2016, the PHP endpoints always ended in ‘/[DGA-string]/index.php’. C2 traffic is often seen to be sent to ‘index.php’ endpoints. Defenders started monitoring the static URI Indicator of Compromise (IoC) ‘index.php’. The malware authors know this as well and have adapted their C2 communication accordingly. As shown in the above screenshots, the PHP endpoint is now in the format of ‘[DGA-string].php’. This further shows that legacy controls – such as static monitoring for quickly outdated Indicators of Compromise – do not scale in today’s threat landscape.

Conclusion

Although the malware authors intended for their implant to stay covert and defeat common security controls, Darktrace instantly alerted on the anomalous behavior. Darktrace’s detections could not have been clearer. The following graphic shows a part of the communication exhibited by the infected device around the time of the infection. Blue lines represent outgoing connections from the device. Every colored dot represents a high-level Darktrace alert:

Using no blacklists or signatures, Darktrace detected this highly anomalous malware behavior instantly. A piece of malware that was meant to stay covert for months was quickly identified using anomaly detection on network data.

Indicators of Compromise:

tequbvchrjar[.]com
jckdxdvvm[.]com
cqyegwug[.]com
66.220.23[.]114
64.71.188[.]178
184.105.76[.]250

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

More in this series

No items found.

Blog

/

Identity

/

July 3, 2025

Top Eight Threats to SaaS Security and How to Combat Them

Default blog imageDefault blog image

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]

Continue reading
About the author
Carlos Gray
Senior Product Marketing Manager, Email

Blog

/

/

July 2, 2025

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

Default blog imageDefault blog image

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

Continue reading
About the author
Your data. Our AI.
Elevate your network security with Darktrace AI