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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
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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

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December 22, 2025

The Year Ahead: AI Cybersecurity Trends to Watch in 2026

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Introduction: 2026 cyber trends

Each year, we ask some of our experts to step back from the day-to-day pace of incidents, vulnerabilities, and headlines to reflect on the forces reshaping the threat landscape. The goal is simple:  to identify and share the trends we believe will matter most in the year ahead, based on the real-world challenges our customers are facing, the technology and issues our R&D teams are exploring, and our observations of how both attackers and defenders are adapting.  

In 2025, we saw generative AI and early agentic systems moving from limited pilots into more widespread adoption across enterprises. Generative AI tools became embedded in SaaS products and enterprise workflows we rely on every day, AI agents gained more access to data and systems, and we saw glimpses of how threat actors can manipulate commercial AI models for attacks. At the same time, expanding cloud and SaaS ecosystems and the increasing use of automation continued to stretch traditional security assumptions.

Looking ahead to 2026, we’re already seeing the security of AI models, agents, and the identities that power them becoming a key point of tension – and opportunity -- for both attackers and defenders. Long-standing challenges and risks such as identity, trust, data integrity, and human decision-making will not disappear, but AI and automation will increase the speed and scale of the cyber risk.  

Here's what a few of our experts believe are the trends that will shape this next phase of cybersecurity, and the realities organizations should prepare for.  

Agentic AI is the next big insider risk

In 2026, organizations may experience their first large-scale security incidents driven by agentic AI behaving in unintended ways—not necessarily due to malicious intent, but because of how easily agents can be influenced. AI agents are designed to be helpful, lack judgment, and operate without understanding context or consequence. This makes them highly efficient—and highly pliable. Unlike human insiders, agentic systems do not need to be socially engineered, coerced, or bribed. They only need to be prompted creatively, misinterpret legitimate prompts, or be vulnerable to indirect prompt injection. Without strong controls around access, scope, and behavior, agents may over-share data, misroute communications, or take actions that introduce real business risk. Securing AI adoption will increasingly depend on treating agents as first-class identities—monitored, constrained, and evaluated based on behavior, not intent.

-- Nicole Carignan, SVP of Security & AI Strategy

Prompt Injection moves from theory to front-page breach

We’ll see the first major story of an indirect prompt injection attack against companies adopting AI either through an accessible chatbot or an agentic system ingesting a hidden prompt. In practice, this may result in unauthorized data exposure or unintended malicious behavior by AI systems, such as over-sharing information, misrouting communications, or acting outside their intended scope. Recent attention on this risk—particularly in the context of AI-powered browsers and additional safety layers being introduced to guide agent behavior—highlights a growing industry awareness of the challenge.  

-- Collin Chapleau, Senior Director of Security & AI Strategy

Humans are even more outpaced, but not broken

When it comes to cyber, people aren’t failing; the system is moving faster than they can. Attackers exploit the gap between human judgment and machine-speed operations. The rise of deepfakes and emotion-driven scams that we’ve seen in the last few years reduce our ability to spot the familiar human cues we’ve been taught to look out for. Fraud now spans social platforms, encrypted chat, and instant payments in minutes. Expecting humans to be the last line of defense is unrealistic.

Defense must assume human fallibility and design accordingly. Automated provenance checks, cryptographic signatures, and dual-channel verification should precede human judgment. Training still matters, but it cannot close the gap alone. In the year ahead, we need to see more of a focus on partnership: systems that absorb risk so humans make decisions in context, not under pressure.

-- Margaret Cunningham, VP of Security & AI Strategy

AI removes the attacker bottleneck—smaller organizations feel the impact

One factor that is currently preventing more companies from breaches is a bottleneck on the attacker side: there’s not enough human hacker capital. The number of human hands on a keyboard is a rate-determining factor in the threat landscape. Further advancements of AI and automation will continue to open that bottleneck. We are already seeing that. The ostrich approach of hoping that one’s own company is too obscure to be noticed by attackers will no longer work as attacker capacity increases.  

-- Max Heinemeyer, Global Field CISO

SaaS platforms become the preferred supply chain target

Attackers have learned a simple lesson: compromising SaaS platforms can have big payouts. As a result, we’ll see more targeting of commercial off-the-shelf SaaS providers, which are often highly trusted and deeply integrated into business environments. Some of these attacks may involve software with unfamiliar brand names, but their downstream impact will be significant. In 2026, expect more breaches where attackers leverage valid credentials, APIs, or misconfigurations to bypass traditional defenses entirely.

-- Nathaniel Jones, VP of Security & AI Strategy

Increased commercialization of generative AI and AI assistants in cyber attacks

One trend we’re watching closely for 2026 is the commercialization of AI-assisted cybercrime. For example, cybercrime prompt playbooks sold on the dark web—essentially copy-and-paste frameworks that show attackers how to misuse or jailbreak AI models. It’s an evolution of what we saw in 2025, where AI lowered the barrier to entry. In 2026, those techniques become productized, scalable, and much easier to reuse.  

-- Toby Lewis, Global Head of Threat Analysis

Conclusion

Taken together, these trends underscore that the core challenges of cybersecurity are not changing dramatically -- identity, trust, data, and human decision-making still sit at the core of most incidents. What is changing quickly is the environment in which these challenges play out. AI and automation are accelerating everything: how quickly attackers can scale, how widely risk is distributed, and how easily unintended behavior can create real impact. And as technology like cloud services and SaaS platforms become even more deeply integrated into businesses, the potential attack surface continues to expand.  

Predictions are not guarantees. But the patterns emerging today suggest that 2026 will be a year where securing AI becomes inseparable from securing the business itself. The organizations that prepare now—by understanding how AI is used, how it behaves, and how it can be misused—will be best positioned to adopt these technologies with confidence in the year ahead.

Learn more about how to secure AI adoption in the enterprise without compromise by registering to join our live launch webinar on February 3, 2026.  

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December 22, 2025

Why Organizations are Moving to Label-free, Behavioral DLP for Outbound Email

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Why outbound email DLP needs reinventing

In 2025, the global average cost of a data breach fell slightly — but remains substantial at USD 4.44 million (IBM Cost of a Data Breach Report 2025). The headline figure hides a painful reality: many of these breaches stem not from sophisticated hacks, but from simple human error: mis-sent emails, accidental forwarding, or replying with the wrong attachment. Because outbound email is a common channel for sensitive data leaving an organization, the risk posed by everyday mistakes is enormous.

In 2025, 53% of data breaches involved customer PII, making it the most commonly compromised asset (IBM Cost of a Data Breach Report 2025). This makes “protection at the moment of send” essential. A single unintended disclosure can trigger compliance violations, regulatory scrutiny, and erosion of customer trust –consequences that are disproportionate to the marginal human errors that cause them.

Traditional DLP has long attempted to mitigate these impacts, but it relies heavily on perfect labelling and rigid pattern-matching. In reality, data loss rarely presents itself as a neat, well-structured pattern waiting to be caught – it looks like everyday communication, just slightly out of context.

How data loss actually happens

Most data loss comes from frustratingly familiar scenarios. A mistyped name in auto-complete sends sensitive data to the wrong “Alex.” A user forwards a document to a personal Gmail account “just this once.” Someone shares an attachment with a new or unknown correspondent without realizing how sensitive it is.

Traditional, content-centric DLP rarely catches these moments. Labels are missing or wrong. Regexes break the moment the data shifts formats. And static rules can’t interpret the context that actually matters – the sender-recipient relationship, the communication history, or whether this behavior is typical for the user.

It’s the everyday mistakes that hurt the most. The classic example: the Friday 5:58 p.m. mis-send, when auto-complete selects Martin, a former contractor, instead of Marta in Finance.

What traditional DLP approaches offer (and where gaps remain)

Most email DLP today follows two patterns, each useful but incomplete.

  • Policy- and label-centric DLP works when labels are correct — but content is often unlabeled or mislabeled, and maintaining classification adds friction. Gaps appear exactly where users move fastest
  • Rule and signature-based approaches catch known patterns but miss nuance: human error, new workflows, and “unknown unknowns” that don’t match a rule

The takeaway: Protection must combine content + behavior + explainability at send time, without depending on perfect labels.

Your technology primer: The three pillars that make outbound DLP effective

1) Label-free (vs. data classification)

Protects all content, not just what’s labeled. Label-free analysis removes classification overhead and closes gaps from missing or incorrect tags. By evaluating content and context at send time, it also catches misdelivery and other payload-free errors.

  • No labeling burden; no regex/rule maintenance
  • Works when tags are missing, wrong, or stale
  • Detects misdirected sends even when labels look right

2) Behavioral (vs. rules, signatures, threat intelligence)

Understands user behavior, not just static patterns. Behavioral analysis learns what’s normal for each person, surfacing human error and subtle exfiltration that rules can’t. It also incorporates account signals and inbound intel, extending across email and Teams.

  • Flags risk without predefined rules or IOCs
  • Catches misdelivery, unusual contacts, personal forwards, odd timing/volume
  • Blends identity and inbound context across channels

3) Proprietary DSLM (vs. generic LLM)

Optimized for precise, fast, explainable on-send decisions. A DSLM understands email/DLP semantics, avoids generative risks, and stays auditable and privacy-controlled, delivering intelligence reliably without slowing mail flow.

  • Low-latency, on-send enforcement
  • Non-generative for predictable, explainable outcomes
  • Governed model with strong privacy and auditability

The Darktrace approach to DLP

Darktrace / EMAIL – DLP stops misdelivery and sensitive data loss at send time using hold/notify/justify/release actions. It blends behavioral insight with content understanding across 35+ PII categories, protecting both labeled and unlabeled data. Every action is paired with clear explainability: AI narratives show exactly why an email was flagged, supporting analysts and helping end-users learn. Deployment aligns cleanly with existing SOC workflows through mail-flow connectors and optional Microsoft Purview label ingestion, without forcing duplicate policy-building.

Deployment is simple: Microsoft 365 routes outbound mail to Darktrace for real-time, inline decisions without regex or rule-heavy setup.

A buyer’s checklist for DLP solutions

When choosing your DLP solution, you want to be sure that it can deliver precise, explainable protection at the moment it matters – on send – without operational drag.  

To finish, we’ve compiled a handy list of questions you can ask before choosing an outbound DLP solution:

  • Can it operate label free when tags are missing or wrong? 
  • Does it truly learn per user behavior (no shortcuts)? 
  • Is there a domain specific model behind the content understanding (not a generic LLM)? 
  • Does it explain decisions to both analysts and end users? 
  • Will it integrate with your label program and SOC workflows rather than duplicate them? 

For a deep dive into Darktrace’s DLP solution, check out the full solution brief.

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About the author
Carlos Gray
Senior Product Marketing Manager, Email
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