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December 1, 2022

Prevent Data Exfiltration & Know When to Respond

Over 300GB of data was exfiltrated from a customer network before Darktrace services intervened. Learn the power of Darktrace in autonomous mode.
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
Anna Gilbertson
Cyber Security Analyst
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01
Dec 2022

Side-by-side, data loss and cyber-attacks are two of the most common concerns expressed by IT directors. Whilst ransom has traditionally been seen as a big cause of this, ransom (and subsequently) data loss appears to be changing. This blog explores an incident seen within a middle eastern financial customer from May-June 2022. This customer suffered large data loss and a high volume of files with a ransom note were written on its network, but the institution’s data was not encrypted. This is an example of a growing trend in ransomware attacks, which involves exfiltration of the victim’s data and the write of a ransom note but no encryption of files. Instead of being extorted for the decryption of their files, companies are being extorted for the return of their stolen data.

Attack Summary

A threat actor spread laterally through a customer network by writing suspicious ‘.dat’ and ‘.exe’ files, exfiltrated more than 300GB of data over a two-month period and made beaconing connections to endpoints identified by OSINT as probable Cobalt Strike C2 servers.  

Attack Details

In May an internal desktop was observed connecting to a rare external endpoint over the SSL protocol using a highly unusual port and the RClone client. These connections continued for several weeks and spiked at the same time as the device’s other malicious activity. It is likely that these connections reflected a potential point of infection. 

The attacker then used the desktop and two other internal devices to perform network scanning and enumeration activity in order to discover other devices on the network it could infect. In total 856 unique IPs were scanned- largely over TCP, UDP and ICMP. Further directory replication service requests were also seen over DCE-RPC, suggesting an attempt to extract user credentials. One device was later seen accessing an unencrypted password file showing successful recon. The attacker then used the already compromised devices to infect other devices on the network by writing suspicious ‘.exe’ and ‘.dat’ files including ‘Bun.dat' and ‘Agent.exe’. 

The compromised devices were then used by the attacker to download nearly 4GB of data from an internal server and to upload a similar amount to a suspicious IP address associated with a young domain. Over the following two months, the compromised devices went on to upload roughly 100GB of data to the same destination each week.  

The attacker also used these devices to make beaconing connections to several rare and external endpoints. Some of these endpoints were created just before the beginning of the compromise and have all been identified by OSINT as probable Cobalt Strike command and control servers. During this beaconing an internal device was also observed making an extremely large number of writes of files that appeared to be ransom notes, these were appropriately named ‘YOURNETWORKDATAHASBEENCOMPROMISED.txt’. 

Darktrace Coverage

 How did this attack remain hidden from the company’s other tools? Neither the initial C2 IP Address, the follow-on Cobalt Strike C2 servers or the exfiltration server were associated with malicious activity in open-source reporting prior to this compromise. This meant they were likely omitted from expected blacklists and standard signature-based security. Furthermore, executable files written by the compromised devices included ‘anydesk.exe’ and ‘procdump64.exe’, both of which are legitimate tools commonly used by administrators. This demonstrates the use of living off the land tactics (LotL) which are typically hard to detect. 

Given the attack’s novelty and LotL techniques, it is no surprise that Darktrace DETECT/Network identified anomalies in usual network behaviour. In particular, Cyber AI Analyst was a large highlight when it came to both Darktrace’s and the security team’s follow-up triage. AI Analyst collated different phases of the compromise such as the network scanning activity, the lateral movement activity over the DCE-RPC protocol, the data exfiltration, and the beaconing activity to the Cobalt Strike endpoints. This helped to map a clear timeline and progression of events along the kill chain. 

Figure 1: An AI Analyst Graph showing the timeline of the beaconing activity to the Cobalt Strike Endpoints and the lateral movement activity over the DCE-RPC protocol.
Figure 2: AI Analyst Graph showing the timeline of overall suspicious activities carried out by the compromised devices.

On top of the DETECT product, Darktrace services had a large role to play in the aftermath. The customer subscribed to Darktrace’s Ask the Expert (ATE) service and only became fully aware of the compromise after submitting an ATE ticket requesting assistance investigating one of the related model breaches. Finally, RESPOND itself (although in human confirmation mode) was still able to be used to manually quarantine devices.

Conclusion

During this incident, the initial point of infection, data exfiltration endpoint, and Cobalt Strike C2 Servers were recently created and had no OSINT associating them with malicious activity. This demonstrates how ineffective traditional signature-based intrusion detection systems can be at detecting compromises and how effective Darktrace is at detecting novel campaigns and attacks. Despite this, detection is clearly not enough, especially as out-of-hours attacks increase in frequency and effectiveness. This customer had RESPOND in human confirmation mode which meant that the activity was acted upon and stopped slower than had it been set autonomously. If malicious data loss is to be stopped in full, companies need to reduce reliance on humans and embrace AI-based protection.

Thanks to Darktrace analyst Steve Robinson for his insights on the above threat investigation.

Appendices

Related AI Analyst Incidents

  • Unusual Repeated Connections
  • Suspicious DCE-RPC Activity 
  • Internal Download and External Upload 
  • Scanning of Multiple Devices 
  • Possible SSL Command and Control Activity to Multiple Endpoints 
  • Suspicious Remote WMI Activity 
  • Access of Probable Unencrypted Password File 
  • Extensive Suspicious DCE-RPC Activity
  • Suspicious Remote Control Service Activity 

Indicators of Compromise 

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
Anna Gilbertson
Cyber Security Analyst

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April 30, 2026

Mythos vs Ethos: Defending in an Era of AI‑Accelerated Vulnerability Discovery

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Anthropic’s Mythos and what it means for security teams

Recent attention on systems such as Anthropic Mythos highlights a notable problem for defenders. Namely that disclosure’s role in coordinating defensive action is eroding.

As AI systems gain stronger reasoning and coding capability, their usefulness in analyzing complex software environments and identifying weaknesses naturally increases. What has changed is not attacker motivation, but the conditions under which defenders learn about and organize around risk. Vulnerability discovery and exploitation increasingly unfold in ways that turn disclosure into a retrospective signal rather than a reliable starting point for defense.

Faster discovery was inevitable and is already visible

The acceleration of vulnerability discovery was already observable across the ecosystem. Publicly disclosed vulnerabilities (CVEs) have grown at double-digit rates for the past two years, including a 32% increase in 2024 according to NIST, driven in part by AI even prior to Anthropic’s Mythos model. Most notably XBOW topped the HackerOne US bug bounty leaderboard, marking the first time an autonomous penetration tester had done so.  

The technical frontier for AI capabilities has been described elsewhere as jagged, and the implication is that Mythos is exceptional but not unique in this capability. While Mythos appears to make significant progress in complex vulnerability analysis, many other models are already able to find and exploit weaknesses to varying degrees.  

What matters here is not which model performs best, but the fact that vulnerability discovery is no longer a scarce or tightly bounded capability.

The consequence of this shift is not simply earlier discovery. It is a change in the defender-attacker race condition. Disclosure once acted as a rough synchronization point. While attackers sometimes had earlier knowledge, disclosure generally marked the moment when risk became visible and defensive action could be broadly coordinated. Increasingly, that coordination will no longer exist. Exploitation may be underway well before a CVE is published, if it is published at all.

Why patch velocity alone is not the answer

The instinctive response to this shift is to focus on patching faster, but treating patch velocity as the primary solution misunderstands the problem. Most organizations are already constrained in how quickly they can remediate vulnerabilities. Asset sprawl, operational risk, testing requirements, uptime commitments, and unclear ownership all limit response speed, even when vulnerabilities are well understood.

If discovery and exploitation now routinely precede disclosure, then patching cannot be the first line of defense. It becomes one necessary control applied within a timeline that has already shifted. This does not imply that organizations should patch less. It means that patching cannot serve as the organizing principle for defense.

Defense needs a more stable anchor

If disclosure no longer defines when defense begins, then defense needs a reference point that does not depend on knowing the vulnerability in advance.  

Every digital environment has a behavioral character. Systems authenticate, communicate, execute processes, and access resources in relatively consistent ways over time. These patterns are not static rules or signatures. They are learned behaviors that reflect how an organization operates.

When exploitation occurs, even via previously unknown vulnerabilities, those behavioral patterns change.

Attackers may use novel techniques, but they still need to gain access, create processes, move laterally, and will ultimately interact with systems in ways that diverge from what is expected. That deviation is observable regardless of whether the underlying weakness has been formally named.

In an environment where disclosure can no longer be relied on for timing or coordination, behavioral understanding is no longer an optional enhancement; it becomes the only consistently available defensive signal.

Detecting risk before disclosure

Darktrace’s threat research has consistently shown that malicious activity often becomes visible before public disclosure.

In multiple cases, including exploitation of Ivanti, SAP NetWeaver, and Trimble Cityworks, Darktrace detected anomalous behavior days or weeks ahead of CVE publication. These detections did not rely on signatures, threat intelligence feeds, or awareness of the vulnerability itself. They emerged because systems began behaving in ways that did not align with their established patterns.

This reflects a defensive approach grounded in ‘Ethos’, in contrast to the unbounded exploration represented by ‘Mythos’. Here, Mythos describes continuous vulnerability discovery at speed and scale. Ethos reflects an understanding of what is normal and expected within a specific environment, grounded in observed behavior.

Revisiting assume breach

These conditions reinforce a principle long embedded in Zero Trust thinking: assume breach.

If exploitation can occur before disclosure, patching vulnerabilities can no longer act as the organizing principle for defense. Instead, effective defense must focus on monitoring for misuse and constraining attacker activity once access is achieved. Behavioral monitoring allows organizations to identify early‑stage compromise and respond while uncertainty remains, rather than waiting for formal verification.

AI plays a critical role here, not by predicting every exploit, but by continuously learning what normal looks like within a specific environment and identifying meaningful deviation at machine speed. Identifying that deviation enables defenders to respond by constraining activity back towards normal patterns of behavior.

Not an arms race, but an asymmetry

AI is often framed as fueling an arms race between attackers and defenders. In practice, the more important dynamic is asymmetry.

Attackers operate broadly, scanning many environments for opportunities. Defenders operate deeply within their own systems, and it’s this business context which is so significant. Behavioral understanding gives defenders a durable advantage. Attackers may automate discovery, but they cannot easily reproduce what belonging looks like inside a particular organization.

A changed defensive model

AI‑accelerated vulnerability discovery does not mean defenders have lost. It does mean that disclosure‑driven, patch‑centric models no longer provide a sufficient foundation for resilience.

As vulnerability volumes grow and exploitation timelines compress, effective defense increasingly depends on continuous behavioral understanding, detection that does not rely on prior disclosure, and rapid containment to limit impact. In this model, CVEs confirm risk rather than define when defense begins.

The industry has already seen this approach work in practice. As AI continues to reshape both offense and defense, behavioral detection will move from being complementary to being essential.

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April 27, 2026

How a Compromised eScan Update Enabled Multi‑Stage Malware and Blockchain C2

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The rise of supply chain attacks

In recent years, the abuse of trusted software has become increasingly common, with supply chain compromises emerging as one of the fastest growing vectors for cyber intrusions. As highlighted in Darktrace’s Annual Threat Report 2026, attackers and state-actors continue to find significant value in gaining access to networks through compromised trusted links, third-party tools, or legitimate software. In January 2026, a supply chain compromise affecting MicroWorld Technologies’ eScan antivirus product was reported, with malicious updates distributed to customers through the legitimate update infrastructure. This, in turn, resulted in a multi‑stage loader malware being deployed on compromised devices [1][2].

An overview of eScan exploitation

According to eScan’s official threat advisory, unauthorized access to a regional update server resulted in an “incorrect file placed in the update distribution path” [3]. Customers associated with the affected update servers who downloaded the update during a two-hour window on January 20 were impacted, with affected Windows devices subsequently have experiencing various errors related to update functions and notifications [3].

While eScan did not specify which regional update servers were affected by the malicious update, all impacted Darktrace customer environments were located in the Europe, Middle East, and Africa (EMEA) region.

External research reported that a malicious 32-bit executable file , “Reload.exe”, was first installed on affected devices, which then dropped the 64-bit downloader, “CONSCTLX.exe”. This downloader establishes persistence by creating scheduled tasks such as “CorelDefrag”, which are responsible for executing PowerShell scripts. Subsequently, it evades detection by tampering with the Windows HOSTS file and eScan registry to prevent future remote updates intended for remediation. Additional payloads are then downloaded from its command-and-control (C2) server [1].

Darktrace’s coverage of eScan exploitation

Initial Access and Blockchain as multi-distributed C2 Infrastructure

On January 20, the same day as the aforementioned two‑hour exploit window, Darktrace observed multiple devices across affected networks downloading .dlz package files from eScan update servers, followed by connections to an anomalous endpoint, vhs.delrosal[.]net, which belongs to the attackers’ C2 infrastructure.

The endpoint contained a self‑signed SSL certificate with the string “O=Internet Widgits Pty Ltd, ST=SomeState, C=AU”, a default placeholder commonly used in SSL/TLS certificates for testing and development environments, as well as in malicious C2 infrastructure [4].

Utilizing a multi‑distributed C2 infrastructure, the attackers also leveraged domains linked with the Solana open‑source blockchain for C2 purposes, namely “.sol”. These domains were human‑readable names that act as aliases for cryptocurrency wallet addresses. As browsers do not natively resolve .sol domains, the Solana Naming System (formerly known as Bonfida, an independent contributor within the Solana ecosystem) provides a proxy service, through endpoints such as sol-domain[.]org, to enable browser access.

Darktrace observed devices connecting to blackice.sol-domain[.]org, indicating that attackers were likely using this proxy to reach a .sol domain for C2 activity. Given this behavior, it is likely that the attackers leveraged .sol domains as a dead drop resolver, a C2 technique in which threat actors host information on a public and legitimate service, such as a blockchain. Additional proxy resolver endpoints, such as sns-resolver.bonfida.workers[.]dev, were also observed.

Solana transactions are transparent, allowing all activity to be viewed publicly. When Darktrace analysts examined the transactions associated with blackice[.]sol, they observed that the earliest records dated November 7, 2025, which coincides with the creation date of the known C2 endpoint vhs[.]delrosal[.]net as shown in WHOIS Lookup information [4][5].

WHOIS Look records of the C2 endpoint vhs[.]delrosal[.]net.
Figure 1: WHOIS Look records of the C2 endpoint vhs[.]delrosal[.]net.
 Earliest observed transaction record for blackice[.]sol on public ledgers.
Figure 2: Earliest observed transaction record for blackice[.]sol on public ledgers.

Subsequent instructions found within the transactions contained strings such as “CNAME= vhs[.]delrosal[.]net”, indicating attempts to direct the device toward the malicious endpoint. A more recent transaction recorded on January 28 included strings such as “hxxps://96.9.125[.]243/i;code=302”, suggesting an effort to change C2 endpoints. Darktrace observed multiple alerts triggered for these endpoints across affected devices.

Similar blockchain‑related endpoints, such as “tumama.hns[.]to”, were also observed in C2 activities. The hns[.]to service allows web browsers to access websites registered on Handshake, a decentralized blockchain‑based framework designed to replace centralized authorities and domain registries for top‑level domains. This shift toward decentralized, blockchain‑based infrastructure likely reflects increased efforts by attackers to evade detection.

In outgoing connections to these malicious endpoints across affected networks, Darktrace / NETWORK recognized that the activity was 100% rare and anomalous for both the devices and the wider networks, likely indicative of malicious beaconing, regardless of the underlying trusted infrastructure. In addition to generating multiple model alerts to capture this malicious activity across affected networks, Darktrace’s Cyber AI Analyst was able to compile these separate events into broader incidents that summarized the entire attack chain, allowing customers’ security teams to investigate and remediate more efficiently. Moreover, in customer environments where Darktrace’s Autonomous Response capability was enabled, Darktrace took swift action to contain the attack by blocking beaconing connections to the malicious endpoints, even when those endpoints were associated with seemingly trustworthy services.

Conclusion

Attacks targeting trusted relationships continue to be a popular strategy among threat actors. Activities linked to trusted or widely deployed software are often unintentionally whitelisted by existing security solutions and gateways. Darktrace observed multiple devices becoming impacted within a very short period, likely because tools such as antivirus software are typically mass‑deployed across numerous endpoints. As a result, a single compromised delivery mechanism can greatly expand the attack surface.

Attackers are also becoming increasingly creative in developing resilient C2 infrastructure and exploiting legitimate services to evade detection. Defenders are therefore encouraged to closely monitor anomalous connections and file downloads. Darktrace’s ability to detect unusual activity amidst ever‑changing tactics and indicators of compromise (IoCs) helps organizations maintain a proactive and resilient defense posture against emerging threats.

Credit to Joanna Ng (Associate Principal Cybersecurity Analyst) and Min Kim (Associate Principal Cybersecurity Analyst) and Tara Gould (Malware Researcher Lead)

Edited by Ryan Traill (Content Manager)

Appendices

Darktrace Model Detections

  • Anomalous File::Zip or Gzip from Rare External Location
  • Anomalous Connection / Suspicious Self-Signed SSL
  • Anomalous Connection / Rare External SSL Self-Signed
  • Anomalous Connection / Suspicious Expired SSL
  • Anomalous Server Activity / Anomalous External Activity from Critical Network Device

List of Indicators of Compromise (IoCs)

  • vhs[.]delrosal[.]net – C2 server
  • tumama[.]hns[.]to – C2 server
  • blackice.sol-domain[.]org – C2 server
  • 96.9.125[.]243 – C2 Server

MITRE ATT&CK Mapping

  • T1071.001 - Command and Control: Web Protocols
  • T1588.001 - Resource Development
  • T1102.001 - Web Service: Dead Drop Resolver
  • T1195 – Supple Chain Compromise

References

[1] https://www.morphisec.com/blog/critical-escan-threat-bulletin/

[2] https://www.bleepingcomputer.com/news/security/escan-confirms-update-server-breached-to-push-malicious-update/

[3] hxxps://download1.mwti.net/documents/Advisory/eScan_Security_Advisory_2026[.]pdf

[4] https://www.virustotal.com/gui/domain/delrosal.net

[5] hxxps://explorer.solana[.]com/address/2wFAbYHNw4ewBHBJzmDgDhCXYoFjJnpbdmeWjZvevaVv

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About the author
Joanna Ng
Associate Principal Analyst
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