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July 26, 2022

Self-Learning AI for Zero-Day and N-Day Attack Defense

Explore the differences between zero-day and n-day attacks on different customer servers to learn how Darktrace detects and prevents cyber threats effectively.
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
Lewis Morgan
Cyber Analyst
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26
Jul 2022

Key Terms:

Zero-day | A recently discovered security vulnerability in computer software that has no currently available fix or patch. Its name come from the reality that vendors have “zero days” to act and respond.

N-day | A vulnerability that emerges in computer software in which a vendor is aware and may have already issued (or are currently working on) a patch or fix. Active exploits often already exist and await abuse by nefarious actors.

Traditional security solutions often apply signature-based-detection when identifying cyber threats, helping to defend against legacy attacks but consequently missing novel ones. Therefore, security teams often lend a lot of focus to ensuring that the risk of zero-day vulnerabilities is reduced [1]. As explored in this blog, however, organizations can face just as much of a risk from n-day attacks, since they invite the most attention from malicious actors [2]. This is due in part to the reduced complexity, cost and time invested in researching and finding new exploits compared with that found when attackers exploit zero-days. 

This blog will examine both a zero-day and n-day attack that two different Darktrace customers faced in the fall of 2021. This will include the activity Darktrace detected, along with the steps taken by Darktrace/Network to intervene. It will then compare the incidents, discuss the possible dangers of third-party integrations, and assess the deprecation of legacy security tools.

Revisiting zero-day attacks 

Zero-days are among the greatest concerns security teams face in the era of modern technology and networking. Defending critical systems from zero-day compromises is a task most legacy security solutions are often unable to handle. Due to the complexity of uncovering new security flaws and developing elaborate code that can exploit them, these attacks are often carried out by funded or experienced groups such as nation-state actors and APTs. One of history’s most prolific zero-days, ‘Stuxnet’, sent security teams worldwide into a global panic in 2010. This involved a widespread attack on Iranian nuclear infrastructure and was widely accepted to be a result of nation-state actors [3]. The Stuxnet worm took advantage of four zero-day exploits, compromising over 200,000 devices and physically damaging around 10% of the 9,000 critical centrifuges at the Natanz nuclear site. 

More recently, 2021 saw the emergence of several critical zero-day vulnerabilities within SonicWall’s product suite [4]. SonicWall is a security hardware manufacturer that provides hardware firewall devices, unified threat management, VPN gateways and network security solutions. Some of these vulnerabilities lie within their Secure Mobile Access (SMA) 100 series (for example, CVE-2019-7481, CVE-2021-20016 and CVE-2021-20038 to name a few). These directly affected VPN devices and often allowed attackers easy remote access to company devices. CVE-2021-20016 in particular incorporates an SQL-Injection vulnerability within SonicWall’s SSL VPN SMA 100 product line [5]. If exploited, this defect would allow an unauthenticated remote attacker to perform their own malicious SQL query in order to access usernames, passwords and other session related information. 

The N-day underdog

The shadow cast by zero-day attacks often shrouds that of n-day attacks. N-days, however, often pose an equal - if not greater - risk to the majority of organizations, particularly those in industrial sectors. Since these vulnerabilities have fixes available, all of the hard work around research is already done; malicious actors only need to view proof of concepts (POCs) or, if proficient in coding, reverse-engineer software to reveal code-changes (binary diffing) in order to exploit these security flaws in the wild. These vulnerabilities are typically attributed to opportunistic hackers and script-kiddies, where little research or heavy lifting is required.  

August 2021 gave rise to a critical vulnerability in Atlassian Confluence servers, namely CVE-2021-26084 [6]. Confluence is a widely used collaboration wiki tool and knowledge-sharing platform. As introduced and discussed a few months ago in a previous Darktrace blog (Explore Internet-Facing System Vulnerabilities), this vulnerability allows attackers to remotely execute code on internet-facing servers after exploiting injection vulnerabilities in Object-Graph Navigation Language (OGNL). Whilst Confluence had patches and fixes available to users, attackers still jumped on this opportunity and began scanning the internet for signs of critical devices serving this outdated software [7]. Once identified, they would  exploit the vulnerability, often installing crypto mining software onto the device. More recently, Darktrace explored a new vulnerability (CVE-2022-26134), disclosed midway through 2022, that affected Confluence servers and data centers using similar techniques to that found in CVE-2021-26084 [8]. 

SonicWall in the wild – 1. Zero-day attack

At the beginning of August 2021, Darktrace prevented an attack from taking place within a European automotive customer’s environment (Figure 1). The attack targeted a vulnerable internet-facing SonicWall VPN server, and while the attacker’s motive remains unclear, similar historic events suggest that they intended to perform ransomware encryption or data exfiltration. 

Figure 1: Timeline of the SonicWall attack 

Darktrace was unable to confirm the definite tactics, techniques and procedures (TTPs) used by the attacker to compromise the customer’s environment, as the device was compromised before Darktrace installation and coverage. However, from looking at recently disclosed SonicWall VPN vulnerabilities and patterns of behaviour, it is likely CVE-2021-20016 played a part. At some point after this initial infection, it is also believed the device was able to move laterally to a domain controller (DC) using administrative credentials; it was this server that then initiated the anomalous activity that Darktrace detected and alerted on. 

On August 5th 2021 , Darktrace observed this compromised domain controller engaging in unusual ICMP scanning - a protocol used to discover active devices within an environment and create a map of an organization’s network topology. Shortly after, the infected server began scanning devices for open RDP ports and enumerating SMB shares using unorthodox methods. SMB delete and HTTP requests (over port 445 and 80 respectively) were made for files named delete.me in the root directory of numerous network shares using the user agent Microsoft WebDAV. However, no such files appeared to exist within the environment. This may have been the result of an attacker probing devices in the network in an effort to see their responses and gather information on properties and vulnerabilities they could later exploit. 

Soon the infected DC began establishing RDP tunnels back to the VPN server and making requests to an internal DNS server for multiple endpoints relating to exploit kits, likely in an effort to strengthen the attacker’s foothold within the environment. Some of the endpoints requested relate to:

-       EternalBlue vulnerability 

-       Petit Potam NTLM hash attack tool

-       Unusual GitHub repositories

-       Unusual Python repositories  

The DC made outgoing NTLM requests to other internal devices, implying the successful installation of Petit Potam exploitation tools. The server then began performing NTLM reconnaissance, making over 1,000 successful logins under ‘Administrator’ to several other internal devices. Around the same time, the device was also seen making anonymous SMBv1 logins to numerous internal devices, (possibly symptomatic of the attacker probing machines for EternalBlue vulnerabilities). 

Interestingly, the device also made numerous failed authentication attempts using a spoofed credential for one of the organization’s security managers. This was likely in an attempt to hide themselves using ‘Living off the Land’ (LotL) techniques. However, whilst the attacker clearly did their research on the company, they failed to acknowledge the typical naming convention used for credentials within the environment. This ultimately backfired and made the compromise more obvious and unusual. 

In the morning of the following day, the initially compromised VPN server began conducting further reconnaissance, engaging in similar activity to that observed by the domain controller. Until now, the customer had set Darktrace RESPOND to run in human confirmation mode, meaning interventions were not made autonomously but required confirmation by a member of the internal security team. However, thanks to Proactive Threat Notifications (PTNs) delivered by Darktrace’s dedicated SOC team, the customer was made immediately aware of this unusual behaviour, allowing them to apply manual Darktrace RESPOND blocks to all outgoing connections (Figure 2). This gave the security team enough time to respond and remediate before serious damage could be done.

Figure 2: Darktrace RESPOND model breach showing the manually applied “Quarantine Device” action taken against the compromised VPN server. This screenshot displays the UI from Darktrace version 5.1

Confluence in the wild – 2. N-day attack

Towards the end of 2021, Darktrace saw a European broadcasting customer leave an Atlassian Confluence internet-facing server unpatched and vulnerable to crypto-mining malware using CVE-2021-26084. Thanks to Darktrace, this attack was entirely immobilized within only a few hours of the initial infection, protecting the organization from damage (Figure 3). 

Figure 3: Timeline of the Confluence attack

On midday on September 1st 2021, an unpatched Confluence server was seen receiving SSL connections over port 443 from a suspicious new endpoint, 178.238.226[.]127.  The connections were encrypted, meaning Darktrace was unable to view the contents and ascertain what requests were being made. However, with the disclosure of CVE-2021-26084 just 7 days prior to this activity, it is likely that the TTPs used involved injecting OGNL expressions to Confluence server memory; allowing the attacker to remotely execute code on the vulnerable server.

Immediately after successful exploitation of the Confluence server, the infected device was observed making outgoing HTTP GET requests to several external endpoints using a new user agent (curl/7.61.1). Curl was used to silently download and configure multiple suspicious files relating to XMRig cryptocurrency miner, including ld.sh, XMRig and config.json. Subsequent outgoing connections were then made to europe.randomx-hub.miningpoolhub[.]com · 172.105.210[.]117 using the JSON-RPC protocol, seen alongside the mining credential maillocal.confluence (Figure 4). Only 3 seconds after initial compromise, the infected device began attempting to mine cryptocurrency using the Minergate protocol but was instantly and autonomously blocked by Darktrace RESPOND. This prevented the server from abusing system resources and generating profits for the attacker.

Figure 4: A graph showing the frequency of external connections using the JSON-RPC protocol made by the breach device over a 48-hour window. The orange-red dots represent models that breached as a result of this activity, demonstrating the “waterfall” effect commonly seen when a device suffers a compromise. This screenshot displays the UI from Darktrace version 5.1

In the afternoon, the malware persisted with its infection. The compromised server began making successive HTTP GET requests to a new rare endpoint 195.19.192[.]28 using the same curl user agent (Figures 5 & 6). These requests were for executable and dynamic library files associated with Kinsing malware (but fortunately were also blocked by Darktrace RESPOND). Kinsing is a malware strain found in numerous attack campaigns which is often associated with crypto-jacking, and has appeared in previous Darktrace blogs [9].

Figure 5: Cyber AI Analyst summarising the unusual download of Kinsing software using the new curl user agent. This screenshot displays the UI from Darktrace version 5.1

The attacker then began making HTTP POST requests to an IP 185.154.53[.]140, using the same curl user agent; likely a method for the attacker to maintain persistence within the network and establish a foothold using its C2 infrastructure. The Confluence server was then again seen attempting to mine cryptocurrency using the Minergate protocol. It made outgoing JSON-RPC connections to a different new endpoint, 45.129.2[.]107, using the following mining credential: ‘42J8CF9sQoP9pMbvtcLgTxdA2KN4ZMUVWJk6HJDWzixDLmU2Ar47PUNS5XHv4Kmfdh8aA9fbZmKHwfmFo8Wup8YtS5Kdqh2’. This was once again blocked by Darktrace RESPOND (Figure 7). 

Figure 6: VirusTotal showing the unusualness of one of these external IPs [10]
Figure 7: Log data showing the action taken by Darktrace RESPOND in response to the device breaching the “Crypto Currency Mining Activity” model. This screenshot displays the UI from Darktrace version 5.1

The final activity seen from this device involved the download of additional shell scripts over HTTP associated with Kinsing, namely spre.sh and unk.sh, from 194.38.20[.]199 and 195.3.146[.]118 respectively (Figure 8). A new user agent (Wget/1.19.5 (linux-gnu)) was used when connecting to the latter endpoint, which also began concurrently initiating repeated connections indicative of C2 beaconing. These scripts help to spread the Kinsing malware laterally within the environment and may have been the attacker's last ditch efforts at furthering their compromise before Darktrace RESPOND blocked all connections from the infected Confluence server [11]. With Darktrace RESPOND's successful actions, the customer’s security team were then able to perform their own response and remediation. 

Figure 8: Cyber AI Analyst revealing the last ditch efforts made by the threat actor to download further malicious software. This screenshot displays the UI from Darktrace version 5.1

Darktrace Coverage: N- vs Zero-days

In the SonicWall case the attacker was unable to achieve their actions on objectives (thanks to Darktrace's intervention). However, this incident displayed tactics of a more stealthy and sophisticated attacker - they had an exploited machine but waited for the right moment to execute their malicious code and initiate a full compromise. Due to the lack of visibility over attacker motive, it is difficult to deduce what type of actor led to this intrusion. However, with the disclosure of a zero-day vulnerability (CVE-2021-20016) not long before this attack, along with a seemingly dormant initially compromised device, it is highly possible that it was carried out by a sophisticated cyber criminal or gang. 

On the other hand, the Confluence case engaged in a slightly more noisy approach; it dropped crypto mining malware on vulnerable devices in the hope that the target’s security team did not maintain visibility over their network or would merely turn a blind eye. The files downloaded and credentials observed alongside the mining activity heavily imply the use of Kinsing malware [11]. Since this vulnerability (CVE-2021-26084) emerged as an n-day attack with likely easily accessible POCs, as well as there being a lack of LotL techniques and the motive being long term monetary gain, it is possible this attack was conducted by a less sophisticated or amateur actor (script-kiddie); one that opportunistically exploits known vulnerabilities in internet-facing devices in order to make a quick profit [12].

Whilst Darktrace RESPOND was enabled in human confirmation mode only during the start of the SonicWall attack, Darktrace’s Cyber AI Analyst still offered invaluable insight into the unusual activity associated with the infected machines during both the Confluence and SonicWall compromises. SOC analysts were able to see these uncharacteristic behaviours and escalate the incident through Darktrace’s PTN and ATE services. Analysts then worked through these tickets with the customers, providing support and guidance and, in the SonicWall case, quickly helping to configure Darktrace RESPOND. In both scenarios, Darktrace RESPOND was able to block abnormal connections and enforce a device’s pattern of life, affording the security team enough time to isolate the infected machines and prevent further threats such as ransomware detonation or data exfiltration. 

Concluding thoughts and dangers of third-party integrations 

Organizations with internet-facing devices will inevitably suffer opportunistic zero-day and n-day attacks. While little can be done to remove the risk of zero-days entirely, ensuring that organizations keep their systems up to date will at the very least help prevent opportunistic and script-kiddies from exploiting n-day vulnerabilities.  

However, it is often not always possible for organizations to keep their systems up to date, especially for those who require continuous availability. This may also pose issues for organizations that rely on, and put their trust in, third party integrations such as those explored in this blog (Confluence and SonicWall), as enforcing secure software is almost entirely out of their hands. Moreover, with the rising prevalence of remote working, it is essential now more than ever that organizations ensure their VPN devices are shielded from external threats, guidance on which has been released by the NSA/CISA [13].

These two case studies have shown that whilst organizations can configure their networks and firewalls to help identify known indicators of compromise (IoC), this ‘rearview mirror’ approach will not account for, or protect against, any new and undisclosed IoCs. With the aid of Self-Learning AI and anomaly detection, Darktrace can detect the slightest deviation from a device’s normal pattern of life and respond autonomously without the need for rules and signatures. This allows for the disruption and prevention of known and novel attacks before irreparable damage is caused- reassuring security teams that their digital estates are secure. 

Thanks to Paul Jennings for his contributions to this blog.

Appendices: SonicWall (Zero-day)

Darktrace model detections

·      AIA / Suspicious Chain of Administrative Credentials

·      Anomalous Connection / Active Remote Desktop Tunnel

·      Anomalous Connection / SMB Enumeration

·      Anomalous Connection / Unusual Internal Remote Desktop

·      Compliance / High Priority Compliance Model Breach

·      Compliance / Outgoing NTLM Request from DC

·      Device / Anomalous RDP Followed By Multiple Model Breaches

·      Device / Anomalous SMB Followed By Multiple Model Breaches

·      Device / ICMP Address Scan

·      Device / Large Number of Model Breaches

·      Device / Large Number of Model Breaches from Critical Network Device

·      Device / Multiple Lateral Movement Model Breaches (PTN/Enhanced Monitoring model)

·      Device / Network Scan

·      Device / Possible SMB/NTLM Reconnaissance

·      Device / RDP Scan

·      Device / Reverse DNS Sweep

·      Device / SMB Session Bruteforce

·      Device / Suspicious Network Scan Activity (PTN/Enhanced Monitoring model)

·      Unusual Activity / Possible RPC Recon Activity

Darktrace RESPOND (Antigena) actions (as displayed in example)

·      Antigena / Network / Manual / Quarantine Device

MITRE ATT&CK Techniques Observed
IoCs

Appendices: Confluence (N-day)

Darktrace model detections

·      Anomalous Connection / New User Agent to IP Without Hostname

·      Anomalous Connection / Posting HTTP to IP Without Hostname

·      Anomalous File / EXE from Rare External Location

·      Anomalous File / Script from Rare Location

·      Compliance / Crypto Currency Mining Activity

·      Compromise / High Priority Crypto Currency Mining (PTN/Enhanced Monitoring model)

·      Device / Initial Breach Chain Compromise (PTN/Enhanced Monitoring model)

·      Device / Internet Facing Device with High Priority Alert

·      Device / New User Agent

Darktrace RESPOND (Antigena) actions (displayed in example)

·      Antigena / Network / Compliance / Antigena Crypto Currency Mining Block

·      Antigena / Network / External Threat / Antigena File then New Outbound Block

·      Antigena / Network / External Threat / Antigena Suspicious Activity Block

·      Antigena / Network / External Threat / Antigena Suspicious File Block

·      Antigena / Network / Significant Anomaly / Antigena Block Enhanced Monitoring

MITRE ATT&CK Techniques Observed
IOCs

References:

[1] https://securitybrief.asia/story/why-preventing-zero-day-attacks-is-crucial-for-businesses

[2] https://electricenergyonline.com/energy/magazine/1150/article/Security-Sessions-More-Dangerous-Than-Zero-Days-The-N-Day-Threat.htm

[3] https://www.wired.com/2014/11/countdown-to-zero-day-stuxnet/

[4] https://cve.mitre.org/cgi-bin/cvekey.cgi?keyword=SonicWall+2021 

[5] https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-20016

[6] https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-26084

[7] https://www.zdnet.com/article/us-cybercom-says-mass-exploitation-of-atlassian-confluence-vulnerability-ongoing-and-expected-to-accelerate/

[8] https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-26134

[9] https://attack.mitre.org/software/S0599/

[10] https://www.virustotal.com/gui/ip-address/195.19.192.28/detection 

[11] https://sysdig.com/blog/zoom-into-kinsing-kdevtmpfsi/

[12] https://github.com/alt3kx/CVE-2021-26084_PoC

[13] https://www.nsa.gov/Press-Room/Press-Releases-Statements/Press-Release-View/Article/2791320/nsa-cisa-release-guidance-on-selecting-and-hardening-remote-access-vpns/

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
Lewis Morgan
Cyber Analyst

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April 21, 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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April 17, 2026

Why Behavioral AI Is the Answer to Mythos

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How AI is breaking the patch-and-prevent security model

The business world was upended last week by the news that Anthropic has developed a powerful new AI model, Claude Mythos, which poses unprecedented risk because of its ability to expose flaws in IT systems.  

Whether it’s Mythos or OpenAI’s GPT-5.4-Cyber, which was just announced on Tuesday, supercharged AI models in the hands of hackers will allow them to carry out attacks at machine speed, much faster than most businesses can stop them.  

This news underscores a stark reality for all leaders: Patching holes alone is not a sufficient control against modern cyberattacks. You must assume that your software is already vulnerable right now. And while LLMs are very good at spotting vulnerabilities, they’re pretty bad at reliably patching them.

Project Glasswing members say it could take months or years for patches to be applied. While that work is done, enterprises must be protected against Zero-Day attacks, or security holes that are still undiscovered.  

Most cybersecurity strategies today are built like a daily multivitamin: broad, preventative, and designed to keep the system generally healthy over time. Patch regularly. Update software. Reduce known vulnerabilities. It’s necessary, disciplined, and foundational. But it’s also built for a world where the risks are well known and defined, cycles are predictable, and exposure unfolds at a manageable pace.

What happens when that model no longer holds?

The AI cyber advantage: Behavioral AI

The vulnerabilities exposed by AI systems like Mythos aren’t the well-understood risks your “multivitamin” was designed to address. They are transient, fast-emerging entry points that exist just long enough to be exploited.

In that environment, prevention alone isn’t enough. You don’t need more vitamins—you need a painkiller. The future of cybersecurity won’t be defined by how well you maintain baseline health. It will be defined by how quickly you respond when something breaks and every second counts.

That’s why behavioral AI gives businesses a durable cyber advantage. Rather than trying to figure out what the attacker looks like, it learns what “normal” looks like across the digital ecosystem of each individual business.  

That’s exactly how behavioral AI works. It understands the self, or what's normal for the organization, and then it can spot deviations in from normal that are actually early-stage attacks.

The Darktrace approach to cybersecurity

At Darktrace, we’ve been defending our 10,000 customers using behavioral AI cybersecurity developed in our AI Research Centre in Cambridge, U.K.

Darktrace was built on the understanding that attacks do not arrive neatly labeled, and that the most damaging threats often emerge before signatures, indicators, or public disclosures can catch up.  

Our AI algorithms learn in real time from your personalized business data to learn what’s normal for every person and every asset, and the flows of data within your organization. By continuously understanding “normal” across your entire digital ecosystem, Darktrace identifies and contains threats emerging from unknown vulnerabilities and compromised supply chain dependencies, autonomously curtailing attacks at machine speed.  

Security for novel threats

Darktrace is built for a world where AI is not just accelerating attacks, but fundamentally reshaping how they originate. What makes our AI so unique is that it's proven time and again to identify cyber threats before public vulnerability disclosures, such as critical Ivanti vulnerabilities in 2025 and SAP NetWeaver exploitations tied to nation-state threat actors.  

As AI reshapes how vulnerabilities are found and exploited, cybersecurity must be anchored in something more durable than a list of known flaws. It requires a real-time understanding of the business itself: what belongs, what does not, and what must be stopped immediately.

What leaders should do right now

The leadership priority must shift accordingly.

First, stop treating unknown vulnerabilities as an edge case. AI‑driven discovery makes them the norm. Security programs built primarily around known flaws, signatures, and threat intelligence will always lag behind an attacker that is operating in real time.

Second, insist on an understanding of what is actually normal across the business. When threats are novel, labels are useless. The earliest and most reliable signal of danger is abnormal behavior—systems, users, or data flows that suddenly depart from what is expected. If you cannot see that deviation as it happens, you are effectively blind during the most critical window.

Finally, assume that the next serious incident will occur before remediation guidance is available. Ask what happens in those first minutes and hours. The organizations that maintain resilience are not the ones waiting for disclosure cycles to catch up—they are the ones that can autonomously identify and contain emerging threats as they unfold.

This is the reality of cybersecurity in an AI‑shaped world. Patching and prevention remain important foundations, but the advantage now belongs to those who can respond instantly when the unpredictable occurs.

Behavioral AI is security designed not just for known threats, but for the ones that AI will discover next.

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About the author
Ed Jennings
President and CEO
Your data. Our AI.
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