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April 7, 2020

Four Ways Cyber-Criminals Fly Under the Radar

Learn how cyber criminals evade detection. Darktrace analyses the four ways they operate under the radar. Read here to stay vigilant against cyber attacks.
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
Oliver Rochford
Technical Director
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07
Apr 2020

The challenge of reliably attributing cyber-threats has amplified in recent years, as adversaries adopt a collection of techniques to ensure that even if their attacks are caught, they themselves escape detection and avoid punishment.

Detecting a threat is, of course, a very different technical challenge compared to tracing that activity back to a human operator. Nevertheless, at some point after the dust has settled, during the post-hoc incident analysis for example, someone somewhere may need to know who the suspects are. And in spite of all of our other advances, and also some recent successes in attributing offensive and cyber-criminal acts, only three out of every 100,000 cyber-crimes are prosecuted. Put simply, this is still an unsolved set of problems. Many of the successes we do have can be attributed more to operational security fails on the criminals’ end than any other active approaches. In fact, some recent trends have actually made reliable attribution even more challenging.

The four cyber-threat trends that make attribution difficult

There are four related trends in how threat-actors can procure and obtain attack capabilities that have resulted in an increase in complexity when attempting to reliably identify Tools, Techniques, and Procedures (TTPs) and attributing them to distinct threat-actors.

A Cybercrime-as-a-Service economy and supply chain allowing cyber-criminals to mix and match off the shelf offensive cyber capabilities.

Expansion of ‘Living off the Land’ (LoL) tool usage by threat-actors to evade traditional, signature-based security defenses, and to obfuscate their activity.

While Code Reuse has always existed in the hacker community, copying nation-state-grade attack code has recently become possible.

The barrier to entry for criminally motivated operators has been lowered, providing the means for less technical criminals, who are only limited by time and their imagination.

Figure 1: The four cyber-threat trends

Threat-actors can mix and match attack tools, creating attack stacks that can be tailored for a variety of campaigns.

Between a professional marketplace of cyber-crime tools and services, the increasing adoption of ‘Living off the Land’ techniques, and the reusing of code leaked from nation-state intelligence services, threat-actors with even the most limited technical ability can conduct highly sophisticated criminal campaigns. Prospective cyber-criminals now have four primary types of attack tools to choose from – with three of them brand new or greatly enhanced. Even more importantly, these threat-actors can mix and match attack tools, creating tactically flexible attack stacks that can be tailored for a variety of campaigns against a diverse set of victims.

Off the shelf attacks

The burgeoning and increasingly professional Cybercrime-as-a-Service market (estimated at $1.6B) provides a thriving marketplace of microservices, attack code, and attack platforms. Anyone with a motive and enough bitcoin and enthusiasm can become the next ‘cyber Don Corleone’. Many of these services offer dedicated account management and professional support 24 hours a day. The commercialization of the cyber-crime supply chain has raised the barrier to entry for Cybercrime-as-a-Service vendors, while at the same time lowering it for cyber-criminal operators.

Living off the Land

‘Living off the Land’ (LoL) and “malware-less” attacks have been on the rise for some time now. What makes these attack methods so dangerous is that they leverage standard operating system tools to conduct their nefarious business, making signature-based approaches that look for malware heuristics ineffective – including signature-based Intrusion Protection Systems.

These attacks in particular demonstrate the need for an approach to cyber security that goes beyond looking at what malware is being used. Rather than relying on static blacklists, security teams are instead turning to a more sophisticated approach that learns ‘normal’ for every user and device across an entire business. From that evolving baseline, this approach to defense can identify and contain anomalous activity indicative of a cyber-threat – all in real time.

Code reuse and repurpose

What is new, and unprecedented, is that cyber-criminals are gaining access to intelligence and nation-state grade attack code.

Hackers have always begged, borrowed, and stolen code from others, including attack code – just two notable examples include the Zeus trojan and RIG exploit kit code leaks that provided the code base for much of the current generation of threats. What is new and unprecedented is that, whether through malice or incompetence, cyber-criminals are gaining access to intelligence and nation-state grade attack code. The Shadowbroker leaks that resulted in Wannacry is one recent example of this trend, and one we expect to accelerate – especially with intelligence services actively outing each other’s methods.

Custom and bespoke techniques

The practice of hackers creating their own tools and researching their own exploits has a long and hallowed tradition, with headline-grabbing zero-days becoming more and more common. Nation-state actors in particular often make a distinction between attack operators and attack code developers, with the ability to request tailored and bespoke code and tools – not unlike the model that has been replicated in the Cybercrime-as-a-Service market. Even when developing custom tools, threat-actors frequently integrate code and exploits from other parties.

Figure 2: The four main attack tool types

When determining who is actually behind these attacks, though, what is most important is the ability to combine all four types of attack tools – this provides a further layer of obfuscation against methods that rely on pattern matching for detection whilst causing additional confusion for would-be investigators. An attacker can use any combination and variation of these tool types to create a different “Chimera” attack stack – making it that much more difficult to identify who is really the operator. Telling apart the operator from the Cybercrime-as-a-Service vendor, for example, is difficult when most of the TTPs that are evaluated are technical and derive from the tooling.

Figure 3: The TTP and Attribution Confusion Chain

Conclusion

As the challenge of attribution intensifies, our focus must turn to defending against cyber-attacks themselves.

The combination of the four threat trends outlined above has lowered the barrier to entry for criminally motivated operators. Less technical adversaries are now able to launch attacks at a speed and scale previously confined to the most organized and well-financed cyber-criminal rings. This change in circumstances has made attribution of offensive cyber activity drastically more complex, and it may be some time before the prosecution rate for cyber-crime gets good enough that it can act as a greater disincentive.

As the challenge of attribution intensifies, our focus must turn to defending against cyber-attacks themselves. You may not ever know who is attacking you, but if you can successfully thwart the full range of threats, new and old, your organization can continue to operate as normal.

Fortunately, defenders’ abilities to detect and respond to cyber-threats have significantly advanced in recent years, thanks to the latest developments in AI and machine learning. Over 3,500 organizations now rely on Cyber AI to detect and contain cyber-threats – whether attackers use pre-existing OS tools to masquerade their attacks or use bespoke and entirely new techniques to bypass rules and signatures. When a threat is identified, AI can respond autonomously by enforcing a user or device’s ‘pattern of life’, allowing ‘business as usual’ whilst ensuring the organization is protected from harm.

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
Oliver Rochford
Technical Director

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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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