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February 9, 2023

Vidar Network: Analyzing a Prolific Info Stealer

Discover the latest insights on the Vidar network-based info stealer from our Darktrace experts and stay informed on cybersecurity threats.
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
Roberto Romeu
Senior SOC Analyst
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09
Feb 2023

In the latter half of 2022, Darktrace observed a rise in Vidar Stealer infections across its client base. These infections consisted in a predictable series of network behaviors, including usage of certain social media platforms for the retrieval of Command and Control (C2) information and usage of certain URI patterns in C2 communications. In the blog post, we will provide details of the pattern of network activity observed in these Vidar Stealer infections, along with details of Darktrace’s coverage of the activity. 

Background on Vidar Stealer

Vidar Stealer, first identified in 2018, is an info-stealer capable of obtaining and then exfiltrating sensitive data from users’ devices. This data includes banking details, saved passwords, IP addresses, browser history, login credentials, and crypto-wallet data [1]. The info-stealer, which is typically delivered via malicious spam emails, cracked software websites, malicious ads, and websites impersonating legitimate brands, is known to access profiles on social media platforms once it is running on a user’s device. The info-stealer does this to retrieve the IP address of its Command and Control (C2) server. After retrieving its main C2 address, the info-stealer, like many other info-stealers, is known to download several third-party Dynamic Link Libraries (DLLs) which it uses to gain access to sensitive data saved on the infected device. The info-stealer then bundles the sensitive data which it obtains and sends it back to the C2 server.  

Details of Attack Chain 

In the second half of 2022, Darktrace observed the following pattern of activity within many client networks:

1. User’s device makes an HTTPS connection to Telegram and/or to a Mastodon server

2. User’s device makes an HTTP GET request with an empty User-Agent header, an empty Host header and a target URI consisting of 4 digits to an unusual, external endpoint

3. User’s device makes an HTTP GET request with an empty User-Agent header, an empty Host header and a target URI consisting of 10 digits followed by ‘.zip’ to the unusual, external endpoint

4. User’s device makes an HTTP POST request with an empty User-Agent header, an empty Host header, and the target URI ‘/’ to the unusual, external endpoint 

Figure 1: The above network logs, taken from Darktrace’s Advanced Search interface, show an infected device contacting Telegram and then making a series of HTTP requests to 168.119.167[.]188
Figure 2:  The above network logs, taken from Darktrace’s Advanced Search interface, show an infected device contacting a Mastadon server and then making a series of HTTP requests to 107.189.31[.]171

Each of these activity chains occurred as the result of a user running Vidar Stealer on their device. No common method was used to trick users into running Vidar Stealer on their devices. Rather, a variety of methods, ranging from malspam to cracked software downloads appear to have been used. 

Once running on a user’s device, Vidar Stealer went on to make an HTTPS connection to either Telegram (https://t[.]me/) or a Mastodon server (https://nerdculture[.]de/ or https://ioc[.]exchange/). Telegram and Mastodon are social media platforms on which users can create profiles. Malicious actors are known to create profiles on these platforms and then to embed C2 information within the profiles’ descriptions [2].  In the Vidar cases observed across Darktrace’s client base, it seems that Vidar contacted Telegram and/or Mastodon servers in order to retrieve the IP address of its C2 server from a profile description. Since social media platforms are typically trusted, this ‘Dead Drop’ method of sharing C2 details with malware samples makes it possible for threat actors to regularly update C2 details without the communication of these changes being blocked. 

Figure 3: A screenshot a profile on the Mastodon server, nerdculture[.]de. The profile’s description contains a C2 address 

After retrieving its C2 address from the description of a Telegram or Mastodon profile, Vidar went on to make an HTTP GET request with an empty User-Agent header, an empty Host header and a target URI consisting of 4 digits to its C2 server. The sequences of digits appearing in these URIs are campaign IDs. The C2 server responded to Vidar’s GET request with configuration details that likely informed Vidar’s subsequent data stealing activities. 

After receiving its configuration details, Vidar went on to make a GET request with an empty User-Agent header, an empty Host header and a target URI consisting of 10 digits followed by ‘.zip’ to the C2 server. This request was responded to with a ZIP file containing legitimate, third-party Dynamic Link Libraries such as ‘vcruntime140.dll’. Vidar used these libraries to gain access to sensitive data saved on the infected host. 

Figure 4: The above PCAP provides an example of the configuration details provided by a C2 server in response to Vidar’s first GET request 
Figure 5: Examples of DLLs included within ZIP files downloaded by Vidar samples

After downloading a ZIP file containing third-party DLLs, Vidar made a POST request containing hundreds of kilobytes of data to the C2 endpoint. This POST request likely represented exfiltration of stolen information. 

Darktrace Coverage

After infecting users’ devices, Vidar contacted either Telegram or Mastodon, and then made a series of HTTP requests to its C2 server. The info-stealer’s usage of social media platforms, along with its usage of ZIP files for tool transfer, complicate the detection of its activities. The info-stealer’s HTTP requests to its C2 server, however, caused the following Darktrace DETECT/Network models to breach:

  • Anomalous File / Zip or Gzip from Rare External Location 
  • Anomalous File / Numeric File Download
  • Anomalous Connection / Posting HTTP to IP Without Hostname

These model breaches did not occur due to users’ devices contacting IP addresses known to be associated with Vidar. In fact, at the time that the reported activities occurred, many of the contacted IP addresses had no OSINT associating them with Vidar activity. The cause of these model breaches was in fact the unusualness of the devices’ HTTP activities. When a Vidar-infected device was observed making HTTP requests to a C2 server, Darktrace recognised that this behavior was highly unusual both for the device and for other devices in the network. Darktrace’s recognition of this unusualness caused the model breaches to occur. 

Vidar Stealer infections move incredibly fast, with the time between initial infection and data theft sometimes being less than a minute. In cases where Darktrace’s Autonomous Response technology was active, Darktrace RESPOND/Network was able to autonomously block Vidar’s connections to its C2 server immediately after the first connection was made. 

Figure 6: The Event Log for an infected device, shows that Darktrace RESPOND/Network autonomously intervened 1 second after the device first contacted the C2 server 95.217.245[.]254

Conclusion 

In the latter half of 2022, a particular pattern of activity was prolific across Darktrace’s client base, with the pattern being seen in the networks of customers across a broad range of industry verticals and sizes. Further investigation revealed that this pattern of network activity was the result of Vidar Stealer infection. These infections moved fast and were effective at evading detection due to their usage of social media platforms for information retrieval and their usage of ZIP files for tool transfer. Since the impact of info-stealer activity typically occurs off-network, long after initial infection, insufficient detection of info-stealer activity leaves victims at risk of attackers operating unbeknownst to them and of powerful attack vectors being available to launch broad compromises. 

Despite the evasion attempts made by the operators of Vidar, Darktrace DETECT/Network was able to detect the unusual HTTP activities which inevitably resulted from Vidar infections. When active, Darktrace RESPOND/Network was able to quickly take inhibitive actions against these unusual activities. Given the prevalence of Vidar Stealer [3] and the speed at which Vidar Stealer infections progress, Autonomous Response technology proves to be vital for protecting organizations from info-stealer activity.  

Thanks to the Threat Research Team for its contributions to this blog.

MITRE ATT&CK Mapping

List of IOCs

107.189.31[.]171 - Vidar C2 Endpoint

168.119.167[.]188 – Vidar C2 Endpoint 

77.91.102[.]51 - Vidar C2 Endpoint

116.202.180[.]202 - Vidar C2 Endpoint

79.124.78[.]208 - Vidar C2 Endpoint

159.69.100[.]194 - Vidar C2 Endpoint

195.201.253[.]5 - Vidar C2 Endpoint

135.181.96[.]153 - Vidar C2 Endpoint

88.198.122[.]116 - Vidar C2 Endpoint

135.181.104[.]248 - Vidar C2 Endpoint

159.69.101[.]102 - Vidar C2 Endpoint

45.8.147[.]145 - Vidar C2 Endpoint

159.69.102[.]192 - Vidar C2 Endpoint

193.43.146[.]42 - Vidar C2 Endpoint

159.69.102[.]19 - Vidar C2 Endpoint

185.53.46[.]199 - Vidar C2 Endpoint

116.202.183[.]206 - Vidar C2 Endpoint

95.217.244[.]216 - Vidar C2 Endpoint

78.46.129[.]14 - Vidar C2 Endpoint

116.203.7[.]175 - Vidar C2 Endpoint

45.159.249[.]3 - Vidar C2 Endpoint

159.69.101[.]170 - Vidar C2 Endpoint

116.202.183[.]213 - Vidar C2 Endpoint

116.202.4[.]170 - Vidar C2 Endpoint

185.252.215[.]142 - Vidar C2 Endpoint

45.8.144[.]62 - Vidar C2 Endpoint

74.119.192[.]157 - Vidar C2 Endpoint

78.47.102[.]252 - Vidar C2 Endpoint

212.23.221[.]231 - Vidar C2 Endpoint

167.235.137[.]244 - Vidar C2 Endpoint

88.198.122[.]116 - Vidar C2 Endpoint

5.252.23[.]169 - Vidar C2 Endpoint

45.89.55[.]70 - Vidar C2 Endpoint

References

[1] https://blog.cyble.com/2021/10/26/vidar-stealer-under-the-lens-a-deep-dive-analysis/

[2] https://asec.ahnlab.com/en/44554/

[3] https://blog.sekoia.io/unveiling-of-a-large-resilient-infrastructure-distributing-information-stealers/

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
Roberto Romeu
Senior SOC Analyst

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April 28, 2026

State of AI Cybersecurity 2026: 87% of security professionals are seeing more AI-driven threats, but few feel ready to stop them

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The findings in this blog are taken from Darktrace’s annual State of AI Cybersecurity Report 2026.

In part 1 of this blog series, we explored how AI is remaking the attack surface, with new tools, models, agents — and vulnerabilities — popping up just about everywhere. Now embedded in workflows across the enterprise, and often with far-reaching access to sensitive data, AI systems are quickly becoming a favorite target of cyber threat actors.

Among bad actors, though, AI is more often used as a tool than a target. Nearly 62% of organizations  experienced a social engineering attack involving a deepfake, or an incident in which bad actors used AI-generated video or audio to try to trick a biometric authentication system, compared to 32% that reported an AI prompt injection attack.

In the hands of attackers, AI can do many things. It’s being used across the entire kill chain: to supercharge reconnaissance, personalize phishing, accelerate lateral movement, and automate data exfiltration. Evidence from Anthropic demonstrates that threat actors have harnessed AI to orchestrate an entire cyber espionage campaign from end to end, allegedly running it with minimal human involvement.

CISOs inhabit a world where these increasingly sophisticated attacks are ubiquitous. Naturally, combatting AI-powered threats is top of mind among security professionals, but many worry about whether their capabilities are up to the challenge.

AI-powered threats at scale: no longer hypothetical

AI-driven threats share signature characteristics. They operate at speed and scale. Automated tools can probe multiple attack paths, search for multiple vulnerabilities and send out a barrage of phishing emails, all within seconds. The ability to attack everywhere at once, at a pace that no human operator could sustain, is the hallmark of an AI-powered threat. AI-powered threats are also dynamic. They can adapt their behavior to spread across a network more efficiently or rewrite their own code to evade detection.

Security teams are seeing the signs that they’re fighting AI-powered threats at every stage of the kill chain, and the sophistication of these threats is testing their resolve and their resources.

  • 73% say that AI-powered cyber threats are having a significant impact on their organization
  • 92% agree that these threats are forcing them to upgrade their defenses
  • 87% agree that AI is significantly increasing the sophistication and success rate of malware
  • 87% say AI is significantly increasing the workload of their security operations team

These teams now confront a challenge unlike anything they’ve seen before in their careers, and the risks are compounding across workflows, tools, data, and identities. It’s no surprise that 66% of security professionals say their role is more stressful today than it was five years ago, or that 47% report feeling overwhelmed at work.

Up all night: Security professionals’ worry list is long

Traditional security methods were never built to handle the complexity and subtlety of AI-driven behavior. Working in the trenches, defenders have deep firsthand experience of how difficult it can be to detect and stop AI-assisted threats.

Increasingly effective social engineering attacks are among their top concerns. 50% of security leaders mentioned hyper-personalized phishing campaigns as one of their biggest worries, while 40% voiced apprehension about deepfake voice fraud. These concerns are legitimate: AI-generated phishing emails are increasingly tailored to individual organizations, business activities, or individuals. Gone are the telltale signs – like grammar or spelling mistakes – that once distinguished malicious communications. Notably, 33% of the malicious emails Darktrace observed in 2025 contained over 1,000 characters, indicating probable LLM usage.

Security leaders also worry about how bad actors can leverage AI to make attacks even faster and more dynamic. 45% listed automated vulnerability scanning and exploit chaining among their biggest concerns, while 40% mentioned adaptive malware.

Confidence is lacking

Protecting against AI demands capabilities that many organizations have not yet built. It requires interpreting new indicators, uncovering the subtle intent within interactions, and recognizing when AI behavior – human or machine – could be suspicious. Leaders know that their current tools aren’t prepared for this. Nearly half don’t feel confident in their ability to defend against AI-powered attacks.

We’ve asked participants in our survey about their confidence for the last three years now. In 2024, 60% said their organizations were not adequately prepared to defend against AI-driven threats. Last year, that percentage shrunk to 45%, a possible indicator that security programs were making progress. Since then, however, the progress has apparently stalled. 46% of security leaders now feel inadequately prepared to protect their organizations amidst the current threat landscape.

Some of these differences are accentuated across different cultures. Respondents in Japan are far less confident (77% say they are not adequately prepared) than respondents in Brazil (where only 21% don’t feel prepared).

Where security programs are falling short

It’s no longer the case that cybersecurity is overlooked or underfunded by executive leadership. Across industries, management recognizes that AI-powered threats are a growing problem, and insufficient budget is near the bottom of most CISO’s list of reasons that they struggle to defend against AI-powered threats.  

It’s the things that money can’t buy – experience, knowledge, and confidence – that are holding programs back. Near the top of the list of inhibitors that survey participants mention is “insufficient knowledge or use of AI-driven countermeasures.” As bad actors embrace AI technologies en masse, this challenge is coming into clearer focus: attack-centric security tools, which rely on static rules, signatures, and historical attack patterns, were never designed to handle the complexity and subtlety of AI-driven attacks. These challenges feel new to security teams, but they are the core problems Darktrace was built to solve.  

Our Self-Learning AI develops a deep understanding of what “normal” looks like for your organization –including unique traffic patterns, end user habits, application and device profiles – so that it can detect and stop novel, dynamic threats at the first encounter. By focusing on learning the business, rather than the attack, our AI can keep pace with AI-powered threats as they evolve.

Explore the full State of AI Cybersecurity 2026 report for deeper insights into how security leaders are responding to AI-driven risks.

Learn more about securing AI in your enterprise.

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April 24, 2026

Email-Borne Cyber Risk: A Core Challenge for the CISO in the Age of Volume and Sophistication

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The challenge for CISOs

Despite continuous advances in security technologies, humans continue to be exploited by attackers. Credential abuse and social actions like phishing are major factors, accounting for around 60% of all breaches. These attacks rely less on technical vulnerabilities and more on exploiting human behavior and organizational processes. 

From my perspective as a former CISO, protecting humans concentrates three of today’s most pressing challenges: the sheer volume of email-based threats, their increasing sophistication, and the limitations of traditional employee awareness programs in moving the needle on risk. 

My personal experience of security awareness training as a CISO

With over 20 years’ experience as an ICT and Cybersecurity leader across various international organizations, I’ve seen security awareness training (SAT) in many guises. And while the cyber landscape is evolving in every direction, the effectiveness of SAT is reaching a plateau.  

Most programs I’ve seen follow a familiar pattern. Training is delivered through a combination of eLearning modules and internal sessions designed to reinforce IT policies. Employees are typically required to complete a slide deck or video, followed by a multiple-choice quiz. Occasional phishing simulations are distributed throughout the year.

The content is often static and unpersonalized, based on known threats that may already be outdated. Every employee regardless of role or risk exposure receives the same training and the same simulated phishing templates, from front-desk staff to the CEO.

The problem with traditional SAT programs

The issue with the approach to SAT outlined above is that the distribution of power is imbalanced. Humans will always be fallible, particularly when faced with increasingly sophisticated attacks. Providing generic, low-context training risks creating false confidence rather than genuine resilience. Let’s look at some of the problems in detail.

Timing and delivery

Employees today operate under constant cognitive load, making lots of rapid decisions every day to reduce their email volumes. Yet if employees are completing training annually, or on an ad hoc basis, it becomes a standalone occurrence rather than a continuous habit.  

As a result, retention is low. Employees often forget the lessons within weeks, a phenomenon known as the ‘Ebbinghaus Forgetting Curve.’

The graph illustrates that when you first learn something, the information disappears at an exponential rate without retention. In fact, according to the curve, you forget 50% of all new information within a day, and 90% of all new information within a week.  

Simultaneously, most training is conducted within a separate interface. Because it takes place away from the actual moment of decision-making, the "teachable moment" is lost. There is a cognitive disconnect between the action (clicking a link in Outlook) and the education (watching a video in a browser). 

People

In the context of professional risk management, the risks faced by different users are different. Static learning such as everyone receiving the same ‘Password Reset’ email doesn’t help users prepare for the specific threats they are likely to face. It also contributes to user fatigue, driven by repetitive training. And if users receive tests at the same time, news spreads among colleagues, hurting the efficacy of the test.  

Staff turnover introduces further risk. In many organizations, new employees gain access to systems before receiving meaningful training, reducing onboarding to little more than policy acknowledgment.

Measuring success

In my experience, solutions are standalone, without any correlation to other tools in the security stack. In some cases, the programs are delivered by HR rather than the security team, creating a complete silo.  

As a result, SAT is often perceived as a compliance exercise rather than a capability building function. The result is that poor-quality training does little to reduce the likelihood of compromise, regardless of completion rates or quiz performance.

What a modern SAT solution should look like

For today’s CISO, email represents the convergence point of high-volume, high-impact, and human-centric threats. Despite significant security investments, it remains one of the most difficult channels to secure effectively. Given these constraints, CISOs must evolve their approach to SAT.

Success lies in a balanced strategy one that combines advanced technology, attack surface reduction, and pragmatic user enablement, without over-relying on human vigilance as the final line of defense.

This means moving beyond traditional SAT toward continuous, contextual awareness, realistic simulations, and tight integration with security outcomes.

Three requirements for a modern SAT solution

  • Invisible protection: The optimum security solution is one that assists users without impeding their experience. The objective is to enhance human capabilities, rather than simply delivering a lecture. 
  • Real-time feedback: Rather than a monthly quiz, the ideal system would provide a prompt or warning when a user is about to engage with something suspicious. 
  • Positive culture: Shifting the focus away from a "gotcha" culture, which is a contributing factor to a resentment, and instead empowers employees to serve as "sensors" for the company. 

Discover how personalized security coaching can strengthen your human layer and make your email defenses more resilient. Explore Darktrace / Adaptive Human Defense.

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About the author
Karim Benslimane
VP, Field CISO
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