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A Surge of Vidar: Network-based details of a prolific info-stealer

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09
Feb 2023
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.

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/

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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.
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The State of AI in Cybersecurity: How AI will impact the cyber threat landscape in 2024

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22
Apr 2024

About the AI Cybersecurity Report

We surveyed 1,800 CISOs, security leaders, administrators, and practitioners from industries around the globe. Our research was conducted to understand how the adoption of new AI-powered offensive and defensive cybersecurity technologies are being managed by organizations.

This blog is continuing the conversation from our last blog post “The State of AI in Cybersecurity: Unveiling Global Insights from 1,800 Security Practitioners” which was an overview of the entire report. This blog will focus on one aspect of the overarching report, the impact of AI on the cyber threat landscape.

To access the full report click here.

Are organizations feeling the impact of AI-powered cyber threats?

Nearly three-quarters (74%) state AI-powered threats are now a significant issue. Almost nine in ten (89%) agree that AI-powered threats will remain a major challenge into the foreseeable future, not just for the next one to two years.

However, only a slight majority (56%) thought AI-powered threats were a separate issue from traditional/non AI-powered threats. This could be the case because there are few, if any, reliable methods to determine whether an attack is AI-powered.

Identifying exactly when and where AI is being applied may not ever be possible. However, it is possible for AI to affect every stage of the attack lifecycle. As such, defenders will likely need to focus on preparing for a world where threats are unique and are coming faster than ever before.

a hypothetical cyber attack augmented by AI at every stage

Are security stakeholders concerned about AI’s impact on cyber threats and risks?

The results from our survey showed that security practitioners are concerned that AI will impact organizations in a variety of ways. There was equal concern associated across the board – from volume and sophistication of malware to internal risks like leakage of proprietary information from employees using generative AI tools.

What this tells us is that defenders need to prepare for a greater volume of sophisticated attacks and balance this with a focus on cyber hygiene to manage internal risks.

One example of a growing internal risks is shadow AI. It takes little effort for employees to adopt publicly-available text-based generative AI systems to increase their productivity. This opens the door to “shadow AI”, which is the use of popular AI tools without organizational approval or oversight. Resulting security risks such as inadvertent exposure of sensitive information or intellectual property are an ever-growing concern.

Are organizations taking strides to reduce risks associated with adoption of AI in their application and computing environment?

71.2% of survey participants say their organization has taken steps specifically to reduce the risk of using AI within its application and computing environment.

16.3% of survey participants claim their organization has not taken these steps.

These findings are good news. Even as enterprises compete to get as much value from AI as they can, as quickly as possible, they’re tempering their eager embrace of new tools with sensible caution.

Still, responses varied across roles. Security analysts, operators, administrators, and incident responders are less likely to have said their organizations had taken AI risk mitigation steps than respondents in other roles. In fact, 79% of executives said steps had been taken, and only 54% of respondents in hands-on roles agreed. It seems that leaders believe their organizations are taking the needed steps, but practitioners are seeing a gap.

Do security professionals feel confident in their preparedness for the next generation of threats?

A majority of respondents (six out of every ten) believe their organizations are inadequately prepared to face the next generation of AI-powered threats.

The survey findings reveal contrasting perceptions of organizational preparedness for cybersecurity threats across different regions and job roles. Security administrators, due to their hands-on experience, express the highest level of skepticism, with 72% feeling their organizations are inadequately prepared. Notably, respondents in mid-sized organizations feel the least prepared, while those in the largest companies feel the most prepared.

Regionally, participants in Asia-Pacific are most likely to believe their organizations are unprepared, while those in Latin America feel the most prepared. This aligns with the observation that Asia-Pacific has been the most impacted region by cybersecurity threats in recent years, according to the IBM X-Force Threat Intelligence Index.

The optimism among Latin American respondents could be attributed to lower threat volumes experienced in the region, but it's cautioned that this could change suddenly (1).

What are biggest barriers to defending against AI-powered threats?

The top-ranked inhibitors center on knowledge and personnel. However, issues are alluded to almost equally across the board including concerns around budget, tool integration, lack of attention to AI-powered threats, and poor cyber hygiene.

The cybersecurity industry is facing a significant shortage of skilled professionals, with a global deficit of approximately 4 million experts (2). As organizations struggle to manage their security tools and alerts, the challenge intensifies with the increasing adoption of AI by attackers. This shift has altered the demands on security teams, requiring practitioners to possess broad and deep knowledge across rapidly evolving solution stacks.

Educating end users about AI-driven defenses becomes paramount as organizations grapple with the shortage of professionals proficient in managing AI-powered security tools. Operationalizing machine learning models for effectiveness and accuracy emerges as a crucial skill set in high demand. However, our survey highlights a concerning lack of understanding among cybersecurity professionals regarding AI-driven threats and the use of AI-driven countermeasures indicating a gap in keeping pace with evolving attacker tactics.

The integration of security solutions remains a notable problem, hindering effective defense strategies. While budget constraints are not a primary inhibitor, organizations must prioritize addressing these challenges to bolster their cybersecurity posture. It's imperative for stakeholders to recognize the importance of investing in skilled professionals and integrated security solutions to mitigate emerging threats effectively.

To access the full report click here.

References

1. IBM, X-Force Threat Intelligence Index 2024, Available at: https://www.ibm.com/downloads/cas/L0GKXDWJ

2. ISC2, Cybersecurity Workforce Study 2023, Available at: https://media.isc2.org/-/media/Project/ISC2/Main/Media/ documents/research/ISC2_Cybersecurity_Workforce_Study_2023.pdf?rev=28b46de71ce24e6ab7705f6e3da8637e

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Sliver C2: How Darktrace Provided a Sliver of Hope in the Face of an Emerging C2 Framework

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17
Apr 2024

Offensive Security Tools

As organizations globally seek to for ways to bolster their digital defenses and safeguard their networks against ever-changing cyber threats, security teams are increasingly adopting offensive security tools to simulate cyber-attacks and assess the security posture of their networks. These legitimate tools, however, can sometimes be exploited by real threat actors and used as genuine actor vectors.

What is Sliver C2?

Sliver C2 is a legitimate open-source command-and-control (C2) framework that was released in 2020 by the security organization Bishop Fox. Silver C2 was originally intended for security teams and penetration testers to perform security tests on their digital environments [1] [2] [5]. In recent years, however, the Sliver C2 framework has become a popular alternative to Cobalt Strike and Metasploit for many attackers and Advanced Persistence Threat (APT) groups who adopt this C2 framework for unsolicited and ill-intentioned activities.

The use of Sliver C2 has been observed in conjunction with various strains of Rust-based malware, such as KrustyLoader, to provide backdoors enabling lines of communication between attackers and their malicious C2 severs [6]. It is unsurprising, then, that it has also been leveraged to exploit zero-day vulnerabilities, including critical vulnerabilities in the Ivanti Connect Secure and Policy Secure services.

In early 2024, Darktrace observed the malicious use of Sliver C2 during an investigation into post-exploitation activity on customer networks affected by the Ivanti vulnerabilities. Fortunately for affected customers, Darktrace DETECT™ was able to recognize the suspicious network-based connectivity that emerged alongside Sliver C2 usage and promptly brought it to the attention of customer security teams for remediation.

How does Silver C2 work?

Given its open-source nature, the Sliver C2 framework is extremely easy to access and download and is designed to support multiple operating systems (OS), including MacOS, Windows, and Linux [4].

Sliver C2 generates implants (aptly referred to as ‘slivers’) that operate on a client-server architecture [1]. An implant contains malicious code used to remotely control a targeted device [5]. Once a ‘sliver’ is deployed on a compromised device, a line of communication is established between the target device and the central C2 server. These connections can then be managed over Mutual TLS (mTLS), WireGuard, HTTP(S), or DNS [1] [4]. Sliver C2 has a wide-range of features, which include dynamic code generation, compile-time obfuscation, multiplayer-mode, staged and stageless payloads, procedurally generated C2 over HTTP(S) and DNS canary blue team detection [4].

Why Do Attackers Use Sliver C2?

Amidst the multitude of reasons why malicious actors opt for Sliver C2 over its counterparts, one stands out: its relative obscurity. This lack of widespread recognition means that security teams may overlook the threat, failing to actively search for it within their networks [3] [5].

Although the presence of Sliver C2 activity could be representative of authorized and expected penetration testing behavior, it could also be indicative of a threat actor attempting to communicate with its malicious infrastructure, so it is crucial for organizations and their security teams to identify such activity at the earliest possible stage.

Darktrace’s Coverage of Sliver C2 Activity

Darktrace’s anomaly-based approach to threat detection means that it does not explicitly attempt to attribute or distinguish between specific C2 infrastructures. Despite this, Darktrace was able to connect Sliver C2 usage to phases of an ongoing attack chain related to the exploitation of zero-day vulnerabilities in Ivanti Connect Secure VPN appliances in January 2024.

Around the time that the zero-day Ivanti vulnerabilities were disclosed, Darktrace detected an internal server on one customer network deviating from its expected pattern of activity. The device was observed making regular connections to endpoints associated with Pulse Secure Cloud Licensing, indicating it was an Ivanti server. It was observed connecting to a string of anomalous hostnames, including ‘cmjk3d071amc01fu9e10ae5rt9jaatj6b.oast[.]live’ and ‘cmjft14b13vpn5vf9i90xdu6akt5k3pnx.oast[.]pro’, via HTTP using the user agent ‘curl/7.19.7 (i686-redhat-linux-gnu) libcurl/7.63.0 OpenSSL/1.0.2n zlib/1.2.7’.

Darktrace further identified that the URI requested during these connections was ‘/’ and the top-level domains (TLDs) of the endpoints in question were known Out-of-band Application Security Testing (OAST) server provider domains, namely ‘oast[.]live’ and ‘oast[.]pro’. OAST is a testing method that is used to verify the security posture of an application by testing it for vulnerabilities from outside of the network [7]. This activity triggered the DETECT model ‘Compromise / Possible Tunnelling to Bin Services’, which breaches when a device is observed sending DNS requests for, or connecting to, ‘request bin’ services. Malicious actors often abuse such services to tunnel data via DNS or HTTP requests. In this specific incident, only two connections were observed, and the total volume of data transferred was relatively low (2,302 bytes transferred externally). It is likely that the connections to OAST servers represented malicious actors testing whether target devices were vulnerable to the Ivanti exploits.

The device proceeded to make several SSL connections to the IP address 103.13.28[.]40, using the destination port 53, which is typically reserved for DNS requests. Darktrace recognized that this activity was unusual as the offending device had never previously been observed using port 53 for SSL connections.

Model Breach Event Log displaying the ‘Application Protocol on Uncommon Port’ DETECT model breaching in response to the unusual use of port 53.
Figure 1: Model Breach Event Log displaying the ‘Application Protocol on Uncommon Port’ DETECT model breaching in response to the unusual use of port 53.

Figure 2: Model Breach Event Log displaying details pertaining to the ‘Application Protocol on Uncommon Port’ DETECT model breach, including the 100% rarity of the port usage.
Figure 2: Model Breach Event Log displaying details pertaining to the ‘Application Protocol on Uncommon Port’ DETECT model breach, including the 100% rarity of the port usage.

Further investigation into the suspicious IP address revealed that it had been flagged as malicious by multiple open-source intelligence (OSINT) vendors [8]. In addition, OSINT sources also identified that the JARM fingerprint of the service running on this IP and port (00000000000000000043d43d00043de2a97eabb398317329f027c66e4c1b01) was linked to the Sliver C2 framework and the mTLS protocol it is known to use [4] [5].

An Additional Example of Darktrace’s Detection of Sliver C2

However, it was not just during the January 2024 exploitation of Ivanti services that Darktrace observed cases of Sliver C2 usages across its customer base.  In March 2023, for example, Darktrace detected devices on multiple customer accounts making beaconing connections to malicious endpoints linked to Sliver C2 infrastructure, including 18.234.7[.]23 [10] [11] [12] [13].

Darktrace identified that the observed connections to this endpoint contained the unusual URI ‘/NIS-[REDACTED]’ which contained 125 characters, including numbers, lower and upper case letters, and special characters like “_”, “/”, and “-“, as well as various other URIs which suggested attempted data exfiltration:

‘/upload/api.html?c=[REDACTED] &fp=[REDACTED]’

  • ‘/samples.html?mx=[REDACTED] &s=[REDACTED]’
  • ‘/actions/samples.html?l=[REDACTED] &tc=[REDACTED]’
  • ‘/api.html?gf=[REDACTED] &x=[REDACTED]’
  • ‘/samples.html?c=[REDACTED] &zo=[REDACTED]’

This anomalous external connectivity was carried out through multiple destination ports, including the key ports 443 and 8888.

Darktrace additionally observed devices on affected customer networks performing TLS beaconing to the IP address 44.202.135[.]229 with the JA3 hash 19e29534fd49dd27d09234e639c4057e. According to OSINT sources, this JA3 hash is associated with the Golang TLS cipher suites in which the Sliver framework is developed [14].

Conclusion

Despite its relative novelty in the threat landscape and its lesser-known status compared to other C2 frameworks, Darktrace has demonstrated its ability effectively detect malicious use of Sliver C2 across numerous customer environments. This included instances where attackers exploited vulnerabilities in the Ivanti Connect Secure and Policy Secure services.

While human security teams may lack awareness of this framework, and traditional rules and signatured-based security tools might not be fully equipped and updated to detect Sliver C2 activity, Darktrace’s Self Learning AI understands its customer networks, users, and devices. As such, Darktrace is adept at identifying subtle deviations in device behavior that could indicate network compromise, including connections to new or unusual external locations, regardless of whether attackers use established or novel C2 frameworks, providing organizations with a sliver of hope in an ever-evolving threat landscape.

Credit to Natalia Sánchez Rocafort, Cyber Security Analyst, Paul Jennings, Principal Analyst Consultant

Appendices

DETECT Model Coverage

  • Compromise / Repeating Connections Over 4 Days
  • Anomalous Connection / Application Protocol on Uncommon Port
  • Anomalous Server Activity / Server Activity on New Non-Standard Port
  • Compromise / Sustained TCP Beaconing Activity To Rare Endpoint
  • Compromise / Quick and Regular Windows HTTP Beaconing
  • Compromise / High Volume of Connections with Beacon Score
  • Anomalous Connection / Multiple Failed Connections to Rare Endpoint
  • Compromise / Slow Beaconing Activity To External Rare
  • Compromise / HTTP Beaconing to Rare Destination
  • Compromise / Sustained SSL or HTTP Increase
  • Compromise / Large Number of Suspicious Failed Connections
  • Compromise / SSL or HTTP Beacon
  • Compromise / Possible Malware HTTP Comms
  • Compromise / Possible Tunnelling to Bin Services
  • Anomalous Connection / Low and Slow Exfiltration to IP
  • Device / New User Agent
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Anomalous File / EXE from Rare External Location
  • Anomalous File / Numeric File Download
  • Anomalous Connection / Powershell to Rare External
  • Anomalous Server Activity / New Internet Facing System

List of Indicators of Compromise (IoCs)

18.234.7[.]23 - Destination IP - Likely C2 Server

103.13.28[.]40 - Destination IP - Likely C2 Server

44.202.135[.]229 - Destination IP - Likely C2 Server

References

[1] https://bishopfox.com/tools/sliver

[2] https://vk9-sec.com/how-to-set-up-use-c2-sliver/

[3] https://www.scmagazine.com/brief/sliver-c2-framework-gaining-traction-among-threat-actors

[4] https://github[.]com/BishopFox/sliver

[5] https://www.cybereason.com/blog/sliver-c2-leveraged-by-many-threat-actors

[6] https://securityaffairs.com/158393/malware/ivanti-connect-secure-vpn-deliver-krustyloader.html

[7] https://www.xenonstack.com/insights/out-of-band-application-security-testing

[8] https://www.virustotal.com/gui/ip-address/103.13.28.40/detection

[9] https://threatfox.abuse.ch/browse.php?search=ioc%3A107.174.78.227

[10] https://threatfox.abuse.ch/ioc/1074576/

[11] https://threatfox.abuse.ch/ioc/1093887/

[12] https://threatfox.abuse.ch/ioc/846889/

[13] https://threatfox.abuse.ch/ioc/1093889/

[14] https://github.com/projectdiscovery/nuclei/issues/3330

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Natalia Sánchez Rocafort
Cyber Security Analyst
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