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A thief in red: Compliance and the RedLine information stealer

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13
Sep 2022
13
Sep 2022
This blog explores Darktrace's detection of a BeamWinHTTP and RedLine info stealer compromise caused by continued torrenting and a malicious download within a telecommunication customer’s environment.

With the continued rise of malware as a service (MaaS), it is now easier than ever to find and deploy information stealers [1]. Given this, it is crucial that companies begin to prioritize good cyber hygiene, and address compliance issues within their environments. Thanks to MaaS, attackers with little to no experience can amplify what might seem like a low-risk attack, into a significant compromise. This blog will investigate a compromise that could have been mitigated with better cyber hygiene and enhanced awareness around compliance issues.

Figure 1: Timeline of the attack

In May 2022 Darktrace DETECT/Network identified a device linked with multiple compliance alerts for ‘torrent’ activity within a Latin American telecommunications company. This culminated in the device downloading a suspicious executable file from an archived webpage. At first, analysis of the downloaded file indicated that it could be a legitimate, albeit outdated software relevant to the client’s industry vertical (SNMPc management tool for GeoDesy GD-300). However, as this was the first event before further suspicious activities, it was also possible that the software downloaded was packaged with malware and marked an initial compromise. Since early April, the device had regularly breached compliance alerts for both BitTorrent and uTorrent (a BitTorrent client). These connections occurred over a common torrenting port, 6881, and may have represented the infection vector.  

Figure 2: View of archived webpage which the suspicious executable was downloaded from

Shortly after the executable was downloaded, Darktrace DETECT alerted a new outbound SSH connection with the following notice in Advanced Search: ‘SSH::Heuristic_Login_Success’. This was highlighted because the breach device did not commonly make connections over this protocol and the destination was a never-before-seen Bulgarian IP address (79.142.70[.]239). The connection lasted 4 minutes, and the device downloaded 31.36 MB of data. 

Following this, the breach device was seen making unusual HTTP connections to rare Russian and Danish endpoints using suspicious user agents. The Russian endpoint was noted for hosting a text file (‘incricinfo[.]com') that listed a single domain which was recently registered. The connections to the Danish endpoint were made to an IP with a URI that OSINT connected to the use of the BeamWinHTTP loader [2]. This loader can be used to download and execute other malware strains, in particular information stealers [3]. 

Figure 3: Screenshot of Russian endpoint with link to incricinfo[.]com 
Figure 4: Cyber AI Analyst highlighting the unusual HTTP connectivity that occurred prior to the multiple suspicious file downloads

At the same time as the connections with the unusual user agents, the device was also seen downloading an executable file from the endpoint, ‘Yuuichirou-hanma[.]s3[.]pl-waw[.]scw[.]cloud’. Analysis of the file indicated that it may be used to deploy further malware and potentially unwanted programs (PUPs). BeamWinHTTP also causes installation of these PUPs which helps to load more nefarious programs and spread compromise. 

This behavior was then seen as the device downloaded 5 different executable files from the endpoint, ‘hakhaulogistics[.]com’. This domain is linked to a Vietnamese logistics company that Darktrace had marked as new within the environment; it is possible that this domain was compromised and being used to host malicious infrastructure. At the point of compromise, several of the downloads were labeled as malicious by popular OSINT [4]. Additionally, at least one of the files was explicitly linked to the RedLine Information Stealer.  

Shortly after, the device made connections to a known Tor relay node. Tor is commonly used as an avenue for C2 communication as it offers a way for attackers to anonymize and obfuscate their activity. It was at this point that the first Proactive Threat Notification (PTN) for this activity occurred. This ensured immediate follow-up investigation from Darktrace SOC and a timeline of events and impacted devices were issued to the customer’s security team directly. 

Figure 5: Cyber AI Analyst highlighting the unusual executable downloads as well as the subsequent Tor connections. The file poweroff[.]exe has been highlighted by several OSINT sources as being potentially malicious

By this point, Darktrace had identified a large volume of unusual outbound HTTP POSTs to a variety of endpoints that seemed to have no obvious function or service. Following these POST requests, the compromised device was seen initiating a long SSL connection to the domain, ‘www[.]qfhwji6fnpiad3gs[.]com’, which is likely to have be generated by an algorithm (DGA). Lastly, a little while after the SSL connections, the device was seen downloading another executable file from the Russian domain ‘test-hf[.]su’. Research on the file again suggested that it was associated with RedLine Stealer [5].  

Figure 6: AIA highlighting additional unusual HTTP connections that were linked with the numeric exe download

Dangers of Non-Compliance 

Whilst the RedLine compromise was a matter of customer concern, the gap in their security was not visibility but rather best practice. It is important to note that prior to these events, the device was commonly seen sending and receiving connections associated with torrenting. In the past it has been observed that RedLine Stealer masquerades as ‘cracked’ software (software that has had its copy protection removed) [6]. In this instance, the initial download of the false ‘SNMPc’ executable may have been proof of this behavior. 

This is a reminder that torrenting is also extremely popular as a peer-to-peer vector for transferring malicious files. Combined with the possibility of network throttling or unapproved VPN use, torrents are usually considered non-compliant within corporate settings. Whether the events here were kickstarted due to a user unwittingly downloading malicious software, or exposure to a malicious actor via BitTorrent use, both cases represent a user circumventing existing compliance controls or a lack of compliance control in general. It is important for organizations to make sure that their users are acting in ways that limit the company’s exposure to nefarious actors. Companies should routinely encourage proper cyber hygiene and implement access controls that block certain activities such as torrenting if threats like these are to be stopped in the future.  

Regardless of what users are doing, Darktrace is positioned to detect and take action on compliance breaches and activity resulting from lack of compliance. The variety of C2 domains used in this blog incident were too quick for most security tools to alert on or for human teams to triage. However, this was no problem for Cyber AI analyst, which was able to draw together aspects of the attack across the kill chain and save a significant amount of time for both the customer security team and Darktrace SOC analysts. If active, Darktrace RESPOND could have blocked activities like the initial BitTorrent connections and incoming download, but with the right preventative measures, it wouldn’t have to. Darktrace PREVENT works continuously to harden defenses and preempt attackers, closing any vulnerabilities before they can be exploited. This includes performing attack surface management, attack path modelling, and security awareness training. In this case, Darktrace PREVENT could have highlighted torrenting activity as part of a potentially harmful attack path and recommended the best actions to mitigate it.

‘No Prior Experience required’ 

In the past, only highly skilled attackers could create and use the tools needed to attack organizations. With Ransomware-as-a-Service (RaaS) proving highly profitable, however, it is no surprise that malware is also becoming a lucrative business. As SaaS can help legitimate companies with no development experience to use and maintain apps, MaaS can help attackers with little to no hacking experience compromise organizations and achieve their goals. RedLine Stealer is readily available, and not prohibitively expensive, meaning attacks can be carried out more frequently, and on a wider range of victims. The incident explored in this blog is proof of this, and a strong indication that security comes not only from strong visibility but also compliance and best practice too. With a powerful defensive tool like PREVENT, security teams can save time while feeling confident that they are keeping ahead of these aspects of security.

Thanks to Adam Stevens for his contributions to this blog.

Appendices

Darktrace Model Breaches

·      Anomalous Connection / Multiple HTTP POSTs to Rare Hostname 

·      Anomalous Connection / New User Agent to IP Without Hostname

·      Anomalous File / EXE from Rare External Location

·      Anomalous File / Multiple EXE from Rare External 

·      Anomalous File / Numeric Exe Download

·      Anomalous Server Activity / New User Agent from Internet Facing System

·      Compliance / SSH to Rare External Destination

·      Compromise / Anomalous File then Tor 

·      Compromise / Possible Tor Usage 

·      Device / Initial Breach Chain Compromise

·      Device / Long Agent Connection to New Endpoint

References

[1] https://blog.sonicwall.com/en-us/2021/12/the-rise-and-growth-of-malware-as-a-service/

[2] https://asec.ahnlab.com/en/33679/  

[3] https://asec.ahnlab.com/en/20930/

[4] https://www.virustotal.com/gui/file/acfc06b4bcda03ecf4f9dc9b27c510b58ae3a6a9baf1ee821fc624467944467b & https://www.virustotal.com/gui/file/dad6311f96df65f40d9599c84907bae98306f902b1489b03768294b7678a5e79 

[5] https://www.virustotal.com/gui/file/ff7574f9f1d15594e409bee206f5db6c76db7c90dda2ae4f241b77cd0c7b6bf6

[6] https://asec.ahnlab.com/en/30445/

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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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