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Remote Desktop Protocol (RDP) Attack Analysis

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16
August 2021
16
August 2021
Das Remote Desktop Protocol (RDP) ist für Angreifer zu einer der beliebtesten Methoden des Eindringens geworden. In diesem Blog wird ein schneller RDP-Angriff analysiert, der sich innerhalb von nur sieben Stunden vom anfänglichen Zugriff bis zum lateral Movement entwickelte.

Am späten Samstagabend wurde ein Unternehmen für physische Sicherheit in den USA Ziel eines Angriffs. Cyberkriminelle hatten einen ungeschu2tzten RDP Server ausgenutzt. Am Sonntag waren alle internen Dienste des Unternehmens unbrauchbar. In diesem Blog werden der Angriff und die Gefahren offener RDP-Ports näher erläutert.

What is RDP?

Mit der Verlagerung in das Home Office haben sich IT-Teams auf remote tools verlassen, um Unternehmensgeräte zu verwalten und den Betrieb aufrechtzuerhalten. Das Remote Desktop Protocol (RDP) ist ein Microsoft-Protokoll, das Administratoren den Zugriff auf Desktop-Computer ermöglicht. Da es dem Benutzer die vollständige Kontrolle über das Gerät gibt, ist es ein wertvoller Einstiegspunkt für Bedrohungsakteure.

‘RDP shops’ selling credentials on the Dark Web have been around for years. xDedic, one of the most notorious crime forums which once boasted over 80,000 hacked servers for sale, was finally shut down by the FBI and Europol in 2019, five years after it had been founded. Selling RDP access is a booming industry because it provides immediate entry into an organization, removing the need to design a phishing email, develop malware, or manually search for zero-days and open ports. For less than $5, an attacker can purchase direct access to their target organization.

In den Monaten nach dem Ausbruch von COVID-19 stieg die Zahl der gefährdeten RDP-Endpunkte um 127 %. Die RDP-Nutzung stieg sprunghaft an, da sich die Unternehmen an die Bedingungen der Telearbeit anpassten, und es wurde für herkömmliche Sicherheitstools fast unmöglich, zwischen der täglichen legitimen Anwendung von RDP und seiner Ausnutzung zu unterscheiden. Dies führte zu einem dramatischen Anstieg der erfolgreichen serverseitigen Angriffe. Nach Angaben des National Cyber Security Centre des Vereinigten Königreichs ist RDP inzwischen der am häufigsten von Cyberkriminellen, insbesondere von Ransomware-Banden, genutzte Angriffsvektor.

Breakdown einer RDP-Kompromittierung

Erstes Eindringen

Bei diesem realen Angriff hatte das Zielunternehmen etwa 7.500 aktive Geräte, von denen eines ein Server mit Internetanschluss war, dessen TCP-Port 3389 - der Standardport für RDP - offen war. Mit anderen Worten: Der Port war so konfiguriert, dass er Netzwerkpakete akzeptiert.

Darktrace detected a successful incoming RDP connection from a rare external endpoint, which utilized a suspicious authentication cookie. Given that the device was subject to a large volume of external RDP connections, it is likely the attacker brute-forced their way in, though they could have used an exploit or bought credentials off the Dark Web.

Da eingehende Verbindungen über Port 3389 zu diesem Dienst alltäglich waren und als Teil des normalen Geschäftsbetriebs erwartet wurden, wurde die Verbindung von keinem anderen Sicherheitstool gemeldet.

Abbildung 1: Timeline des Angriffs - die Gesamtverweildauer betrug einen Tag

Internal Reconnaissance

Nach der anfänglichen Kompromittierung wurde das Gerät bei Netzwerk-Scan-Aktivitäten innerhalb seines eigenen Subnetzes beobachtet, um seinen Zugriff zu erweitern. Nach dem Scan stellte das Gerät WMI-Verbindungen (Windows Management Instrumentation) zu mehreren Geräten über DCE-RPC her, was mehrere Darktrace Alarme auslöste.

Abbildung 2: Das Diagramm zeigt Ausschläge von ungewöhnlichen Aktivitäten und eine große Anzahl von Modellverletzungen

Command and control (C2)

Das Gerät stellte dann eine neue RDP-Verbindung an einem nicht standardmäßigen Port her, wobei es ein administratives Authentifizierungs-Cookie zu einem Endpunkt verwendete, der im Netzwerk noch nie gesehen wurde. Danach wurden Tor-Verbindungen beobachtet, was auf eine mögliche C2-Kommunikation hindeutet.

Figure 3: Cyber AI Analyst - Darktrace's AI investigation tool - breaks down the different stages of the incident

Lateral movement

Der Angreifer versuchte dann, sich über SMB Service Control Pipes und PsExec auf fünf Geräte im Subnetz des Angreifers zu bewegen, die wahrscheinlich während des Netzwerkscans identifiziert wurden.

Durch die Verwendung nativer Windows-Verwaltungstools (PsExec, WMI und svcctl) für laterale Bewegungen gelang es dem Angreifer, "von der Bildfläche zu verschwinden" und sich der Erkennung durch den Rest des Sicherheits-Stacks zu entziehen.

Ask the Expert

Die internen Dienste der Organisation waren nicht verfügbar, also wandte man sich an den 24/7 Ask the Expert Service von Darktrace. Die Darktrace Cyberexperten konnten mithilfe der KI schnell den Umfang sowie die Art des Angriffs feststellen und mit der Behebung des Problems beginnen. So konnte die Bedrohung neutralisiert werden, bevor der Angreifer seine Ziele erreichte, zu denen unter anderem Krypto-Mining, die Verbreitung von Ransomware oder das Exfiltrieren sensibler Daten gehörten.

RDP-Schwachstelle: Gefahren durch ungeschützte Server

Vor den oben beschriebenen Ereignissen hatte Darktrace eingehende Verbindungen über RDP und SQL von einer Vielzahl seltener externer Endpunkte beobachtet, was darauf hindeutet, dass der Server schon viele Male zuvor angegriffen worden war. Wenn unnötige Dienste für das Internet offen gelassen werden, ist eine Kompromittierung unvermeidlich - es ist nur eine Frage der Zeit.

Dies gilt insbesondere für RDP. In diesem Fall ist es dem Angreifer gelungen, durch seinen ersten Zugriff auf den RDP-Port, erfolgreich Zugang zu verschaffen und eine externe Kommunikation zu eröffnen. Bedrohungsakteure sind immer auf der Suche nach einer Möglichkeit, in das System einzudringen. Was also als Compliance-Problem betrachtet werden könnte, kann sich leicht und schnell zu einer Kompromittierung entwickeln.

Die remote Kontrolle ist außer Kontrolle 

Der Angriff fand außerhalb der Geschäftszeiten statt - zu einem Zeitpunkt, an dem das Sicherheitsteam seinen Samstagabend genoss - und entwickelte sich mit bemerkenswerter Geschwindigkeit, wobei er sich in weniger als sieben Stunden von "Initial intrusion" bis zum "Lateral Movement" entwickelte. Es kommt häufig vor, dass Angreifer diese menschlichen Schwachstellen ausnutzen, um sich schnell bewegen und unentdeckt zu bleiben, bis das IT-Team am Montagmorgen wieder an seinem Schreibtisch sitzt.

It is for this reason that a security solution which does not sleep – and which can detect and autonomously respond to threats around the clock – is critical. Self-Learning AI can keep up with threats which escalate at machine speed, stopping them at every turn.

Wir danken dem Analysten Steven Sosa von Darktrace für seine Erkenntnisse über die oben genannte Bedrohung.

Erfahren Sie, wie ein RDP-Angriff zur Verbreitung von Ransomware führte

Darktrace Modell-Erkennungen:


  • Compliance / Incoming Remote Desktop
  • Device / Network Scan
  • Device / New or Uncommon WMI Activity
  • Device / Suspicious Network Scan Activity
  • Device / RDP Scan
  • Device / Anomalous RDP Followed By Multiple Model Breaches
  • Anomalous Connection / Outbound RDP to Unusual Port
  • Compliance / Possible Tor Usage
  • Compliance / High Priority Compliance Model Breach
  • Device / New or Unusual Remote Command Execution
  • Anomalous Connection / New or Uncommon Service Control
  • Device / New or Uncommon SMB Named Pipe
  • Device / Multiple Lateral Movement Model Breaches
  • Anomalous Connection / High Volume of New or Uncommon Service Control
  • Compliance / Outbound RDP
  • Anomalous Server Activity / Domain Controller Initiated to Client

EINBLICKE IN DAS SOC-Team
Darktrace Cyber-Analysten sind erstklassige Experten für Threat Intelligence, Threat Hunting und Incident Response. Sie bieten Tausenden von Darktrace Kunden auf der ganzen Welt rund um die Uhr SOC-Support. Einblicke in das SOC-Team wird ausschließlich von diesen Experten verfasst und bietet Analysen von Cyber-Vorfällen und Bedrohungstrends, die auf praktischen Erfahrungen in diesem Bereich basieren.
AUTOR
ÜBER DEN AUTOR
Oakley Cox
Analyst Technical Director, APAC

Oakley is a technical expert with 5 years’ experience as a Cyber Analyst. After leading a team of Cyber Analysts at the Cambridge headquarters, he relocated to New Zealand and now oversees the defense of critical infrastructure and industrial control systems across the APAC region. His research into cyber-physical security has been published by Cyber Security journals and CISA. Oakley is GIAC certified in Response and Industrial Defense (GRID), and has a Doctorate (PhD) from the University of Oxford.

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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].

Schlussfolgerung

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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About the author
Natalia Sánchez Rocafort
Cyber Security Analyst
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