WannaCry: a resposta de Darktrace à campanha global de ransomware
16
May 2017
16
May 2017
With Darktrace, these kind of infections are not hard to detect. WannaCry and other ransomware cause highly anomalous behavioral patterns that our machine-learning technology is ideally placed to recognize.
Over 200,000 organisations and private individuals were victims of Friday’s global cyber-attack. This number is likely to increase over the coming weeks, as copy-cat criminals develop variants of the same ransomware and new methods of delivering similar attacks.
Some background on the WannaCry campaign
The WannaCry outbreak does not appear to have targeted specific countries or industries. Instead, it targeted outdated computer systems, using exploit kits leaked earlier this year to infect devices and drop the initial ransomware file. Once inside a network, WannaCry will attempt to locate other vulnerable computers by conducting internal and external SMB scanning. Having established itself, the malware encrypts files and demands a ransom of around $300 to unlock them, payable in Bitcoin. However, dealing with criminals means that there is no guarantee of the files being released if that money is paid out. Strong security measures and effective response mechanisms are the only reliable ways in which to prevent extensive damage.
Leveraging Darktrace, these kind of infections are not hard to detect: WannaCry and other ransomware cause highly anomalous behavioral patterns that our machine-learning technology is ideally placed to recognize.
To demonstrate, let’s take a walk-through of how Darktrace was able to detect the WannaCry attack on a client. Note that device names have been obfuscated for security purposes.
Following the initial compromise, Darktrace detected unusual activity originating from an infected device, as it scanned the network in an attempt to locate other devices open to SMB connections:
Example of an internal scan.
The worm was scanning the network to locate devices with the DoublePulsar backdoor already present, through which the WannaCry ransomware can be dropped. If this backdoor was not found to be present, the worm used an exploit known as EternalBlue to infect the device, installing both WannaCry and the DoublePulsar backdoor.
This installation of the worm on vulnerable devices allowed it to continue to spread laterally inside the network.
Simultaneously, infected devices scanned random external IPs on port 445 (SMB), to continue spreading the worm to other devices on the internet:
Internal devices scanning external destinations.
As soon as infected devices started scanning both inside and outside network, Darktrace detected these activities as serious deviations in the devices’ usual pattern of life:
External and internal connections by one of the network devices 48 hours either side of the WannaCry campaign. Every orange dot represents a model breach.
For many of these devices, the deviation from typical pattern of life was such that it took Darktrace one second to detect anomalous behavior:
As this unusual activity persisted in the network, the confidence of Darktrace’s machine learning increased and attributed higher scores to these anomalous events:
These high scores caused Darktrace models to breach in real time, alerting the customer to the severity of the unusual connections occurring inside their network:
In these recent cyber-attacks, the level of disruption was attributed to the speed with which this infection was able to spread like wildfire through networks. Unlike more common forms of malware, which rely on human-mediated methods such as phishing to co-opt people into triggering the payload, this type of attack uses a worm to move from machine to machine without human intervention. Fortunately, it is precisely this – a dramatic change in internal activity – which has allowed us to effectively fight back.
Darktrace Antigena acts automatically to neutralise in-progress attacks, taking targeted action against deviations in the expected ‘pattern of life’. This allows organisations to react before humans have even become aware of a breach. So it follows that the extent of deviation produced by an attack is fundamentally linked to the ability of a self-aware network to protect itself.
The potential gravity of this situation has proven that infections traveling at machine speed require an equivalent response time – only possible with machine-learning technology – in order to stop and contain future threats.
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Os analistas cibernéticos da Darktrace são especialistas de classe mundial em inteligência de ameaças, caça de ameaças e resposta a incidentes, e fornecem suporte 24/7 SOC a milhares de Darktrace clientes em todo o mundo. Dentro do SOC é de autoria exclusiva desses especialistas, fornecendo análises de incidentes cibernéticos e tendências de ameaças, com base na experiência do mundo real na área.
AUTOR
SOBRE O AUTOR
Andrew Tsonchev
VP of Technology
Andrew is a technical expert on cyber security and advises Darktrace’s strategic customers on advanced threat defense, AI and autonomous response. He has a background in threat analysis and research, and holds a first-class degree in physics from Oxford University and a first-class degree in philosophy from King’s College London. His comments on cyber security and the threat to critical national infrastructure have been reported in international media, including CNBC and the BBC World.
The State of AI in Cybersecurity: How AI will impact the cyber threat landscape in 2024
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.
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.
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.
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
Sliver C2: How Darktrace Provided a Sliver of Hope in the Face of an Emerging C2 Framework
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.
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.
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.
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].
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:
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].
Conclusão
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
