Protecting critical infrastructure: Mapping and patching CVEs is not enough for robust defense
25
Aug 2021
25
Aug 2021
Mapping and patching common vulnerabilities and exposure (CVEs) is not enough to achieve truly robust defense. This blog discusses the limits of vulnerability tracking and how self-learning technology can help achieve the goals of Biden’s National Security Memorandum on Improving Cybersecurity for Critical Infrastructure Control Systems.
While increased public focus and funding are urgently needed, they are only the first steps in achieving stronger security for critical infrastructure. For these efforts to be successful, the Biden administration needs to turn its focus to cutting-edge capabilities and direct its funding toward sophisticated technologies that can achieve these ends.
Focusing on achieving the right security capabilities
For years, the security community for operational technology (OT) and Industrial Control Systems (ICS) has put a strong emphasis on mapping and patching common vulnerabilities and exposures (CVEs). While this vulnerability tracking is often necessary, mapping and patching vulnerabilities alone is not a sufficient strategy to arm organizations against attacks.
First, known vulnerabilities simply do not represent all risks, as attackers frequently leverage unknown vulnerabilities called zero-days and misuse legitimate operations in ways that cannot be trivially recognized as malicious. This is particularly true for control system environments, with one-third of ICS flaws designated as zero-days when disclosed.
Devices with zero or few known vulnerabilities may also simply lack specific research into them, rather than being more secure. For example, millions of devices are put at risk by a recently discovered vulnerability in control systems used in building systems — including heating and air conditioning — and this also affects PLCs widely used in manufacturing and energy utilities.
Rather than merely tracking and patching CVEs, Biden’s recent National Security Memorandum realigns OT security to place focus onto the right security goals. The memorandum candidly addresses the problem with vulnerability tracking and other legacy security approaches, as this document affirms that “we cannot address threats we cannot see.”
Investing in the right security technologies
The memorandum specifically focuses on “facilitating deployment of technologies and systems that provide threat visibility, indications, detection, and warnings, and that facilitate response capabilities” for ICS and OT. The next step in achieving these aims is to identify the technologies that can provide these capabilities.
By learning a sense of ‘self’ from scratch for every human, device, and the whole ecosystem of an organization, Self-Learning AI autonomously implements machine-speed detection, investigations, and response. This solution is particularly effective at stopping ransomware before operation disruption, which would have allowed Colonial Pipeline to avoid manual shutdowns.
AI is also uniquely capable of thwarting insider threats. It could have immediately identified the threat in the Florida water facility incident, as its understanding of the ‘pattern of life’ for every user, device, and all the connections between them would have illuminated the insider’s subtle unusual behavior.
The sensitivity and essential nature of critical infrastructure demand the most sophisticated technologies for robust cyber defense. Fortunately, Self-Learning AI technology is already available and currently used by all 16 critical infrastructure sectors designated by CISA. As the threat continues to grow, and the Biden administration’s efforts are pursued more aggressively, Self-Learning AI stands ready to safeguard the systems that our society relies on.
Like this and want more?
Receive the latest blog in your inbox
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
NEWSLETTER
Like this and want more?
Stay up to date on the latest industry news and insights.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
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.
AUTHOR
ABOUT ThE AUTHOR
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.
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].
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
