Misconfigurations - whether accidental or malicious - are a growing threat in the face of rapidly expanding digital footprints comprising of cloud assets and bespoke OT technology. This blog explains how these are uncovered and remediated with Darktrace PREVENT.
During an initial demo with a water management company, Darktrace PREVENT found an industrial control system exposed to the internet. Immediately, the organization went into incident response mode because this system was mission critical and could potentially impact the water facilities, as it had the power to adjust water flow. This asset was exposed because of a simple misconfiguration, highlighting to the customer the need for proactive monitoring of its attack surface. In this case, the engineer who set up the system had simply not noticed the mistake, but these scenarios could be more dangerous and more likely if insider threat is involved.
Misconfigurations arise when vital security settings are either not applied or applied incorrectly. Such misconfigurations produce vulnerable security openings that can be exploited by attackers to either gain a foothold in the asset or generate a more dangerous attack, like altering water flow or deploying ransomware. There is a wide variety of assets that are subject to potential misconfigurations, including web or application servers, cloud containers, custom code, network devices like desktops or servers, and entire databases.
Unfortunately, the pervasiveness of misconfigurations is only increasing. In the past 12 months, there has been a 310% increase in hackers reporting misconfiguration vulnerabilities to the HackerOne platform.
Every digital environment has its own characteristics that alter the prevalence or the impact of misconfigurations. For example, industrial devices that support critical infrastructure are all the more sensitive to these types of changes, as these devices often have limited integrated security, despite their highly delicate functions. Because with every vendor and device has its own recommended configuration, security teams must take additional precautions.
In cloud environments, the ease of deployment and increased capabilities also tend to produce more misconfigurations. Digital footprints are growing at such a pace that security departments may opt to skip onboarding processes of technologies to avoid becoming an obstacle for the business. It has become so easy for any department, regardless of their technical knowledge, to add cloud applications, software, or even hardware to the company's architecture. This is why shadow IT is so troublesome: it's impossible for the security team to ensure something is well configured if they don’t even know it exists.
In addition, due to rapid growth, security and IT teams aren’t experts in every technology included within the enterprise architecture. So, the teams may do their best to apply security controls while being unaware the current configuration is a misconfiguration. With digital assets’ constant evolution, they may even be configured correctly at one point but become misconfigured in the future if not updated.
Mitigating Misconfiguration
It’s human nature that we make mistakes, and the more assets and third parties that are introduced, the more mistakes are possible. However, there are certain steps organizations can take towards reducing the frequency and the impact of misconfigurations.
Any organization needs to have discovery processes to maintain an updated inventory of their assets, and should categorize these assets based on their exposure as well as their criticality to the business. This information should feed into the organization’s risk analysis, which in turn informs the priority of mitigation actions or controls. This process, when done manually, can be long and arduous, and is not continuous: as organizations’ digital footprints are evolving so rapidly, these analyses can become obsolete quickly.
On the other hand, organizations must also monitor the activity of these assets and not just assess them at face value. As with anything in security, security teams need to be weary of the symptoms. Inappropriate configurations will often generate alerts such as slow performance, multiple suspicious login attempts, bloatware, unexpected application behavior such as redirects or shutdowns.
The Power of PREVENT
Misconfigurations are easier to identify, prioritize and remediate with an AI solution that provides continuous analysis of the organization’s external and internal attack surface. Darktrace PREVENT – consisting of PREVENT/Attack Surface Management (ASM) and PREVENT/End-to-End (E2E) – achieve exactly this.
With ASM, security teams gain visibility of the entire external attack surface, including elusive assets like shadow IT and legacy devices. It frequently uncovers misconfigurations and recommends how to mitigate the risks caused by them. Some examples include email spoofing, no SPF records, no DKIM records, no DMARC records, subdomain takeover possible, and missing routes for netblocks.
The truly unique aspect of a Self-Learning technology is that security teams receive notifications tailored to the precise assets within their architectures. In other words, the tool will only provide the misconfiguration recommendations for the specific assets that require it, instead of having to reverse engineer state-of-the-art security and then trying to see where it can apply within the organization. With Darktrace, security teams are already getting that information directly. In fact, it doesn’t stop there, as PREVENT can then prioritize the misconfigurations by the risk inherited. The security team only has to check the list of misconfigurations in order of priority and take action on them.
From an internal perspective, PREVENT/E2E will map those misconfigurations to potential attack paths, answering the question of what damage each misconfiguration can lead to and more importantly how: an attacker could go from that initial misconfiguration through each lateral movement, whether it is via a device or a user, and then reach the most critical devices within the infrastructure.
Often in security, the focus can drift to the latest tactics and techniques being used by large Advanced Persistent Threats, but a simple misconfiguration caused by a rushed or distracted employee can pose an equally large threat. An innocent mistake can often open an even larger weakness in the digital architecture, as the attacker doesn’t have to force to open the window to break in.
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
Carlos Gray
Product Manager
Carlos Gonzalez Gray is a Product Marketing Manager at Darktrace. Based in the Madrid Office, Carlos engages with the global product team to ensure each product supports the company’s overall strategy and goals throughout their entire lifecycle. Previous to his position in the product team, Carlos worked as a Cyber Technology Specialist where he specialized in the OT sector protecting critical infrastructure. His background as a consultant in Spain to IBEX 35 companies led him to become well versed in matters of compliance, auditing and data privacy as well. Carlos holds an Honors BA in Political Science and a Masters in Cybersecurity from IE University.
The State of AI in Cybersecurity: The Impact of AI on Cybersecurity Solutions
13
May 2024
About the AI Cybersecurity Report
Darktrace 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.
Overwhelming alert volumes, high false positive rates, and endlessly innovative threat actors keep security teams scrambling. Defenders have been forced to take a reactive approach, struggling to keep pace with an ever-evolving threat landscape. It is hard to find time to address long-term objectives or revamp operational processes when you are always engaged in hand-to-hand combat.
The impact of AI on the threat landscape will soon make yesterday’s approaches untenable. Cybersecurity vendors are racing to capitalize on buyer interest in AI by supplying solutions that promise to meet the need. But not all AI is created equal, and not all these solutions live up to the widespread hype.
Do security professionals believe AI will impact their security operations?
Yes! 95% of cybersecurity professionals agree that AI-powered solutions will level up their organization’s defenses.
Not only is there strong agreement about the ability of AI-powered cybersecurity solutions to improve the speed and efficiency of prevention, detection, response, and recovery, but that agreement is nearly universal, with more than 95% alignment.
This AI-powered future is about much more than generative AI. While generative AI can help accelerate the data retrieval process within threat detection, create quick incident summaries, automate low-level tasks in security operations, and simulate phishing emails and other attack tactics, most of these use cases were ranked lower in their impact to security operations by survey participants.
There are many other types of AI, which can be applied to many other use cases:
Supervised machine learning: Applied more often than any other type of AI in cybersecurity. Trained on attack patterns and historical threat intelligence to recognize known attacks.
Natural language processing (NLP): Applies computational techniques to process and understand human language. It can be used in threat intelligence, incident investigation, and summarization.
Large language models (LLMs): Used in generative AI tools, this type of AI applies deep learning models trained on massively large data sets to understand, summarize, and generate new content. The integrity of the output depends upon the quality of the data on which the AI was trained.
Unsupervised machine learning: Continuously learns from raw, unstructured data to identify deviations that represent true anomalies. With the correct models, this AI can use anomaly-based detections to identify all kinds of cyber-attacks, including entirely unknown and novel ones.
What are the areas of cybersecurity AI will impact the most?
Improving threat detection is the #1 area within cybersecurity where AI is expected to have an impact.
The most frequent response to this question, improving threat detection capabilities in general, was top ranked by slightly more than half (57%) of respondents. This suggests security professionals hope that AI will rapidly analyze enormous numbers of validated threats within huge volumes of fast-flowing events and signals. And that it will ultimately prove a boon to front-line security analysts. They are not wrong.
Identifying exploitable vulnerabilities (mentioned by 50% of respondents) is also important. Strengthening vulnerability management by applying AI to continuously monitor the exposed attack surface for risks and high-impact vulnerabilities can give defenders an edge. If it prevents threats from ever reaching the network, AI will have a major downstream impact on incident prevalence and breach risk.
Where will defensive AI have the greatest impact on cybersecurity?
Cloud security (61%), data security (50%), and network security (46%) are the domains where defensive AI is expected to have the greatest impact.
Respondents selected broader domains over specific technologies. In particular, they chose the areas experiencing a renaissance. Cloud is the future for most organizations, and the effects of cloud adoption on data and networks are intertwined. All three domains are increasingly central to business operations, impacting everything everywhere.
Responses were remarkably consistent across demographics, geographies, and organization sizes, suggesting that nearly all survey participants are thinking about this similarly—that AI will likely have far-reaching applications across the broadest fields, as well as fewer, more specific applications within narrower categories.
Going forward, it will be paramount for organizations to augment their cloud and SaaS security with AI-powered anomaly detection, as threat actors sharpen their focus on these targets.
How will security teams stop AI-powered threats?
Most security stakeholders (71%) are confident that AI-powered security solutions are better able to block AI-powered threats than traditional tools.
There is strong agreement that AI-powered solutions will be better at stopping AI-powered threats (71% of respondents are confident in this), and there’s also agreement (66%) that AI-powered solutions will be able to do so automatically. This implies significant faith in the ability of AI to detect threats both precisely and accurately, and also orchestrate the correct response actions.
There is also a high degree of confidence in the ability of security teams to implement and operate AI-powered solutions, with only 30% of respondents expressing doubt. This bodes well for the acceptance of AI-powered solutions, with stakeholders saying they’re prepared for the shift.
On the one hand, it is positive that cybersecurity stakeholders are beginning to understand the terms of this contest—that is, that only AI can be used to fight AI. On the other hand, there are persistent misunderstandings about what AI is, what it can do, and why choosing the right type of AI is so important. Only when those popular misconceptions have become far less widespread can our industry advance its effectiveness.
Connecting the Dots: Darktrace’s Detection of the Exploitation of the ConnectWise ScreenConnect Vulnerabilities
10
May 2024
Introduction
Across an ever changing cyber landscape, it is common place for threat actors to actively identify and exploit newly discovered vulnerabilities within commonly utilized services and applications. While attackers are likely to prioritize developing exploits for the more severe and global Common Vulnerabilities and Exposures (CVEs), they typically have the most success exploiting known vulnerabilities within the first couple years of disclosure to the public.
Addressing these vulnerabilities in a timely manner reduces the effectiveness of known vulnerabilities, decreasing the pace of malicious actor operations and forcing pursuit of more costly and time-consuming methods, such as zero-day related exploits or attacking software supply chain operations. While actors also develop tools to exploit other vulnerabilities, developing exploits for critical and publicly known vulnerabilities gives actors impactful tools at a low cost they are able to use for quite some time.
Between January and March 2024, the Darktrace Threat Research team investigated one such example that involved indicators of compromise (IoCs) suggesting the exploitation of vulnerabilities in ConnectWise’s remote monitoring and management (RMM) software ScreenConnect.
What are the ConnectWise ScreenConnect vulnerabilities?
CVE-2024-1708 is an authentication bypass vulnerability in ScreenConnect 23.9.7 (and all earlier versions) that, if exploited, would enable an attacker to execute remote code or directly impact confidential information or critical systems. This exploit would pave the way for a second ScreenConnect vunerability, CVE-2024-1709, which allows attackers to directly access confidential information or critical systems [1].
ConnectWise released a patch and automatically updated cloud versions of ScreenConnect 23.9.9, while urging security temas to update on-premise versions immediately [3].
If exploited in conjunction, these vulnerabilities could allow a malicious actor to create new administrative accounts on publicly exposed instances by evading existing security measures. This, in turn, could enable attackers to assume an administrative role and disable security tools, create backdoors, and disrupt RMM processes. Access to an organization’s environment in this manner poses serious risk, potentially leading to significant consequences such as deploying ransomware, as seen in various incidents involving the exploitation of ScreenConnect [2]
Darktrace’s anomaly-based detection was able to identify evidence of exploitation related to CVE-2024-1708 and CVE-2024-1709 across two distinct timelines; these detections included connectivity with endpoints that were later confirmed to be malicious by multiple open-source intelligence (OSINT) vendors. The activity observed by Darktrace suggests that threat actors were actively exploiting these vulnerabilities across multiple customer environments.
In the cases observed across the Darktrace fleet, Darktrace DETECT™ and Darktrace RESPOND™ were able to work in tandem to pre-emptively identify and contain network compromises from the onset. While Darktrace RESPOND was enabled in most customer environments affected by the ScreenConnect vulnerabilities, in the majority of cases it was configured in Human Confirmation mode. Whilst in Human Confirmation mode, RESPOND will provide recommended actions to mitigate ongoing attacks, but these actions require manual approval from human security teams.
When enabled in autonomous response mode, Darktrace RESPOND will take action automatically, shutting down suspicious activity as soon as it is detected without the need for human intervention. This is the ideal end state for RESPOND as actions can be taken at machine speed, without any delays waiting for user approval.
Looking within the patterns of activity observed by Darktrace , the typical attack timeline included:
Darktrace observed devices on affected customer networks performing activity indicative of ConnectWise ScreenConnect usage, for example connections over 80 and 8041, connections to screenconnect[.]com, and the use of the user agent “LabTech Agent”. OSINT research suggests that this user agent is an older name for ConnectWise Automate [5] which also includes ScreenConnect as standard [6].
Figure 1: Darktrace DETECT model alert highlighting the use of a remote management tool, namely “screenconnect[.]com”.
This activity was typically followed by anomalous connections to the external IP address 108.61.210[.]72 using URIs of the form “/MyUserName_DEVICEHOSTNAME”, as well as additional connections to another external, IP 185.62.58[.]132. Both of these external locations have since been reported as potentially malicious [14], with 185.62.58[.]132 in particular linked to ScreenConnect post-exploitation activity [2].
Figure 2: Darktrace DETECT model alert highlighting the unusual connection to 185.62.58[.]132 via port 8041.
Figure 3: Darktrace DETECT model alert highlighting connections to 108.61.210[.]72 using a new user agent and the “/MyUserName_DEVICEHOSTNAME” URI.
Same Exploit, Different Tactics?
While the majority of instances of ConnectWise ScreenConnect exploitation observed by Darktrace followed the above pattern of activity, Darktrace was able to identify some deviations from this.
In one customer environment, Darktrace’s detection of post-exploitation activity began with the same indicators of ScreenConnect usage, including connections to screenconnect[.]com via port 8041, followed by connections to unusual domains flagged as malicious by OSINT, in this case 116.0.56[.]101 [16] [17]. However, on this deployment Darktrace also observed threat actors downloading a suspicious AnyDesk installer from the endpoint with the URI “hxxp[:]//116.0.56[.]101[:]9191/images/Distribution.exe”.
Figure 4: Darktrace DETECT model alert highlighting the download of an unusual executable file from 116.0.56[.]101.
Further investigation by Darktrace’s Threat Research team revealed that this endpoint was associated with threat actors exploiting CVE-2024-1708 and CVE-2024-1709 [1]. Darktrace was additionally able to identify that, despite the customer being based in the United Kingdom, the file downloaded came from Pakistan. Darktrace recognized that this represented a deviation from the device’s expected pattern of activity and promptly alerted for it, bringing it to the attention of the customer.
Figure 5: External Sites Summary within the Darktrace UI pinpointing the geographic locations of external endpoints, in this case highlighting a file download from Pakistan.
Darktrace’s Autonomous Response
In this instance, the customer had Darktrace enabled in autonomous response mode and the post-exploitation activity was swiftly contained, preventing the attack from escalating.
As soon as the suspicious AnyDesk download was detected, Darktrace RESPOND applied targeted measures to prevent additional malicious activity. This included blocking connections to 116.0.56[.]101 and “*.56.101”, along with blocking all outgoing traffic from the device. Furthermore, RESPOND enforced a “pattern of life” on the device, restricting its activity to its learned behavior, allowing connections that are considered normal, but blocking any unusual deviations.
Figure 6: Darktrace RESPOND enforcing a “pattern of life” on the offending device after detecting the suspicious AnyDesk download.
Figure 7: Darktrace RESPOND blocking connections to the suspicious endpoint 116.0.56[.]101 and “*.56.101” following the download of the suspicious AnyDesk installer.
The customer was later able to use RESPOND to manually quarantine the offending device, ensuring that all incoming and outgoing traffic to or from the device was prohibited, thus preventing ay further malicious communication or lateral movement attempts.
Figure 8: The actions applied by Darktrace RESPOND in response to the post-exploitation activity related to the ScreenConnect vulnerabilities, including the manually applied “Quarantine device” action.
Conclusion
In the observed cases of the ConnectWise ScreenConnect vulnerabilities being exploited across the Darktrace fleet, Darktrace was able to pre-emptively identify and contain network compromises from the onset, offering vital protection against disruptive cyber-attacks.
While much of the post-exploitation activity observed by Darktrace remained the same across different customer environments, important deviations were also identified suggesting that threat actors may be adapting their tactics, techniques and procedures (TTPs) from campaign to campaign.
While new vulnerabilities will inevitably surface and threat actors will continually look for novel ways to evolve their methods, Darktrace’s Self-Learning AI and behavioral analysis offers organizations full visibility over new or unknown threats. Rather than relying on existing threat intelligence or static lists of “known bads”, Darktrace is able to detect emerging activity based on anomaly and respond to it without latency, safeguarding customer environments whilst causing minimal disruption to business operations.
Credit: Emma Foulger, Principal Cyber Analyst for their contribution to this blog.
Appendices
Darktrace Model Coverage
DETECT Models
Compromise / Agent Beacon (Medium Period)
Compromise / Agent Beacon (Long Period)
Anomalous File / EXE from Rare External Location
Device / New PowerShell User Agent
Anomalous Connection / Powershell to Rare External
Anomalous Connection / New User Agent to IP Without Hostname
User / New Admin Credentials on Client
Device / New User Agent
Anomalous Connection / Multiple HTTP POSTs to Rare Hostname
Anomalous Server Activity / Anomalous External Activity from Critical Network Device
Compromise / Suspicious Request Data
Compliance / Remote Management Tool On Server
Anomalous File / Anomalous Octet Stream (No User Agent)
![Darktrace DETECT model alert highlighting the use of a remote management tool, namely “screenconnect[.]com”.](https://assets-global.website-files.com/626ff4d25aca2edf4325ff97/663e65c6b0ee97378e4509af_Screenshot%202024-05-10%20at%2011.21.43%20AM.png)
![Figure 2: Darktrace DETECT model alert highlighting the unusual connection to 185.62.58[.]132 via port 8041.](https://assets-global.website-files.com/626ff4d25aca2edf4325ff97/663e66b4de1b5491cb1af256_Screenshot%202024-05-10%20at%2011.22.47%20AM.png)
![Figure 3: Darktrace DETECT model alert highlighting connections to 108.61.210[.]72 using a new user agent and the “/MyUserName_DEVICEHOSTNAME” URI.](https://assets-global.website-files.com/626ff4d25aca2edf4325ff97/663e66d3c53f1631e9d6f3ec_Screenshot%202024-05-10%20at%2011.23.04%20AM.png)
![Figure 4: Darktrace DETECT model alert highlighting the download of an unusual executable file from 116.0.56[.]101.](https://assets-global.website-files.com/626ff4d25aca2edf4325ff97/663e671300e1a5b26483fcea_Screenshot%202024-05-10%20at%2011.27.20%20AM.png)
![Figure 7: Darktrace RESPOND blocking connections to the suspicious endpoint 116.0.56[.]101 and “*.56.101” following the download of the suspicious AnyDesk installer.](https://assets-global.website-files.com/626ff4d25aca2edf4325ff97/663e67ff976d5b71245b32a4_Screenshot%202024-05-10%20at%2011.31.17%20AM.png)
