Learn how AI can enhance security measures by detecting malicious assets, and safeguarding against vulnerabilities. Stay secure with advanced technology.
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.
Written by
Willem Van Zwieten
Data Science & Analytics Lead
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19
Jan 2022
The internet is huge and is expanding every day. While this has many positives for businesses, managing the potential risks in this environment can be daunting. The reality is that across the internet brands are susceptible to everything from brand abuse to phishing websites. So, how is it possible that organisations can keep track of the web-based assets that belong to them, and at the same time distinguish them from something that may only look like it’s theirs? Finding and verifying all of a company’s web assets across the entire internet is a massive undertaking. You essentially need to filter the whole internet and try to pick out what is relevant, and then set about detecting the risks – or even potential risks – within what you have found.
This isn’t a process that can be managed manually. The staff-hours alone would make this hugely prohibitive, and that’s without taking into account the potential margin for error. Instead, it requires a different approach, one based around automation. At Darktrace, my team and I work on exactly those kind of solutions. We’ve developed our own algorithms to define what distinguishes a client’s brand-owned site from everything else on the internet. We refer to that as a company’s Brand DNA. These special characteristics help us to predict and identify where any brand-related assets are across the entire internet, and how they should be investigated further. The concept of an organisation’s Brand DNA breaks down into two areas: what is unique by design and what is uniqueby comparison.
Unique by Design
All brands have different design elements that help set them apart from other brands. This can be everything from their name and logo, to the different fonts and colours they use in all their communications and websites. By ingesting this data into our algorithms we are able to scan the internet for any web-based assets that may be relevant to a company, based on these key brand elements. While the elements of ‘unique by design’ are relatively easily understandable by humans, no organisations want to have their time taken up manually searching through millions of images every day in order to help them locate the web properties that might belong to their organisation.
Unique by Comparison
Conversely, ‘unique by comparison’ focuses more on the elements that a human would probably not be able to figure out by themselves. As we suggest potential new domains to customers, we build up a pool of assets. Automation allows us to find patterns in these assets that might not be immediately obvious to humans, such as elements of metadata,nameserver details, or even where a website is hosted. Although unique by design is more about what you actually see on a website and unique by comparison focuses on the back-end, in reality there are overlaps and the two things feed into each other.
As a very basic example: if a domain is hosted on nameserver where other company assets are hosted, AND the company logo is on the page, then the chances are this domain is a company-owned asset. In effect, the two approaches strengthen each other. By analysing all these details together, our algorithms can increasingly accurately score how likely an asset is to be owned by a company. I should add here that unique by comparison is based on comparing a lot of features at once, so it is often not as clear cut as the above example.
Combining Humans and AI
Ultimately, automating the process in this way helps to create a minimal-touch process for companies. The algorithms do all the filtering, enabling the creation of a much-reduced list of assets for the company to look through. Basically, we’re able to break down that list to avery small percentage of the internet that they actually need to look at and then analyse the risk those assets poseto the organisation.
We also use something which we internally label “AI2”(artificial intelligence with analyst interaction). This essentially means we’re adding a human layer to the automated process, for both input and output checks. While the algorithms do all the heavy lifting and aid scalability, the human element allows us to finetune or dive deeper into certain automated findings.
Detecting Malicious Assets
While the algorithms are principally focused on establishing a brand’s attack surface, a useful byproduct is that they can also locate malicious assets, such as potential phishing sites. For example, if something looks like it belongs to the customer, but doesn’t actually belong inside their directdigital infrastructure, then clearly there is an increased likelihood that it is either a brand abuse or phishing site.
On top of this, as part of our search process we can also automatically create combinations of possible URLs that cover common search errors such as typos or “fat finger”errors within brand names, and then hunt for those – clearly the likelihood of these URLs being rogue sites is greatly enhanced.
The Clearest View of the Attack Surface
By combining all these elements, companies are able to get the most complete view of their potential attack surface.And with the use of enhanced automation techniques they can do so with minimum effort. From this position companies are able to easily and quickly home in on the genuine items, and the areas that pose them the most risk. They can then use the resulting list to form the foundations from which they can apply the rest of their security strategy.
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.
State of AI Cybersecurity 2026: 92% of security professionals concerned about the impact of AI agents
Findings from the State of AI Cybersecurity 2026 reveal that security teams are struggling to adapt to enterprise AI adoption. The need is clear: as embedded AI features go mainstream, organizations need to find an intelligent approach to securing AI at scale.
In today's threat landscape, blending in to normal activity is the key to success for attackers and the growing reliance on residential proxies shows a significant shift in how threat actors are attempting to bypass IP detection tools.
The increasing dependency on residential proxies has exposed how prevalent proxy services are and how reliant a diverse range of threat actors are on them. From cybercriminal groups to state‑sponsored actors, the need to bypass IP detection tools is fundamental to the success of these groups. One malware that has quietly become notorious for its ability to avoid anomaly detection is GhostSocks, a malware that turns compromised devices into residential proxies.
What is GhostSocks?
Originally marketed on the Russian underground forum xss[.]is as a Malware‑as‑a‑Service (MaaS), GhostSocks enables threat actors to turn compromised devices into residential proxies, leveraging the victim's internet bandwidth to route malicious traffic through it.
How does Ghostsocks malware work?
The malware offers the threat actor a “clean” IP address, making it look like it is coming from a household user. This enables the bypassing of geographic restrictions and IP detection tools, a perfect tool for avoiding anomaly detection. It wasn’t until 2024, when a partnership was announced with the infamous information stealer Lumma Stealer, that GhostSocks surged into widespread adoption and alluded to who may be the author of the proxy malware.
Written in GoLang, GhostSocks utilizes the SOCKS5 proxy protocol, creating a SOCKS5 connection on infected devices. It uses a relay‑based C2 implementation, where an intermediary server sits in between the real command-and-control (C2) server and the infected device.
How does Ghostsocks malware evade detection?
To further increase evasion, the Ghostsocks malware wraps its SOCKS5 tunnels in TLS encryption, allowing its malicious traffic to blend into normal network traffic.
Early variants of GhostSocks do not implement a persistence mechanism; however, later versions achieve persistence via registry run keys, ensuring sustained proxy operational time [1].
While proxying is its primary purpose, GhostSocks also incorporates backdoor functionality, enabling malicious actors to run arbitrary commands and download and deploy additional malicious payloads. This was evident with the well‑known ransomware group Black Basta, which reportedly used GhostSocks as a way of maintaining long‑term access to victims’ networks [1].
Darktrace’s detection of GhostSocks Malware
Darktrace observed a steady increase in GhostSocks activity across its customer base from late 2025, with its Threat Research team identifying multiple incidents involving the malware. In one notable case from December 2025, Darktrace detected GhostSocks operating alongside Lumma Stealer, reinforcing that the partnership between Lumma and GhostSocks remains active despite recent attempts to disrupt Lumma’s infrastructure.
Darktrace’s first detection of GhostSocks‑related activity came when a device on the network of a customer in the education sector began making connections to an endpoint with a suspicious self‑signed certificate that had never been seen on the network before.
The endpoint in question, 159.89.46[.]92 with the hostname retreaw[.]click, has been flagged by multiple open‑source intelligence (OSINT) sources as being associated with Lumma Stealer’s C2 infrastructure [2], indicating its likely role in the delivery of malicious payloads.
Figure 1: Darktrace’s detection of suspicious SSL connections to retreaw[.]click, indicating an attempted link to Lumma C2 infrastructure.
Less than two minutes later, Darktrace observed the same device downloading the executable (.exe) file “Renewable.exe” from the IP 86.54.24[.]29, which Darktrace recognized as 100% rare for this network.
Figure 2: Darktrace’s detection of a device downloading the unusual executable file “Renewable.exe”.
Both the file MD5 hash and the executable itself have been identified by multiple OSINT vendors as being associated with the GhostSocks malware [3], with the executable likely the backdoor component of the GhostSocks malware, facilitating the distribution of additional malicious payloads [4].
Following this detection, Darktrace’s Autonomous Response capability recommended a blocking action for the device in an early attempt to stop the malicious file download. In this instance, Darktrace was configured in Human Confirmation Mode, meaning the customer’s security team was required to manually apply any mitigative response actions. Had Autonomous Response been fully enabled at the time of the attack, the connections to 86.54.24[.]29 would have been blocked, rendering the malware ineffective at reaching its C2 infrastructure and halting any further malicious communication.
Figure 3: Darktrace’s Autonomous Response capability suggesting blocking the suspicious connections to the unusual endpoint from which the malicious executable was downloaded.
As the attack was able to progress, two days later the device was detected downloading additional payloads from the endpoint www.lbfs[.]site (23.106.58[.]48), including “Setup.exe”, “,.exe”, and “/vp6c63yoz.exe”.
Figure 4: Darktrace’s detection of a malicious payload being downloaded from the endpoint www.lbfs[.]site.
Once again, Darktrace recognized the anomalous nature of these downloads and suggested that a “group pattern of life” be enforced on the offending device in an attempt to contain the activity. By enforcing a pattern of life on a device, Darktrace restricts its activity to connections and behaviors similar to those performed by peer devices within the same group, while still allowing it to carry out its expected activity, effectively preventing deviations indicative of compromise while minimizing disruption. As mentioned earlier, these mitigative actions required manual implementation, so the activity was able to continue. Darktrace proceeded to suggest further actions to contain subsequent malicious downloads, including an attempt to block all outbound traffic to stop the attack from progressing.
Figure 5: An overview of download activity and the Autonomous Response actions recommended by Darktrace to block the downloads.
Around the same time, a third executable download was detected, this time from the hostname hxxp[://]d2ihv8ymzp14lr.cloudfront.net/2021-08-19/udppump[.]exe, along with the file “udppump.exe”.While GhostSocks may have been present only to facilitate the delivery of additional payloads, there is no indication that these CloudFront endpoints or files are functionally linked to GhostSocks. Rather, the evidence points to broader malicious file‑download activity.
Shortly after the multiple executable files had been downloaded, Darktrace observed the device initiating a series of repeated successful connections to several rare external endpoints, behavior consistent with early-stage C2 beaconing activity.
Cyber AI Analyst’s investigation
Figure 7: Darktrace’s detection of additional malicious file downloads from malicious CloudFront endpoints.
Throughout the course of this attack, Darktrace’s Cyber AI Analyst carried out its own autonomous investigation, piecing together seemingly separate events into one wider incident encompassing the first suspicious downloads beginning on December 4, the unusual connectivity to many suspicious IPs that followed, and the successful beaconing activity observed two days later. By analyzing these events in real-time and viewing them as part of the bigger picture, Cyber AI Analyst was able to construct an in‑depth breakdown of the attack to aid the customer’s investigation and remediation efforts.
Figure 8: Cyber AI Analyst investigation detailing the sequence of events on the compromised device, highlighting its extensive connectivity to rare endpoints, the related malicious file‑download activity, and finally the emergence of C2 beaconing behavior.
Conclusion
The versatility offered by GhostSocks is far from new, but its ability to convert compromised devices into residential proxy nodes, while enabling long‑term, covert network access—illustrates how threat actors continue to maximise the value of their victims’ infrastructure. Its growing popularity, coupled with its ongoing partnership with Lumma, demonstrates that infrastructure takedowns alone are insufficient; as long as threat actors remain committed to maintaining anonymity and can rapidly rebuild their ecosystems, related malware activity is likely to persist in some form.
Credit to Isabel Evans (Cyber Analyst), Gernice Lee (Associate Principal Analyst & Regional Consultancy Lead – APJ) Edited by Ryan Traill (Content Manager)
AI is already embedded in day-to-day enterprise activity, with 78% of participants in one recent survey reporting that their organizations are using generative AI in at least one business function. Generative AI now acts as an always-on assistant, researcher, creator, and coach across an expanding array of departments and functions. Autonomous agents are performing multi-step operational workflows from end to end. AI features have been layered on top of every SaaS application. And vibe coding is making it possible for employees without deep technical expertise to build their own AI-powered automations.
According to Gartner, more than 80% of enterprises will have deployed GenAI models, applications, or APIs in production environments by the end of this year, up from less than 5% in 2023. Companies report a 130% increase in spending on AI over the same period, with 72% of business leaders using AI tools at least weekly. The outsized efficiency and productivity gains that were once a future vision are quickly becoming everyday reality.
AI is currently driving business growth and innovation, and organizations risk falling behind peers if they don’t keep up with the pace of adoption, but it is also quietly expanding the enterprise attack surface. The modern CISO is challenged to both enable innovation and protect the business from these emerging threats.
AI agents introduce new risks and vulnerabilities
AI agents are playing growing roles in enterprise production environments. In many cases, these agents act with broad permissions across multiple software systems and platforms. This means they’re granted far-reaching access – to sensitive data, business-critical applications, tokens and APIs, and IT and security tools. With this access comes risk for security leaders – 92% are concerned about the use of AI agents across the workforce and their impact on security.
These agents must be governed as identities, with least-privilege access and ongoing monitoring. They can’t be thought of as invisible aspects of the application estate. Understanding how AI agents behave, and how to manage their permissions, control their behavior, and limit their data access will be a top security priority throughout 2026.
Generative AI prompts: The next frontier
Prompts are how users – both human and agentic – interact with AI systems, and they’re where natural language gets translated into model behavior. Natural language is infinite in its potential combinations and permutations, making this aspect of the attack surface open-ended and far more complex than traditional CVEs. With carefully crafted prompts, bad actors may be able to coax models into disclosing sensitive data, bypassing guardrails, or initiating undesirable actions.
Among security leaders, the biggest worries about AI usage in their environments all involve ways that systems might be manipulated to bypass traditional controls.
61% are most concerned about the exposure of sensitive data
56% are most concerned about potential data security and policy violations
51% are most concerned about the misuse or abuse of AI tools
The more employees rely on AI in their day-to-day workflows, the more critical it becomes for security teams to understand how prompt behavior determines model behavior – and where that behavior could go wrong.
What does “securing AI” mean in practice?
AI adoption opens new security risks that blur the boundaries between traditional security disciplines. A single malicious interaction with an AI model could involve identity misuse, sensitive data exposure, application logic abuse, and supply chain risk – all within a single workflow. Protecting this dynamic and rapidly evolving attack surface requires an approach that spans identity security, cloud security, application security, data security, software development security, and more.
The task for security leaders is to implement the tools, policies, and frameworks to mitigate these novel, expansive, and cross-disciplinary risks.
However, within most enterprises, AI policy creation remains in its infancy. Just 37% of security leaders report that their organization has a formal AI policy, representing a small but worrisome decrease from last year. Conversations about AI abound: in 52% of organizations, there’s discussion about an AI policy. Still, talk is cheap, and leaders will need to take action if they’re to successfully enable secure AI innovation.
To govern and protect their AI systems, organizations must take a multi-pronged approach. This requires building out policies, but it also demands that they are able to:
Monitor the prompts driving GenAI assistants and agents in real time. Organizations must be able to inspect prompts, sessions, and responses across enterprise GenAI tools, low- and high-code environments, and SaaS and SASE so that they can detect clever conversational prompt attacks and malicious chaining.
Secure all business AI agent identities. Security teams need to identify all the agents acting within their environment and supply chain, map their connections and interactions via MCP and services like Amazon S3, and audit their behavior across the cloud, SaaS environments, and on the network and endpoint devices.
Maintain centralized, comprehensive visibility. Understanding intent, assessing risks, and enforcing policies all require that security teams have a single view that spans AI interactions across the entire business.
Discover and control shadow AI. Teams need to be able to identify unsanctioned AI activities, distinguish the misuse of legitimate tools from their appropriate use, and apply policies to protect data, while guiding users towards approved solutions.
Scaling AI safely and responsibly
The approach that most cybersecurity vendors have taken – using historical patterns to predict future threats – doesn’t work well for AI systems. Because AI changes its behavior in response to the information it encounters while taking action, previous patterns don’t indicate what it will do next. Looking at past attacks can’t tell you how complex models will behave in your individual business.
Securing AI requires interpreting ambiguous interactions, uncovering subtleties that reveal intent within extended conversations, understanding how access accumulates over time, and recognizing when behavior – both human and machine – begins to drift towards areas of risk. To do this, you need to understand what “normal” looks like in each unique organization: how users, systems, applications, and AI agents behave, how they communicate, and how data flows between them.
Darktrace has spent more than a decade designing AI-powered solutions that can understand and adapt to evolving behavior in complex environments. This technology learns directly from the environment it protects, identifying malicious actions that deviate from normal operations, so that it can stop AI-related threats on the very first encounter.
As AI adoption reshapes enterprise operations, humans and machines will collaborate more and more often. This collaboration might dramatically expand the attack surface, but it also has the potential to be a force multiplier for defenders.
Explore the full State of AI Cybersecurity 2026 report for deeper insights into how security leaders are responding to AI-driven risks.
![Darktrace’s detection of suspicious SSL connections to retreaw[.]click, indicating an attempted link to Lumma C2 infrastructure.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/69c5739b43fdfed6f1b02053_Screenshot%202026-03-26%20at%2010.57.44%E2%80%AFAM.png)
![Darktrace’s detection of a malicious payload being downloaded from the endpoint www.lbfs[.]site.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/69c57465977a5ae3f07c6d69_Screenshot%202026-03-26%20at%2011.01.05%E2%80%AFAM.png)
