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
Max Heinemeyer
Global Field CISO
Written by
Adam Stevens
Senior Director of Product, Cloud | Darktrace
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09
Aug 2023
TLDR: There are too many technical vulnerabilities and there is too little organizational context for IT teams to patch effectively. Attack path modelling provides the organizational context, allowing security teams to prioritize vulnerabilities. The result is a system where CVEs can be parsed in, organizational context added, and attack paths considered, ultimately providing a prioritized list of vulnerabilities that need to be patched.
Figure 1: The Darktrace user interface presents risk-prioritized vulnerabilities
This blog post explains how Darktrace addresses the challenge of vulnerability prioritization. Most of the industry focusses on understanding the technical impact of vulnerabilities globally (‘How could this CVE generally be exploited? Is it difficult to exploit? Are there pre-requisites to exploitation? …’), without taking local context of a vulnerability into account. We’ll discuss here how we create that local context through attack path modelling and map it to technical vulnerability information. The result is a stunningly powerful way to prioritize vulnerabilities.
We will explore:
1) The challenge and traditional approach to vulnerability prioritization 2) Creating local context through machine learning and attack path modelling 3) Examining the result – contextualized, vulnerability prioritization
The Challenge
Anyone dealing with Threat and Vulnerability Management (TVM) knows this situation:
You have a vulnerability scanning report with dozens or hundreds of pages. There is a long list of ‘critical’ vulnerabilities. How do you start prioritizing these vulnerabilities, assuming your goal is reducing the most risk?
Sometimes the challenge is even more specific – you might have 100 servers with the same critical vulnerability present (e.g. MoveIT). But which one should you patch first, as all of those have the same technical vulnerability priority (‘critical’)? Which one will achieve the biggest risk reduction (critical asset e.g.)? Which one will be almost meaningless to patch (asset with no business impact e.g.) and thus just a time-sink for the patch and IT team?
There have been recent improvements upon flat CVE-scoring for vulnerability prioritization by adding threat-intelligence about exploitability of vulnerabilities into the mix. This is great, examples of that additional information are Exploit Prediction Scoring System (EPSS) and Known Exploited Vulnerabilities Catalogue (KEV).
With CVE and CVSS scores we have the theoretical technical impact of vulnerabilities, and with EPSS and KEV we have information about the likelihood of exploitation of vulnerabilities. That’s a step forward, but still doesn’t give us any local context. Now we know even more about the global and generic technical risk of a vulnerability, but we still lack the local impact on the organization.
Let’s add that missing link via machine learning and attack path modelling.
Adding Attack Path Modelling for Local Context
To prioritize technical vulnerabilities, we need to know as much as we can about the asset on which the vulnerability is present in the context of the local organization. Is it a crown jewel? Is it a choke point? Does it sit on a critical attack path? Is it a dead end, never used and has no business relevance? Does it have organizational priority? Is the asset used by VIP users, as part of a core business or IT process? Does it share identities with elevated credentials? Is the human user on the device susceptible to social engineering?
Those are just a few typical questions when trying to establish local context of an asset. Knowing more about the threat landscape, exploitability, or technical information of a CVE won’t help answer any of the above questions. Gathering, evaluating, maintaining, and using this local context for vulnerability prioritization is the hard part. This local context often resides informally in the head of the TVM or IT team member, having been assembled by having been at the organization for a long time, ‘knowing’ systems, applications and identities in question and talking to asset and application owners if time permits. This does unfortunately not scale, is time-consuming and heavily dependent on individuals.
Understanding all attack paths for an organization provides this local context programmatically.
We discover those attack paths, and these are bespoke for each organization through Darktrace PREVENT™, using the following method (simplified):
1) Build an adaptive model of the local business. Collect, combine, and analyze (using machine learning and non-machine learning techniques) data from various data domains:
a. Network, Cloud, IT, and OT data (network-based attack paths, communication patterns, peer-groups, choke-points, …). Natively collected by Darktrace technology.
b. Email data (social engineering attack paths, phishing susceptibility, external exposure, security awareness level, …). Natively collected by Darktrace technology.
c. Identity data (account privileges, account groups, access levels, shared permissions, …). Collected via various integrations, e.g. Active Directory.
d. Attack surface data (internet-facing exposure, high-impact vulnerabilities, …). Natively collected by Darktrace technology.
e. SaaS information (further identity context). Natively collected by Darktrace
f. Vulnerability information (CVEs, CVSS, EPSS, KEV, …). Collected via integrations, e.g. Vulnerability Scanners or Endpoint products.
Figure 3: Darktrace PREVENT revealing each stage of an attack path
2) Understand what ‘crown jewels’ are and how to get to them. Calculate entity importance (user, technical asset), exposure levels, potential damage levels (blast radius) weakness levels, and other scores to identify most important entities and their relationships to each other (‘crown jewels’).
Various forms of machine learning and non-machine learning techniques are used to achieve this. Further details on some of the exact methods can be found here. The result is a holistic, adaptive and dynamic model of the organization that shows most important entities and how to get to them across various data domains.
The combination of local context and technical context, around the severity and likelihood of exploitation, creates the Darktrace Vulnerability Score. This enables effective risk-based prioritisation of CVE patching.
Figure 4: List of devices with the highest damage potential in the organization - local context
3) Map the attack path model of the organization to common cyber domain knowledge. We can then combine things like MITRE ATT&CK techniques with those identified connectivity patterns and attack paths – making it easy to understand which techniques, tools and procedures (TTPs) can be used to move through the organization, and how difficult it is to exploit each TTP.
Figure 5: An example attack path with associated MITRE techniques and difficulty scores for each TTP
We can now easily start prioritizing CVE patching based on actual, organizational risk and local context.
Bringing It All Together
Finally, we overlay the attack paths calculated by Darktrace with the CVEs collected from a vulnerability scanner or EDR. This can either happen as a native integration in Darktrace PREVENT, if we are already ingesting CVE data from another solution, or via CSV upload.
Figure 6: Darktrace's global CVE prioritization in action.
But you can also go further than just looking at the CVE that delivers the biggest risk reduction globally in your organization if it is patched. You can also look only at certain group of vulnerabilities, or a sub-set of devices to understand where to patch first in this reduced scope:
Figure 7: An example of the information Darktrace reveals around a CVE
This also provides the TVM team clear justification for the patch and infrastructure teams on why these vulnerabilities should be prioritized and what the positive impact will be on risk reduction.
Attack path modelling can be utilized for various other use cases, such as threat modelling and improving SOC efficiency. We’ll explore those in more depth at a later stage.
Want to explore more on using machine learning for vulnerability prioritization? Want to test it on your own data, for free? Arrange a demo today.
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.
SEO Poisoning and Fake PuTTY sites: Darktrace’s Investigation into the Oyster backdoor
What is SEO poisoning?
Search Engine Optimization (SEO) is the legitimate marketing technique of improving the visibility of websites in organic search engine results. Businesses, publishers, and organizations use SEO to ensure their content is easily discoverable by users. Techniques may include optimizing keywords, creating backlinks, or even ensuring mobile compatibility.
SEO poisoning occurs when attackers use these same techniques for malicious purposes. Instead of improving the visibility of legitimate content, threat actors use SEO to push harmful or deceptive websites to the top of search results. This method exploits the common assumption that top-ranking results are trustworthy, leading users to click on URLs without carefully inspecting them.
As part of SEO poisoning, the attacker will first register a typo-squatted domain, slightly misspelled or otherwise deceptive versions of real software sites, such as putty[.]run or puttyy[.]org. These sites are optimized for SEO and often even backed by malicious Google ads, increasing the visibility when users search for download links. To achieve that, threat actors may embed pages with strategically chosen, high-value keywords or replicate content from reputable sources to elevate the domain’s perceived authority in search engine algorithms [4]. In more advanced operations, these tactics are reinforced with paid promotion, such as Google ads, enabling malicious domains to appear above organic search results as sponsored links. This placement not only accelerates visibility but also impacts an unwarranted sense of legitimacy to unsuspected users.
Once a user lands on one of these fake pages, they are presented with what looks like a legitimate software download option. Upon clicking the download indicator, the user will be redirected to another separate domain that actually hosts the payload. This hosting domain is usually unrelated to the nominally referenced software. These third-party sites can involve recently registered domains but may also include legitimate websites that have been recently compromised. By hosting malware on a variety of infrastructure, attackers can prolong the availability of distribution methods for these malicious files before they are taken down.
What is the Oyster backdoor?
Oyster, also known as Broomstick or CleanUpLoader, is a C++ based backdoor malware first identified in July 2023. It enables remote access to infected systems, offering features such as command-line interaction and file transfers.
Oyster has been widely adopted by various threat actors, often as an entry point for ransomware attacks. Notable examples include Vanilla Tempest and Rhysida ransomware groups, both of which have been observed leveraging the Oyster backdoor to enhance their attack capabilities. Vanilla Tempest is known for using Oyster’s stealth persistence to maintain long-term access within targeted networks, often aligning their operations with ransomware deployment [5]. Rhysida has taken this further by deploying Oyster as an initial access tool in ransomware campaigns, using it to conduct reconnaissance and move laterally before executing encryption activities [6].
Once installed, the backdoor gathers basic system information before communicating with a command-and-control (C2) server. The malware largely relies on a ‘cmd.exe’ instance to execute commands and launch other files [1].
In previous SEO poisoning cases, the file downloaded from the fake pages is not just PuTTY, but a trojanized version that includes the stealthy Oyster backdoor. PuTTY is a free and open-source terminal emulator for Windows that allows users to connect to remote servers and devices using protocols like SSH and Telnet. In the recent campaign, once a user visits the fake software download site, ranked highly through SEO poisoning, the malicious payload is downloaded through direct user interaction and subsequently installed on the local device, initiating the compromise. The malware then performs two actions simultaneously: it installs a fully functional version of PuTTY to avoid user suspicion, while silently deploying the Oyster backdoor. Given PuTTY’s nature, it is prominently used by IT administrators with highly privileged account as opposed to standard users in a business, possibly narrowing the scope of the targets.
Oyster’s persistence mechanism involves creating a Windows Scheduled Task that runs every few minutes. Notably, the infection uses Dynamic Link Library (DLL) side loading, where a malicious DLL, often named ‘twain_96.dll’, is executed via the legitimate Windows utility ‘rundll32.exe’, which is commonly used to run DLLs [2]. This technique is frequently used by malicious actors to blend their activity with normal system operations.
Darktrace’s Coverage of the Oyster Backdoor
In June 2025, security analysts at Darktrace identified a campaign leveraging search engine manipulation to deliver malware masquerading as the popular SSH client, PuTTY. Darktrace / NETWORK’s anomaly-based detection identified signs of malicious activity, and when properly configured, its Autonomous Response capability swiftly shut down the threar before it could escalate into a more disruptive attack. Subsequent analysis by Darktrace’s Threat Research team revealed that the payload was a variant of the Oyster backdoor.
The first indicators of an emerging Oyster SEO campaign typically appeared when user devices navigated to a typosquatted domain, such as putty[.]run or putty app[.]naymin[.]com, via a TLS/SSL connection.
Figure 1: Darktrace’s detection of a device connecting to the typosquatted domain putty[.]run.
The device would then initiate a connection to a secondary domain that hosts the malicious installer, likely triggered by user interaction with redirect elements on the landing page. This secondary site may not have any immediate connection to PuTTY itself but is instead a hijacked blog, a file-sharing service, or a legitimate-looking content delivery subdomain.
Figure 2: Darktrace’s detection of the device making subsequent connections to the payload domain.
Following installation, multiple affected devices were observed attempting outbound connectivity to rare external IP addresses, specifically requesting the ‘/secure’ endpoint as noted within the declared URIs. After the initial callback, the malware continued communicating with additional infrastructure, maintaining its foothold and likely waiting for tasking instructions. Communication patterns included:
· Endpoints with URIs /api/kcehc and /api/jgfnsfnuefcnegfnehjbfncejfh
· Endpoints with URI /reg and user agent “WordPressAgent”, “FingerPrint” or “FingerPrintpersistent”
This tactic has been consistently linked to the Oyster backdoor, which has shown similar URI patterns across multiple campaigns [3].
Darktrace analysts also noted the sophisticated use of spoofed user agent strings across multiple investigated customer networks. These headers, which are typically used to identify the application making an HTTP request, are carefully crafted to appear benign or mimic legitimate software. One common example seen in the campaign is the user agent string “WordPressAgent”. While this string references a legitimate web application or plugin, it does not appear to correspond to any known WordPress services or APIs. Its inclusion is most likely designed to mimic background web traffic commonly associated with WordPress-based content management systems.
Figure 3: Cyber AI Analyst investigation linking the HTTP C2 activity.
Case-Specific Observations
While the previous section focused on tactics and techniques common across observed Oyster infections, a closer examination reveals notable variations and unique elements in specific cases. These distinct features offer valuable insights into the diverse operational approaches employed by threat actors. These distinct features, from unusual user agent strings to atypical network behavior, offer valuable insights into the diverse operational approaches employed by the threat actors. Crucially, the divergence in post-exploitation activity reflects a broader trend in the use of widely available malware families like Oyster as flexible entry points, rather than fixed tools with a single purpose. This modular use of the backdoor reflects the growing Malware-as-a-Service (MaaS) ecosystem, where a single initial infection can be repurposed depending on the operator’s goals.
From Infection to Data Egress
In one observed incident, Darktrace observed an infected device downloading a ZIP file named ‘host[.]zip’ via curl from the URI path /333/host[.]zip, following the standard payload delivery chain. This file likely contained additional tools or payloads intended to expand the attacker’s capabilities within the compromised environment. Shortly afterwards, the device exhibited indicators of probable data exfiltration, with outbound HTTP POST requests featuring the URI pattern: /upload?dir=NAME_FOLDER/KEY_KEY_KEY/redacted/c/users/public.
This format suggests the malware was actively engaged in local host data staging and attempting to transmit files from the target machine. The affected device, identified as a laptop, aligns with the expected target profile in SEO poisoning scenarios, where unsuspecting end users download and execute trojanized software.
Irregular RDP Activity and Scanning Behavior
Several instances within the campaign revealed anomalous or unexpected Remote Desktop Protocol (RDP) sessions occurring shortly after DNS requests to fake PuTTY domains. Unusual RDP connections frequently followed communication with Oyster backdoor C2 servers. Additionally, Darktrace detected patterns of RDP scanning, suggesting the attackers were actively probing for accessible systems within the network. This behavior indicates a move beyond initial compromise toward lateral movement and privilege escalation, common objectives once persistence is established.
The presence of unauthorized and administrative RDP sessions following Oyster infections aligns with the malware’s historical role as a gateway for broader impact. In previous campaigns, Oyster has often been leveraged to enable credential theft, lateral movement, and ultimately ransomware deployment. The observed RDP activity in this case suggests a similar progression, where the backdoor is not the final objective but rather a means to expand access and establish control over the target environment.
Cryptic User Agent Strings?
In multiple investigated cases, the user agent string identified in these connections featured formatting that appeared nonsensical or cryptic. One such string containing seemingly random Chinese-language characters translated into an unusual phrase: “Weihe river is where the water and river flow.” Legitimate software would not typically use such wording, suggesting that the string was intended as a symbolic marker rather than a technical necessity. Whether meant as a calling card or deliberately crafted to frame attribution, its presence highlights how subtle linguistic cues can complicate analysis.
Figure 4: Darktrace’s detection of malicious connections using a user agent with randomized Chinese-language formatting.
Strategic Implications
What makes this campaign particularly noteworthy is not simply the use of Oyster, but its delivery mechanism. SEO poisoning has traditionally been associated with cybercriminal operations focused on opportunistic gains, such as credential theft and fraud. Its strength lies in casting a wide net, luring unsuspecting users searching for popular software and tricking them into downloading malicious binaries. Unlike other campaigns, SEO poisoning is inherently indiscriminate, given that the attacker cannot control exactly who lands on their poisoned search results. However, in this case, the use of PuTTY as the luring mechanism possibly indicates a narrowed scope - targeting IT administrators and accounts with high privileges due to the nature of PuTTY’s functionalities.
This raises important implications when considered alongside Oyster. As a backdoor often linked to ransomware operations and persistent access frameworks, Oyster is far more valuable as an entry point into corporate or government networks than small-scale cybercrime. The presence of this malware in an SEO-driven delivery chain suggests a potential convergence between traditional cybercriminal delivery tactics and objectives often associated with more sophisticated attackers. If actors with state-sponsored or strategic objectives are indeed experimenting with SEO poisoning, it could signal a broadening of their targeting approaches. This trend aligns with the growing prominence of MaaS and the role of initial access brokers in today’s cybercrime ecosystem.
Whether the operators seek financial extortion through ransomware or longer-term espionage campaigns, the use of such techniques blurs the traditional distinctions. What looks like a mass-market infection vector might, in practice, be seeding footholds for high-value strategic intrusions.
Credit to Christina Kreza (Cyber Analyst) and Adam Potter (Senior Cyber Analyst)
Appendices
MITRE ATT&CK Mapping
· T1071.001 – Command and Control – Web Protocols
· T1008 – Command and Control – Fallback Channels
· T0885 – Command and Control – Commonly Used Port
· T1571 – Command and Control – Non-Standard Port
· T1176 – Persistence – Browser Extensions
· T1189 – Initial Access – Drive-by Compromise
· T1566.002 – Initial Access – Spearphishing Link
· T1574.001 – Persistence – DLL
Indicators of Compromise (IoCs)
· 85.239.52[.]99 – IP address
· 194.213.18[.]89/reg – IP address / URI
· 185.28.119[.]113/secure – IP address / URI
· 185.196.8[.]217 – IP address
· 185.208.158[.]119 – IP address
· putty[.]run – Endpoint
· putty-app[.]naymin[.]com – Endpoint
· /api/jgfnsfnuefcnegfnehjbfncejfh
· /api/kcehc
Darktrace Model Detections
· Anomalous Connection / New User Agent to IP Without Hostname
· Anomalous Connection / Posting HTTP to IP Without Hostname
· Compromise / HTTP Beaconing to Rare Destination
· Compromise / Large Number of Suspicious Failed Connections
· Compromise / Beaconing Activity to External Rare
· Compromise / Quick and Regular Windows HTTP Beaconing
· Device / Large Number of Model Alerts
· Device / Initial Attack Chain Activity
· Device / Suspicious Domain
· Device / New User Agent
· Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block
The content provided in this blog is published by Darktrace for general informational purposes only and reflects our understanding of cybersecurity topics, trends, incidents, and developments at the time of publication. While we strive to ensure accuracy and relevance, the information is provided “as is” without any representations or warranties, express or implied. Darktrace makes no guarantees regarding the completeness, accuracy, reliability, or timeliness of any information presented and expressly disclaims all warranties.
Nothing in this blog constitutes legal, technical, or professional advice, and readers should consult qualified professionals before acting on any information contained herein. Any references to third-party organizations, technologies, threat actors, or incidents are for informational purposes only and do not imply affiliation, endorsement, or recommendation.
Darktrace, its affiliates, employees, or agents shall not be held liable for any loss, damage, or harm arising from the use of or reliance on the information in this blog.
The cybersecurity landscape evolves rapidly, and blog content may become outdated or superseded. We reserve the right to update, modify, or remove any content without notice.
The benefits of bringing together network and email security
In many organizations, network and email security operate in isolation. Each solution is tasked with defending its respective environment, even though both are facing the same advanced, multi-domain threats.
This siloed approach overlooks a critical reality: email remains the most common vector for initiating cyber-attacks, while the network is the primary stage on which those attacks progress. Without direct integration between these two domains, organizations risk leaving blind spots that adversaries can exploit.
A modern security strategy needs to unify email and network defenses, not just in name, but in how they share intelligence, conduct investigations, and coordinate response actions. Let’s take a look at how this joined-up approach delivers measurable technical, operational, and commercial benefits.
Technical advantages
Pre-alert intelligence: Gathering data before the threat strikes
Most security tools start working when something goes wrong – an unusual login, a flagged attachment, a confirmed compromise. But by then, attackers may already be a step ahead.
By unifying network and email security under a single AI platform (like the Darktrace Active AI Security Platform), you can analyze patterns across both environments in real time, even when there are no alerts. This ongoing monitoring builds a behavioral understanding of every user, device, and domain in your ecosystem.
That means when an email arrives from a suspicious domain, the system already knows whether that domain has appeared on your network before – and whether its behavior has been unusual. Likewise, when new network activity involves a domain first spotted in an email, it’s instantly placed in the right context.
This intelligence isn’t built on signatures or after-the-fact compromise indicators – it’s built on live behavioral baselines, giving your defenses the ability to flag threats before damage is done.
Alert-related intelligence: Connecting the dots in real time
Once an alert does fire, speed and context matter. The Darktrace Cyber AI Analyst can automatically investigate across both environments, piecing together network and email evidence into a single, cohesive incident.
Instead of leaving analysts to sift through fragmented logs, the AI links events like a phishing email to suspicious lateral movement on the recipient’s device, keeping the full attack chain intact. Investigations that might take hours – or even days – can be completed in minutes, with far fewer false positives to wade through.
This is more than a time-saver. It ensures defenders maintain visibility after the first sign of compromise, following the attacker as they pivot into network infrastructure, cloud services, or other targets. That cross-environment continuity is impossible to achieve with disconnected point solutions or siloed workflows.
Operational advantages
Streamlining SecOps across teams
In many organizations, email security is managed by IT, while network defense belongs to the SOC. The result? Critical information is scattered between tools and teams, creating blind spots just when you need clarity.
When email and network data flow into a single platform, everyone is working from the same source of truth. SOC analysts gain immediate visibility into email threats without opening another console or sending a request to another department. The IT team benefits from the SOC’s deeper investigative context.
The outcome is more than convenience: it’s faster, more informed decision-making across the board.
Reducing time-to-meaning and enabling faster response
A unified platform removes the need to manually correlate alerts between tools, reducing time-to-meaning for every incident. Built-in AI correlation instantly ties together related events, guiding analysts toward coordinated responses with higher confidence.
Instead of relying on manual SIEM rules or pre-built SOAR playbooks, the platform connects the dots in real time, and can even trigger autonomous response actions across both environments simultaneously. This ensures attacks are stopped before they can escalate, regardless of where they begin.
Commercial advantages
While purchasing “best-of-breed" for all your different tools might sound appealing, it often leads to a patchwork of solutions with overlapping costs and gaps in coverage. However good a “best-in-breed" email security solution might be in the email realm, it won't be truly effective without visibility across domains and an AI analyst piecing intelligence together. That’s why we think “best-in-suite" is the only “best-in-breed" approach that works – choosing a high-quality platform ensures that every new capability strengthens the whole system.
On top of that, security budgets are under constant pressure. Managing separate vendors for email and network defense means juggling multiple contracts, negotiating different SLAs, and stitching together different support models.
With a single provider for both, procurement and vendor management become far simpler. You deal with one account team, one support channel, and one unified strategy for both environments. If you choose to layer on managed services, you get consistent expertise across your whole security footprint.
Even more importantly, an integrated AI platform sets the stage for growth. Once email and network are under the same roof, adding coverage for other attack surfaces – like cloud or identity – is straightforward. You’re building on the same architecture, not bolting on new point solutions that create more complexity.
