A modern organization protects its information systems with a whole suite of technologies that generate thousands of security alerts every day. Looking at these alerts in bulk, you’ll see that a significant share are false positives. Ideally, these alerts would be filtered out automatically so that analysts could focus on the ones that may point to a real incident. However, writing filters is a time-consuming task in itself. To create a correct filter, an analyst needs to:

• Review the logs
• Identify the source of the activity
• Define filtering boundaries
• Choose filtering criteria
• Write a rule
• Check that the filter is robust

This post looks at how filter creation itself can be automated to reduce analyst workload.

Filter validity criteria

In the context of security alerts, a filter is a combination of event field values that marks a detection as a false positive and not worth the analyst’s attention. Filters can be generated for any combination of event fields, but not every combination yields a valid filter. That’s why we need to define validity criteria: conditions showing that a specific filter actually removes noise and doesn’t suppress useful alerts.

Detection rules in security systems typically contain a set of conditions, and each of them can generate an alert. A specific event triggers a given fragment of a detection rule. The set of events that a given fragment responds to is what we’ll call its context. A good filter should cover the entire context where possible, without going beyond it. Filters that are too broad or too narrow are useless. For example, a filter based solely on PowerShell would exclude too many events, including some that deserve the analyst’s attention. Most of the filtered events would also be unrelated to the rule in question. A filter on a specific ID, on the other hand, would cover only one event, which may never recur.

Problems in filter generation

We can run into a number of problems when creating filters. First, events often contain an excessive number of fields (around 400 on average), and most of them aren’t needed for filtering. At the same time, generating and ranking combinations of these fields is also fairly resource-intensive. So, the first step is to discard the irrelevant fields and keep only those worth using in the filter. For example, out of 400 fields in an event, four may be enough for filtering.

Another problem is string normalization. Field values (for example, the path to the same application on different systems) can vary depending on system settings, user folder names, and so on. For a filter to account for all these variations, the strings must be normalized. For file paths, in particular, you can replace the drive letter and user folders with standard environment variables and convert all paths to a single case.

Finally, if a filter completely mirrors the query of a detection rule, it effectively disables that rule. To fix this, remove the event fields used in the detection rule from the filter. For example, if a rule triggers on a file path, there are two ways to filter out unwanted alerts:

• In the filter, use fields other than the file path.
• Use a part of the file path that the rule doesn’t use. For example, for the rule file_path: ".../something.exe", you can set up a filter that excludes events with the user/unique_files/ directory in the file path.

Filtering security alerts with an algorithm

Given the problems described above, automatically generating filters for security alerts should consist of the following steps:

1. An analyst receives a security alert that they assess as a false positive and uses the filter generator.
2. The generator takes the 400-field event from the alert, along with the rule that triggered on it, and determines the context.
3. The generator selects the fields that are relevant to the filter.
4. The generator normalizes the strings.
5. The generator builds different combinations of the selected fields. To do this, we applied the FP-Growth (frequent pattern growth) algorithm to the context determined earlier. FP Growth builds a tree of frequently occurring items (fields) from a dataset (in our case, the context) and discards items that aren’t frequent enough. This reduces the number of combinations to check, which lowers the cost of finding the most relevant ones.
6. The generator ranks the resulting combinations and offers the analyst the top three to choose from.

For example, consider an event described by the following JSON:

After generating and ranking the combinations, we got three filters based on file paths:

• processcmdline:("nodejs\\node.exe" AND "*****\\appdata\\roaming\\npm\/node_modules\/pi-coding-agent\/dist\/cli.js")

• file_path:"*****/.pi\\agent\\bin\\fd.exe"

• processcmdline: "programfiles\\nodejs\\node.exe"

The last filter turned out to be too broad, but the analyst marked the other two as valid.

Pros and cons of the algorithmic approach

The algorithmic approach to filter generation has several advantages. First, it’s simple and deterministic: the same alert always produces the same result. Second, it’s interpretable, meaning we can see why a given combination ranked near the top of the list and why a particular field didn’t make it into the list of candidate fields at all. Third, the stages of the process are independent of each other, so individual steps can be improved and tuned without the risk of breaking the algorithm elsewhere. Finally, it has no external dependencies or models, which makes it easy to deploy in any customer environment.

However, it also has drawbacks. The first is its dependence on context. If there’s no context for some reason — for example, the rule’s first-ever trigger — a filter can’t be created. The second problem is string normalization. There’s a high chance of running into atypical cases that the normalization rules don’t cover. Often such cases can’t be described correctly at all, because they contradict each other.

These problems can be solved with large language models (LLMs). When an LLM generates the filters, no context is needed, because the LLM doesn’t iterate over or rank combinations. The LLM also takes a more flexible approach to string normalization, including non-standard cases. That said, an LLM doesn’t have to be involved at every stage of the generation process. We decided to keep the algorithmic solution where it works well. For example, a simple algorithm selects suitable fields without errors, so we didn’t move this stage to the model.

Filter generation with an LLM

The LLM-based filter generation looks like this:

The input is still an alert containing an event with 400 fields, plus the rule that triggered on that event. Then an algorithm selects the most suitable fields for the filter. Next, the LLM processes the input — the event, the selected fields, and the detection rule — and generates three filter options along with an explanation for them. The explanation helps the analyst understand why these particular filters were suggested and make an informed choice.

Consider the following event in JSON format:

The LLM suggested three filter options that all contain the same command line fragment:

• file_path: "\\WindowsPowerShell\\powershell.exe" AND filecmdline: "Get-VM -Name kafka\*"

• filecmdline: "Get-VM -Name kafka\*"

• filecmdline: "kafka*"

As in the previous example, two of the three output filters were valid. The last one was too broad to be usable. The analyst chose the first filter with minor edits:

file_path: "powershell.exe" AND filecmdline: "Get-VM -Name kafka\*"

Pros and cons of generating filters with an LLM

Using an LLM solved the problems of purely algorithmic filter generation: dependence on context and errors in string normalization. However, the single-request approach had its own drawbacks. First, it had no output validation. The LLM returned filters as-is, with no syntax check and no way to correct errors. Second, unlike in the algorithmic version, all filter generation tasks were handled by a single system prompt. As a result, a minor fix in one place could break the logic in another.

Using multiple AI agents

To address the problems of generating filters with a single model and a single system prompt, we tried distributing the tasks across different LLM agents. In this version, we brought in agents as early as the stage of selecting relevant fields. The chain includes four agents in total:

• Selector: selects the relevant fields.
• Generator: generates filters.
• Validator: evaluates the generated filters. If the filters are found to be invalid, it returns the task to the generator. If the selected fields are found to be irrelevant, it starts a new field selection cycle. If the filters pass validation, or the limit on regeneration attempts is reached, the valid filters (or the best ones available) are passed to the analyst.
• Commentator: analyzes the output filters and explains which field values were used and why.

For the following JSON event, the agents produce three valid filters based on the file path.

The filters differ in the additional parameters that support the detection:

• file_path:"sysvol\\domain\\scripts\\logon.bat"

• file_path:"sysvol\\domain\\scripts\\logon.bat" AND file_md5:"0x7..6E"

• autorun_entry: "Group Policy\\State" AND file_path: "SYSVOL\\domain\\scripts\\logon.bat"

Pros and cons of the agent-based approach

LLM agents give us a system that can validate and correct filters. Its modular architecture also lets us tune and improve different stages of generation independently. However, this approach makes more LLM calls than the previous ones, so the system is slower. It also depends on access to a model, which may not be available in the organization’s isolated environment.

Evaluating the filter generation system

To evaluate how well the filter generation system works, we use two main metrics:

• Approval rate. For each security alert, the system suggests three filters. If the analyst selects at least one of them, we count the generation as successful. For example, in the case below, the approval rate is 2/3.
  
• False positive reduction rate (FP drop). This metric shows how much a filter helps reduce the number of false positives. It can only be measured over time, because we need to compare the number of false positive alerts received during a set period before and after the filter is deployed.

Below is a comparison of the three filter generation approaches by these metrics:

As the results show, LLMs significantly improve the efficiency of filter generation. At the same time, the LLM agentless and agent-based approaches perform almost identically. So we chose the simpler and faster option: an LLM with a single system prompt. As a result, we cut false positives by 99%, increased the number of filters generated per day by 28%, and made analysts’ alert handling 17% more efficient.

Takeaways

Practice has shown that filter generation can be automated effectively. We’ve looked at several options for automatically generating filters for false-positive security alerts: a purely algorithmic approach, an LLM-based approach, and an approach built on a group of LLM agents. All of them can generate valid filters and make the analyst’s work much easier, but each has its own pros and cons.

The algorithmic approach scored lower on the evaluation metrics, but in two-thirds of cases, it still produces filters that satisfy the analyst. It works especially well when there’s already a set of similar events to filter out, meaning enough context has accumulated. The algorithmic generator can also be deployed in any environment. Both standalone LLM and multi-agent LLM approaches show high accuracy, and their results differ only slightly, so the slower agent-based approach looks excessive at this stage. If an LLM is available, the best option appears to be a single model that handles several stages of the generation cycle.

Detection engineering is both a role and an activity. It’s the discipline of building and maintaining the logic that identifies malicious or risky behavior within your organization. It’s where security analysis meets software engineering, turning raw logs and telemetry into actionable detections that defenders can rely on.

Detection engineers bridge the gaps between security operations, threat intelligence, and infrastructure. They understand how attackers operate, how defenders respond, and how systems generate the data that supports those detections. A well-built detection can mean the difference between an alert that matters and one that drowns in noise.

Precision and Recall: The Balancing Act

Every detection faces a trade-off between precision and recall.

Precision measures how much you can trust your alerts: of everything that fired, how much was actually malicious? High precision = fewer false positives = analysts trust the tool.

Recall measures how much you’re catching: of everything malicious that happened, how much did you detect? High recall = fewer blind spots = threats don’t slip through.

Detection engineering lives in the tension between the two: too much precision risks missing subtle threats; too much recall floods analysts with false positives. The goal is calibration through testing, iteration, and feedback.

Core Components of Detection Engineering

1. Scoping and Prioritization

Start with intent. What are you trying to detect, and why? Define the threats, behaviors, or assets that matter most, then align your work with frameworks such as MITRE ATT&CK®. Evaluate risk and value to determine which detections provide the highest impact for the effort required. Prioritize based on business importance and available telemetry.

2. Data and Telemetry

Great detections rely on great data. Keep inventory of your logs. Validate that each contains the data you need. Normalize and enrich your logs so detections remain consistent across tools and environments. Understanding how logs are generated is essential to writing logic that fits your environment.

3. Detection Logic Development

This is where hypothesis meets code. Write queries or rules that detect behaviors, not just static indicators. Combine multiple approaches:

• Signatures for known indicators of compromise (IoCs)
• Behavioral logic for Tactics, Techniques, and Procedures (TTPs) and patterns
• Anomaly models for statistical outliers

4. Testing and Validation

Use adversary emulations or frameworks like Atomic Red Team to verify that your detections actually fire and your logging is working. Measure quality through precision, recall, and additional factors like confidence (how certain you are it’s malicious) and criticality (how severe the impact would be).

5. Deployment and Tuning

Monitor performance, including trigger frequency, false positive rates, and analyst response time. Detection engineering is iterative and ongoing, not “write once and done.”

6. Documentation and Knowledge Transfer

Good detections come with context. Record what the detection looks for, why it matters, what data it uses, and how it was tested. Version control your rules using Git and promote collaboration so analysts, hunters, and engineers can learn from one another. Playbooks and detection libraries ensure institutional knowledge does not disappear when staff change roles.

7. Metrics and Continuous Improvement

Track measurable outcomes such as coverage across ATT&CK® techniques, number of validated detections, false positive rates, and mean time to detect. Regularly retire stale detections, update ones that drift, and automate validation wherever possible. Detection-as-Code practices keep everything maintainable and scalable.

Building the Skillset

Detection engineering requires a strong foundation in logging, security tooling, and incident response. You should understand when it’s best to detect, when to block, and how to use data sources effectively.

Whether you’re building your first rule or managing hundreds, the mindset remains the same: test everything, learn constantly, and make each detection better than the last.

Get Started

Explore resources like:

detectionengineering.net and SigmaHQ.

Follow community leaders like:

Zack Allen (Detection Engineering Weekly) and Haider Dost (Threat Detection Maturity Framework).

Study detections and see how others approach logic design at:

research.splunk.com

Read:

Practical Threat Detection Engineering.

For more resources check out:

https://bit.ly/notlogic

Over the past year, Microsoft uncovered a Chinese-language cybercriminal ecosystem, an interconnected network of phishing kit operators, developers, and an illicit marketplace selling tools and infrastructure for large-scale phishing, enabling cybercriminal operations worldwide.

Threat actors purchased and used these tools and infrastructure, including phishing kits and access to compromised or fraudulent Microsoft or Google cloud environments and related credentials, to target organizations across the healthcare, financial services, wholesale distribution, and information technology (IT) sectors in Japan, the United States, Australia, and India. In collaboration with law enforcement agencies and other partners worldwide, Microsoft’s Digital Crimes Unit (DCU) facilitated a disruption of the ecosystem’s primary developers and infrastructure.

Microsoft’s research into one phishing kit unraveled a larger ecosystem, including the phishing kit developers, an infrastructure dealer, and the threat actors who buy from both. In this blog, we examine several phishing kits marketed and sold through YAM Store, an underground marketplace tracked by Microsoft as Storm-3119. We share our analysis of the technical aspects of this Chinese-language phishing kit ecosystem, the shared infrastructure, provisioning methods, and downstream activity by threat actors using these tools.

Uncovering the shadow world of a phishing kit ecosystem

Microsoft, alongside industry partner Resecurity, identified three phishing kits sold through the YAM Store that make up this Chinese-language cybercriminal network: CoGUI (also known as NX or FA), Haozi (also known as Phoenix), and Iron Man System. Microsoft tracks the cluster of activity and the developers behind all three phishing kits as Storm-2990. Since January 2026, the threat actors behind these kits have been responsible for facilitating the sending of over one billion phishing emails and SMS messages to over 75 thousand organizations worldwide.

Microsoft assesses with high confidence that these phishing kits were developed by Chinese-speaking threat actors, and, despite their distinct branding and separate Telegram-based sales channels, exhibit highly similar capabilities and functionality. The phishing kits provide phishing templates impersonating legitimate brands and organizations (including government, financial, and logistics) worldwide, and enable criminal actors to facilitate high-volume phishing and credential stealing operations. The similarity in their infrastructure and overlap in activity suggest either shared development origins, code reuse, or close collaboration among their developers.

These phishing kits have been advertised through YAM Store, first CoGUI and Haozi, followed by Iron Man System starting in June 2026. YAM Store and affiliated phishing kit companies accepted payments through cryptocurrency, including USDT and Ethereum, adding another layer to conceal illicit activity. The scale and proliferation of this ecosystem demonstrate how readily available criminal infrastructure can lower the barrier to entry, enabling less skilled cybercriminals to target organizations at scale and contribute to fraud losses.

By mapping the relationship between YAM Store and these phishing kits, we provide insight into the commercial infrastructure that supports modern phishing operations.

Storm-3119: YAM Store, the power broker

YAM Store is an initial access broker (IAB) that sells infrastructure using compromised or fraudulently obtained environments in providers like Microsoft and Google. The marketplace offers a variety of infrastructure based on age, subscription type, and environment types such as education (.edu) or non-profit (.org).

The YAM Store marketplace provides cybercriminals with a platform to purchase fraudulently acquired Microsoft and Google domains associated with legitimate, expired, or previously used organization numbers, allowing actors to use seemingly legitimate infrastructure and obtain workspaces at relatively low cost. Storm-3119 uses automation to identify expired environment or organization names and leverages legitimate customer information associated with them to help circumvent existing detections.

Anatomy of YAM Store operations: How Storm-3119 acquires, controls, and monetizes infrastructure

During this investigation, Microsoft uncovered a five-stage attack pipeline often used in Storm-3119’s operations. YAM Store functions as an access broker, an intermediary that hijacks Microsoft 365 and Azure for Education tenants and resells the stolen accounts, licenses, and storage as a service to downstream operations, including the phishing kits tracked as Storm-2990. The pipeline below describes the supply side of that business: how the actor acquires, controls, and monetizes compromised environments.

Stage 1 – Reconnaissance: Storm-3119 conducts automated reconnaissance of target environments. The actor uses Flask, a lightweight Python web framework, to covertly identify and profile cloud-enabled organizations. The tooling is designed to discover Microsoft 365 tenants, assess enrollment opportunities, gather licensing and Azure cloud information, and perform these activities anonymously through Tor infrastructure.

Stage 2 – Environment takeover and app-only admin panels: Once a viable environment is identified, the threat actor centralizes associated information in an actor-controlled platform used to administer compromised Microsoft 365 environments. This platform is a separate set of administrative panels (implemented in PHP, Java/Spring, and Cloudflare Workers) that lets the operator maintain, monetize, and expand access using stolen application credentials. Leveraging the compromised environment’s client secret through an application-only authentication flow, the panel performs full directory control: it resets passwords silently (without forcing a change at next sign-in), assigns education licenses, and grants privileged attributes that the actor can later monetize. Because this access is application-level rather than tied to a single user session, the resulting persistence is more durable, more scalable, and harder to detect.

Stage 3 – Storage and resource abuse: With the stolen privileged credentials, the threat actor signs in to a compromised Global Administrator account and self-provisions the PnP Management Shell service principal, which represents the actor’s application inside the compromised tenant. Through this service principal, the threat actor grants themselves broad Microsoft Graph permissions, including read and write access to directory data and AppCatalog.ReadWrite.All, along with full control over the SharePoint API. The actor then abuses the compromised tenant’s SharePoint and OneDrive as bulk hosting and converts the hijacked storage into a sellable product.

Stage 4 – Phishing and DNS infrastructure support: The threat actor maintains dedicated infrastructure to support and manage phishing operations on behalf of their customers. A custom DNS management platform enables centralized control of phishing domains, SSL certificates, and domain-routing configurations through Cloudflare.

Stage 5 – Storefront and monetization: Storm-3119 resells the hijacked accounts, licenses, and storage through an automated storefront, operating as the access broker that supplies these resources to downstream operations. Payment is accepted through Alipay, WeChat Pay, and USDT (TRC20), and the storefront supports a sub-reseller tier. Telegram is used for order notifications and buyer contact rather than as the primary sales channel.

In addition to targeting existing tenants, Storm-3119 also separately creates fraudulent organizations and supporting web presence to establish tenants on Microsoft’s platform, likely using AI-generated content and forged documentation to make the fabricated entities appear legitimate. In one case, the actor created an environment, likely using AI, named “Jewish Choice” under the guise of a nonprofit organization and associated it with jewishchoice[.]org, a domain registered only within the past several months that did not appear to represent a legitimate organization. This activity indicates that Storm-3119 likely established the website and associated domain to lend credibility to the fabricated nonprofit identity and facilitate tenant creation.

Storm-2990: The Iron Man System phishing kit

YAM Store sells its illicitly acquired infrastructure, as well as the prominent phishing kits tracked as Storm-2990, to other threat actors. The platforms are commercially licensed phishing-as-a-service (PaaS) products. They are designed to impersonate organizations across several industry verticals to steal information, including sign-in credentials (usernames, passwords) and financial data (credit card information), used to gain access to network environments.

Iron Man System’s customers purchase a subscription that provides access to the kit’s license server, with the domain hard-coded into the kit. This restriction adds a layer of obfuscation by limiting site access to paying customers. The kits allow attackers to steal money by abusing the stolen credit card information.

The Iron Man System phishing kit contains over 2,335 templates tailored to over 139 countries including Australia, Japan, India, and the United States, with the United States accounting for the most phishing templates. The phishing lures used by Iron Man System mimic various trusted brands and organizations, such as local government including authorities associated with traffic and parking fees, law enforcement, healthcare, finance, transportation, and retail. These lures are designed to trick targets into providing sensitive information. Most phishing lures used in the United States are transportation or government-related.

The phishing kits are capable of the following advanced evasion techniques to varying degrees:

• Geofencing, a technique used to restrict access to configured countries only, so only users from specific countries can access the phishing content from their web browser
• Fake CAPTCHA gates, used to prevent or block automated browsing from threat research tools by making the user click a box or execute an action
• IP address block list and allow list, used to block or allow IP addresses to prevent threat researcher interaction
• Anti-detection configuration related tosettings to evade security researcher detection
• Telegram bot configuration,including bot tokens and chat IDs for real-time phishing that enables credential exfiltration. It allows the kit operator to receive automatic notifications on their Telegram account when a target takes action on a phishing lure, including entering credentials, credit card data submission, and verification codes.

After the target provides the sensitive information (sign-in details, credit card info), the phishing kit collects it using a WebSocket, offering customers real-time victim interaction through Telegram notifications during credential harvesting. This confirms that the kit supports live, interactive phishing where the operator monitors each victim’s progress and manually controls what the victim sees next, a hallmark of sophisticated PaaS platforms. The attacker then harvests the information to use in follow-on fraudulent financial activities.

The Iron Man System phishing kit panel is predominately hosted on China-based hosting providers, with a smaller number of panels found on hosting providers in other countries. The fully featured, multi-tenant phishing kit uses the GoFrame application development framework, an open-source Go framework with roots in the Chinese developer ecosystem.

Downstream activity from Storm-2990 phishing kits

Microsoft observed downstream activity by threat actors using infrastructure affiliated with the Storm-2990 phishing kits. In analyzed cases, the actors used similar common phishing techniques, such as urgency in the message and attaching malicious links that redirect to actor-controlled infrastructure that allows these downstream threat actors to perform the following activities:

• Credential stealing: The actors use phishing pages that impersonate legitimate services to trick targets into providing credentials or other authentication information, enabling downstream actors to access compromised accounts.
• Device code phishing: In some cases, the actors abuse the device code authentication flow to trick victims into authenticating to legitimate applications, allowing downstream actors to obtain authentication tokens and access compromised accounts.
• Financial fraud: The end goal for the downstream actors is to use stolen payment data to make fraudulent purchases of high-end goods or fraudulent transactions on legitimate financial platforms.

The threat actors using the phishing kits conduct phishing campaigns at scale whether it is a missing toll payment or a traffic citation. The following screenshots show email or SMS phishing lures used in Storm-2990 phishing kits.

Mitigation and protection guidance

This interconnected network of Chinese-language phishing kit operators and developers operates as an enabling ecosystem, providing infrastructure and services that facilitate criminal activity rather than functioning as a standalone malware tool. Microsoft assesses that the ecosystem is not limited to the abuse of Microsoft services and extends to other providers.

While Microsoft notes that the identified organizations impacted by attacks stemming from Storm-3119 or Storm-2990 operations include financial, healthcare, and IT companies, the activity conducted by threat actors using these tools are common attacks that could affect any organization.

Most of the activity that stems from this phishing kit ecosystem consists of social engineering, phishing operations, and account and storage takeovers. Microsoft recommends the following to mitigate the impact of Storm-3119 and Storm-2990-related threats.

• In nearly all the cases observed by Microsoft, the phishing lures used by the Storm-2990 operators followed prescriptive scam red flags, including urgency, time-sensitive tasks such as clicking a provided link with official branding. Users should verify with the purported sender of the message or email, even when the task involves signing in to a known, secure account not provided by the link or calling the purported organization or company directly.
• Educate users about protecting personal and business information in social media, filtering unsolicited communication, identifying lures in spear-phishing emails and watering holes, and reporting reconnaissance attempts and other suspicious activities.
• Educate users about common phishing techniques. Sign-in prompts should clearly identify the application being authenticated to. As of 2021, Microsoft Azure interactions prompt the user to confirm (Cancel or Continue) that they are signing in to the app they expect, which is an option frequently missing from phishing sign-ins.
• Review our recommended settings for Exchange Online Protection and Microsoft Defender for Office 365 to ensure your organization has established essential defenses and knows how to monitor and respond to threat activity.
• Configure the Microsoft Defender for Office 365 Safe Links policy to apply to internal recipients.

Hardening credentials and cloud identities is also necessary to defend against phishing attacks, which seek to gain valid credentials and access tokens. As an initial step, use passwordless solutions like passkeys and implement multifactor authentication (MFA) throughout your environment:

• Invoke the principles of least privilege and Zero Trust to significantly slow down threat actors who might have been able to gain initial access and buy time for defenders to respond. Since Storm-3119 operators have been observed obtaining Global Administrator access, implementing these principles can help lock down Global Administrator roles and who has access to specific resources.
• Use the Microsoft Authenticator app for passkeys and MFA, and complement MFA with Conditional Access policies, where sign-in requests are evaluated using additional identity-driven signals.
• Conditional Access policies can also be scoped to strengthen privileged accounts with phishing-resistant MFA.
• Your passkey and MFA policy can be further secured by only allowing MFA and passkey registrations from trusted locations and devices.
• Enable network protection to prevent applications or users from accessing malicious domains and other malicious content on the internet.

Microsoft Defender detections

Microsoft Defender customers can refer to the list of applicable detections below. Microsoft Defender coordinates detection, prevention, investigation, and response across endpoints, identities, email, and apps to provide integrated protection against attacks like the threat discussed in this blog.

The coverage below applies to the downstream activity detected by Microsoft that was identified using Storm-2990 infrastructure.

Microsoft Security Copilot

Microsoft Security Copilot is embedded in Microsoft Defender and provides security teams with AI-powered capabilities to summarize incidents, analyze files and scripts, summarize identities, use guided responses, and generate device summaries, hunting queries, and incident reports.

Customers can also deploy AI agents, including the following Microsoft Security Copilot agents, to perform security tasks efficiently:

• Threat Intelligence Briefing agent
• Phishing Triage agent
• Threat Hunting agent
• Dynamic Threat Detection agent

Security Copilot is also available as a standalone experience where customers can perform specific security-related tasks, such as incident investigation, user analysis, and vulnerability impact assessment. In addition, Security Copilot offers developer scenarios that allow customers to build, test, publish, and integrate AI agents and plugins to meet unique security needs.

Threat intelligence reports

Microsoft Defender XDR customers can use the following threat analytics reports in the Defender portal (requires license for at least one Defender XDR product) to get the most up-to-date information about the activity and techniques discussed in this blog. These reports provide intelligence, protection information, and recommended actions to prevent, mitigate, or respond to associated threats found in customer environments.

• Threat Overview Profile: Evolving phishing threats

Microsoft Security Copilot customers can also use the Microsoft Security Copilot integration in Microsoft Defender Threat Intelligence, either in the Security Copilot standalone portal or in the embedded experience in the Microsoft Defender portal to get more information about the activity discussed in this blog.

Hunting queries

Microsoft Defender XDR

Microsoft Defender XDR customers can run the following advanced hunting queries to find generic activity that may have some overlap with Storm-2990-related activity. Note that these queries are not specific to Storm-2990:

Hunt for suspicious device code authentication

The following query identifies risky sign-ins using the device-code authentication flow that could indicate device-code phishing and subsequent unauthorized account access.

EntraIdSignInEvents
| where Timestamp > ago(30d)
| where AuthenticationProtocol =~ "deviceCode"
| where RiskLevelDuringSignIn in~ ("medium", "high")
or RiskLevelAggregated in~ ("medium", "high")
or RiskEventTypes_V2 != "[]"
| project Timestamp, AccountUpn, IPAddress, Country, Application, ResourceDisplayName,
AuthenticationProtocol, RiskLevelDuringSignIn, RiskLevelAggregated,
RiskEventTypes_V2, SessionId
| order by Timestamp desc

Hunt for potentially compromised accounts using multiple risk signals

The following query identifies accounts exhibiting risky authentication activity across multiple IP addresses or countries that may indicate credential compromise following phishing.

EntraIdSignInEvents
| where Timestamp > ago(30d)
| where RiskLevelDuringSignIn in~ ("medium", "high")
    or RiskLevelAggregated in~ ("medium", "high")
    or RiskEventTypes_V2 != "[]"
| summarize FirstSeen = min(Timestamp), LastSeen = max(Timestamp),
            IPAddresses = make_set(IPAddress, 20), Countries = make_set(Country, 20),
            Applications = make_set(Application, 20), RiskEvents = make_set(RiskEventTypes_V2, 20),
            SignInCount = count() by AccountUpn
| where array_length(IPAddresses) > 1 or array_length(Countries) > 1
| order by LastSeen desc

Hunt for phishing clicks followed by Defender alerts

The following query correlates email URL clicks with subsequent Defender alerts for the same user to identify potential phishing-related account compromise.

let Lookback = 30d;
let PhishClicks =
    UrlClickEvents
    | where Timestamp > ago(Lookback)
    | project ClickTime = Timestamp, AccountUpn, Url, ClickAction = ActionType;
let IdentityAlerts =
    AlertEvidence
    | where Timestamp > ago(Lookback)
    | where EntityType =~ "User"
    | project AlertTime = Timestamp, AccountUpn, AlertId, Title, Severity, DetectionSource;
PhishClicks
| join kind=inner IdentityAlerts on AccountUpn
| where AlertTime between (ClickTime .. ClickTime + 24h)
| project ClickTime, AlertTime, AccountUpn, Url, ClickAction, AlertId, Title, Severity, DetectionSource
| order by ClickTime desc

Hunt for phishing URL clicks followed by suspicious sign-ins

The following query identifies users who clicked URLs in email and subsequently generated risky sign-in activity, which may help identify credential compromise following phishing.

let Lookback = 30d;
let PhishClicks =
    UrlClickEvents
    | where Timestamp > ago(Lookback)
    | where ActionType in~ ("ClickAllowed", "ClickBlocked")
    | project ClickTime = Timestamp, AccountUpn, Url, Workload, ClickAction = ActionType;
let SuspiciousSignins =
    EntraIdSignInEvents
    | where Timestamp > ago(Lookback)
    | where RiskLevelDuringSignIn =~ "high"
        or RiskLevelAggregated =~ "high"
        or RiskEventTypes_V2 != "[]"
    | project SignInTime = Timestamp, AccountUpn, IPAddress, Country, Application,
              RiskLevelDuringSignIn, RiskLevelAggregated, RiskEventTypes_V2;
PhishClicks
| join kind=inner SuspiciousSignins on AccountUpn
| where SignInTime between (ClickTime .. ClickTime + 24h)
| project ClickTime, SignInTime, AccountUpn, Url, IPAddress, Country, Application,
          RiskLevelDuringSignIn, RiskEventTypes_V2
| order by ClickTime desc

Microsoft Sentinel

Microsoft Sentinel customers can use the TI Mapping analytics (a series of analytics all prefixed with ‘TI map’) to automatically match the malicious activity mentioned in this blog post with data in their workspace. If the TI Map analytics are not currently deployed, customers can install the Threat Intelligence solution from the Microsoft Sentinel Content Hub to have the analytics rule deployed in their Sentinel workspace.

• Possible phishing with clicked/selected link and network session
• Threat Intelligence URL match with UrlClickEvents
• Correlate unfamiliar sign-in properties and atypical travel alerts
• Possible AiTM phishing attempt against Microsoft Entra ID
• Business email compromise – Mailbox rule

References

• CoGUI Phish Kit Targets Japan with Millions of Messages | Proofpoint US
• IronToll: Global Government-Impersonation PhaaS | PhishEye

Learn more

For the latest security research from the Microsoft Threat Intelligence community, check out the Microsoft Threat Intelligence Blog.

To get notified about new publications and to join discussions on social media, follow us on LinkedIn, X (formerly Twitter), and Bluesky.

To hear stories and insights from the Microsoft Threat Intelligence community about the ever-evolving threat landscape, listen to the Microsoft Threat Intelligence podcast.

The post The hidden network: Inside the phishing infrastructure ecosystem powering global cybercrime appeared first on Microsoft Security Blog.

Gamers around the world are holding their breath for November 19, 2026, the day Grand Theft Auto VI is expected to drop. Many of them have been waiting for the next installment in the hit series for over a decade. For die-hard fans, the final months before launch could prove especially agonizing. Sadly, scammers know all about this hotly anticipated release too, and they’ve decided to use it as a lure to launch all sorts of attacks on gamers. We’ve spotted a great many bait sites aimed at impatient GTA VI fans. Today, we walk you through exactly how these crooks try to trick gamers, and how you can avoid becoming their next target.

1. “GTA VI has already leaked, and all you have to do is pay”

One site our experts found, called CyberLeek, claims to have an early build of GTA VI, and pretends to offer three different ways to get hold of the game before its official release. The first and priciest option is to buy the “full game” of GTA VI for US$1000. For that price, buyers are offered an early build of the game, exclusive content, and a direct download link. This is claimed to be a limited offer: only one copy per customer.

An alleged GTA VI leak advertised for US$1000

For those not ready to part with a thousand dollars, there’s a cheaper option: for a hundred dollars, users can get 24-hour access to the GTA VI demo.

Finally, the third option relies on crowdfunding. Playing modern-day Robin Hoods, the site’s creators claim they’ll raise $50,000 and then release the game to the public for free, with everyone who donates at least $100 getting priority early access. But dig into the fine print, and the fundraising goal for that paid early access suddenly jumps from $50,000 to $500,000. Looks like one of the scammers sat on their keyboard hit an extra zero by accident.

An urgent fundraiser for US$50,000 — or is it US$500,000? — to release a leaked GTA VI build, plus advertising opportunities for businesses

But CyberLeek’s lineup of tempting “offers” doesn’t stop there; there are also services for businesses. For US$5000, the site’s creators offer to place an ad right inside Grand Theft Auto VI: a logo, a product, or a brand mention that will supposedly show up in some unspecified “upcoming Grand Theft Auto video content”. What exactly that means is unclear: in-game clips or cutscenes, trailers, or just some gameplay footage. And honestly, the price seems suspiciously low for product placement in one of the most anticipated games in recent years.

This offer looks especially amusing next to the supposedly complete early build of the game with full access available on the same website. So, apparently, the game is already finished enough to sell in full, but you can still somehow slap ads into it.

One can only pay for all these incredible services with cryptocurrency. CyberLeek accepts Bitcoin, Ethereum, USDT, and Litecoin, and the site’s creators make a point of saying that all payments are final and no refunds are possible. Well, we weren’t expecting refunds anyway.

That said, this is just one example. CyberLeek started out as the nickname of the hacker behind both the recent Rockstar Games breach and GTA VI content leak. Later on, other cybercriminals began using that same name to build a whole bunch of fake sites. Despite having completely different designs, all of these sites attempt to project an association with CyberLeek, enticing users with (false) promises of access to “official” leaked versions of GTA VI. Whichever of these sites you come across, we’d strongly advise against using your own computer to check what’s actually hiding inside the archives and installers they hand out.

Another CyberLeek site offering access to a “leaked build” of GTA VI invites the user to download some unknown launcher, or “unlock” the full version of the game — whatever that means

2. Preordering Grand Theft Auto VI — with a catch

This bait targets slightly more sensible gamers — the kind who aren’t trying to download GTA VI right this second but wouldn’t say no to grabbing a free preorder code ahead of time. Our experts found a site that is claiming to hand out a redeem code for the PlayStation 5 or Xbox Series X|S. All one needs do is pick a platform and provide an email address.

To make it all look convincing, the site even shows off its own “statistics”: it supposedly has 3480 active users, and only 121 codes are still available as of today.

To get the “exclusive code”, the user is asked to pick a console and provide their email address

After picking a console, entering an email address, and clicking Reveal Code, the user lands on a new page. It shows the first four characters of a supposedly generated code, and to get the rest the user is asked to complete an additional check. They’re given 15 minutes to do so.

The site shows the first four characters of the coveted code, and promises to send the rest after an additional check, which the user has 15 minutes to complete

Nobody tells the gamer upfront what this check actually involves. But the next page, which opens after clicking Reveal Full Code, inexplicably turns out to be in German. Here, the user is asked to pass a “human verification”: download a free AI chat app for their computer, run it, and accept the terms of use.

The real point of this “verification” most likely boils down to driving traffic to third-party, often shady, or outright fraudulent sites, with GTA VI simply used as bait to pump up visitor numbers.

To get the rest of the “code”, the site demands that the user download and run an AI chat app and accept its terms of use

A simpler variation involves fake sites that simply collect money under the pretext of placing preorders for Grand Theft Auto VI. For example, a fake PlayStation Store site targeting Brazilian users offers preorders for R$449.9, which actually matches the official price in the real PS Store. The user can pay with a credit card or through Pix (Brazil’s instant payment system), but along with their money, they’d also have to hand over personal data, including their CPF — the Brazilian equivalent of a taxpayer identification number.

In Brazil, a fake PlayStation Store site collects money for GTA VI preorders at a price that looks legit

3. You’ve missed it: GTA VI is already out!

Some scammers don’t even bother with elaborate stories about leaks, beta tests, or preorder deals. They simply build websites that copy the look and feel of GTA, and invite users to start playing GTA VI right here, right now. We came across a few examples of these sites.

No leaks, no tests, no codes needed: visitors are simply invited to click Try GTA 6 and start playing

One of these sites tells users they can download GTA VI, as long as they first pick their platform. The range of options is surprisingly generous: a PC version as well as versions for PlayStation 5 and Xbox Series X|S.

Users are invited to pick a version of GTA VI for PlayStation 5, Xbox Series X|S, or PC

Just to be clear, GTA VI’s official release is still more than a month away, so there’s no way one can download and install the game right now. Nobody knows exactly what users would be getting on their devices instead of the game if they download from one of these sites, but it’s definitely nothing useful. And no matter how carefully scammers copy GTA’s signature style and slap on the Rockstar logo, the address bar usually gives the game away: check the domain and compare it with the official Rockstar Games website address.

4. The icing on the cake: GTA VI for tokens

And finally, what would 2026 be without cryptocurrency? We found a site whose creators were probably inspired by the CyberLeek story and decided to ride the wave of GTA VI’s popularity to promote a token with the refreshingly straightforward name $GTAVI. The whole site is styled after GTA VI and Vice City.

The creators of the project have gone out of their way to highlight the perks of holding the token. They call $GTAVI “the only GTA VI coin that actually rewards its holders”. Whatever the meaning of that tagline, the project’s declared ultimate goal sounds even more tempting: every token holder will get access to Grand Theft Auto VI just by virtue of owning $GTAVI. Think about that for a second: it’s not exactly a direct promise of a free copy of GTA VI, but more like an aspiration on the part of the crypto developers.

The token’s creators claim that they’re striving to eventually provide every $GTAVI holder with access to GTA VI

To add a bit of credibility, the page also throws in some crypto stats. The claimed market cap of $GTAVI is 51,600 dollars, with a 24-hour trading volume of 7880 dollars. There’s also a mysterious metric called “Characters” with a value of 12, and what exactly it means is anyone’s guess. There’s also a separate mention of a certain $BOOBIE as the Top Gainer — apparently, this is how the site’s creators labeled whoever supposedly made the most money off $GTAVI. The site doesn’t explain where any of these numbers come from or how accurate they actually are.

All in all, this looks like an attempt to hype up yet another meme coin by riding the wave of GTA VI’s popularity. Projects like this often end up following the classic pump-and-dump playbook: the token gets promoted for a while to drive its price up, and then the insiders and major holders cash out, leaving everyone else stuck with heavily devalued crypto. As for the stated goal behind $GTAVI — letting every token holder get a copy of GTA VI just for owning it — the site gives us no reason to believe that’s actually going to happen.

How to wait for GTA VI without falling for scammers

Unfortunately, we don’t know of a legitimate way to get early access to GTA VI, and honestly, we doubt anyone else does either. It looks like we’ll all just have to wait until November. To make sure you don’t fall for tempting scam offers in the meantime, here’s what we recommend:

• Only install games from official sources.
• Check the website address. Even if a page looks like the official Rockstar Games website or the PlayStation Store, carefully examine the domain in the address bar.
• Never enter your payment details on sites promising GTA VI before its official release — especially if they ask you to pay in some unusual way like with cryptocurrency.
• Don’t download unknown launchers, archives, or installation files, even if they’re billed as a “leaked build” or as software you need in order to be a beta tester.
• Don’t hand over your personal information for a “beta test”, “key”, “early access”, or any other offer that sounds too good to be true.
• Never disable security features just because a website tells you to. If a site asks you to ignore a Windows warning, run some mysterious command, or launch an unknown file as an administrator, treat it as a giant red flag.
• Install a reliable security solution built for gamers, one that warns you before you land on a scam site and blocks malicious files, but won’t slow your games down.

What other tricks do scammers pull to fool even the tech-savviest gamers? Find out in our other posts:

• Bad advice for gamers:ClickFix hits Steam forums
• Hentai games with a nasty twist
• Stealka stealer: the new face of game cheats, mods, and cracks
• Gamers under fire: malware on official websites
• Arcane stealer instead of Minecraft cheats

In July 2026, Microsoft Defender Experts observed phishing campaigns targeting organizations across multiple industries that distributed a masqueraded MSP360 Remote Monitoring and Management (RMM) installer through meeting invitations, PDF-themed lures, software update prompts, and other social-engineering content. Once executed, the legitimate MSP360 installer, distributed under a deceptive file name established remote management access on affected devices and enabled threat actors to gain an initial foothold using trusted administrative software.

Microsoft observed the MSP360 deployment being used to download and install a ConnectWise ScreenConnect client, creating a secondary remote-access channel that provided redundant access to compromised systems. Microsoft did not observe exploitation of ScreenConnect software itself; rather, threat actors abused legitimately obtained remote administration software to establish and maintain access. After access was established, threat actors used these remote administration channels to deploy additional tools and conduct post-compromise activity, including information collection and credential-access operations.

This activity highlights how threat actors continue to abuse legitimate remote administration software to blend into normal IT operations while maintaining persistent access and reducing detection opportunities. Microsoft Defender for Endpoint detects suspicious and uncommon remote-management activity, while the hunting queries and mitigations in this post can help organizations identify and restrict unapproved RMM use.

Attack chain overview

The observed multi-stage intrusion chain began when phishing lures delivered a legitimate, digitally signed MSP360 RMM v2.5.0.67 installer under deceptive filenames. Following successful User Account Control (UAC) elevation, the installer established MSP360 services for persistent access and leveraged the RMM agent to invoke PowerShell, download, and silently install ConnectWise ScreenConnect.

This effectively introduced a second remote administration channel on the compromised device, which the threat actor subsequently used to transfer and execute additional tooling supporting credential access, local data collection, and other post-compromise activity.

Initial Access: Phishing Campaign Delivering Masqueraded MSP360 RMM Installer

Microsoft observed multiple phishing campaigns that used a multi-stage delivery chain to distribute legitimate, digitally signed MSP360 RMM software (v2.5.0.67). Phishing emails directed users to actor-controlled landing pages that impersonated document-sharing portals, invitation workflows, Adobe Reader download pages, Zoom installation pages, and business collaboration platforms.

Upon user interaction, victims were redirected to download locations hosted on both attacker-controlled infrastructure and legitimate cloud services including Amazon S3, Cloudflare R2, Dropbox, GitLab, and Supabase. The downloaded executables used filenames crafted to resemble legitimate business content, meeting invitations, PDF documents, and software installers. Analysis of downloaded samples showed that many ultimately contained the same MSP360 RMM installer package despite appearing as different files to the victim.

MSP360 SHA256: 108ef7e628d7a20bd6241a5b57149e27a6061f467123eb64061975559f8f73dc

MSP360 SHA1: f34330d4c6e0aa978dc3af40360c14b31ad51127

Observed lure themes:

We have observed the threat actor using multiple social-engineering themes, including:

• Workplace meeting requests
• Zoom and Google Meet installation prompts
• Adobe Acrobat and PDF reader updates
• RSVP invitations and e-cards
• Job offer documents
• Document review and signature requests
• DHL and package-delivery themed content

Examples of observed filenames included:

• VIP_ECARD_INVITATION_rmm_v2.5.0.67_oid[redacted].exe
• ZoomSetup_Installation_v2.5.0.67_ oid[redacted].exe
• PDF Reader & Editor the Adobe Acrobatte_rmm_v2.5.0.67_ oid[redacted].exe
• RSVP_INVITATION_E_CARD_rmm_v2.5.0.67_ oid[redacted].exe
• SSA.GOV_STATEMENT_rmm_v2.5.0.67_ oid[redacted].exe

The campaign relied on a diverse set of payload-hosting mechanisms. Microsoft observed the actor distributing the payload through attacker-controlled domains, websites assessed to be compromised, and legitimate cloud-hosted services. Cloud-hosted services used for payload distribution included Amazon S3, Cloudflare R2, Dropbox, GitLab, and Supabase.

This approach enabled the actor to rapidly rotate delivery infrastructure while continuing to distribute the same MSP360 installer using different lure themes and filenames.

RMM platforms are attractive to threat actors because they are designed to provide administrators with broad remote management capabilities across managed endpoints, including remote command execution, software deployment, file transfer, and persistent service-based access. When abused, these same capabilities can give threat actors a flexible post-compromise channel for maintaining access, deploying additional tooling, and conducting follow-on activity while blending in with legitimate remote administration workflows.

MSP360 RMM installation and foothold establishment

After victims downloaded and executed the masqueraded MSP360 installer, the binary launched from the user’s Downloads directory under a filename designed to resemble a legitimate business document.

The installer subsequently dropped multiple installation components, including System.dll, nsExec.dll, and UAC.dll, to the following folder paths before relaunching itself through an elevation workflow generated by the installer framework. Next, the installer invoked a Windows User Account Control (UAC) elevation workflow. In observed successful installations, the process continued with elevated privileges, allowing deployment of MSP360 components and services. In unsuccessful installations, the elevation did not complete, and deployment terminated before the software was fully installed.

Following elevation, the installer initiated the MSP360 installation workflow and recorded installation status messages using Windows eventcreate.exe. The installer generated “Begin installation” and “End installation. MSP360 de Success.” events under the event source: MSP360 RMM Agent installer. The installer dropped multiple MSP360 plugins and binaries within the installation directory: C:\Program Files\RMM Agent\.

The installer also performed prerequisite discovery by enumerating installed .NET runtimes using: dotnet –list-runtimes. To establish long-term access on the affected device, the installer registered two Windows services: RMM.Agent.exe & RMM.Agent.Launcher.exe. Microsoft observed events indicating stopping any existing MSP360 services and installing new MSP360 services.

In addition to service-based persistence, the installer created registry-based autorun entries for MSP360 user interface components, ensuring the tray applications would automatically launch when users signed in.

The installation routine also modified the Windows Firewall configuration by creating an inbound allow rule for the MSP360 agent. The rule allowed inbound UDP traffic to C:\Program Files\RMM Agent\RMM.Agent.exe on port 48678. This configuration enabled network communications required by the remote management platform.

Not every execution of the installer resulted in a successful deployment. In some instances, the installer was launched by the user and began its installation routine but subsequently attempted to obtain elevated privileges through User Account Control (UAC). When elevation was denied or aborted, the installation terminated before completing deployment of the MSP360 RMM components. The process execution flow showed installation initialization activity followed by events indicating that administrative privileges were required, with no subsequent evidence of the persistence mechanisms, services, or remote management functionality observed during successful installations.

Remote command execution from MSP360 Agent and ScreenConnect deployment

Following successful installation of MSP360 RMM, the newly installed RMM.Agent.exe service launched PowerShell. The PowerShell process modified the execution policy for the current session and executed Invoke-WebRequest commands to download an MSI package named ClientSetup.msi from actor-controlled infrastructure. The downloaded package was then installed silently through msiexec.exe using the /qn switch, eliminating visible user interaction. This installation activity resulted in the deployment of a ConnectWise ScreenConnect client on the compromised endpoint, including ScreenConnect.ClientService.exe, ScreenConnect.WindowsClient.exe, and supporting components.

The installer additionally created Windows service registrations, application uninstall entries, and authentication-related registry modifications associated with the newly deployed ScreenConnect client.

Following deployment, the ScreenConnect client service launched with configuration parameters referencing actor-controlled infrastructure and subsequently established successful outbound communications. The service then spawned ScreenConnect.WindowsClient.exe, providing an additional remote access channel independent of MSP360. Following establishment of the ScreenConnect session, the threat actor used ScreenConnect to transfer and stage additional executables in the following directories:

• C:\Users\%user%\OneDrive\Documents\ScreenConnect\Temp\
• C:\Users\%user%\Documents\ScreenConnect\Temp\

Examples of frequently observed files included:

• WindVerify.exe
• WindowsUpdate.exe
• WindowsSecurity_PIN.exe
• WindowsSecurity_Password.exe
• WindowsPassKey.exe
• SCHider.exe
• PIN.exe
• phonepc.exe
• DefenderDT.exe
• DefenderControl.exe
• phonelinkupdate.exe
• PhoneLinkPrompt.exe
• Passwords.EXE
• OpenCamera.exe
• open_phone_link.exe
• MouseHiderGUI.exe
• HideUL.exe
• HideMouseApp.dll
• HideMouse.exe
• HideFromControlPanel.exe
• HideCursor.exe
• BannerHider.exe
• WebBrowserBookmarksView.exe
• WebBrowserPassView.exe

These files were among the most frequently observed utilities delivered by the threat actor following establishment of a ScreenConnect session. Several filenames were intentionally chosen to resemble legitimate Windows, Microsoft Defender, Phone Link, and security-related components, likely to reduce user suspicion and blend into normal operating system activity. These utilities were observed during post-compromise activity and were used to support subsequent operations on affected systems. Several of the observed tools are commonly associated with credential access, information collection, execution of additional payloads, and efforts to reduce defender visibility.

The execution of these files occurred through ScreenConnect’s built-in RunFile functionality, which allows files to be transferred to and executed on managed endpoints. This activity demonstrates how the threat actor leveraged a legitimate MSP360 RMM deployment to establish an initial foothold before deploying ConnectWise ScreenConnect as a secondary remote access platform. The combination of MSP360 and ScreenConnect provided the threat actor with redundant remote administration channels and enabled the transfer, execution, and management of additional tooling during subsequent stages of the intrusion.

The threat actor subsequently used these remote access platforms to facilitate follow-on activities including information collection, credential access, and the deployment of additional utilities on compromised systems.

Microsoft also observed separate activity during July in which FaronicsDeployAgent.exe, a legitimate deployment and remote access application, was used in a similar manner to MSP360 RMM. In this activity, the threat actor leveraged FaronicsDeployAgent.exe as the initial remote management platform and subsequently used it to download and install ScreenConnect. This observation demonstrates that the deployment of ScreenConnect was not limited to MSP360-based intrusions, with additional legitimate remote administration software also being leveraged to establish remote access and facilitate ScreenConnect installation.

Attribution

Microsoft has not attributed this activity to a named threat actor. The campaigns are tracked as unattributed activity.

Mitigation and protection guidance

Microsoft recommends the following actions to help organizations reduce their exposure to this activity. Check the recommendations card for the deployment status of monitored mitigations.

• Govern approved RMM tools: For approved RMM systems used in your environment, enforce security settings where possible to implement multi-factor authentication (MFA).
• Restrict unauthorized software: Use Application Control for Windows to create policies to block unapproved IT management tools. Both solutions include functionality to block specific software publisher certificates:
  • Application Control for Windows file rule levels allow administrators to specify the level at which they want to trust their applications, including listing certificates as untrusted.
  • AppLocker’s publisher rule condition is available for files that are digitally signed, which can enable organizations to block non-approved RMM instances that include publisher information.
• Block specific signed applications: Microsoft Defender for Endpoint also provides functionality to block specific signed applications using the block certificate action.
• Consider searching for unapproved RMM software installations (see the Advanced hunting section). If an unapproved installation is discovered, reset passwords for accounts used to install the RMM services. If a system-level account was used to install the software, further investigation may be warranted.
• Strengthen endpoint protection: Turn on cloud-delivered protection in Microsoft Defender Antivirus or the equivalent for your antivirus product to cover rapidly evolving attacker tools and techniques. Cloud-based machine learning protections provide near-instant, automated protection against new and emerging threats.
• Investigate unauthorized installations: Turn on the following attack surface reduction rule to block or audit activity associated with this threat:
  • Use advanced protection against ransomware
  • Block process creations originating from PsExec and WMI commands. Some organizations may experience compatibility issues with this rule on certain server systems but should deploy it to other systems to prevent lateral movement originating from PsExec and WMI

Microsoft Defender XDR detections

Microsoft Defender XDR customers can refer to the list of applicable detections below. Microsoft Defender XDR coordinates detection, prevention, investigation, and response across endpoints, identities, email, and apps to provide integrated protection against attacks like the threat discussed in this blog.

Threat intelligence reports

Microsoft customers can use Microsoft Defender XDR Threat Analytics and related Microsoft threat intelligence reporting to stay current on the malicious activity, indicators, detection coverage, and recommended response actions associated with this campaign. These reports provide investigation context, protection guidance, and updated intelligence that security teams can use to prevent, mitigate, or respond to related activity in their environments.

Advanced hunting

// Run this query to identify the presence of MSP360 RMM agent

let MSP360RMM = "108ef7e628d7a20bd6241a5b57149e27a6061f467123eb64061975559f8f73dc";
DeviceFileEvents
| where Timestamp >= ago(30d)
| where SHA256 =~ MSP360RMM

// Run this query to identify the remote command execution from MSP360 RMM Agent

DeviceProcessEvents
| where Timestamp >= ago(30d)  
| where (InitiatingProcessVersionInfoCompanyName == "MSP360" and ProcessCommandLine == "\"powershell.exe\"") or ( InitiatingProcessCommandLine == "\"powershell.exe\"" and ProcessCommandLine has_all ("msiexec.exe","\\Temp\\",".msi") and InitiatingProcessParentFileName == "RMM.Agent.exe")

// Run this query to identify the suspicious network connections from the threat actor’s use of ScreenConnect

let MaliciousURLs =  DeviceNetworkEvents
| where Timestamp >= ago(30d)
| where InitiatingProcessCommandLine == "\"powershell.exe\""
| where InitiatingProcessParentFileName == "RMM.Agent.exe"
| where isnotempty(RemoteUrl)
| distinct  RemoteUrl;
DeviceNetworkEvents
| where Timestamp >= ago(30d)
| where InitiatingProcessFileName has_any ( "ScreenConnect.ClientService.exe","ScreenConnect.WindowsClient.exe","ScreenConnect.Client.exe")
| where isnotempty(RemoteUrl)
| where RemoteUrl in (MaliciousURLs)

// Run this query to identify the suspicious payloads dropped and launched by the threat actor’s use of ScreenConnect

DeviceProcessEvents
| where Timestamp >= ago(30d)  
| where InitiatingProcessCommandLine  has_all ("ScreenConnect.WindowsClient.exe", "RunFile","\\Documents\\","\\Temp\\")

MITRE ATT&CK Techniques observed

This campaign has exhibited use of the following attack techniques. For standard industry documentation about these techniques, refer to the MITRE ATT&CK framework.

Resource Development

• T1583.001 Acquire Infrastructure: Domains | Domains were used to host phishing landing pages, payload delivery infrastructure, and ScreenConnect services associated with the campaign.
• T1583.006 Acquire Infrastructure: Web Services | The threat actor leveraged legitimate cloud-hosting providers including Amazon S3, Cloudflare R2, Dropbox, GitLab, and Supabase for payload distribution.

Initial Access

• T1566.002 Phishing: Spearphishing Link | Victims received workplace meeting, Zoom, Google Meet, invitation, RSVP, PDF, and Adobe-themed phishing emails containing links that redirected to malicious payload download locations.
• T1204 User Execution | Victims downloaded and executed a masqueraded MSP360 installer distributed under deceptive filenames designed to resemble legitimate business documents and software.

Execution

• T1059.001 Command and Scripting Interpreter: PowerShell | The MSP360 RMM.Agent.exe service launched PowerShell to download and install ConnectWise ScreenConnect.
• T1059.003 Command and Scripting Interpreter: Windows Command Shell | Installation workflows used cmd.exe for logging, prerequisite checks, file management, and installation-related tasks.
• T1218.007 System Binary Proxy Execution: Msiexec | The downloaded ClientSetup.msi package was installed silently using msiexec.exe /qn.

Persistence

• T1543.003 Create or Modify System Process: Windows Service | MSP360 and ScreenConnect were installed as Windows services to establish persistent access.
• T1547.001 Boot or Logon Autostart Execution: Registry Run Keys / Startup Folder | MSP360 created registry Run entries to automatically launch tray application components at user logon.

Defense Evasion

• T1036 Masquerading | The actor distributed legitimate MSP360 software under filenames designed to resemble meeting applications, invitations, PDFs, and business documents.
• T1036.005 Match Legitimate Name or Location | Follow-on tooling used filenames that closely resembled legitimate Windows, Microsoft Defender, security, and Phone Link applications.
• T1112 Modify Registry | MSP360 and ScreenConnect modified registry locations associated with services, persistence, credential provider components, protocol handlers, and uninstall entries.

Command and Control

• T1219 Remote Access Software | The threat actor abused legitimate remote administration software including MSP360 RMM and ConnectWise ScreenConnect to maintain access to victim systems.
• T1105 Ingress Tool Transfer | MSP360 downloaded ScreenConnect, while the threat actor’s use of ScreenConnect was subsequently used to transfer and execute additional tooling on compromised endpoints.
• T1071.001 Application Layer Protocol: Web Protocols | PowerShell and ScreenConnect communicated with actor-controlled infrastructure over HTTP/HTTPS.
• T1573 Encrypted Channel | Payload retrieval and remote access communications occurred over encrypted network channels.
• T1102 Web Service | Multiple cloud-hosted web services were used throughout the delivery and command-and-control infrastructure.

Discovery

• T1082 System Information Discovery | The installer enumerated installed .NET runtimes using dotnet –list-runtimes during the installation workflow.

Collection

• T1005 Data from Local System | Additional tooling delivered through ScreenConnect was observed in post-compromise activity and used to collect information from victim devices.

Indicators of compromise (IOCs)

Observed indicators associated with this campaign are listed below.

Learn More

For the latest security research from the Microsoft Threat Intelligence community, check out the Microsoft Threat Intelligence Blog.

To get notified about new publications and to join discussions on social media, follow us on LinkedIn, X (formerly Twitter), and Bluesky.

To hear stories and insights from the Microsoft Threat Intelligence community about the ever-evolving threat landscape, listen to the Microsoft Threat Intelligence podcast.

Review our documentation to learn more about our real-time protection capabilities and see how to enable them within your organization.  

• Learn more about securing Copilot Studio agents with Microsoft Defender 
• Evaluate your AI readiness with our latest Zero Trust for AI workshop.
• Microsoft 365 Copilot AI security documentation
• How Microsoft discovers and mitigates evolving attacks against AI guardrails
• Prompt injection protection in Microsoft Defender for Office 365 – official documentation of the prompt injection protection in Microsoft Defender for Office 365.
• How Microsoft discovers and mitigates evolving attacks against AI guardrails | Microsoft Security Blog
• Manipulating AI memory for profit: The rise of AI Recommendation Poisoning | Microsoft Security Blog – a related example of an AI-era technique observed in email traffic
• Defending the inbox against prompt injection attacks | Microsoft Defender for Office 365 Blog – feature announcement introducing prompt injection protection in Microsoft Defender for Office 365.
• Learn how Microsoft is reimagining the SOC for the agentic era with ISOC in Microsoft Defender

The post Phishing Abuses RMM Tools for Persistent Access appeared first on Microsoft Security Blog.

Since January 2026, Microsoft has observed Russian state threat actor Star Blizzard evolve their detection evasion capabilities through large-scale phishing campaigns, the use of accounts on compromised websites, and a novel malware delivery technique that Microsoft tracks as “RedFlick”. These changes represent a notable shift in the actor’s operational tradecraft and support ongoing cyberespionage activity targeting Ukrainian individuals and institutions as well as international non-government organizations (NGOs), Western think tanks, governments, and other organizations associated with international policy—particularly those with a nexus in supporting Ukraine.

As part of this evolution, Star Blizzard adopted RedFlick, a malware delivery technique that helps evade detection by initiating a set of scheduled tasks to deploy the actor’s custom backdoor, CosmicPulse. This technique is a notable departure from the actor’s previous use of ClickFix-based infection chains which required victims to complete multiple actions before CosmicPulse could be installed. By contrast, the RedFlick infection flow only requires a single user interaction, reducing friction in the compromise process. Combined with the actor’s shift toward large-scale phishing operations during the same period, these changes likely improve Star Blizzard’s ability to reach more targets, evade detection, and increase the likelihood of successful compromise.

This blog provides updated technical analysis of Star Blizzard’s tactics, techniques, and procedures (TTPs) observed throughout 2026, building on our 2025 and 2023 blogs. It details the actor’s evolving phishing, persistence, and malware delivery techniques, and provides recommendations, indicators of compromise (IOCs), detections, and hunting guidance to help organizations identify and defend against RedFlick-related activity. As with any observed nation-state actor activity, Microsoft directly notifies customers that have been targeted or compromised, providing them with recommendations and mitigations to secure their accounts.

Star Blizzard TTPs observed in 2026

Star Blizzard is attributed by the United States Cybersecurity and Infrastructure Agency (CISA) as subordinate to the Russian Federal Security Service Centre (FSB) Centre 18. Star Blizzard periodically overhauls their TTPs to avoid detection, often in response to public exposure of the actor’s campaigns that have involved targeted social engineering through messaging apps and credential theft. Since Google Threat Intelligence Group published its report on Star Blizzard’s COLDCOPY malware in October 2025, Microsoft observed the actor refine their initial access and evasive techniques to include:

• Moving away from targeted spear phishing to large-scale initial contact phishing campaigns
• Using compromised websites to create accounts to send phishing emails
• Updating malware deployment to facilitate the installation of a CosmicPulse downloader

As of the writing of this blog, the RedFlick campaigns have targeted Ukrainian individuals and institutions, as well as international NGOs, think tanks, governments, and financial institutions that have supported Ukraine politically or financially. Microsoft has observed this activity affect over 100 organizations primarily in the United States and United Kingdom, consistent with Star Blizzard’s longstanding targeting priorities.

Microsoft continues to observe some previously reported Star Blizzard phishing techniques throughout 2026; however, the TTPs discussed in this blog have been associated primarily with the actor’s new larger-scale phishing campaigns.

Larger-scale initial contact phishing

Microsoft previously reported on Star Blizzard’s spear-phishing campaigns observed during 2023-2024. During these operations, the actor continued to rely on their conventional TTPs such as initiating email contact with targets before sending a follow-up containing a malicious link, to actor-controlled/compromised infrastructure purposed for credential theft, while impersonating known political or diplomatic figures to lure victims into responding.

In 2026, Microsoft observed Star Blizzard shift from exclusively targeted spear-phishing operations to also conducting larger-scale phishing campaigns. The larger-scale phishing operations were observed at a scale not previously seen from the actor, ranging from tens to hundreds of email messages per campaign. This change likely reflects the actor’s adoption of a mass-mailing phishing platform to automate campaign execution and increase the likelihood of successful compromises by significantly expanding the initial targeting pool. The progression of these campaigns is summarized in the following timeline, including examples of Star Blizzard’s phishing subject lines, targeting details, and the malware delivery methods observed across each campaign:

• January
  • “Повідомлення про результати податкової перевірки” (Notice of tax audit results) – Campaign targeting unidentified Ukraine persons with an attached RedFlick lure.
• February
  • “списання з Вашого рахунку за оплату штрафу” (Debiting from your account for payment of a fine) – Campaign targeting unidentified Ukraine persons with an attached RedFlick lure.
• March
  • “Invitation to an IISS [Private Roundtable/Closed-Door Discussion] on European Security” – Initial contact campaign targeting government officials, security researchers, academia, media, and NGOs. Respondents received a RedFlick lure attachment.
  • “Invitation to a Closed CES Roundtable Discussion” – Initial contact campaign targeting US and Europe technology-sector organizations. Respondents received a RedFlick lure attachment.
  • “Atlantic Council Closed-Door Strategic Discussion” – Initial contact campaign targeting government officials, foreign policy practitioners, security researchers, academia, media, and NGOs. Respondents received a link to DarkSword iOS backdoor installation.
• April
  • “Closed-Door Online Session on Global Capital Allocation & M&A” – Initial contact campaign targeting international financial organizations and researchers. Respondents received a RedFlick lure attachment.
• May
  • “Future of Peace Operations Forum – A Closed Strategic Dialogue” – Initial contact campaign targeting diplomatic and multilateral organizations. Respondents received a RedFlick lure attachment.
  • “Future of Liberty Forum” – Initial contact campaign targeting employees of a US-based think tank. Respondents received a RedFlick lure attachment.
• June
  • “Invitation to the MAMA Summit on the Current Situation Surrounding the Ukrainian Crisis” – Initial contact campaign targeting incumbent/former diplomatic staff. Respondents received a RedFlick lure attachment.
  • “Invitation to the Chatham House London Conference 2026 – 9 July 2026” – Initial contact campaign targeting think tanks, NGOs, and national parliamentary. Respondents received a RedFlick lure attachment.
• July
  • “Thought you might find this USUBC roundtable of interest” – Initial contact campaign targeting think tanks, NGOs, and Ukraine civil society. Respondents received a RedFlick lure attachment.
  • “Інформація щодо тимчасового відключення водопостачання” (Information about temporary water supply shutdown) – Targeted Kyiv-based hotels with an attached RedFlick lure.
• August
  • “Payment Advice Note from 06.08.2026” – Targeted employees of an international financial organization with an attached RedFlick lure.

Once a recipient responds to the initial phishing email, Star Blizzard typically sends a follow-up message containing a password-protected RAR or ZIP archive. The archive contains files that initiate the RedFlick infection flow. The password is included as an image in the follow-up email shown in Figure 1, along with additional examples of Star Blizzard follow-up emails below:

Since January 2026, Microsoft observed at least 13 distinct large-scale phishing campaigns targeting primarily NGOs, think tanks, and government organizations worldwide. The earliest campaigns, observed between January and February, targeted unspecified users of the Ukraine email provider Ukr.net. These emails impersonated Ukrainian authorities and were themed as notifications of a tax audit or outstanding fine.

Beginning in March 2026, Star Blizzard expanded targeting outside of Ukraine. Subsequent campaigns frequently used lures themed as invitations to conferences or events purportedly organized by a reputable think tank or NGO. Microsoft also observed the actor target multiple individuals within the same organization, with phishing emails often crafted to appear as internal communications originating from the targeted organization itself. The actor’s shift from Ukraine-focused operations to global targets could indicate Star Blizzard initially targeted Ukraine to test their new capabilities.

The large-scale campaigns have demonstrated capabilities previously unassociated with Star Blizzard. For example, a campaign observed in mid-August 2026 employed steganography to conceal identifiers. Throughout 2026, Star Blizzard has also continued to develop additional operational capabilities. Notably, ProofPoint reported in March that the actor had targeted vulnerable Apple iOS devices to deploy the DarkSword backdoor.

Creating accounts on compromised websites

Since March 2026, Star Blizzard has made another notable change to their TTPs to support the higher-volume phishing operations, using accounts created on compromised websites to send phishing emails to targets. This change coincided with the actor’s shift to larger-scale phishing campaigns and has been observed almost exclusively in support of these operations. Previously, the actor would create accounts on free email services (predominantly using Protonmail and Microsoft consumer accounts) to impersonate an individual that would be well known to the prospective target, whether it was a political figure, academic, or former diplomat. In the large-scale campaigns, Star Blizzard has used accounts created on websites hosted on CPanel and WordPress, using the same account name across multiple website domains. Microsoft Threat Intelligence assesses with high confidence that these websites have been compromised by Star Blizzard for this purpose.

Updating malware delivery and installation TTPs

Between January and August 2026, Microsoft observed multiple waves of Star Blizzard phishing campaigns alongside notable changes in the actor’s initial access TTPs, particularly the use of RedFlick scheduled tasks. During this period, Microsoft observed three significant shifts in the threat actor’s delivery techniques.

From ClickFix to VHDX

In mid-January 2026, Microsoft observed the use of a malicious Virtual Hard Disk v2 (VHDX), delivered through a phishing email containing a password-protected ZIP file. The VHDX file ships a malicious LNK file disguised as a PDF document, alongside a hidden directory containing a BAT script and a legitimate decoy PDF.

When the victim opens the LNK file, conhost.exe is launched in a hidden window and cmd.exe is subsequently spawned to execute the embedded BAT script:

The BAT file opens the decoy PDF and invokes SSH.exe with PermitLocalCommand enabled, ultimately downloading and executing a remotely hosted MSI installer:

In multiple campaigns, Microsoft observed an MSI installer creating a scheduled task that uses control.exe to download and execute a remotely hosted CosmicPulse downloader masquerading as a Control Panel applet (CPL). Unlike earlier campaigns that relied on ClickFix lures and user interaction, this approach relies on compiling the CosmicPulse downloader as a Control Panel applet DLL.

The shift from interactive user execution to remote execution and masquerading as a CPL item highlights an evolution in both persistence and defense evasion techniques.

CosmicPulse downloader

The Control Panel applet DLL is a downloader with the sole purpose of downloading and installing a version of CosmicPulse, a malicious Python backdoor, on the target device. The downloader is also known publically as NOROBOT or BAITSWITCH.

Similar to previous versions, this downloader downloads, persists, and executes a version of CosmicPulse on the infected device. Upon execution, it downloads two ZIP files and stores them on disk. It then writes an encrypted AES key to the HKEY_CURRENT_USER\Software\Classes\.mollis registry key. One ZIP file contains a Python 3.8 64-bit package and a Python file that serves as the CosmicPulse bootstrapper. The bootstrapper reads the encrypted key from the registry, recovers it using an embedded key in AES-ECB mode, and then uses the recovered key to decode the CosmicPulse payload (also known as YESROBOT) contained in the second ZIP file. The below image shows the process of the CosmicPulse installation.

Since January 2026, Microsoft has observed the CosmicPulse backdoor going through various little changes to circumvent existing signatures. However, the capabilities and purpose of this backdoor remain the same as described in previous articles.

Persistence through multiple scheduled tasks

In April 2026, Microsoft observed Star Blizzard changing persistence tactics to include RedFlick scheduled tasks. Whereas a malicious MSI file installed a single scheduled task in January—by April, the actor’s MSI installer created three scheduled tasks masquerading as legitimate network components, each with their own purpose. The TTPs have overlaps with the spear-phishing campaigns reported by the Digital Security Lab Ukraine (DLUA) in June 2026. In at least one incident, Microsoft has observed either the first or third task deploying an instance of CosmicPulse on the infected machine.

Task 1 – Registration beaconing and dynamic DLL execution task

The first task masquerades as a legitimate Internet Quality Test Connection task. The scheduled command serves two purposes. First, it sends a UTF-16 and Base64-encoded string containing the network or computer name and the username of the infected device to the C2 server. Thereby, it exfiltrates basic information about the infected host.

Second, an attacker-controlled DLL can be executed remotely through invoking Control_RunDLL from Shell32.dll. Microsoft was unable to obtain a variant of this payload. The task uses a WebDAV UNC path to access the remote payload, allowing the resource to be retrieved over HTTP rather than through a conventional SMB network share.

Task 2 – Supporting task

The second installed task, named Network Configuration Manager, supports the WebDAV-based execution used by the other tasks. The invoked command causes Windows to treat the UNC-style path as a WebDAV resource and process it through the WebDAV redirector and associated WebClient functionality.

Through WebDAV, Windows applications can interact with a remote web resource as though it is a file or folder path while the underlying communication is performed using HTTP/HTTPS rather than conventional SMB. The task therefore prepares the WebDAV client functionality required by the other scheduled tasks.

Task 3 – CosmicPulse downloader execution

The third installed task, named System Health Monitor, uses control.exe to access a remote path hosted on the hardcoded C2 server and execute the next stage.

Hiding payloads in PDF files

In July 2026, Microsoft observed another change in Star Blizzard’s delivery techniques, this time involving a multistage execution chain.

The phishing email contains a password-protected RAR archive nested inside a ZIP file. After the archive is opened, it exposes an LNK file.

When executed, the LNK file uses conhost.exe and curl to download a PDF from an actor-controlled server:

After downloading the PDF, the command runs a second PowerShell payload. This payload searches the PDF for the magic header cAB, extracts the subsequent 208 bytes of Base64-encoded data, and executes the decoded command through PowerShell. The resulting conhost command, shown below, attempts to download and install another MSI file:

As in previous campaigns, the Python installer contains obfuscated PowerShell code. When executed, the code attempts to create two additional scheduled tasks. The first scheduled task again serves as a helper task, consistent with the supporting tasks observed in previous campaigns. The second scheduled task attempts to download and execute a CPL applet again.

Star Blizzard’s shift from ClickFix-based delivery chains to VHDX files, expanded use of scheduled tasks for persistence, and concealment of payloads within PDF files demonstrate the actor’s continued ability to adapt their delivery methods in response to evolving defenses. The actor’s recent use of PDFs to conceal payloads further illustrates efforts to bypass layered defenses through increasingly complex execution chains. Taken together, these changes demonstrate Star Blizzard’s continued efforts to streamline malware deployment, reduce required user interaction, and improve operational scalability while maintaining its longstanding espionage objectives.

Defending against Star Blizzard and RedFlick-related activity

Microsoft Threat Intelligence advises organizations that are most likely at risk—primarily those in government, NGOs, or think tanks adjacent to Ukraine policy or support—to implement the following recommendations to mitigate against Star Blizzard activity.

Protecting against Star Blizzard phishing

As previously reported, Star Blizzard’s success relies on sophisticated, targeted phishing lures that trick users into engaging with the actor by impersonating trusted contacts. While Star Blizzard has changed their overall TTPs, the actor continues to employ the same phishing patterns against users:

• Star Blizzard continues to impersonate trusted contacts that users or organizations would expect an email from. Star Blizzard also continues using email from free providers such as Proton @proton[.]me, particularly in Evilginx spear-phishing operations observed throughout 2026. However, despite the actor’s recent shift toward leveraging compromised legitimate websites that impersonate real individuals associated with a target’s organization, users can still remain vigilant for other Star Blizzard TTPs, several of which have been observed in previous campaigns:
  • Star Blizzard continues to make initial contact with a target by sending an email, usually without an attachment.
  • If a user engages, Star Blizzard will follow up with an email with an attachment. In this particular campaign, it has been an archive file such as a RAR or ZIP.
  • The email sender contains names of actual persons and organizations they belong to—however, the organization is not within the root or registered domain itself but in the username or local part of the email address. This should draw a red flag to the email’s authenticity. When in doubt, directly contact the person you think sent the email using a previously established and trusted contact method such as known email address or phone number.
  • The emails in these RedFlick campaigns are often sent in bulk.
• To best mitigate against this activity, Microsoft suggests the following policies to strengthen network environments against Star Blizzard phishing operations:
  • Using phishing resistant authentication methods.
  • Lockdown account access using Conditional Access policies.
  • Use advanced anti-phishing solutions like Microsoft Defender for Office 365 that monitor and scan incoming emails and visited websites.
  • Turn on Safe Links and Safe Attachments for Office 365.

Microsoft also recommends the following mitigations to reduce the impact of this threat

• Run endpoint detection and response (EDR) in block mode so that Microsoft Defender for Endpoint can block malicious artifacts, even when your non-Microsoft antivirus does not detect the threat, or when Microsoft Defender Antivirus is running in passive mode. EDR in block mode works behind the scenes to remediate malicious artifacts that are detected post-compromise.
• Encourage users to use Microsoft Edge and other web browsers that support Microsoft Defender SmartScreen, which identifies and blocks malicious websites, including phishing sites, scam sites, and sites that host malware.
• Configure investigation and remediation in full automated mode to allow Microsoft Defender for Endpoint to take immediate action on alerts to resolve breaches, significantly reducing alert volume.
• Turn on cloud-delivered protection and automatic sample submission in Microsoft Defender Antivirus to cover rapidly evolving attacker tools, techniques, and behaviors. These capabilities use artificial intelligence and machine learning to quickly identify and stop new and unknown threats.
• Use security defaults as a baseline set of policies to improve identity security posture. For more granular control, enable Conditional Access policies. Conditional Access policies evaluate sign-in requests using additional identity driven signals like user or group membership, IP location information, and device status, among others, and are enforced for suspicious sign-ins. Organizations can protect themselves from attacks that leverage stolen credentials by enabling policies such as compliant devices or trusted IP address requirements.
• Implement continuous access evaluation.
• Turn on Microsoft Defender Antivirus real-time protection.
• Continuously monitor suspicious or anomalous activities. Investigate sign-in attempts with suspicious characteristics (for example, location, ISP, user agent, and use of anonymizer services).
• Turn on Zero-hour auto purge (ZAP) in Defender for Office 365 to quarantine sent mail in response to newly-acquired threat intelligence and retroactively neutralize malicious phishing, spam, or malware messages that have already been delivered to mailboxes.
• Enable network protection to prevent applications or users from accessing malicious domains and other malicious content on the internet.
• Configure Microsoft Defender for Office 365 to recheck links on click. Safe Links provides URL scanning and rewriting of inbound email messages in mail flow, and time-of-click verification of URLs and links in email messages, other Office 365 applications such as Teams, and other locations such as SharePoint Online. Safe Links scanning occurs in addition to the regular anti-spam and anti-malware protection in inbound email messages in Exchange Online Protection (EOP). Safe Links scanning can help protect your organization from malicious links that are used in phishing and other attacks.
• Use the Attack Simulator in Microsoft Defender for Office 365 to organize realistic, yet safe, simulated phishing and password attack campaigns in your organization by training end users against clicking URLs in unsolicited messages and disclosing their credentials. Training should include checking for poor spelling and grammar in phishing emails or the application’s consent screen as well as spoofed app names, logos, and domain URLs appearing to originate from legitimate applications or companies. Note that Attack Simulator testing only supports phishing emails containing links at this time.
• Microsoft Defender customers can turn on attack surface reduction rules to prevent common attack techniques:
  • Block executable files from running unless they meet a prevalence, age, or trusted list criterion
  • Block execution of potentially obfuscated scripts
• Utilize Windows Firewall, Windows Firewall with Advanced Security, or an enterprise firewall/filtering solution to help prevent or restrict outbound SSH connection attempts to external or public networks that are not essential for business.

Microsoft Defender detections

Microsoft Defender customers can refer to the list of applicable detections below. Microsoft Defender coordinates detection, prevention, investigation, and response across endpoints, identities, email, apps to provide integrated protection against attacks like the threat discussed in this blog.

Microsoft Security Copilot

Microsoft Security Copilot is embedded in Microsoft Defender and provides security teams with AI-powered capabilities to summarize incidents, analyze files and scripts, summarize identities, use guided responses, and generate device summaries, hunting queries, and incident reports.

Customers can also deploy AI agents, including the following Microsoft Security Copilot agents, to perform security tasks efficiently:

• Threat Intelligence Briefing agent
• Phishing Triage agent
• Threat Hunting agent
• Dynamic Threat Detection agent

Security Copilot is also available as a standalone experience where customers can perform specific security-related tasks, such as incident investigation, user analysis, and vulnerability impact assessment. In addition, Security Copilot offers developer scenarios that allow customers to build, test, publish, and integrate AI agents and plugins to meet unique security needs.

Threat intelligence reports

Microsoft Defender XDR customers can use the following threat analytics reports in the Defender portal (requires license for at least one Defender XDR product) to get the most up-to-date information about the threat actor, malicious activity, and techniques discussed in this blog. These reports provide the intelligence, protection information, and recommended actions to prevent, mitigate, or respond to associated threats found in customer environments.

• Actor profile: Star Blizzard
• Tool profile: CosmicPulse
• Activity profile: New Star Blizzard spearphishing campaign targets WhatsApp accounts
• Threat overview: Evolving phishing threats

Microsoft Security Copilot customers can also use the Microsoft Security Copilot integration in Microsoft Defender Threat Intelligence, either in the Security Copilot standalone portal or in the embedded experience in the Microsoft Defender portal to get more information about this threat actor.

Hunting queries

Microsoft Defender XDR

Microsoft Defender XDR customers can run the following advanced hunting queries to find related activity in their networks:

Conhost.exe invokes curl

The following query will detect the use of conhost.exe to launch curl to download a decoy PDF from an actor-controlled server, which Star Blizzard used in July 2026. (Note that this query may detect activity not related to Star Blizzard or necessarily malicious. Please investigate findings to determine if the activity is legitimate.)

DeviceProcessEvents
| where Timestamp > ago(7d)
| where FileName == "conhost.exe"
| where ProcessCommandLine has "curl"
| project Timestamp, DeviceName, AccountName, ProcessCommandLine, InitiatingProcessCommandLine, InitiatingProcessFileName, DeviceId, ProcessId, ProcessUniqueId, InitiatingProcessUniqueId

Invoke SSH to launch local command line

The following query will detect the invocation of SSH to initiate a local command prompt, which Star Blizzard used in January 2026 to download and execute a remotely hosted MSI installer. (Note that this query may detect activity not related to Star Blizzard or necessarily malicious. Please investigate findings to determine if the activity is legitimate.)

DeviceProcessEvents
| where Timestamp > ago(7d)
| where ProcessCommandLine has "ssh.exe"
| where ProcessCommandLine has "PermitLocalCommand=yes"
| where ProcessCommandLine has "LocalCommand=cmd.exe"
| project Timestamp, DeviceName, AccountName, ProcessCommandLine, InitiatingProcessCommandLine, InitiatingProcessParentFileName, DeviceId, ProcessId, InitiatingProcessId, ReportId

Persistence through scheduled tasks

The following query will detect Star Blizzard’s uniquely named scheduled tasks that attempt to masquerade as legitimately-named tasks, used for persistence by Star Blizzard in April 2026.

union isfuzzy=true
(
DeviceProcessEvents
| where Timestamp > ago(7d)
| where ProcessCommandLine has_any ("Internet Quality Test Connection","Network Configuration Manager","System Health Monitor") or AdditionalFields has_any ("Internet Quality Test Connection","Network Configuration Manager","System Health Monitor")
| project Timestamp, DeviceName, ActionType, ProcessCommandLine, AdditionalFields
, RegistryKey = tostring(dynamic(null)), RegistryValueName = tostring(dynamic(null))
, SourceTable = "DeviceProcessEvents"
, ProcessId, AccountName, AccountDomain, AccountSid
)
,
(
DeviceEvents
| where Timestamp > ago(7d)
| where ProcessCommandLine has_any ("Internet Quality Test Connection","Network Configuration Manager","System Health Monitor") or AdditionalFields has_any ("Internet Quality Test Connection","Network Configuration Manager","System Health Monitor") or RegistryKey has_any ("Internet Quality Test Connection","Network Configuration Manager","System Health Monitor") or RegistryValueName has_any ("Internet Quality Test Connection","Network Configuration Manager","System Health Monitor")
| project Timestamp, DeviceName, ActionType, ProcessCommandLine, AdditionalFields, RegistryKey, RegistryValueName
, SourceTable = "DeviceEvents"
, ProcessId = long(null), AccountName = "", AccountDomain = "", AccountSid = ""
)
,
(
DeviceRegistryEvents
| where Timestamp > ago(7d)
| where RegistryKey has_any ("Internet Quality Test Connection","Network Configuration Manager","System Health Monitor") or RegistryValueName has_any ("Internet Quality Test Connection","Network Configuration Manager","System Health Monitor") or RegistryValueData has_any ("Internet Quality Test Connection","Network Configuration Manager","System Health Monitor") or InitiatingProcessCommandLine has_any ("Internet Quality Test Connection","Network Configuration Manager","System Health Monitor")
| project Timestamp, DeviceName, ActionType
, ProcessCommandLine = InitiatingProcessCommandLine, AdditionalFields = ""
, RegistryKey, RegistryValueName
, SourceTable = "DeviceRegistryEvents"
, ProcessId = long(null), AccountName = "", AccountDomain = "", AccountSid = ""
)

Microsoft Sentinel

Microsoft Sentinel customers can use the TI Mapping analytics (a series of analytics all prefixed with ‘TI map’) to automatically match the malicious domain indicators mentioned in this blog post with data in their workspace. If the TI Map analytics are not currently deployed, customers can install the Threat Intelligence solution from the Microsoft Sentinel Content Hub to have the analytics rule deployed in their Sentinel workspace.

Detect network IP and domain indicators of compromise using ASIM

The following query checks IP addresses and domain IOCs across data sources supported by ASIM network session parser.

let lookback = 30d;
let ioc_ip_addr = dynamic(["103.245.231.248", "2.57.241.246", "89.125.209.168", "103.245.231.79", "45.84.59.66", "103.160.59.97"]);
let ioc_domains = dynamic(["etia.ca", "groy.cc", "gliderrompercycl.com", "muvb.net", "divekickspolic.org", "matjk.click", "bpdaersa.click", "stuseamandesilt.org", "Itechx.tel", "guach.net", "ruten.observer", "byveo.org", "secure-dns-hub.com", "qumel.link", "cyrna.top", "drasw.club"]);
_Im_NetworkSession(starttime=todatetime(ago(lookback)), endtime=now())
| where DstIpAddr in (ioc_ip_addr) or DstDomain has_any (ioc_domains)
| summarize FirstSeen=min(TimeGenerated), LastSeen=max(TimeGenerated), EventCount=count()
    by SrcIpAddr, DstIpAddr, DstDomain, Dvc, EventProduct, EventVendor

Detect Web Sessions IP and file hash indicators of compromise using ASIM

The following query checks IP addresses, domains, and file hash IOCs across data sources supported by ASIM web session parser.

let lookback = 30d;
let ioc_ip_addr = dynamic(["103.245.231.248", "2.57.241.246", "89.125.209.168", "103.245.231.79", "45.84.59.66", "103.160.59.97"]);
let ioc_domains = dynamic(["etia.ca", "groy.cc", "gliderrompercycl.com", "muvb.net", "divekickspolic.org", "matjk.click", "bpdaersa.click", "stuseamandesilt.org", "Itechx.tel", "guach.net", "ruten.observer", "byveo.org", "secure-dns-hub.com", "qumel.link", "cyrna.top", "drasw.club"]);
_Im_WebSession(starttime=todatetime(ago(lookback)), endtime=now())
| where DstIpAddr in (ioc_ip_addr)
    or DstDomain has_any (ioc_domains)
    or Url has_any (ioc_domains)
| summarize FirstSeen=min(TimeGenerated), LastSeen=max(TimeGenerated), EventCount=count()
    by SrcIpAddr, DstIpAddr, DstDomain, Url, Dvc, EventProduct, EventVendor

Indicators of compromise

References

• https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-341a
• https://www.zscaler.com/blogs/security-research/coldriver-updates-arsenal-baitswitch-and-simplefix
• https://dslua.org/publications/spearphishing-via-fake-urc-2026-invitations-targets-ukrainian-csos/
• https://cloud.google.com/blog/topics/threat-intelligence/new-malware-russia-coldriver
• https://x.com/threatinsight/status/2037560142880006266

Learn more

For the latest security research from the Microsoft Threat Intelligence community, check out the Microsoft Threat Intelligence Blog.

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The post Star Blizzard refines phishing and malware delivery with the RedFlick technique appeared first on Microsoft Security Blog.

Apart from the privacy concerns around smart glasses, researchers have found that some cheap brands come with barely any security at all.

ABC Australia reports that researchers from NSB Cyber and Abstract Shield tested two inexpensive pairs, costing A$60 and A$110 (around US$42 and US$78), and found more than a dozen flaws across the smart glasses themselves, their app, and an associated website.

The main problem they uncovered was insecure Bluetooth pairing: If the glasses were powered on and not connected to their owner’s phone, an attacker could connect first, with no password or meaningful pairing confirmation.

After connecting, an attacker could reportedly control the glasses to capture photos or recordings, copy existing media, and intercept data moving between the glasses and the phone. An attacker could also make another device appear to be the victim’s glasses, allowing it to connect to the owner’s mobile app.

The testing also found that a Bluetooth-visible device identifier could allegedly be used with a weakness in the app’s website to retrieve a user’s email address and date of birth.

The research uncovered another privacy issue. ABC reports that voice or text submitted to the built-in AI, along with images sent to it, was first transmitted to a server in Shenzhen and could be forwarded elsewhere, depending on the function. The tests did not establish how the data was subsequently used.

The server locations, combined with some of the AI’s answers to specific questions, led researchers to conclude that the chatbot companion relied at least in part on Chinese sovereign AI models. Professor Kimberlee Weatherall, a technology regulation expert involved in the testing, said the failure to identify China in the privacy policy appeared to violate the Australian Privacy Principles,

The timing of the testing is also relevant because Australia’s smart-device security standards apply to most consumer smart devices manufactured on or after March 4, 2026. They require, among other measures, no universal default passwords, a way to report vulnerabilities, and information about the minimum security-update period. Devices made before that date are outside the requirements.

Experts quoted by ABC said the devices violated Australia’s privacy laws and were also likely to breach several sections of the Cyber Security Act. However, whether the new smart-device standards apply would depend partly on when the glasses were manufactured, and the rules have not yet been tested in a known enforcement action.

Professor Weatherall said:

“The rules say that the password must be unique. It doesn’t even seem like they were applying a password, which might mean that their standards are so low they don’t even technically breach that rule, which I find amazing.”

What to do

We’re not fans of smart glasses, expensive or cheap. But if you own a pair:

• Avoid pairing or using inexpensive camera glasses that lack a clear physical pairing step, account authentication, a published security-contact process, and a stated update-support period.
• Stop using their AI or cloud features for sensitive material, remove unnecessary permissions from the companion app, check for firmware and app updates, and consider returning the product if the vendor cannot document a fix.
• Ask permission before recording people, and check your local laws to understand what is and isn’t allowed.

From reporting threats to removing them.

Cybersecurity risks should never spread beyond a headline. Keep threats off your devices by downloading Malwarebytes today.

Executive summary

In April 2026, we at Kaspersky’s Global Emergency Response Team (GERT) responded to a security incident at a manufacturing organization in the Middle East. The threat actor obtained domain admin-equivalent control of the organization’s Active Directory environment and authored a malicious Group Policy Object (GPO) named PAYLOAD, linking it at the domain root. Through that single object, the actor delivered ransom notes, hijacked the desktop wallpaper and lock screen, enforced a logon banner, and disabled the local administrator account across every domain-joined Windows workstation — all without dropping a ransomware binary or encrypting any data. The only ransomware we found in this incident was PAYLOAD sample targeting ESXi on Linux servers. Besides that, data exfiltration was observed originating from the file servers and several additional systems, and was later published on the dark web.

This case is an example of two converging trends that define the 2026 ransomware landscape:

1. Living-off-the-land abuse of trusted AD infrastructure. Group Policy is a signed, allowlisted, SYSTEM-privileged distribution channel that the majority of endpoint detection and response tools is designed not to inspect. By delivering impact through GPO rather than through malware, the actor sidestepped the entire file- and process-based detection stack.
2. Encryptionless extortion. Industry telemetry shows extortion-only incidents grow significantly year-on-year. PAYLOAD fits this model; the leverage is operational disruption and the threat of escalation rather than cryptographic denial of data.

We confirmed that no files were encrypted on Windows machines, no malicious binaries were resident on disk, no endpoint persistence was established, and no malicious processes were running at the time of analysis. The entire attack lived inside Active Directory itself. The defensive implication is stark: an organization whose detection strategy depends on catching a ransomware executable would have seen nothing until the first endpoint rebooted and the ransom wallpaper appeared.

In this article, we will describe the GPO attack chain and provide operational advice on how to detect such threats, including detailed remediation recommendations.

Group Policy as an attack surface

Attacks through group policies are nothing new. They can inflict significant, domain-wide damage with multiple malicious capabilities. A Group Policy Object (GPO) is essentially a combination of a Group Policy Container (GPC) in Active Directory and a Group Policy Template (GPT) in SYSVOL. The Group Policy scope depends on whether the GPC is linked to the directory tree at the domain, site, or organization unit (OU) level. A link at the domain root means the policy applies to every computer and user object beneath it. Thus, a GPO compromised at the domain root can affect all in-scope domain users and computers, potentially granting an attacker complete control over the corporate network. What makes GPO abuse even more dangerous is that group policies are processed in a trusted, high-privilege environment, ensuring persistence because endpoint cleanup is not enough to remove them.

We have already discussed GPO architecture and ways it can be compromised in greater detail in an earlier blog post. Other public threat intelligence has also repeatedly documented this technique in ransomware operations. Microsoft observed Ryuk operators distributing ransomware through Group Policy, SYSVOL startup items, and PsExec. LockBit affiliates have been documented modifying SYSVOL Group Policy files, including ScheduledTasks.xml, to support ransomware execution and propagation. BlackCat/ALPHV operators have also abused GPOs to create scheduled tasks and deploy ransomware.

Another notable example of GPO abuse is PAYLOAD ransomware, which weaponizes GPO Preferences and policy settings for pure impact rather than as a launcher for an encryptor. Let’s take a closer look at this attack, which is detailed further below.

Attack timeline

During the April 2026 investigation, we managed to reconstruct the attack timeline as outlined below:

Next, we will discuss each of these stages in more detail and share the findings from our incident response activities.

Incident overview

Initial access

The entry vector was a compromised valid account (MITRE ATT&CK T1078) used to authenticate through the organization’s FortiGate SSL VPN — an external remote service (T1133). Insufficient logging on the FortiGate appliance prevented us from reconstructing how the credential was originally compromised.

Three hypotheses were considered plausible in the context of the attack, in no particular order:

• Password spraying or credential stuffing against the SSL VPN portal.
• Phishing-led credential harvesting.
• Purchase of pre-compromised credentials from an initial access broker (IAB).

Once on the internal network, the actor operated with the compromised security principal’s privileges. Because the account was able to create and link a GPO at the domain root, it held either domain admin privileges or a delegated equivalent (e.g., membership of Group Policy Creator Owners combined with link rights on the domain object).

FortiGate SSL VPN authentication logs and ESXi/virtualization privilege escalation logs were insufficient to reconstruct the lateral movement and privilege escalation chain between initial VPN access and the GPO write privilege level. The most common real-world routes to GPO control — DCSync, Kerberoasting of privileged service accounts, and Pass-the-Hash/Pass-the-Ticket — could not be confirmed or ruled out.

Execution

Rather than deploying an encryptor, the actor abused the victim’s Active Directory policy infrastructure, resulting in malicious GPOs being configured and linked at the domain root to deliver the observed impact across domain-joined systems. The two GPOs below constitute the entire offensive toolkit observed.

The PAYLOAD GPO

We performed a Resultant Set of Policy (RSOP) analysis, which helped us list all policy settings on the affected workstations. From these results, we reconstructed the following changes delivered by PAYLOAD:

The PAYLOAD GPO enabled the following actions:

1. Drop SYSVOL\hello.txt to the desktop and root directories C:\ and D:\ as a read-only README-payload.txt by tampering with the “Group Policy Files” client-side extension (CSE).
2. Modify the HKLM\SOFTWARE\Microsoft\Windows\CurrentVersion\Policies\System\ registry key, changing the legalnoticecaption value to "Welcome to Payload!" and the legalnoticetext to the ransom note text through the “Group Policy Registry” CSE.
3. Set payload.jpg located in the targeted domain controller’s SYSVOL as the lock screen image by altering the personalization policy, and as the wallpaper by altering the desktop policy at the user level.
4. Revoke administrator rights for the administrator account (locking the account) by editing the Security Settings CSE in the GptTmpl.inf file.

This activity was enabled entirely through a legitimate group policy mechanism, meaning there is no malware code for security solutions to look for because the malicious logic is contained within the policy configuration.

The win Firewall Off GPO

A second domain-root-linked GPO, named “win Firewall Off”, disabled Windows Firewall across the domain, private and public profiles on all endpoints (T1562.004). Deployed independently of PAYLOAD, this object degraded host defenses and ensured the actor retained unimpeded network reach to endpoints for any follow-on activity.

Settings changed by win Firewall Off GPO

The one-day delay detonation

The most forensically instructive detail is the one-day gap between GPO creation (13 April) and visible impact (14 April). Analysis of Master File Table (MFT) timestamps and the Group Policy History registry key confirmed the policy was written to SYSVOL and cached on endpoints on 13 April. However, computer configuration settings (wallpaper and lock screen machine policy, security settings, firewall disabling) only apply upon reboot or policy refresh — and no endpoint had rebooted in the interim. The attack therefore remained dormant in the GPO cache for one day before detonating en masse when machines were restarted in accordance with standard procedures.

We have to point out here that this delayed policy application is characteristic of GPO-based operations and has two potential consequences for defenders. Firstly, it may grant the actor a quiet window for exfiltration, persistence, or further staging between weaponization and impact. Secondly, it can be used to sever the temporal link between the cause (a GPO-creation event in the directory log) and the effect (mass user-visible disruption occurring at a later time), complicating timeline reconstruction unless directory service auditing is in place.

Forensic findings

Initial incident response engagement revealed a number of findings associated with the attack that we share below.

1. No file encryption
   A full review of the MFT on affected workstations found no files bearing a .payload extension characteristic to PAYLOAD ransomware and no evidence of bulk renaming or encryption I/O patterns. The attackers’ objectives were operational disruption and extortion via visual impact and access denial.
2. No endpoint persistence
   All standard persistence locations were clean:
   • Scheduled tasks — no malicious tasks
   • Run/RunOnce keys — clean
   • Startup folders (user and system) — clean
   • Services — no malicious service installed
   • WMI event subscriptions — none
   • Boot sector/MBR — unmodified

   The attack’s persistence mechanism is the GPO link itself on the domain controller, with no endpoint-resident component.

3. No active malicious processes
   Live process and memory analysis revealed no injected threads, process hollowing or anomalous outbound connections.
4. Registry timeline analysis
   Group Policy History, Shadow and State registry keys on the workstation recorded the application of PAYLOAD on 13 April:
   HKLM\…\Group Policy\History\{35378EAC-683F-11D2-A89A-00C04FBBCFA2}\1
   HKLM\…\Group Policy\Shadow\{827D319E-6EAC-11D2-A4EA-00C04F79F83A}\0
   HKLM\…\Group Policy\State\Machine\GPO-List\7
   HKCU\…\Group Policy\History\{7150F9BF-48AD-4da4-A49C-29EF4A8369BA}\1
   HKCU\…\Group Policy\State\S-1-5-21-…\Loopback-GPO-List\5
   The presence of the “Loopback-GPO-List” entry indicates the GPO was processed in loopback mode, ensuring the user configuration (wallpaper) applied regardless of which user logged on to the machine.
5. SYSVOL artifacts
   Two files were staged on the domain controller’s SYSVOL share and served to endpoints through the Files Group Policy Preference CSE:
   • payload.jpg — the ransom image used for both wallpaper and lock screen.
   • hello.txt — the ransom note, distributed to desktops and drive roots as README-payload.txt.

Detection engineering

Because no malicious binary or process exists, the detection logic should focus on the directory service and SYSVOL indicators. Below are the highest-value telemetry sources for identifying the described malicious activity.

• Directory service change auditing (DS Access)
  Enable Advanced Audit Policy → DS Access → Audit Directory Service Changes on all domain controllers and set up alerts for the following events:

  Event ID	Meaning	Hunt focus
  5137	A directory service object was created	The account that created new groupPolicyContainer objects must be an authorized GPO administrator
  5136	A directory service object was modified	Changes to gPLink on the domain root or sensitive organizational units; changes to gPCMachineExtensionNames/gPCUserExtensionNames/gPCFileSysPath/versionNumber for all GPOs
  5141	A directory service object was deleted	GPO deletions (relevant for tamper detection and remediation assessment)

  A gPLink modification at the domain root by a non-standard account is one of the most telling indicators of this attack class.

• SYSVOL file integrity monitoring
  Monitor \\DC.THECOMPANY.local\sysvol\THECOMPANY.local\Policies\ for unexpected files, particularly image files, text files, scripts, ScheduledTasks.xml, and modified registry.pol/GptTmpl.inf files that were created by sources other than legitimate replication.
• Endpoint policy application telemetry
  On endpoints, GPO application is logged in the Group Policy Operational log (Microsoft-Windows-GroupPolicy/Operational) and reflected in the History and Shadow registry keys shown above. A sudden domain-wide change to the applied policy set is a strong post-detonation signal.
• Missing 5136 events
  If the contents of a GPO’s SYSVOL change without a corresponding 5136 event, this can indicate direct SYSVOL/template editing (e.g., via open-source tools such as PowerView or SharpGPOAbuse) that bypasses the normal Group Policy Management Console (GPMC) modification path. While we recommend treating the absence of expected audit events as suspicious, it should be noted that this can be caused by misconfigured auditing.

Anti-forensics and recovery-inhibition capabilities

Confirmed PAYLOAD family capabilities

The Windows endpoint attack described in this report was implemented through malicious Group Policy Objects and did not involve a recovered ransomware executable, resident endpoint malware, confirmed file encryption or active malicious processes at the time of the forensic examination. However, PAYLOAD cryptomalware for Windows does exist, and other sources, including public analysis of its samples, reveal further malicious capabilities of this ransomware that could be used by security teams to enrich detection logic and security policies.

The behaviors documented in this section must therefore be interpreted as family-level capabilities identified through the public reverse engineering of PAYLOAD ransomware samples. They were not confirmed as having executed during the GPO-based incident unless corresponding evidence was identified in the host, memory, process, event-log, or hypervisor.

Windows Event Log clearing

Public reverse engineering of the PAYLOAD Windows variant reveals an optional event log clearing capability (T1685.005, formerly T1070.001). The ransomware dynamically resolves Windows Event Log APIs, enumerates available event log channels, and clears individual channels.

Implementation artifacts include:

• Runtime loading of wevtapi.dll
• EvtOpenChannelEnum
• EvtNextChannelPath
• EvtClearLog
• Enumeration of Security, System, Application, and PowerShell logs, and operational channels
• A command-line option controlling whether log clearing is performed

Clearing event logs reduces the availability of records related to process execution, authentication, PowerShell, service control, and system changes. However, it does not necessarily remove events that were already forwarded to a SIEM, Windows Event Collector, EDR backend, or protected log archive.

Forensic indicators

• Windows Security Event ID 1102
• Event logging shutdown or failure events
• Sudden reductions in event record identifiers
• Multiple event channels restarting from low record numbers
• Gaps between endpoint and centrally forwarded telemetry
• Execution of utilities or APIs associated with log management
• Direct access to EVTX files (Windows Event Log files) under the Windows event log directory

We should note that these indicators can’t be interpreted on their own. For instance, Event ID 1102 should be correlated with a number of aggravating factors, such as:

• Event ID 4688 or Sysmon Event ID 1 for process creation
• Event ID 4624 for the associated administrative logon
• EDR process trees
• PowerShell operational logging
• Windows Event Forwarding records
• Privileged access management activity

At the same time, the absence of Event ID 1102 does not prove that log clearing did not occur. Direct deletion, truncation, service impairment, or an incomplete audit configuration may produce different evidence.

Security process and service termination

PAYLOAD analysis reveals that the Windows ransomware variant contains logic that targets security processes and services (T1685 and T1489). This capability is intended to stop security products, backup software, database services, and applications that may lock files targeted for encryption.
Process and service termination serves several objectives:

1. Reduce endpoint detection and response visibility.
2. Release file handles so data can be modified.
3. Interrupt backup and recovery services.
4. Prevent databases and enterprise applications from protecting active data.
5. Reduce interference with encryption operations.

Forensic indicators

• Security agent services changing from running to stopped
• Event ID 7036 service state changes
• Event ID 7040 service start type changes
• Event ID 4688 or Sysmon Event ID 1 for service control utilities
• Sysmon Event ID 5 for terminated processes
• EDR health degradation affecting many endpoints
• Repeated termination attempts against security or backup processes
• Execution of dedicated process killing utilities

VSS deletion, backup and recovery suppression

Public PAYLOAD sample analysis reports the deletion of Windows Volume Shadow Copies before encryption (T1490). This behavior removes local restore points and reduces the victim’s ability to recover files without external backups.

Recovery inhibition is broader than VSS deletion and may include multiple other malicious techniques, including the following:

• Deleting shadow copies, backup catalogs, and hypervisor snapshots
• Disabling recovery services
• Modifying boot recovery settings
• Compromising backup management platforms
• Deleting or encrypting backup repositories

In the PAYLOAD Windows sample that is proven to perform encryption, VSS deletion is confirmed to have taken place before encryption. A broader compromise of the backup platform was not established in the investigated incident.

Forensic indicators

• Execution of shadow copy management utilities
• Backup catalog deletion attempts
• Recovery configuration changes
• Backup service termination
• Mass snapshot deletions
• Backup administrator logons outside normal maintenance windows
• Backup retention or immutability changes
• Backup jobs failing immediately before disruptive activity

Ecosystem-relevant ransomware techniques

Above, we discussed the techniques specific to PAYLOAD ransomware. In this section, we’ll review other common ransomware techniques, such as BYOVD abuse and ESXi policy weakening, that remain relevant risks in the ransomware ecosystem. However, these techniques should not be attributed to this incident without supporting driver load, process, memory, ESXi, vCenter, or backup platform telemetry. We describe these techniques below as they may prove useful in other ransomware-related forensic investigations.

The examples used in this section are therefore ecosystem-relevant: they are commonly observed in ransomware operations but not conclusively attributed to the analyzed PAYLOAD samples.

ETW suppression and in-memory patching

Event Tracing for Windows (ETW) is a kernel-supported tracing architecture used by Windows components, diagnostic utilities, security products, and endpoint monitoring systems.

Although we didn’t encounter this technique in our case, some ransomware families can patch ETW-related functions inside the ransomware process (T1685, formerly T1562.001). The reported routine changes the memory protection of code pages, overwrites the beginning of selected ETW functions so they return without producing events, restores the original protection, and refreshes the instruction cache.

Reported target functions include EtwEventWrite, EtwEventWriteFull, EtwEventWriteTransfer, and EtwRegister.

This modification applies to the process-local mapping of ntdll.dll. It does not disable ETW globally across Windows, but it can reduce the telemetry generated by the modified process.

Forensic indicators

• Writable or recently modified executable pages within ntdll.dll
• Memory protection changes affecting ETW function addresses
• In-memory code differing from the corresponding clean DLL on disk
• EDR memory tampering alerts
• Unexpected calls to memory protection APIs followed by telemetry silence
• Process execution visible in network or filesystem telemetry but absent from expected ETW-derived sources
• Inconsistent telemetry between EDR, Sysmon, Windows Event Logs, and network monitoring

It should be noted that telemetry gaps are not conclusive evidence of ETW patching. Other factors include agent upgrades, network outages, endpoint shutdowns, SIEM ingestion failures, collector backpressure, and filtering or licensing changes.

Memory acquisition is the strongest validation source when process-local ETW patching is suspected.

Vulnerable signed driver abuse, BYOVD

Bring Your Own Vulnerable Driver (BYOVD) involves introducing or abusing a legitimately signed but vulnerable kernel driver (T1068 and T1685). The attacker exploits the driver’s exposed functionality to gain kernel-level capabilities, modify protected memory, terminate security processes, remove security callbacks, or bypass operating system security controls.

BYOVD is highly relevant to contemporary ransomware operations. However, the reviewed public PAYLOAD analyses do not provide sufficient evidence to conclude that BYOVD is an intrinsic PAYLOAD capability.

It should therefore be presented as an ecosystem-relevant technique rather than a confirmed PAYLOAD feature.

Forensic indicators

• Unexpected .sys files written to user-writable or temporary directories
• New kernel driver services
• Sysmon Event ID 6
• Driver hashes matching Microsoft or community vulnerable driver blocklists
• Invalid, revoked, expired, or unusual driver signatures
• Driver load followed by EDR termination
• New privileged processes immediately following driver loading
• Code Integrity or Defender alerts
• Service creation events referencing driver files

ESXi security policy weakening

Ransomware operators are increasingly targeting ESXi and vCenter because compromising the virtualization layer provides access to many business-critical virtual machines.

Public research typically describes operators performing the following activities (T1685, T1490 and T1489):

• Enabling SSH on ESXi hosts
• Changing root passwords
• Disabling execInstalledOnly
• Modifying lockdown mode exceptions
• Stopping virtual machines
• Deleting snapshots and backups
• Changing host firewall policies
• Copying custom ransomware binaries to hypervisors

On the target organization’s Linux servers we saw an ESXi PAYLOAD variant, which makes this behavior operationally relevant. Nevertheless, the reviewed evidence does not indicate that PAYLOAD operators used any of these policy-weakening actions in the investigated incident.

Forensic indicators

• Unexpected ESXi SSH enablement
• Changes to execInstalledOnly
• Secure Boot enforcement changes
• Lockdown mode configuration changes
• Root password changes
• New or unusual administrator accounts
• Bulk VM shutdown activity
• Bulk snapshot deletion
• New binaries in datastore or temporary paths
• vCenter tasks originating from unexpected accounts or systems
• Missing or disabled remote syslog forwarding

Remediation

To contain the described threat and limit possible damage, several remediation steps should be taken. Below, we recommend a four-phase remediation approach for containing this threat and restoring the affected environment.

Phase 1 — domain controller actions (to be performed first)

When dealing with PAYLOAD GPO, the first priority of the remediation plan should be to remove the source GPOs. Until this is done, endpoint cleanup is ineffective because the next policy refresh re-infects cleaned machines.

The immediate domain controller actions to be taken are:

1. Delete PAYLOAD GPO. Remove {C897F2C7-C2AC-4E6F-BF48-58036FF29E79} via GPMC.
2. Delete or revert win Firewall Off GPO. Remove {22099AD2-E062-4F56-B574-5099BBA4E7A6}.
3. Clean SYSVOL. Delete jpg and hello.txt from \\DC.THECOMPANY.local\sysvol\THECOMPANY.local\.
4. Rotate credentials. Reset the compromised account; audit all privileged accounts and group memberships for unauthorized changes; rotate krbtgt twice if domain admin compromise is confirmed.
5. Force refresh. Perform gpupdate /force across endpoints after deletion; re-enable local administrator and firewall via clean policy.

Phase 2 — Active Directory and GPO hardening

• Adopt the AD tiered administration model and confine domain admins to Tier 0 with no interactive logon to workstations and member servers.
• Separate GPO creation from GPO linking rights, and grant both only to a dedicated, audited administrator role.
• Enable GPO change auditing (Event IDs 5136, 5137, 5141) and forward to a SIEM.
• Monitor SYSVOL with file integrity tooling.

Phase 3 — credential and access hardening

• Enforce phishing-resistant MFA on all VPN and remote access entry points.
• Deploy Windows LAPS to generate unique, rotated local administrator passwords.
• Enable Credential Guard to protect LSASS-resident secrets.
• Adopt Privileged Access Workstations (PAW) for all administration.
• Implement just-in-time elevation through Privileged Identity Management (PIM) for domain admin.

Phase 4 — detection and monitoring

• SIEM rule: GPO creation or domain-root gPLink change by a non-standard account.
• SIEM rule: file creation events under SYSVOL from non-replication sources.
• Implement enhanced, centrally collected FortiGate SSL VPN logging (auth events, session detail, source IP geolocation, impossible travel check) and detailed perimeter monitoring.
• Deploy a canary GPO whose application anywhere signals attacker GPO write access.

Conclusion

PAYLOAD demonstrates a maturing tactic of turning the victim’s own trusted infrastructure into a weapon. By weaponizing Group Policy, the actor achieved a domain-wide impact without a single malicious binary on any endpoint, evaded file- and process-based detection entirely, and caused organization-wide disruption within seconds of the first reboot.

The absence of encryption is the most strategically significant finding and is consistent with the 2026 trend toward encryptionless extortion. At Kaspersky GERT, we assess with moderate confidence that the missing encryption reflects one of two scenarios: (1) a deliberate decision to stay below the irreversible data destruction threshold while preserving the option of a follow-on encryption phase, or (2) an operation interrupted before full execution.

The absence of encrypted files must not be mistaken for the absence of a serious compromise — the most critical impact lies in the actor gaining domain admin-level control. This incident proves that a threat actor with domain admin access and a working knowledge of group policy internals can inflict domain-wide disruption equivalent to a ransomware attack without writing a single malicious file to any endpoint.

Detection strategies anchored solely in file- or process-based indicators are blind to this attack class. Effective controls include directory service change auditing, SYSVOL integrity monitoring, and privileged access governance, which allow for GPO protection.

Detection by Kaspersky solutions

Kaspersky security solutions effectively detect the described malicious activity at various stages of the attack. Possible detection scenarios are listed below.
To protect organizations that use our Kaspersky SIEM system, we have prepared a package of correlation rules designed to help detect this type of malicious activity. The rules are now available for customers to download from the SIEM repository; the package name is: [OOTB] Group policy hijacked: PAYLOAD ransomware – ENG.

The “Group policy hijacked: PAYLOAD ransomware” package contains rules that detect suspicious file creation or modification in the SYSVOL shared folder on a domain controller, as well as changes to critical attributes and settings of domain group policies. Some rules may require adjustment if they trigger in response to legitimate activity, such as synchronization between domain controllers or the configuration of a new group policy.

To ensure the detection rules function correctly, verify that events from Windows systems are being received in full, including events with the following identifiers: Sysmon: 11, Security — 4663, 5136, 4657.

We also recommend applying the following rules available in the repository to detect all attack stages.

• Detection of Windows Event Log clearing to cover up traces of an attack: R050_03_Windows Event Log was cleared
• Detection of suspicious access to the LSASS process, which may indicate attempts to dump credentials: R262_Suspicious access to the LSASS process
• Detection of the Volume Shadow Copy service being started, which is used to create shadow copies before deleting them: R231_20_Running the Volume Shadow Copy service
• Detection of shadow copy deletion: R321_Shadow copy deletion
• Detection of Windows Defender disablement: R076_01_Windows Defender Antivirus was disabled
• Detection of attempts to disable or modify the system firewall: R240_03_Disabling system firewall via the registry
• Detection of a service installation from a non-system folder, which may indicate malware persistence techniques: R281_01_Installation of a service from a non-system folder

For the rules in this list to function correctly, it is necessary to configure Security event auditing for event IDs 4663, 5136, 4657, 7036, and 1102.

In addition to the SIEM system, when audit settings are configured correctly, the process of creating, modifying, and deleting GPOs generates a large number of characteristic artifacts on the domain controller, enabling Kaspersky Endpoint Detection and Response Expert to promptly alert the user to anomalies in the infrastructure.

The creation of a new GPO is covered by the gpo_creation rule, which triggers in response to the corresponding event in the infrastructure. Changes to existing policies are detected by the following rules:

• The setting_the_gpcmachineextensionname_attribute rule detects changes to the gPCMachineExtensionNames attribute.
• The setting_the_gpcfilesyspath_attribute rule detects the use of the gPCFileSysPath attribute of a Group Policy Object, which specifies the path to the GPO’s contents in SYSVOL.
• The broader rule setting_the_grouppolicycontainer_class detects a change to the groupPolicyContainer class.

GPO deletion is covered by the gpo_deletion rule.

In the next major version update of Kaspersky EDR Expert, a new event type, GPO, will be introduced, allowing users to track changes to Group Policy Objects.

For information security officers, GPO events will serve as an additional source of context when investigating activity in Active Directory and will help detect changes that could affect the configuration, access rights, and security of endpoints before a potentially malicious policy is deployed.

MITRE ATT&CK mapping

Indicators of compromise

GPOs
PAYLOAD ransomware GPO: {C897F2C7-C2AC-4E6F-BF48-58036FF29E79}
win Firewall Off GPO: {22099AD2-E062-4F56-B574-5099BBA4E7A6}

File names and MD5 hashes
payload.jpg: Ransom wallpaper and lock screen image (SYSVOL)
hello.txt: Ransom note source (SYSVOL)
README-payload.txt: Ransom note dropped to desktops and drive roots
killer.exe (0108656A3E1ADE6CA4F21B084F5E1208): process killer tool
kill.exe (BEA5E267F24D7DA59F6821BFFDBFF293): process killer tool

IP addresses
37.19.210[.]12
146.70.117[.]239
149.102.229[.]154
104.164.55[.]46
104.28.162[.]228
104.28.163[.]162
64.190.76[.]14
192.42.116[.]50
192.42.116[.]12
192.42.116[.]56
192.42.116[.]97
192.42.116[.]52

Registry keys
HKLM\...\Policies\System\legalnoticecaption = Welcome to Payload!

SYSVOL path
\\DC.THECOMPANY.local\sysvol\THECOMPANY.local\payload.jpg

Event IDs
5137: GPO object creation on DC — review creator account
5136: GPO attribute modification — watch gPLink at domain root

Apart from the privacy concerns around smart glasses, researchers have found that some cheap brands come with barely any security at all.

ABC Australia reports that researchers from NSB Cyber and Abstract Shield tested two inexpensive pairs, costing A$60 and A$110 (around US$42 and US$78), and found more than a dozen flaws across the smart glasses themselves, their app, and an associated website.

The main problem they uncovered was insecure Bluetooth pairing: If the glasses were powered on and not connected to their owner’s phone, an attacker could connect first, with no password or meaningful pairing confirmation.

After connecting, an attacker could reportedly control the glasses to capture photos or recordings, copy existing media, and intercept data moving between the glasses and the phone. An attacker could also make another device appear to be the victim’s glasses, allowing it to connect to the owner’s mobile app.

The testing also found that a Bluetooth-visible device identifier could allegedly be used with a weakness in the app’s website to retrieve a user’s email address and date of birth.

The research uncovered another privacy issue. ABC reports that voice or text submitted to the built-in AI, along with images sent to it, was first transmitted to a server in Shenzhen and could be forwarded elsewhere, depending on the function. The tests did not establish how the data was subsequently used.

The server locations, combined with some of the AI’s answers to specific questions, led researchers to conclude that the chatbot companion relied at least in part on Chinese sovereign AI models. Professor Kimberlee Weatherall, a technology regulation expert involved in the testing, said the failure to identify China in the privacy policy appeared to violate the Australian Privacy Principles,

The timing of the testing is also relevant because Australia’s smart-device security standards apply to most consumer smart devices manufactured on or after March 4, 2026. They require, among other measures, no universal default passwords, a way to report vulnerabilities, and information about the minimum security-update period. Devices made before that date are outside the requirements.

Experts quoted by ABC said the devices violated Australia’s privacy laws and were also likely to breach several sections of the Cyber Security Act. However, whether the new smart-device standards apply would depend partly on when the glasses were manufactured, and the rules have not yet been tested in a known enforcement action.

Professor Weatherall said:

“The rules say that the password must be unique. It doesn’t even seem like they were applying a password, which might mean that their standards are so low they don’t even technically breach that rule, which I find amazing.”

What to do

We’re not fans of smart glasses, expensive or cheap. But if you own a pair:

• Avoid pairing or using inexpensive camera glasses that lack a clear physical pairing step, account authentication, a published security-contact process, and a stated update-support period.
• Stop using their AI or cloud features for sensitive material, remove unnecessary permissions from the companion app, check for firmware and app updates, and consider returning the product if the vendor cannot document a fix.
• Ask permission before recording people, and check your local laws to understand what is and isn’t allowed.

From reporting threats to removing them.

Cybersecurity risks should never spread beyond a headline. Keep threats off your devices by downloading Malwarebytes today.

Hackers captured a Flock camera and got a look (alternate link) at the software:

While much of the automatic license plate reader’s (ALPR) most sensitive storage remained encrypted and inaccessible, the joint analysis of the recovered data shows that software running on the device explicitly detects people as well as vehicles, license plates, and bicycles. The camera can produce dozens of images of a single passing vehicle and, according to several weeks of recovered logs, generated more than a million images. Its computer-vision software also sometimes isolated bumper stickers and other graphics, including, in one case, an American flag patch on a motorcyclist’s saddlebag.

If you’re wondering how the hackers got by disk encryption, one of the unencrypted partitions contained the key for an encrypted partition. That’s pretty bad security engineering.

Executive summary

In April 2026, we at Kaspersky’s Global Emergency Response Team (GERT) responded to a security incident at a manufacturing organization in the Middle East. The threat actor obtained domain admin-equivalent control of the organization’s Active Directory environment and authored a malicious Group Policy Object (GPO) named PAYLOAD, linking it at the domain root. Through that single object, the actor delivered ransom notes, hijacked the desktop wallpaper and lock screen, enforced a logon banner, and disabled the local administrator account across every domain-joined Windows workstation — all without dropping a ransomware binary or encrypting any data. The only ransomware we found in this incident was PAYLOAD sample targeting ESXi on Linux servers. Besides that, data exfiltration was observed originating from the file servers and several additional systems, and was later published on the dark web.

This case is an example of two converging trends that define the 2026 ransomware landscape:

1. Living-off-the-land abuse of trusted AD infrastructure. Group Policy is a signed, allowlisted, SYSTEM-privileged distribution channel that the majority of endpoint detection and response tools is designed not to inspect. By delivering impact through GPO rather than through malware, the actor sidestepped the entire file- and process-based detection stack.
2. Encryptionless extortion. Industry telemetry shows extortion-only incidents grow significantly year-on-year. PAYLOAD fits this model; the leverage is operational disruption and the threat of escalation rather than cryptographic denial of data.

We confirmed that no files were encrypted on Windows machines, no malicious binaries were resident on disk, no endpoint persistence was established, and no malicious processes were running at the time of analysis. The entire attack lived inside Active Directory itself. The defensive implication is stark: an organization whose detection strategy depends on catching a ransomware executable would have seen nothing until the first endpoint rebooted and the ransom wallpaper appeared.

In this article, we will describe the GPO attack chain and provide operational advice on how to detect such threats, including detailed remediation recommendations.

Group Policy as an attack surface

Attacks through group policies are nothing new. They can inflict significant, domain-wide damage with multiple malicious capabilities. A Group Policy Object (GPO) is essentially a combination of a Group Policy Container (GPC) in Active Directory and a Group Policy Template (GPT) in SYSVOL. The Group Policy scope depends on whether the GPC is linked to the directory tree at the domain, site, or organization unit (OU) level. A link at the domain root means the policy applies to every computer and user object beneath it. Thus, a GPO compromised at the domain root can affect all in-scope domain users and computers, potentially granting an attacker complete control over the corporate network. What makes GPO abuse even more dangerous is that group policies are processed in a trusted, high-privilege environment, ensuring persistence because endpoint cleanup is not enough to remove them.

We have already discussed GPO architecture and ways it can be compromised in greater detail in an earlier blog post. Other public threat intelligence has also repeatedly documented this technique in ransomware operations. Microsoft observed Ryuk operators distributing ransomware through Group Policy, SYSVOL startup items, and PsExec. LockBit affiliates have been documented modifying SYSVOL Group Policy files, including ScheduledTasks.xml, to support ransomware execution and propagation. BlackCat/ALPHV operators have also abused GPOs to create scheduled tasks and deploy ransomware.

Another notable example of GPO abuse is PAYLOAD ransomware, which weaponizes GPO Preferences and policy settings for pure impact rather than as a launcher for an encryptor. Let’s take a closer look at this attack, which is detailed further below.

Attack timeline

During the April 2026 investigation, we managed to reconstruct the attack timeline as outlined below:

Next, we will discuss each of these stages in more detail and share the findings from our incident response activities.

Incident overview

Initial access

The entry vector was a compromised valid account (MITRE ATT&CK T1078) used to authenticate through the organization’s FortiGate SSL VPN — an external remote service (T1133). Insufficient logging on the FortiGate appliance prevented us from reconstructing how the credential was originally compromised.

Three hypotheses were considered plausible in the context of the attack, in no particular order:

• Password spraying or credential stuffing against the SSL VPN portal.
• Phishing-led credential harvesting.
• Purchase of pre-compromised credentials from an initial access broker (IAB).

Once on the internal network, the actor operated with the compromised security principal’s privileges. Because the account was able to create and link a GPO at the domain root, it held either domain admin privileges or a delegated equivalent (e.g., membership of Group Policy Creator Owners combined with link rights on the domain object).

FortiGate SSL VPN authentication logs and ESXi/virtualization privilege escalation logs were insufficient to reconstruct the lateral movement and privilege escalation chain between initial VPN access and the GPO write privilege level. The most common real-world routes to GPO control — DCSync, Kerberoasting of privileged service accounts, and Pass-the-Hash/Pass-the-Ticket — could not be confirmed or ruled out.

Execution

Rather than deploying an encryptor, the actor abused the victim’s Active Directory policy infrastructure, resulting in malicious GPOs being configured and linked at the domain root to deliver the observed impact across domain-joined systems. The two GPOs below constitute the entire offensive toolkit observed.

The PAYLOAD GPO

We performed a Resultant Set of Policy (RSOP) analysis, which helped us list all policy settings on the affected workstations. From these results, we reconstructed the following changes delivered by PAYLOAD:

The PAYLOAD GPO enabled the following actions:

1. Drop SYSVOL\hello.txt to the desktop and root directories C:\ and D:\ as a read-only README-payload.txt by tampering with the “Group Policy Files” client-side extension (CSE).
2. Modify the HKLM\SOFTWARE\Microsoft\Windows\CurrentVersion\Policies\System\ registry key, changing the legalnoticecaption value to "Welcome to Payload!" and the legalnoticetext to the ransom note text through the “Group Policy Registry” CSE.
3. Set payload.jpg located in the targeted domain controller’s SYSVOL as the lock screen image by altering the personalization policy, and as the wallpaper by altering the desktop policy at the user level.
4. Revoke administrator rights for the administrator account (locking the account) by editing the Security Settings CSE in the GptTmpl.inf file.

This activity was enabled entirely through a legitimate group policy mechanism, meaning there is no malware code for security solutions to look for because the malicious logic is contained within the policy configuration.

The win Firewall Off GPO

A second domain-root-linked GPO, named “win Firewall Off”, disabled Windows Firewall across the domain, private and public profiles on all endpoints (T1562.004). Deployed independently of PAYLOAD, this object degraded host defenses and ensured the actor retained unimpeded network reach to endpoints for any follow-on activity.

Settings changed by win Firewall Off GPO

The one-day delay detonation

The most forensically instructive detail is the one-day gap between GPO creation (13 April) and visible impact (14 April). Analysis of Master File Table (MFT) timestamps and the Group Policy History registry key confirmed the policy was written to SYSVOL and cached on endpoints on 13 April. However, computer configuration settings (wallpaper and lock screen machine policy, security settings, firewall disabling) only apply upon reboot or policy refresh — and no endpoint had rebooted in the interim. The attack therefore remained dormant in the GPO cache for one day before detonating en masse when machines were restarted in accordance with standard procedures.

We have to point out here that this delayed policy application is characteristic of GPO-based operations and has two potential consequences for defenders. Firstly, it may grant the actor a quiet window for exfiltration, persistence, or further staging between weaponization and impact. Secondly, it can be used to sever the temporal link between the cause (a GPO-creation event in the directory log) and the effect (mass user-visible disruption occurring at a later time), complicating timeline reconstruction unless directory service auditing is in place.

Forensic findings

Initial incident response engagement revealed a number of findings associated with the attack that we share below.

1. No file encryption
   A full review of the MFT on affected workstations found no files bearing a .payload extension characteristic to PAYLOAD ransomware and no evidence of bulk renaming or encryption I/O patterns. The attackers’ objectives were operational disruption and extortion via visual impact and access denial.
2. No endpoint persistence
   All standard persistence locations were clean:
   • Scheduled tasks — no malicious tasks
   • Run/RunOnce keys — clean
   • Startup folders (user and system) — clean
   • Services — no malicious service installed
   • WMI event subscriptions — none
   • Boot sector/MBR — unmodified

   The attack’s persistence mechanism is the GPO link itself on the domain controller, with no endpoint-resident component.

3. No active malicious processes
   Live process and memory analysis revealed no injected threads, process hollowing or anomalous outbound connections.
4. Registry timeline analysis
   Group Policy History, Shadow and State registry keys on the workstation recorded the application of PAYLOAD on 13 April:
   HKLM\…\Group Policy\History\{35378EAC-683F-11D2-A89A-00C04FBBCFA2}\1
   HKLM\…\Group Policy\Shadow\{827D319E-6EAC-11D2-A4EA-00C04F79F83A}\0
   HKLM\…\Group Policy\State\Machine\GPO-List\7
   HKCU\…\Group Policy\History\{7150F9BF-48AD-4da4-A49C-29EF4A8369BA}\1
   HKCU\…\Group Policy\State\S-1-5-21-…\Loopback-GPO-List\5
   The presence of the “Loopback-GPO-List” entry indicates the GPO was processed in loopback mode, ensuring the user configuration (wallpaper) applied regardless of which user logged on to the machine.
5. SYSVOL artifacts
   Two files were staged on the domain controller’s SYSVOL share and served to endpoints through the Files Group Policy Preference CSE:
   • payload.jpg — the ransom image used for both wallpaper and lock screen.
   • hello.txt — the ransom note, distributed to desktops and drive roots as README-payload.txt.

Detection engineering

Because no malicious binary or process exists, the detection logic should focus on the directory service and SYSVOL indicators. Below are the highest-value telemetry sources for identifying the described malicious activity.

• Directory service change auditing (DS Access)
  Enable Advanced Audit Policy → DS Access → Audit Directory Service Changes on all domain controllers and set up alerts for the following events:

  Event ID	Meaning	Hunt focus
  5137	A directory service object was created	The account that created new groupPolicyContainer objects must be an authorized GPO administrator
  5136	A directory service object was modified	Changes to gPLink on the domain root or sensitive organizational units; changes to gPCMachineExtensionNames/gPCUserExtensionNames/gPCFileSysPath/versionNumber for all GPOs
  5141	A directory service object was deleted	GPO deletions (relevant for tamper detection and remediation assessment)

  A gPLink modification at the domain root by a non-standard account is one of the most telling indicators of this attack class.

• SYSVOL file integrity monitoring
  Monitor \\DC.THECOMPANY.local\sysvol\THECOMPANY.local\Policies\ for unexpected files, particularly image files, text files, scripts, ScheduledTasks.xml, and modified registry.pol/GptTmpl.inf files that were created by sources other than legitimate replication.
• Endpoint policy application telemetry
  On endpoints, GPO application is logged in the Group Policy Operational log (Microsoft-Windows-GroupPolicy/Operational) and reflected in the History and Shadow registry keys shown above. A sudden domain-wide change to the applied policy set is a strong post-detonation signal.
• Missing 5136 events
  If the contents of a GPO’s SYSVOL change without a corresponding 5136 event, this can indicate direct SYSVOL/template editing (e.g., via open-source tools such as PowerView or SharpGPOAbuse) that bypasses the normal Group Policy Management Console (GPMC) modification path. While we recommend treating the absence of expected audit events as suspicious, it should be noted that this can be caused by misconfigured auditing.

Anti-forensics and recovery-inhibition capabilities

Confirmed PAYLOAD family capabilities

The Windows endpoint attack described in this report was implemented through malicious Group Policy Objects and did not involve a recovered ransomware executable, resident endpoint malware, confirmed file encryption or active malicious processes at the time of the forensic examination. However, PAYLOAD cryptomalware for Windows does exist, and other sources, including public analysis of its samples, reveal further malicious capabilities of this ransomware that could be used by security teams to enrich detection logic and security policies.

The behaviors documented in this section must therefore be interpreted as family-level capabilities identified through the public reverse engineering of PAYLOAD ransomware samples. They were not confirmed as having executed during the GPO-based incident unless corresponding evidence was identified in the host, memory, process, event-log, or hypervisor.

Windows Event Log clearing

Public reverse engineering of the PAYLOAD Windows variant reveals an optional event log clearing capability (T1685.005, formerly T1070.001). The ransomware dynamically resolves Windows Event Log APIs, enumerates available event log channels, and clears individual channels.

Implementation artifacts include:

• Runtime loading of wevtapi.dll
• EvtOpenChannelEnum
• EvtNextChannelPath
• EvtClearLog
• Enumeration of Security, System, Application, and PowerShell logs, and operational channels
• A command-line option controlling whether log clearing is performed

Clearing event logs reduces the availability of records related to process execution, authentication, PowerShell, service control, and system changes. However, it does not necessarily remove events that were already forwarded to a SIEM, Windows Event Collector, EDR backend, or protected log archive.

Forensic indicators

• Windows Security Event ID 1102
• Event logging shutdown or failure events
• Sudden reductions in event record identifiers
• Multiple event channels restarting from low record numbers
• Gaps between endpoint and centrally forwarded telemetry
• Execution of utilities or APIs associated with log management
• Direct access to EVTX files (Windows Event Log files) under the Windows event log directory

We should note that these indicators can’t be interpreted on their own. For instance, Event ID 1102 should be correlated with a number of aggravating factors, such as:

• Event ID 4688 or Sysmon Event ID 1 for process creation
• Event ID 4624 for the associated administrative logon
• EDR process trees
• PowerShell operational logging
• Windows Event Forwarding records
• Privileged access management activity

At the same time, the absence of Event ID 1102 does not prove that log clearing did not occur. Direct deletion, truncation, service impairment, or an incomplete audit configuration may produce different evidence.

Security process and service termination

PAYLOAD analysis reveals that the Windows ransomware variant contains logic that targets security processes and services (T1685 and T1489). This capability is intended to stop security products, backup software, database services, and applications that may lock files targeted for encryption.
Process and service termination serves several objectives:

1. Reduce endpoint detection and response visibility.
2. Release file handles so data can be modified.
3. Interrupt backup and recovery services.
4. Prevent databases and enterprise applications from protecting active data.
5. Reduce interference with encryption operations.

Forensic indicators

• Security agent services changing from running to stopped
• Event ID 7036 service state changes
• Event ID 7040 service start type changes
• Event ID 4688 or Sysmon Event ID 1 for service control utilities
• Sysmon Event ID 5 for terminated processes
• EDR health degradation affecting many endpoints
• Repeated termination attempts against security or backup processes
• Execution of dedicated process killing utilities

VSS deletion, backup and recovery suppression

Public PAYLOAD sample analysis reports the deletion of Windows Volume Shadow Copies before encryption (T1490). This behavior removes local restore points and reduces the victim’s ability to recover files without external backups.

Recovery inhibition is broader than VSS deletion and may include multiple other malicious techniques, including the following:

• Deleting shadow copies, backup catalogs, and hypervisor snapshots
• Disabling recovery services
• Modifying boot recovery settings
• Compromising backup management platforms
• Deleting or encrypting backup repositories

In the PAYLOAD Windows sample that is proven to perform encryption, VSS deletion is confirmed to have taken place before encryption. A broader compromise of the backup platform was not established in the investigated incident.

Forensic indicators

• Execution of shadow copy management utilities
• Backup catalog deletion attempts
• Recovery configuration changes
• Backup service termination
• Mass snapshot deletions
• Backup administrator logons outside normal maintenance windows
• Backup retention or immutability changes
• Backup jobs failing immediately before disruptive activity

Ecosystem-relevant ransomware techniques

Above, we discussed the techniques specific to PAYLOAD ransomware. In this section, we’ll review other common ransomware techniques, such as BYOVD abuse and ESXi policy weakening, that remain relevant risks in the ransomware ecosystem. However, these techniques should not be attributed to this incident without supporting driver load, process, memory, ESXi, vCenter, or backup platform telemetry. We describe these techniques below as they may prove useful in other ransomware-related forensic investigations.

The examples used in this section are therefore ecosystem-relevant: they are commonly observed in ransomware operations but not conclusively attributed to the analyzed PAYLOAD samples.

ETW suppression and in-memory patching

Event Tracing for Windows (ETW) is a kernel-supported tracing architecture used by Windows components, diagnostic utilities, security products, and endpoint monitoring systems.

Although we didn’t encounter this technique in our case, some ransomware families can patch ETW-related functions inside the ransomware process (T1685, formerly T1562.001). The reported routine changes the memory protection of code pages, overwrites the beginning of selected ETW functions so they return without producing events, restores the original protection, and refreshes the instruction cache.

Reported target functions include EtwEventWrite, EtwEventWriteFull, EtwEventWriteTransfer, and EtwRegister.

This modification applies to the process-local mapping of ntdll.dll. It does not disable ETW globally across Windows, but it can reduce the telemetry generated by the modified process.

Forensic indicators

• Writable or recently modified executable pages within ntdll.dll
• Memory protection changes affecting ETW function addresses
• In-memory code differing from the corresponding clean DLL on disk
• EDR memory tampering alerts
• Unexpected calls to memory protection APIs followed by telemetry silence
• Process execution visible in network or filesystem telemetry but absent from expected ETW-derived sources
• Inconsistent telemetry between EDR, Sysmon, Windows Event Logs, and network monitoring

It should be noted that telemetry gaps are not conclusive evidence of ETW patching. Other factors include agent upgrades, network outages, endpoint shutdowns, SIEM ingestion failures, collector backpressure, and filtering or licensing changes.

Memory acquisition is the strongest validation source when process-local ETW patching is suspected.

Vulnerable signed driver abuse, BYOVD

Bring Your Own Vulnerable Driver (BYOVD) involves introducing or abusing a legitimately signed but vulnerable kernel driver (T1068 and T1685). The attacker exploits the driver’s exposed functionality to gain kernel-level capabilities, modify protected memory, terminate security processes, remove security callbacks, or bypass operating system security controls.

BYOVD is highly relevant to contemporary ransomware operations. However, the reviewed public PAYLOAD analyses do not provide sufficient evidence to conclude that BYOVD is an intrinsic PAYLOAD capability.

It should therefore be presented as an ecosystem-relevant technique rather than a confirmed PAYLOAD feature.

Forensic indicators

• Unexpected .sys files written to user-writable or temporary directories
• New kernel driver services
• Sysmon Event ID 6
• Driver hashes matching Microsoft or community vulnerable driver blocklists
• Invalid, revoked, expired, or unusual driver signatures
• Driver load followed by EDR termination
• New privileged processes immediately following driver loading
• Code Integrity or Defender alerts
• Service creation events referencing driver files

ESXi security policy weakening

Ransomware operators are increasingly targeting ESXi and vCenter because compromising the virtualization layer provides access to many business-critical virtual machines.

Public research typically describes operators performing the following activities (T1685, T1490 and T1489):

• Enabling SSH on ESXi hosts
• Changing root passwords
• Disabling execInstalledOnly
• Modifying lockdown mode exceptions
• Stopping virtual machines
• Deleting snapshots and backups
• Changing host firewall policies
• Copying custom ransomware binaries to hypervisors

On the target organization’s Linux servers we saw an ESXi PAYLOAD variant, which makes this behavior operationally relevant. Nevertheless, the reviewed evidence does not indicate that PAYLOAD operators used any of these policy-weakening actions in the investigated incident.

Forensic indicators

• Unexpected ESXi SSH enablement
• Changes to execInstalledOnly
• Secure Boot enforcement changes
• Lockdown mode configuration changes
• Root password changes
• New or unusual administrator accounts
• Bulk VM shutdown activity
• Bulk snapshot deletion
• New binaries in datastore or temporary paths
• vCenter tasks originating from unexpected accounts or systems
• Missing or disabled remote syslog forwarding

Remediation

To contain the described threat and limit possible damage, several remediation steps should be taken. Below, we recommend a four-phase remediation approach for containing this threat and restoring the affected environment.

Phase 1 — domain controller actions (to be performed first)

When dealing with PAYLOAD GPO, the first priority of the remediation plan should be to remove the source GPOs. Until this is done, endpoint cleanup is ineffective because the next policy refresh re-infects cleaned machines.

The immediate domain controller actions to be taken are:

1. Delete PAYLOAD GPO. Remove {C897F2C7-C2AC-4E6F-BF48-58036FF29E79} via GPMC.
2. Delete or revert win Firewall Off GPO. Remove {22099AD2-E062-4F56-B574-5099BBA4E7A6}.
3. Clean SYSVOL. Delete jpg and hello.txt from \\DC.THECOMPANY.local\sysvol\THECOMPANY.local\.
4. Rotate credentials. Reset the compromised account; audit all privileged accounts and group memberships for unauthorized changes; rotate krbtgt twice if domain admin compromise is confirmed.
5. Force refresh. Perform gpupdate /force across endpoints after deletion; re-enable local administrator and firewall via clean policy.

Phase 2 — Active Directory and GPO hardening

• Adopt the AD tiered administration model and confine domain admins to Tier 0 with no interactive logon to workstations and member servers.
• Separate GPO creation from GPO linking rights, and grant both only to a dedicated, audited administrator role.
• Enable GPO change auditing (Event IDs 5136, 5137, 5141) and forward to a SIEM.
• Monitor SYSVOL with file integrity tooling.

Phase 3 — credential and access hardening

• Enforce phishing-resistant MFA on all VPN and remote access entry points.
• Deploy Windows LAPS to generate unique, rotated local administrator passwords.
• Enable Credential Guard to protect LSASS-resident secrets.
• Adopt Privileged Access Workstations (PAW) for all administration.
• Implement just-in-time elevation through Privileged Identity Management (PIM) for domain admin.

Phase 4 — detection and monitoring

• SIEM rule: GPO creation or domain-root gPLink change by a non-standard account.
• SIEM rule: file creation events under SYSVOL from non-replication sources.
• Implement enhanced, centrally collected FortiGate SSL VPN logging (auth events, session detail, source IP geolocation, impossible travel check) and detailed perimeter monitoring.
• Deploy a canary GPO whose application anywhere signals attacker GPO write access.

Conclusion

PAYLOAD demonstrates a maturing tactic of turning the victim’s own trusted infrastructure into a weapon. By weaponizing Group Policy, the actor achieved a domain-wide impact without a single malicious binary on any endpoint, evaded file- and process-based detection entirely, and caused organization-wide disruption within seconds of the first reboot.

The absence of encryption is the most strategically significant finding and is consistent with the 2026 trend toward encryptionless extortion. At Kaspersky GERT, we assess with moderate confidence that the missing encryption reflects one of two scenarios: (1) a deliberate decision to stay below the irreversible data destruction threshold while preserving the option of a follow-on encryption phase, or (2) an operation interrupted before full execution.

The absence of encrypted files must not be mistaken for the absence of a serious compromise — the most critical impact lies in the actor gaining domain admin-level control. This incident proves that a threat actor with domain admin access and a working knowledge of group policy internals can inflict domain-wide disruption equivalent to a ransomware attack without writing a single malicious file to any endpoint.

Detection strategies anchored solely in file- or process-based indicators are blind to this attack class. Effective controls include directory service change auditing, SYSVOL integrity monitoring, and privileged access governance, which allow for GPO protection.

Detection by Kaspersky solutions

Kaspersky security solutions effectively detect the described malicious activity at various stages of the attack. Possible detection scenarios are listed below.
To protect organizations that use our Kaspersky SIEM system, we have prepared a package of correlation rules designed to help detect this type of malicious activity. The rules are now available for customers to download from the SIEM repository; the package name is: [OOTB] Group policy hijacked: PAYLOAD ransomware – ENG.

The “Group policy hijacked: PAYLOAD ransomware” package contains rules that detect suspicious file creation or modification in the SYSVOL shared folder on a domain controller, as well as changes to critical attributes and settings of domain group policies. Some rules may require adjustment if they trigger in response to legitimate activity, such as synchronization between domain controllers or the configuration of a new group policy.

To ensure the detection rules function correctly, verify that events from Windows systems are being received in full, including events with the following identifiers: Sysmon: 11, Security — 4663, 5136, 4657.

We also recommend applying the following rules available in the repository to detect all attack stages.

• Detection of Windows Event Log clearing to cover up traces of an attack: R050_03_Windows Event Log was cleared
• Detection of suspicious access to the LSASS process, which may indicate attempts to dump credentials: R262_Suspicious access to the LSASS process
• Detection of the Volume Shadow Copy service being started, which is used to create shadow copies before deleting them: R231_20_Running the Volume Shadow Copy service
• Detection of shadow copy deletion: R321_Shadow copy deletion
• Detection of Windows Defender disablement: R076_01_Windows Defender Antivirus was disabled
• Detection of attempts to disable or modify the system firewall: R240_03_Disabling system firewall via the registry
• Detection of a service installation from a non-system folder, which may indicate malware persistence techniques: R281_01_Installation of a service from a non-system folder

For the rules in this list to function correctly, it is necessary to configure Security event auditing for event IDs 4663, 5136, 4657, 7036, and 1102.

In addition to the SIEM system, when audit settings are configured correctly, the process of creating, modifying, and deleting GPOs generates a large number of characteristic artifacts on the domain controller, enabling Kaspersky Endpoint Detection and Response Expert to promptly alert the user to anomalies in the infrastructure.

The creation of a new GPO is covered by the gpo_creation rule, which triggers in response to the corresponding event in the infrastructure. Changes to existing policies are detected by the following rules:

• The setting_the_gpcmachineextensionname_attribute rule detects changes to the gPCMachineExtensionNames attribute.
• The setting_the_gpcfilesyspath_attribute rule detects the use of the gPCFileSysPath attribute of a Group Policy Object, which specifies the path to the GPO’s contents in SYSVOL.
• The broader rule setting_the_grouppolicycontainer_class detects a change to the groupPolicyContainer class.

GPO deletion is covered by the gpo_deletion rule.

In the next major version update of Kaspersky EDR Expert, a new event type, GPO, will be introduced, allowing users to track changes to Group Policy Objects.

For information security officers, GPO events will serve as an additional source of context when investigating activity in Active Directory and will help detect changes that could affect the configuration, access rights, and security of endpoints before a potentially malicious policy is deployed.

MITRE ATT&CK mapping

Indicators of compromise

GPOs
PAYLOAD ransomware GPO: {C897F2C7-C2AC-4E6F-BF48-58036FF29E79}
win Firewall Off GPO: {22099AD2-E062-4F56-B574-5099BBA4E7A6}

File names and MD5 hashes
payload.jpg: Ransom wallpaper and lock screen image (SYSVOL)
hello.txt: Ransom note source (SYSVOL)
README-payload.txt: Ransom note dropped to desktops and drive roots
killer.exe (0108656A3E1ADE6CA4F21B084F5E1208): process killer tool
kill.exe (BEA5E267F24D7DA59F6821BFFDBFF293): process killer tool

IP addresses
37.19.210[.]12
146.70.117[.]239
149.102.229[.]154
104.164.55[.]46
104.28.162[.]228
104.28.163[.]162
64.190.76[.]14
192.42.116[.]50
192.42.116[.]12
192.42.116[.]56
192.42.116[.]97
192.42.116[.]52

Registry keys
HKLM\...\Policies\System\legalnoticecaption = Welcome to Payload!

SYSVOL path
\\DC.THECOMPANY.local\sysvol\THECOMPANY.local\payload.jpg

Event IDs
5137: GPO object creation on DC — review creator account
5136: GPO attribute modification — watch gPLink at domain root

Malicious extensions and ClickFix attacks are two hugely popular topics on our blog, and for a good reason: they’re both very common. In this post, we bring them together. Here’s the thing: in our posts about malicious ClickFix instructions, we don’t always explain exactly how users end up on the attackers’ pages in the first place. A recent study gives us a chance to fill that gap and talk about one of those methods: malicious extensions.

Readers who are not deep into cybersecurity may already have a bunch of questions at this point: how can extensions be malicious? where do they come from? and what exactly is ClickFix, anyway? Fret not: this post answers all these questions.

The danger of browser extensions

Let’s start with the basics. Browser extensions or add-ons are lightweight tools installed in your browser to give it extra capabilities or change how it works with web pages. For example, extensions can block ads, translate pages, download audio and video, save passwords, or take screenshots.

Most browser developers offer users an official extension store. For instance, for the most popular browser, Chrome, that store is the Chrome Web Store. There are also alternative platforms for distributing extensions, though browser developers have recently made it much harder to install and use extensions that don’t come from an official source.

Just like any other software, extensions can be useful, but they can also be malicious. In fact, cybercriminals often spread malicious extensions through those very same official stores, and we’ve covered plenty of such cases on our blog before.

It’s no accident that criminals love spreading malicious plug-ins. To work properly, most extensions need permission to read and modify the content of every web page the user visits. Without that permission, add-ons would be basically useless. For instance, a translation extension needs to read all the text on a page before it can swap the original for the translation.

Cybercriminals take advantage of these capabilities for their own purposes: for example, they intercept logins, passwords and other data entered on websites, swap out payment details, or show victims fake forms and messages. And all of this happens on legitimate sites that users have come to trust without a second thought.

Now let’s move on to a fresh piece of research about a campaign that spread 19 malicious add-ons through the Chrome and Edge extension stores. We’ll use it as an example to look at what these tools can actually do.

Almost twenty malicious extensions for Chrome and Edge

In late August, cybersecurity researchers reported finding 18 malicious extensions for Chrome and one for Edge. They said all 19 add-ons were probably linked to the same campaign. They base this on the similarity of the malicious code and the methods the attackers used. You’ll find the full list of malicious extensions at the end of this post.

Most of the extensions discovered, 14 in total, were built by the attackers themselves. Their first versions weren’t malicious at all and did exactly what users downloaded them for. But after a while, once users had come to trust the add-on, the attackers pushed an update that slipped in malicious functionality.

As for the remaining five extensions, their creators had nothing to do with the cybercriminals at all. They simply sold their creations to them. This way of getting hold of extensions is also popular among cybercrooks. There are plenty of reasons for that: ready-made add-ons can be bought for relatively little money, and criminals don’t have to build or promote them from scratch. And most importantly, along with the extension, they also inherit its existing user base. Upon buying the add-on, all the attackers need to do is add malicious functions and push an update that installs automatically for users. Users are never told that the extension’s real owner has changed.

In this case, the add-on cybercriminals bought most often was Enable Right Click & Copy — Smart Unlock + OCR. It had 70 000 users in Chrome and 10 000 in Edge. This purchase is exactly why there’s a single Edge extension among all its Chrome relatives. The add-on itself originally served a perfectly benign purpose: it let users select and copy text on sites where that’s normally blocked, and it could also pull text out of images with optical character recognition (OCR).

At the time of the research, the Edge version of the extension had more than 10 000 users. Source

The malicious functionality introduced into the extension makes it reach out to the attackers’ command-and-control (C2) server to download additional modules of nefarious code. These modules can change over time. Since Chrome automatically updates all installed extensions by default, the malicious version lands on the device without users having to do anything at all.

For the downloaded code to run on the websites the user visits, the extension disables one of their defenses — Content Security Policy (CSP). This mechanism allows website owners to declare which scripts and other resources are allowed to load and run on a page. Once this protection is turned off, the extension can inject code it gets from the C2 server right into the pages open in the browser. The website itself isn’t hacked, and it looks the same to everyone else, but for that one infected user, the legitimate page quietly turns malicious.

What these 19 extensions can do

Now let’s take a closer look at what the malicious modules loaded by the extension are actually capable of. Researchers found 16 such modules, built for all sorts of tasks: from stealing data and hijacking cryptocurrency to the ClickFix attacks we’re focusing on here.

Some of the modules simply collect user data. They intercept logins, passwords, email addresses, and other valuable information the user enters into web forms, including payment details. On top of that, the add-ons steal data from Facebook and LinkedIn accounts and collect browsing history. Criminals can use this information to take over accounts, steal money, and launch further attacks against the victim.

A few other modules focus specifically on stealing cryptocurrency and related data directly. One of them is a crypto drainer, a tool that empties crypto wallets. It works with Ethereum and compatible blockchains as well as Solana and Tron. It replaces the real Connect Wallet and Swap buttons on websites with its own fake ones. When a user clicks one of these buttons, the drainer takes over all further interaction with the wallet and walks the victim through confirming the actions needed to steal their cryptocurrency.

Other modules target users of Ledger and Trezor hardware wallets. Right on the official websites of these services, the malicious extension displays a fake wallet update-and-restore page that asks for a seed phrase of 12, 18, or 24 words. The seed phrase the user enters is forwarded straight to the attackers, giving them full control over the wallet.

The fake form asks the user to enter a seed phrase of 12, 18, or 24 words, which then ends up in the attackers’ hands. Source

Finally, some modules gather information from crypto service pages open in the browser, such as OKX, MEXC, Kraken, KuCoin, Coinbase, Binance, Bybit, and MetaMask, including balance details and active session data that can be used to hijack accounts.

And now we’ve finally reached the module that matters most for this post: the one used for ClickFix attacks. We’ll talk about it in the next part.

How malicious extensions are used in ClickFix attacks

The ClickFix attack technique has become extremely popular among cybercriminals lately. It relies on social engineering: the victim is shown fake instructions supposedly needed to complete some action, such as passing a CAPTCHA check, fixing an error, or updating a program. Following these instructions, the user copies the command suggested by the attackers, pastes it into a system tool built for running these commands, and runs it, all by themselves.

We’ve written about attacks like this and their consequences more than once on our blog. But we rarely dig into exactly how a victim ends up on a page with fake instructions in the first place. A campaign that spread malicious extensions through the Chrome Web Store shows us one way this can happen.

Add-ons turn out to be a very handy tool for ClickFix attacks. As we mentioned earlier, when a user visits a page, extensions are allowed reading and changing its content in order to work. That gives attackers the chance to swap in a fake ClickFix instruction at just the right moment, replacing the content of the page the person actually wanted to open.

What’s more, the ClickFix technique itself lets attackers break out of the browser and gain a foothold directly in the system. That means removing the malicious extension won’t necessarily undo the damage: after the user has run the ClickFix command, malware may already be installed outside the browser.

In the specific attack example the researchers describe, the user sees a fake notification saying their browser needs to be updated, along with instructions on how to do it. The instructions change depending on the operating system, and the extension picks up that information automatically too.

In the macOS version of this attack, the user is told to open Terminal, copy a command, paste it there and run it, then restart Chrome to supposedly finish the update. Source

How to avoid falling victim to infected extensions

First off, we recommend checking whether any of the following malicious extensions are already installed in your browser:

• Enable Right Click & Copy — Smart Unlock + OCR
• RapidLens – Google Lens for Screen Search & Images
• QuickLens – Search Screen with Google Lens
• Password Protect PDF
• Allow Copy – Select & Enable Right Click
• PixelCheck
• Creative Library – Ad Spy Tool
• Website Traffic Checker: MirrorSphere SEO Stats
• Site Signal – Website Traffic & SEO Checker
• SEO Pulse Pro – Website Traffic & SEO Analyzer
• Private Crypto News Reader
• Blockfolio: Address Monitor
• Crypto Rates & Fiat Converter
• Crypto Alerter: Price Alarms & Volatility Warnings
• DeFi Pulse Tracker
• Crypto Price Badge: Quick Glance
• Multi-Chain Explorer
• LedgerLook: Wallet Checker
• Meta & Facebook Ad Library Spy — Save Ads, Finder, Downloader | FeedX-Ray

If you find any of these extensions on your device, we recommend removing it as soon as you can and running a full system scan with a reliable antivirus. As we mentioned above, simply deleting the add-on might not be enough. You can use a free trial version to detect and remove the infection.

How to avoid falling victim to malicious extensions and ClickFix attacks in the future?

• Don’t install too many extensions and regularly check the list of those you have.
• Remove extensions you don’t use.
• Trust the warnings from your operating system and security software, rather than the instructions — or even the demands — of websites and apps.
• Install a trusted security solution on all your devices.

What else makes browser extensions and the ClickFix technique dangerous? Find out in our other posts:

• Bad advice for gamers: ClickFix hits Steam forums
• ClickFix on macOS: how Apple users are being scammed
• Infostealer has entered the chat
• AI sidebar spoofing: a new attack on AI browsers
• Dangerous browser extensions

In short, WordPress security plugins are tools you add to your site to strengthen your settings, scan for malware, watch for file changes, and block suspicious activity from within WordPress. Most sites should use one, along with a firewall that filters traffic before WordPress even loads. Look for layered protection, good detection, clear handling of false alarms, minimal impact on performance, and real human support.

If you manage a WordPress site, you have probably searched for “best WordPress security plugin” at some point.

Continue reading WordPress Security Plugins: How to Choose the Right One at Sucuri Blog.

Threat actors are increasingly improving their tactics to make suspicious emails look like legitimate email notifications to potential victims, deploying techniques that impersonate internally sent emails from executive team members. While this technique is not new, the adoption of AI has enabled threat actors to improve their campaign templates and construct emails tailored to their recipients. Additionally, threat actors are incorporating multiple techniques within the same email to improve the overall narrative further.

In this blog, we will discuss a recent campaign observed using third-party email delivery infrastructure to send out over a million financial fraud scam emails that displayed multiple indicators consistent with the use of generative AI during email template creation. The threat actor impersonated CEOs of multiple target companies, attempting to convince accounts payable departments of the same companies to process an Automated Clearing House (ACH) payment of nearly $50,000. To add legitimacy, the actor included a forwarded email thread (and a fabricated invoice) between the impersonated CEO and ServiceNow (which was also being impersonated).

Attack chain overview

The campaign follows steps before and during the execution of the campaign: threat actors register impersonation domains, send executive-themed payment requests through trusted infrastructure, embed fabricated invoices and supporting conversations, and attempt to convince finance personnel to initiate ACH transfers.

Email Delivery

Between August 3 and 5, Microsoft detected a campaign consisting of more than a million emails targeting enterprise users. The attacker used multiple third-party email service accounts to send out the emails. A huge majority of these emails were sent to users in the United States (87.7% of the total campaign).

Unlike traditional invoice scams that rely on a single social engineering lure, this campaign layered executive impersonation, vendor branding, fabricated invoices, and supporting email conversations into a unified narrative intended to reduce recipient skepticism.

The threat actor impersonated executive team members (such as a CEO, CFO, President) of multiple targeted companies, attempting to convince accounts payable departments of the same companies to process an ACH payment of nearly $50,000. More specifically, the CEOs were impersonated in multiple places in the email such as in the sender display name, reply-to display name, and in the email signature. Email bodies contained a simple and direct “approval” of the “invoice below” as well as urged users to request a PDF version if they need it. Additionally, as mentioned earlier, the email signature contained certain details about the spoofed CEO such as name and email address.

Important note: Throughout this campaign, threat actors impersonated legitimate organizations using attacker-controlled infrastructure, fabricated communications, and lookalike domains. Microsoft found no evidence that the legitimate organizations referenced in the lures, including ServiceNow, were compromised or involved in the activity. Rather, the campaign relied on fraudulent domains and content designed to mimic trusted brands and individuals.

The threat actor did not stop there. To add further legitimacy, directly below the CEO signature, the actor included “forwarded” content , specifically a professional looking but fabricated “ServiceNow Platform — Annual Subscription” invoice. The extremely detailed invoice contains various ServiceNow branding and logos. It has basic invoice details such as invoice number, issue and due dates, currency, amount due, payment method, and itemized line items. The payment method instructed is a bank transfer to accounts controlled by the threat actor. Microsoft observed the use of multiple financial institutions across samples, indicating that payment destinations may vary between targets. Certain parts of the invoice are personalized to the recipient. Specifically, the “BILLED TO” section has the recipient company name and executive name.

The invoice shown below is a threat actor-created impersonation and was not issued by ServiceNow.

Finally, directly below the fake invoice, two more “forwarded” emails are included which are essentially a short conversation between the two spoofed executives (the targeted company executive, and ServiceNow President). The two executives are seen discussing the ServiceNow purchase, implementation and handling of the invoice.

From a defender point of view there are several indicators within the email indicating that the email and the “forwarded” thread are not genuine.

• “From” headers from the spoofed thread lack any data headers like actual forwarded emails.

• Suspicious language used in the spoofed thread such as “no need to copy me”.
• Suspicious language in headers i.e display name not matching sender address, subjects using financial lure keywords like ‘due bill’, ‘ACH Parment’ etc.
• Despite the sophistication of the generated content, several inconsistencies remained visible to defenders
• In real email threads, the previous threads are normally tabbed or otherwise visually grouped, while the previous threads in this example were left aligned.
• An additional inconsistency was observed where the targeted company’s CEO requested the recipient to send the invoice directly to victims and not CC the sender. However, in the most recent thread, the CEO stated that the invoice is approved and the invoice is sent from his address.

Domain registration

Before initiating the campaign, the threat actor registered several domains. A ‘ServiceNow’ lookalike domain service-nowinc[.]com was registered on July 31, shortly before the campaign activity was observed. This domain was used for the spoofed email address of ServiceNow President. It was also used in several places in the fabricated invoice such as in the contact email in case of any questions. The actor also registered another domain on the same day. The domain domainlify[.]net was used in the Reply-To email.

Generative AI usage

Microsoft observed several indicators consistent with AI-assisted template development. These included extensive HTML comments, structured section labeling, and highly uniform template construction. While these indicators suggest generative AI involvement, they do not independently establish the extent to which AI generated campaign content.

Examples:

Additionally, the use of ‘em dash’ (“—”) and banner ‘===========’ have also become other indicators associated with AI usage.

One possible indication of template-based generation is that invoice identifiers and narrative structure remained largely consistent across samples while organization-specific details changed between targets.

Mitigation and protection guidance

Microsoft provides layered protection against this type of executive-impersonation and invoice-fraud campaign. Properly configured email authentication, spoof protection, mail-flow connectors, and Microsoft Defender for Office 365 help identify and block suspicious messages before delivery; messages later determined to be malicious can be quarantined or removed through post-delivery remediation, including Zero-hour Auto Purge. Security teams can then use Microsoft Defender XDR and Security Copilot to investigate related alerts, affected users, and campaign indicators, coordinate response, and take remediation actions.

Together, these capabilities help reduce the likelihood that fraudulent payment requests reach finance personnel and support faster containment if a message is delivered.

To defend against social engineering campaigns involving executive impersonation, invoice fraud, and potentially AI-assisted content development, Microsoft recommends the following mitigations:

Configure automatic attack disruption in Microsoft Defender XDR. Automatic attack disruption is designed to contain attacks in progress, limit the impact on an organization’s assets, and provide more time for security teams to remediate the attack fully.

Enable Zero-hour auto purge (ZAP) in Office 365 to quarantine sent mail in response to newly acquired threat intelligence and retroactively neutralize malicious phishing, spam, or malware messages that have already been delivered to mailboxes.

Invest in advanced anti-phishing solutions that monitor and scan incoming emails and visited websites. For example, organizations can leverage web browsers like Microsoft Edge that automatically identify and block malicious websites, including those used in this phishing campaign, and solutions that detect and block malicious emails, links, and files.

These links provide information on how to properly configure mail flow with connectors:

• Manage mail flow using a third-party cloud service with Exchange Online
• Configure mail flow using connectors in Exchange Online
• Mail flow rules (transport rules) in Exchange Online
• Enhanced filtering for connectors in Exchange Online

These links provide information on configuring SPF, DKIM, and DMARC:

• Email authentication in cloud organizations
• Set up SPF to identify valid email sources for your custom cloud domains
• Set up DKIM to sign mail from your cloud domain

Microsoft Defender detections

Microsoft Defender customers can refer to the list of applicable detections below. Microsoft Defender coordinates detection, prevention, investigation, and response across endpoints, identities, email, and apps to provide integrated protection against attacks like the threat discussed in this blog.

Microsoft Security Copilot

Security Copilot customers can use the standalone experience to create their own prompts or run the following prebuilt promptbooks to automate incident response or investigation tasks related to this threat:

• Incident investigation
• Microsoft User analysis
• Threat actor profile
• Threat Intelligence 360 report based on MDTI article
• Vulnerability impact assessment

Note that some promptbooks require access to plugins for Microsoft products such as Microsoft Defender XDR or Microsoft Sentinel.

Threat intelligence reports

Microsoft Defender XDR customers can use Threat Analytics reports in the Defender portal (requires license for at least one Defender XDR product) to get the most up-to-date information about the malicious activity and techniques discussed in this blog. These reports provide the intelligence, protection information, and recommended actions to prevent, mitigate, or respond to associated threats found in customer environments.

MITRE ATT&CK Techniques observed

This threat has exhibited use of the following attack techniques. For standard industry documentation about these techniques, refer to the MITRE ATT&CK framework.

Reconnaissance

T1591 – Gather Victim Organization Information
Threat actors collect publicly available information about target organizations, executives, finance personnel, vendors, and business relationships to build convincing invoice-fraud narratives.

T1598 – Phishing for Information
Information gathered from victims and public sources is used to craft highly targeted business email compromise (BEC) lures.

Resource Development

T1583.001 – Acquire Infrastructure: Domains
Threat actors register domains that impersonate trusted organizations, vendors, or business partners.

T1585.002 – Establish Accounts: Email Accounts
Attacker-controlled email accounts are created to support impersonation and fraudulent communications.

T1583 – Acquire Infrastructure
Third-party email delivery infrastructure and supporting services are leveraged to distribute campaigns.

Initial Access

T1566 – Phishing
Targeted phishing emails are delivered to finance personnel using executive and vendor impersonation themes.

T1566.001 – Spearphishing Attachment
Fraudulent invoices or supporting documents are attached to phishing emails.

T1566.003 – Spearphishing via Service
Third-party email services are used to distribute phishing messages and improve legitimacy.

Defense evasion

T1036 – Masquerading
Attackers disguise emails, domains, invoices, and business correspondence as legitimate communications.

T1656 – Impersonation
Executives, vendors, and trusted business entities are impersonated to establish credibility and influence payment decisions.

Impact

T1657 – Financial Theft
Victims are deceived into transferring funds to attacker-controlled financial accounts through fraudulent invoice payment requests.

Indicators of compromise (IOC)

Learn More

For the latest security research from the Microsoft Threat Intelligence community, check out the Microsoft Threat Intelligence Blog.

To get notified about new publications and to join discussions on social media, follow us on LinkedIn, X (formerly Twitter), and Bluesky.

To hear stories and insights from the Microsoft Threat Intelligence community about the ever-evolving threat landscape, listen to the Microsoft Threat Intelligence podcast.

Review our documentation to learn more about our real-time protection capabilities and see how to enable them within your organization.  

• Learn more about securing Copilot Studio agents with Microsoft Defender 
• Evaluate your AI readiness with our latest Zero Trust for AI workshop.
• Microsoft 365 Copilot AI security documentation
• How Microsoft discovers and mitigates evolving attacks against AI guardrails

• How Microsoft discovers and mitigates evolving attacks against AI guardrails | Microsoft Security Blog
• Manipulating AI memory for profit: The rise of AI Recommendation Poisoning | Microsoft Security Blog – a related example of an AI-era technique observed in email traffic
• Defending the inbox against prompt injection attacks | Microsoft Defender for Office 365 Blog – feature announcement introducing prompt injection protection in Microsoft Defender for Office 365.
• Prompt injection protection in Microsoft Defender for Office 365 – official documentation of the prompt injection protection in Microsoft Defender for Office 365.

The post Protecting organizations from AI-assisted executive impersonation and invoice fraud appeared first on Microsoft Security Blog.

Every wave of technology excitement creates a new opportunity for cyberattackers, and AI is no exception. Microsoft Threat Intelligence has published research showing a growing set of campaigns that impersonate popular AI platforms and tools, including ChatGPT, Microsoft Copilot, DeepSeek, and Claude.¹ The goal is to make phishing, search-driven malware campaigns, and malvertising—which is malicious advertising that uses online ads to lure users to harmful sites, downloads, or redirect chains—more convincing. A ChatGPT-themed phishing campaign sent up to 100,000 emails in a single day, tricking users into updating their ChatGPT Plus payment information and stealing personal and credit card data. These campaigns do not represent a compromise of the AI services being referenced. They represent something more familiar—cyberattackers doing what they have always done: borrowing trust. Right now, AI brands can carry significant trust and curiosity, making them attractive themes for cyberattackers to exploit.

Understanding why this trend matters and what it means for security teams is critical to shaping a modern protection strategy. The tactics are the same ones cyberattackers have always refined: urgency, curiosity, and impersonation of something familiar to lower a user’s guard. What has changed is the wrapper. A message about a new model release, a policy update from a familiar AI assistant, or a plugin that promises to make the workday easier is today’s version of the fake invoice or the shipping notification. AI-themed lures deserve attention not because they are a passing trend tied to one product cycle, but because AI remains a genuine source of excitement and urgency for employees and consumers alike, and cyberattackers are exploiting the human instinct to explore what is new, useful, or urgent.

The attack pattern is evolving

Microsoft’s research team recently observed several AI brand campaigns including:

• A ChatGPT-themed phishing kit built to harvest credit card data.
• A Claude-themed campaign that harvested credentials and access tokens through adversary-in-the-middle (AiTM) techniques.
• Malvertising for a fake AI Windows plugin that delivered the Vidar stealer.
• Fraudulent DeepSeek installers distributed through GitHub.

In one case, an initial access broker tracked as Storm-3075 used AI-themed malvertising to distribute payloads for multiple downstream actors, a sign of how quickly this tactic is being commoditized across the criminal ecosystem.

What ties these campaigns together is not sophistication in the traditional sense. It is patience and precision in exploiting a moment. Threat actors are capitalizing on anticipated launches and emerging trends, layering multi-stage redirection chains and disposable infrastructure to slip past both users and defenses. That has real implications for security leaders: it means these incidents cannot be evaluated one surface at a time. A single AI-themed lure can begin as an email, become a malicious link, trigger a suspicious download, and end as an identity or endpoint compromise. Organizations that assess each of those as an isolated event are always a step behind. Organizations that connect them see the full shape of the cyberattack, often early enough to stop it.

Turning AI lures into dead ends with Microsoft Defender

In practice, protection starts before the user ever engages with the lure. Microsoft Defender’s anti-phishing policies can help detect spoofing and impersonation attempts, including user and domain impersonation, first-contact messages, mailbox intelligence signals, and other suspicious sender characteristics. For an AI-themed lure, that might look like a fake “Copilot policy update,” a spoofed support notice, or a lookalike domain designed to make a credential collection page feel legitimate.

If the campaign relies on links, Defender’s Safe Links provides URL scanning and detonation during mail flow, plus time-of-click verification when a user selects a link in email, Microsoft Teams, or supported Microsoft 365 apps. That is important when cyberattackers use redirect chains, delayed activation, or links that appear benign at delivery but later resolve to phishing infrastructure, fake sign-in pages, or malicious downloads.

For campaigns that use fake installers, malicious downloads, or weaponized attachments, Safe Attachments adds another layer by detonating attachments in a virtual environment before delivery when policies are configured. For example, if a message promotes a “new AI plugin” but includes a harmful attachment, Safe Attachments can analyze the file for malware, ransomware, or phishing behavior before it reaches the user. If a cyberthreat is identified after delivery, Defender’s post-delivery filtering capabilities help remove malicious content from mailboxes and reduce the window of exposure.

Protect against multi-stage attacks with attack disruption

But AI-powered attacks don’t stop at email. Their objective is to gain the highest level of access possible, using compromised accounts as a foothold to move across identities, devices, and data. When a cyberattack moves beyond the inbox, Defender helps connect the evidence. Signals from email and collaboration tools, endpoints, identities, and software as a service (SaaS) apps are correlated into an attack story so analysts can see whether the same lure led to a clicked link, a downloaded payload, risky sign-in behavior, or endpoint activity.

As cyberattackers expand beyond email to gain broader access across the environment, Defender moves from detection to disruption. For multi-stage, multi-domain attacks like business email compromise or AiTM, Defender’s powerful, built-in response capability, attack disruption, will contain the compromised asset during the attack to prevent further lateral movement while security teams investigate and remediate. Attack disruption contains more than 81,000 compromised user accounts monthly and is now disrupting more than 45,000 AiTM attacks each month.

In a recent case study, Defender disrupted a business email compromise attack within four minutes of the initial activity (Figure 4). While response times may vary by scenario, this case shows the impact of attack disruption on a real cyberthreat. The cyberattacker used a convincing document-sharing lure to trick a user to start a legitimate Microsoft device code sign-in flow, which avoided traditional credential theft techniques. Defender recognized the resulting device code authentication and follow-on activity as suspicious, correlated signals across identity and email telemetry, and disrupted the attack within four minutes before the attacker could establish persistence, create inbox rules, or execute payroll fraud.

The takeaway

AI brands are the new bait, but the underlying lesson is bigger than any single campaign. As cyberattackers continue to exploit the momentum around AI, organizations should expect social engineering to become more targeted, more believable, and more difficult to evaluate in isolation.

The answer is not to treat every new lure as a brand-new category of risk. It is to build a protection model that makes trust harder to exploit across the full attack chain. Microsoft Defender helps organizations do that by connecting prevention, detection, investigation, and response across the attack path, so AI-themed lures are harder to deliver, harder to trust, and harder to turn into broader compromise.

To learn more about Microsoft Security solutions, visit our website. Bookmark the Security blog to keep up with our expert coverage on security matters. Also, follow us on LinkedIn (Microsoft Security) and X (@MSFTSecurity) for the latest news and updates on cybersecurity.

¹AI brands as bait: How threat actors are using the AI hype in social engineering, Microsoft Threat Intelligence. June 8, 2026.

The post Detect and disrupt AI-themed attacks with Microsoft Defender appeared first on Microsoft Security Blog.

Microsoft Security Research is tracking active cloud-based intrusions spanning multiple accounts in which unusual sign-ins were followed by threat actor-added authentication methods, high-volume Microsoft Graph activity, SharePoint and OneDrive downloads, and email collection through REST APIs. Microsoft Security Research assesses that this sequence is consistent with automated collection from compromised cloud identities using proxy-associated infrastructure, the activity has been observed since May 2026.

The activity begins with identity-focused social engineering and impersonation infrastructure, proceeds through authentication persistence and cloud reconnaissance, and is followed by targeted data access and activity consistent with data collection and potential exfiltration. Domains, IP addresses, and hosting providers can change quickly, but the recurring sequence of identity compromise, persistence, reconnaissance, content discovery, and exfiltration provides a more durable basis for investigation. Defenders should investigate this sequence across identity, Microsoft Graph, SharePoint, OneDrive, and Exchange signals, then revoke sessions and remove unauthorized authentication methods for confirmed compromises.

Attack chain overview

Step 1-2 : Initial access: Passkey and SSO lures

The attack often begins with a seemingly routine call or message on a user’s personal phone number from someone claiming to be from the organization’s IT helpdesk. The caller creates a sense of urgency, explaining that a passkey, multifactor authentication (MFA), or single sign-on (SSO) configuration must be updated immediately to avoid disruption. Employees are directed to a website that closely resembles a legitimate Microsoft sign-in experience and may receive the link through SMS messages sent directly to their personal mobile phones.

Despite the frequent use of passkey-themed lures, passkey enrollment is often not the actor’s true objective. Instead, the passkey narrative serves as a convincing pretext to guide victims through adversary-in-the-middle (AiTM) phishing or device-code authentication flows. In AiTM scenarios, the actor captures credentials and session tokens; in device code attacks, the victim unknowingly authorizes access on the actor’s behalf. This initial interaction may leave very little forensic evidence. If the victim opens the phishing link on a personal mobile device that is not onboarded to Microsoft Defender for Endpoint, the related activity may be absent from endpoint telemetry.

In many investigations, the employee’s recollection of a phone call or text message becomes the earliest and sometimes the only evidence explaining how the compromise began. As a result, investigators must often reconstruct the attack by connecting these reports with subsequent sign-ins, device code authentication events, token activity, and authentication method changes.

Reconnaissance on targeted organization

The actor appears to invest heavily in pre-attack research, likely gathering information about employees and organizational structure from public sources such as social networking and professional profiling platforms.

Reusable domains, personalized targeting

In a smaller number of cases, actors take advantage of already compromised accounts to expand their reach. Using a trusted employee identity, they send similar passkey-themed messages through Microsoft Teams, making the request appear legitimate and significantly increasing the likelihood of engagement. To support these operations, the actors rapidly deploy convincing phishing infrastructure built around themes such as passkeys, SSO enrollment, account activation, and identity verification.

A commonly observed technique involves registering generic domains and embedding the target organization’s name as a subdomain, creating URLs that appear familiar at first glance. Multiple domains may be created for the same organization, allowing the actor to rotate infrastructure as needed. These domains are often registered with Nicenic registrar (observed in previous extortion campaigns) and operational within hours, giving defenders little opportunity to identify and block the infrastructure before employees encounter it. Registration alone should not be interpreted as evidence of registrar involvement in the activity. For example, company-name.integratedsso[.]com and company-name.secure-passkey[.]com illustrate how the same company name can appear under different actor-controlled domains.

Together, the phone-based social engineering, personalized targeting, trusted internal messaging, and rapidly changing phishing infrastructure form the opening chapter of a highly coordinated intrusion designed to blend technical deception with human trust.

The actor creates domains following the pattern companyname[.]maliciousdomain[.]com to impersonate organization-specific authentication portals. Including the victim organization’s name in the URL helps establish credibility and can persuade users to proceed with authentication. Example: contoso[.]add-passkey[.]com.

Step 3-4 : User identity compromise

From one sign-in to broader application access

In one investigated attack sequence, the activity began with an anomalous sign-in to Microsoft OfficeHome application from an unmanaged device, possibly attacker-owned. Once MFA was completed, the actor began accessing identity portals such as My Sign-Ins and enterprise application stores such as My Apps. Sign-in artifacts, including user-agent patterns, indicated possible AiTM phishing.

Using the same session, the actor further accessed several management applications, including Microsoft Approval Management, which is used for identity and approval-related services. SharePoint Online and OneDrive were used to enumerate sensitive files, primarily through the Graph API. The investigation revealed that the actor’s sessions persisted for approximately one hour while enumerating sensitive files and internal applications.

Passkey lure leads to device code phishing

In another investigated attack sequence, the actor was observed using the device code flow to compromise the session token after the passkey lure. In device code phishing, the user is persuaded to enter a code on the legitimate Microsoft authentication page. This approval issues a token to an attacker-controlled client, which can then access permitted resources without stealing a browser cookie. Following the device code flow, the actor successfully replayed the compromised token, effectively bypassing MFA and conducting enumeration and further attack progression.

Reusing the same credentials after an earlier compromise

The third attack pattern involved the actor signing in with compromised credentials, with MFA approved using a previously registered PhoneAppOTP method. This suggests that the attacker had registered the authenticator app days before launching the campaign. Once the sign in was successful, the actor followed the same reconnaissance pattern observed in other attack sequences. This activity was primarily carried out using an automated system developed with Node.js and Microsoft Graph.

To illustrate how the activity unfolded over time, the following timeline summarizes the key events identified during the investigation.

Step 5 : New MFA device for persistence

Following initial access, the actor’s first objective was to transform a temporary compromise into a persistent foothold. Rather than relying solely on stolen credentials, the actor enrolled an MFA method under their control, typically by registering a new phone number, authenticator application, or software-based one-time password (OTP) token. This effectively inserted an actor-controlled factor into the victim’s identity, allowing future authentication challenges to be satisfied without the user’s involvement.

By registering an actor-controlled MFA method, the threat actor ensured that future authentication challenges could be satisfied using a factor they controlled. While MFA enrollment alone does not survive a complete credential and session reset, it provides a durable persistence mechanism when combined with stolen tokens, unrevoked sessions, or subsequent access to valid credentials. As a result, actors frequently establish MFA persistence early in the intrusion to increase the likelihood of maintaining long-term access to the compromised identity.

Phone or authenticator device addition

Detects a newly registered MFA device with a populated device token. The query compares the previous and updated authentication method values and returns newly added device records.

CloudAppEvents
 | where ActionType == "Update user."
 | where tostring(RawEventData.ResultStatus) == "Success"
 | where RawEventData has_any ("StrongAuthenticationPhoneAppDetail", "StrongAuthenticationUserDetails")
 | extend AccountObjectId = extract(@"User_([a-f0-9\-]+)", 1, tostring(RawEventData.Target))
 | where isnotempty(AccountObjectId)
 | mvexpand ModifiedProp = RawEventData.ModifiedProperties
 | where tostring(ModifiedProp.Name) in ("StrongAuthenticationPhoneAppDetail", "StrongAuthenticationUserDetails")
 | extend OldValue = tostring(ModifiedProp.OldValue),
          NewValue = tostring(ModifiedProp.NewValue)
 | extend OldDeviceCount = countof(OldValue, @"""Id"""),
          NewDeviceCount = countof(NewValue, @"""Id""")
 | where NewDeviceCount > OldDeviceCount

Software token addition

Below is a real-world example of attacker controlled Software token added to the user’s identity with Update user operation. This is added as a second NewValue entry containing the device name NO_DEVICE, device token NO_DEVICE_TOKEN, and the SoftwareTokenActivated device tag.

{
[EF1.1][KR1.2]   "Id": "[GUID_REDACTED]",
   "CreationTime": "2026-09-04T16:57:42.0000000Z",
   "OrganizationId": "[GUID_REDACTED]",
   "Operation": "Update user.",
   "RecordType": 8,
   "Workload": "AzureActiveDirectory",
   "ResultStatus": "Success",
   "UserKey": "Not Available",
   "UserId": "ServicePrincipal_[GUID_REDACTED]",
   "Version": 1,
   "UserType": 4,
   "ObjectId": "[EMAIL_REDACTED]",
   "ModifiedProperties": [
     {
       "Name": "StrongAuthenticationPhoneAppDetail",
       "OldValue": [
         {
           "DeviceName": "[DEVICE_NAME_REDACTED]",
           "DeviceToken": "[DEVICE_TOKEN_REDACTED]",
           "DeviceTag": "iOS",
           "PhoneAppVersion": "6.8.53",
           "OathTokenTimeDrift": 0,
           "DeviceId": "[GUID_REDACTED]",
           "Id": "[GUID_REDACTED]",
           "TimeInterval": 0,
           "AuthenticationType": 3,
           "NotificationType": 2,
           "LastAuthenticatedTimestamp": "2026-09-04T16:54:47.6425033Z",
           "AuthenticatorFlavor": "Authenticator",
           "HashFunction": null,
           "TenantDeviceId": null,
           "SecuredPartitionId": 20111,
           "SecuredKeyId": 7
         }
       ],
       "NewValue": [
         {
           "DeviceName": "[DEVICE_NAME_REDACTED]",
           "DeviceToken": "[DEVICE_TOKEN_REDACTED]",
           "DeviceTag": "iOS",
           "PhoneAppVersion": "6.8.53",
           "OathTokenTimeDrift": 0,
           "DeviceId": "[GUID_REDACTED]",
           "Id": "[GUID_REDACTED]",
           "TimeInterval": 0,
           "AuthenticationType": 3,
           "NotificationType": 2,
           "LastAuthenticatedTimestamp": "2026-09-04T16:54:47.6425033Z",
           "AuthenticatorFlavor": "Authenticator",
           "HashFunction": null,
           "TenantDeviceId": null,
           "SecuredPartitionId": 20111,
           "SecuredKeyId": 7
         },
         {
           "DeviceName": "NO_DEVICE",
           "DeviceToken": "NO_DEVICE_TOKEN",
           "DeviceTag": "SoftwareTokenActivated",
           "PhoneAppVersion": "NO_PHONE_APP_VERSION",
           "OathTokenTimeDrift": 0,
           "DeviceId": "[GUID_REDACTED]",
           "Id": "[GUID_REDACTED]",
           "TimeInterval": 0,
           "AuthenticationType": 2,
           "NotificationType": 1,
           "LastAuthenticatedTimestamp": "2026-09-04T16:57:42.4514487Z",
           "AuthenticatorFlavor": "Authenticator",
           "HashFunction": "hmacsha1",
           "TenantDeviceId": null,
           "SecuredPartitionId": 20111,
           "SecuredKeyId": 7
         }
       ]
     },
     {
       "Name": "Included Updated Properties",
       "OldValue": "",
       "NewValue": "StrongAuthenticationPhoneAppDetail"
     },
     {
       "Name": "TargetId.UserType",
       "OldValue": "",
       "NewValue": "Member"
     }
   ]
 }

Step 6 : Graph reconnaissance

Once MFA persistence was established, the actor initiated an extensive internal reconnaissance phase using Microsoft Graph to inventory users, groups, permissions, resources, and accessible content across the tenant with the compromised identity. The actor deliberately rotated infrastructure throughout the attack lifecycle, with separate IP addresses often used for authentication, reconnaissance, and exfiltration activities. As a result, piecing together the full intrusion required correlating activity across multiple stages rather than relying on individual network indicators.

The attack underscores a critical detection challenge: Microsoft Graph abuse rarely appears suspicious when viewed through a single API call. Requests to endpoints such as /users, /groups, or /sites are commonplace in enterprise environments. However, when the same identity, application, or access token systematically traverses multiple tenant resources, evaluates privilege and authentication settings, and subsequently accesses mail, files, attachments, or document content, those actions collectively form a clear reconnaissance-to-exfiltration chain. This attack serves as a strong example of why Graph activity must be assessed holistically, with emphasis on behavioral progression and cross-event correlation rather than individual API requests in isolation.

Graph reconnaissance pattern matrix initiated by the actor

Hunt for broad Graph reconnaissance in one session

Find identities or applications touching several reconnaissance categories from the same IP within 30 minutes.

let Lookback = 24h; [MI25.1][IM25.2]
GraphAPIAuditEvents 
| where Timestamp > ago(Lookback) 
| where toint(ResponseStatusCode) between (200 .. 299) 
| extend Uri = tolower(RequestUri), 
        ActorId = coalesce(AccountObjectId,  
                           ServicePrincipalId, ApplicationId), 
        Path = tostring(split(tolower(RequestUri), "?")[0]) 
| extend ReconType = case( 
   Uri has "/organization" or Uri has "/subscribedskus", "Tenant", 
   Uri has "/users" or Uri has "/groups", "Directory", 
   Uri has "/directoryroles" or Uri has "/rolemanagement", "Privilege", 
   Uri has "/applications" or Uri has "/serviceprincipals" 
       or Uri has "/oauth2permissiongrants", "Application", 
   Uri has "/sites" or Uri has "/drive", "Repository", 
   Uri has "/messages" or Uri has "/mailfolders", "Mailbox", 
   "Other") 
| where ReconType != "Other" and isnotempty(ActorId) 
| summarize Requests=count(), Categories=dcount(ReconType), 
           DistinctPaths=dcount(Path), 
           ReconTypes=make_set(ReconType, 10), 
           SampleUris=make_set(RequestUri, 10) 
   by ActorId, IpAddress, ApplicationId, bin(Timestamp, 30m) 
| where Requests >= 10 and Categories >= 3 and DistinctPaths >= 6 
| order by Categories desc, Requests desc

Hunt for privilege, MFA, application, and consent discovery

Highlight sensitive control-plane reconnaissance that can expose persistence or escalation opportunities.

GraphAPIAuditEvents 
| where Timestamp > ago(24h) 
| where toint(ResponseStatusCode) between (200 .. 299) 
| extend Uri = tolower(RequestUri), 
         ActorId = coalesce(AccountObjectId,
                            ServicePrincipalId, ApplicationId) 
| where Uri has_any ("/directoryroles", "/rolemanagement", 
                     "/authentication/methods", "/applications", 
                     "/serviceprincipals", "/oauth2permissiongrants", 
                     "/approleassign") 
| summarize Requests=count(), DistinctPaths=dcount(tostring(split(Uri, "?")[0])), 
            ScopesSeen=make_set(Scopes, 10), 
            SampleUris=make_set(RequestUri, 12) 
    by ActorId, IpAddress, ApplicationId, bin(Timestamp, 30m) 
| where Requests >= 4 and DistinctPaths >= 2 
| order by Requests desc

Hunt for SharePoint and OneDrive repository discovery

Detect search, child traversal, delta queries, and paging used to map file repositories.

GraphAPIAuditEvents 
| where Timestamp > ago(24h) 
| where toint(ResponseStatusCode) between (200 .. 299) 
| extend Uri = tolower(RequestUri), 
         ActorId = coalesce(AccountObjectId,
                            ServicePrincipalId, ApplicationId), 
         Path = tostring(split(tolower(RequestUri), "?")[0]) 
| where Uri has_any ("/sites", "/drives", "/drive/") 
| where Uri has_any ("/search", "/children", "/delta", 
                     "$skiptoken", "%24skiptoken", "$top", "%24top") 
| summarize Requests=count(), DistinctPaths=dcount(Path), 
            SampleUris=make_set(RequestUri, 12) 
    by ActorId, IpAddress, ApplicationId, bin(Timestamp, 20m) 
| where Requests >= 8 and DistinctPaths >= 4 
| order by Requests desc

Hunt for mailbox and attachment reconnaissance

Find concentrated enumeration of messages, mail folders, and attachments.

GraphAPIAuditEvents 
| where Timestamp > ago(24h) 
| where toint(ResponseStatusCode) between (200 .. 299) 
| extend Uri = tolower(RequestUri), 
         ActorId = coalesce(AccountObjectId, 
                            ServicePrincipalId, ApplicationId), 
         Path = tostring(split(tolower(RequestUri), "?")[0]) 
| where Uri has_any ("/messages", "/mailfolders", "/attachments") 
| summarize Requests=count(), 
            DistinctPaths=dcount(Path), 
            MessageRequests=countif(Uri has "/messages"), 
            AttachmentRequests=countif(Uri has "/attachments"), 
            TotalResponseBytes=sum(coalesce(ResponseSize, 0)), 
            SampleUris=make_set(RequestUri, 12) 
    by ActorId, IpAddress, ApplicationId, bin(Timestamp, 30m) 
| where (Requests >= 8 and DistinctPaths >= 4) or AttachmentRequests >= 3 
| order by AttachmentRequests desc, Requests desc

Step 7-8 : High-volume cloud data collection and suspected exfiltration

Following reconnaissance, the actor transitioned into large-scale data collection across Microsoft 365 workloads using the compromised identities. Microsoft observed high-volume access and download activity targeting Microsoft SharePoint Online and Microsoft OneDrive for Business, with some intrusions extending into Microsoft Exchange Online through REST API-based access to email content. Across SharePoint and OneDrive, the activity generated significant volumes of FileAccessed and FileDownloaded events, indicating systematic retrieval of cloud-hosted documents and organizational data.

The activity frequently exhibited characteristics of automation rather than interactive user behavior. In several cases, Microsoft observed the python-httpx user agent associated with high-volume SharePoint and OneDrive access patterns. However, the user agent alone should not be treated as malicious. Instead, such activity should be evaluated in the broader context of data volume, affected identities, source infrastructure, prior reconnaissance activity, and evidence of identity compromise.

Unlike rapid smash-and-grab operations, data exfiltration was typically measured and sustained, often spanning several hours to multiple days depending on the volume of files and email content available to the compromised user. The actors generally maintained a controlled pace of collection, with fewer than 1,000 files or emails accessed within any one-hour period, likely helping the activity blend with normal enterprise usage while enabling the gradual extraction of large amounts of sensitive data over time.

Hunt for exfiltration through Exchange Online

Exfiltration of data through REST API using Microsoft Office or One Outlook Web

CloudAppEvents 
| where isempty(AccountObjectId) 
| where ApplicationId == '20893' 
| where AccountDisplayName in ("One Outlook Web", "9199bf20-a13f-4107-85dc-02114787ef48", "d3590ed6-52b3-4102-aeff-aad2292ab01c") 
| where isnotempty(IPAddress) 
| extend AccountObjectId = tostring(RawEventData.TokenObjectId) 
| summarize ExchangeRestEventCount=count() by IPAddress, AccountObjectId, bin(Timestamp,1h) 
| where ExchangeRestEventCount >= 500

Hunt for exfiltration through Microsoft SharePoint Online, OneDrive for Business

Exfiltration of data through python-httpx user agent

CloudAppEvents 
| where ApplicationId == "20892" or ApplicationId == "15600"
| where ActionType in ("FileDownloaded", "FileAccessed", "SyncDownloadedFull")
| where isnotempty(AccountObjectId) 
| where isnotempty(IPAddress) 
| where isnotempty(UserAgent) 
| where UncommonForUser has_any("ISP","UserAgent") 
| where UserAgent has 'python-httpx' 
| project Timestamp, AccountObjectId, IPAddress, ISP, UserAgent 
| summarize FilesAccessedLastWindow = count() by AccountObjectId, IPAddress, ISP, UserAgent, bin(Timestamp,2h) 
| where FilesAccessedLastWindow >=100

Hunt for anomalous high-volume exfiltration

Exfiltration of data through anonymous proxy

CloudAppEvents
| where ApplicationId in (20892, 20893, 15600)
| where ActionType in~ ("FileDownloaded", "FileAccessed", "FilePreviewed")
| where IsAnonymousProxy == true
| where UserAgent !has "ODMTADemand"
| extend FileSizeBytes = coalesce(tolong(RawEventData.FileSizeBytes), 0)
| summarize
    FileSizeBytes = sum(FileSizeBytes),
    FirstSeen = min(Timestamp),
    LastSeen = max(Timestamp),
    EventCount = count(),
    ActionTypes = make_set(ActionType),
    Applications = make_set(Application)
    by AccountObjectId, IPAddress,
       TimeBucket = bin(Timestamp, 2h), UserAgent, ISP
| extend FileSizeGB =
    round(FileSizeBytes / 1024.0 / 1024.0 / 1024.0, 2)
| where FileSizeGB >= 5 or EventCount >= 1000
| order by EventCount desc

Attribution

Microsoft Threat Intelligence assesses that the initial access activity observed in this campaign is used by a range of threat actors, including Storm-3121, Storm-3032, and others. Storm-3121 conducts initial access activity leading to ShinyHunters and Falcon extortion. Storm-3032 represents a set of actors that splintered from the BlackFile group and now operate under the Helix extortion banner. That being said, Microsoft Defender has detection coverage for the known tactics, techniques and procedures from Storm-3121, Storm-3032 and other operators in the same ecosystem.

Mitigation and protection guidance

Microsoft recommends that organizations investigate identity and cloud-workload signals as a connected sequence, with priority given to unusual sign-ins followed bys authentication method enrollment, Microsoft Graph reconnaissance, token issuance, and abnormal SaaS download or mailbox activity.

Investigate

• Review newly registered authentication methods and devices for users with risky or unusual sign-ins and remove unauthorized methods after validating the user.
• Investigate high-volume or programmatic Microsoft Graph activity involving directory enumeration, role discovery, service principal discovery, SharePoint, OneDrive, or sensitivity-label discovery.
• Correlate SharePoint and OneDrive download anomalies, Exchange REST activity, and mailbox or attachment searches with identity and authentication events.

Contain and remediate

• Revoke active sessions and refresh tokens for confirmed compromised identities, reset credentials, remove attacker-registered authentication methods, remove attacker created mailbox rules, and require secure re-registration of authentication methods.

Reduce future risk

• Do not treat an IP or domain match as conclusive on its own. Validate workload behavior, affected identities, persistence events, and data access volume.
• Enforce phishing-resistant MFA (FIDO2/passkeys, Windows Hello for Business) via Conditional Access
• Enforce Conditional Access that requires a managed, compliant device for Exchange, SharePoint, and Graph-privileged apps
• Enforce strict conditional access controls for security info registration, including setting required sign-in frequency to always (require a new interactive auth), requiring managed devices and/or named locations, and requiring phish-resistant MFA as a required authentication strength, and in a separate policy blocking security info registration with a high sign-in risk condition
• Enforce risk-based access policies for risky sign-ins and risky users – remediate elevated risk with phishing-resistant MFA or secure password change, and block access at the highest risk levels.
• Train users against voice and email phishing that targets MFA and passkey enrollment. Provide a verified channel to report unsolicited authentication requests.
• Block the device code and authentication transfer flows via Conditional Access, except where an explicit business need exists.
• Restrict user consent for applications, require admin approval, and regularly review service principals holding high-privilege Graph permissions such as Mail.Read, Files.Read.All, and Directory.Read.All.
• Limit access from unmanaged devices to web-only sessions without download or sync, and disable anonymous sharing links in SharePoint and OneDrive.
• Enable Microsoft Graph activity logs and mailbox auditing, and alert on anomalous enumeration, authentication-method registration, and high-volume file or mail access.
• Educational training: Verify user identity through a rigorous process before performing any helpdesk-initiated credential or MFA reset, and alert on every such reset.

Microsoft Defender XDR detections

Microsoft Defender XDR customers can refer to the list of applicable detections below. Microsoft Defender XDR coordinates detection, prevention, investigation, and response across endpoints, identities, email, and apps to provide integrated protection against attacks like the threat discussed in this blog.

Customers with provisioned access can also use Microsoft Security Copilot in Microsoft Defender to investigate and respond to incidents, hunt for threats, and protect their organization with relevant threat intelligence.

Microsoft Security Copilot

Security Copilot customers can use the standalone experience to create their own prompts or run the following prebuilt promptbooks to investigate activity associated with this intrusion pattern:

• Incident investigation – Generates investigation summaries and helps analysts understand incidents involving compromised identities, suspicious sign-ins, persistence activity, and cloud-based data access.
• Microsoft User analysis – Analyses user accounts, sign-in activity, authentication events, risk indicators, and related identity signals that may help identify compromised accounts.

Customers can also use Microsoft Security Copilot together with Microsoft Threat Intelligence to investigate indicators, threat activity, and related intelligence associated with suspicious sign-ins, Microsoft Graph reconnaissance, and cloud data exfiltration activity.

Note that some promptbooks require access to plugins for Microsoft products such as Microsoft Defender XDR or Microsoft Sentinel.

Threat intelligence reports

Microsoft customers can use the following reports in Microsoft products to get the most up-to-date information about the threat actor, malicious activity, and techniques discussed in this blog. These reports provide intelligence, protection information, and recommended actions to prevent, mitigate, or respond to associated threats found in customer environments.

MITRE ATT&CK Techniques observed

Reconnaissance

• T1591 Gather Victim Org Information | Use of themed domains for credential phishing pages
• T1598.003 Phishing for Information: Spear-phishing Link | Use of spear-phishing emails with links to spoofed sign-in pages

Resource Development

• T1583.001 Acquire Infrastructure: Domains | Use of themed domains for credential phishing pages
• T1585.002 Establish Accounts: Email Accounts | Delivery infrastructure that is comprised of accounts from consumer email providers like Hotmail

Initial Access

• T1078.004 Valid Accounts: Cloud Accounts | The compromised cloud identity is used to authenticate to Microsoft cloud services.

Persistence

• T1556.006 Modify Authentication Process: Multi-Factor Authentication | After gaining account access, the threat actor registers a new authentication method, mobile device, or software-based OTP to establish persistent access to the compromised identity.

Discovery

• T1087.004 Account Discovery: Cloud Account | The threat actor uses Microsoft Graph to enumerate tenant users and identities, including information useful for identifying high-value identities and privileged accounts.
• T1069.003 Permission Groups Discovery: Cloud Groups | Microsoft Graph reconnaissance enumerates group memberships and directory roles, allowing the threat actor to understand privilege relationships and identify potentially valuable identities.

Collection

• T1530 Data from Cloud Storage | The actor searches and accesses SharePoint and OneDrive content and performs high-volume file access/download activity to collect targeted cloud-hosted information.
• T1114 Email Collection | Mailboxes, messages, and attachments are searched for material of interest and email is collected through REST APIs.
• T1213 Data from Information Repositories | The actor searches enterprise cloud repositories, including SharePoint content and other organizational cloud data, to identify information of value for collection.

Exfiltration

• T1567 Exfiltration Over Web Service | The campaign performs high-volume cloud data access and uses automated Python-based activity for rapid collection and exfiltration.

Advanced hunting queries

Additional advanced hunting query for Graph reconnaissance:

Hunt for automated pagination, delta, and search behavior

Identify actors walking large Graph result sets or repeatedly querying for data.

GraphAPIAuditEvents 
| where Timestamp > ago(24h) 
| where toint(ResponseStatusCode) between (200 .. 299) 
| extend Uri = tolower(RequestUri), 
         ActorId = coalesce(AccountObjectId, 
                            ServicePrincipalId, ApplicationId), 
         Path = tostring(split(tolower(RequestUri), "?")[0]) 
| where Uri has_any ("$top", "%24top", "$skip", "%24skip", 
                     "$skiptoken", "%24skiptoken", "$count", 
                     "%24count", "/delta", "/search") 
| summarize AutomatedRequests=count(), DistinctPaths=dcount(Path), 
            SampleUris=make_set(RequestUri, 12) 
    by ActorId, IpAddress, ApplicationId, bin(Timestamp, 15m) 
| where AutomatedRequests >= 8 and DistinctPaths >= 4 
| order by AutomatedRequests desc

Hunt for reconnaissance progressing to content collection

Prioritize sessions where broad discovery and content retrieval occur together.

GraphAPIAuditEvents 
| where Timestamp > ago(24h) 
| where toint(ResponseStatusCode) between (200 .. 299) 
| extend Uri = tolower(RequestUri), 
         ActorId = coalesce(AccountObjectId,
                            ServicePrincipalId, ApplicationId) 
| extend ActivityType = case( 
    Uri has "/content" or Uri has "/attachments", "ContentCollection", 
    Uri has "/users" or Uri has "/groups", "DirectoryRecon", 
    Uri has "/directoryroles" or Uri has "/rolemanagement" 
        or Uri has "/authentication/methods", "PrivilegeRecon", 
    Uri has "/applications" or Uri has "/serviceprincipals", "ApplicationRecon", 
    Uri has "/sites" or Uri has "/drives" or Uri has "/drive/", "RepositoryRecon", 
    Uri has "/messages" or Uri has "/mailfolders", "MailboxRecon", 
    "Other") 
| where ActivityType != "Other" 
| summarize DiscoveryFirst=minif(Timestamp, ActivityType != "ContentCollection"), 
            CollectionFirst=minif(Timestamp, ActivityType == "ContentCollection"), 
            DiscoveryCategories=dcountif(ActivityType, ActivityType != "ContentCollection"), 
            ContentRequests=countif(ActivityType == "ContentCollection"), 
            TotalResponseBytes=sum(coalesce(ResponseSize, 0)), 
            SampleUris=make_set(RequestUri, 15) 
    by ActorId, IpAddress, ApplicationId, bin(Timestamp, 1h) 
| where isnotnull(DiscoveryFirst) and isnotnull(CollectionFirst) 
| where CollectionFirst >= DiscoveryFirst 
    and DiscoveryCategories >= 2 and ContentRequests >= 1 
| order by CollectionFirst desc

Hunt for exfiltration through Microsoft Graph

Prioritize sessions where broad discovery and content retrieval occur together.

GraphAPIAuditEvents 
| where Timestamp > ago(24h) 
| where toint(ResponseStatusCode) between (200 .. 299) 
| extend Uri = tolower(RequestUri), 
         ActorId = coalesce(AccountObjectId, 
                            ServicePrincipalId, ApplicationId) 
| extend ActivityType = case( 
    Uri has "/content" or Uri has "/attachments", "ContentCollection", 
    Uri has "/users" or Uri has "/groups", "DirectoryRecon", 
    Uri has "/directoryroles" or Uri has "/rolemanagement" 
        or Uri has "/authentication/methods", "PrivilegeRecon", 
    Uri has "/applications" or Uri has "/serviceprincipals", "ApplicationRecon", 
    Uri has "/sites" or Uri has "/drives" or Uri has "/drive/", "RepositoryRecon", 
    Uri has "/messages" or Uri has "/mailfolders", "MailboxRecon", 
    "Other") 
| where ActivityType != "Other" 
| summarize DiscoveryFirst=minif(Timestamp, ActivityType != "ContentCollection"), 
            CollectionFirst=minif(Timestamp, ActivityType == "ContentCollection"), 
            DiscoveryCategories=dcountif(ActivityType, ActivityType != "ContentCollection"), 
            ContentRequests=countif(ActivityType == "ContentCollection"), 
            TotalResponseBytes=sum(coalesce(ResponseSize, 0)), 
            SampleUris=make_set(RequestUri, 15) 
    by ActorId, IpAddress, ApplicationId, bin(Timestamp, 1h) 
| where isnotnull(DiscoveryFirst) and isnotnull(CollectionFirst) 
| where CollectionFirst >= DiscoveryFirst 
    and DiscoveryCategories >= 2 and ContentRequests >= 1 
| order by CollectionFirst desc

Indicators of compromise (IOC)

Learn more

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The post Passkey-themed social engineering leads to identity and cloud compromise appeared first on Microsoft Security Blog.

Microsoft researchers observed a high-volume phishing campaign using invisible Unicode tag characters, a technique popularized in AI prompt injection research as ASCII Smuggling. Instead of using these characters to hide instructions from people while exposing them to AI models, the attacker used them to split financial lure words such as ‘funding’ to prevent email filters from parsing them.

The finding emerged from Microsoft Defender for Office 365 prompt injection protection research, showing how AI-era evasion techniques can surface in traditional phishing campaigns. In Microsoft telemetry, hits on a hunting signature designed to detect ASCII-smuggling increased sharply beginning February 9, 2026, and remained elevated on weekdays for approximately three months. Microsoft Defender for Office 365 telemetry showed that the majority of messages were flagged by layered protections rather than by reliance on a single Unicode-specific signal.

What is ASCII smuggling?

“ASCII smuggling” refers to the use of invisible or non-rendering Unicode characters to hide content inside text that looks normal. The most abused range is the Unicode Tags block, U+E0000 to U+E007F. This block contains a shadow copy of the printable ASCII characters (for example, U+E0041 mirrors ‘A’, U+E0061 mirrors ‘a’). The block was originally intended for language tagging and is now largely deprecated.

The important property for an attacker is this: most of these code points are not rendered by typical fonts and user interfaces. A string can therefore carry a message that is not readable to a human but will be processed by any language model or other software that receives a copy of the email content.

Why the AI-security world made it famous

Over the past year, ASCII smuggling became a recurring technique in the prompt injection and cross-prompt injection (XPIA) literature. The attack pattern is straightforward:

1. An attacker hides instructions inside invisible tag characters embedded in a web page, document, email, or other content.
2. A human (and many user interfaces) sees nothing unusual.
3. An AI assistant that ingests the raw text does “see” the hidden characters, decodes them as text, and may be induced to follow threat actor-controlled instructions, potentially including data exposure or unauthorized actions depending on the assistant’s permissions and safeguards.

Because this technique cleanly demonstrates the gap between what the human sees and what the model reads, it appeared frequently in AI red-teaming write-ups, conference talks, and tooling throughout 2025. That attention put a spotlight on the U+E0000-U+E007F range.

Because tag characters are invisible to humans but exist at the text-processing level, the same property that makes them useful for smuggling instructions into a model also makes them useful for obfuscating keywords before a detector evaluates them. The intent is inverted, but the mechanism is similar and a user’s suspicions are not raised.

Writing a practical ASCII-smuggling signature

As part of work on Microsoft Defender for Office 365 prompt injection protection, we built hunting logic for email-borne XPIA and prompt obfuscation patterns: content that looks harmless to users but may carry hidden instructions for an AI system that ingests the raw message. The same hunt designed to identify prompt injection risk in email became the starting point for this phishing-evasion discovery.

One practical way to hunt for ASCII smuggling is to look for messages carrying characters from the Unicode tags block (U+E0000-U+E007F), the hallmark of attempts to hide instructions from, or for, an AI model. That broad signature is a useful starting point, but it needs enough Unicode context to avoid mistaking legitimate tag-character sequences for abuse.

The first version simply flagged any code point in that range, which proved too blunt. It kept firing on a small subset of perfectly legitimate messages – which, on inspection, all contained one of three subdivision flag emojis: the flags of England, Scotland, and Wales – because those emojis are encoded using tag characters.

After those exclusions, remaining hits were mostly benign artifacts from email-security gateways, mailbox providers, and security or AI researchers forwarding or testing messages that contained tag characters. This provided a good baseline where any spikes would indicate abuse of this technique by attackers.

Figure 1. The three subdivision flag emojis – England, Scotland, and Wales – that tripped the naive signature. Each is encoded as a sequence of invisible Unicode tag characters (U+E0000-U+E007F).

Figure 2. The Wales flag emoji pasted into the ASCII Smuggler tool from Embrace The Red. What renders as a single flag is actually a base flag code point (U+1F3F4) followed by an invisible tag-character sequence spelling gbwls (U+E0067 U+E0062 U+E0077 U+E006C U+E0073) and a terminating tag (U+E007F) – the same U+E0000-U+E007F range the signature watches for.

What we observed: ASCII smuggling repurposed for phishing

New activity emerges in telemetry

The tuned ASCII-smuggling signature began as an AI-security hunt for hidden prompt injection content in email. Instead, it surfaced finance-themed phishing messages using the same Unicode range for filter evasion.

On February 9, 2026, signature hits increased sharply. The following chart reflects Microsoft Defender for Office 365 telemetry for the hunting signature over the measured period:

The day before onset (February 8) the signature fired on roughly 21,000 messages; the next day it fired on more than 1.3 million. Most of the emails can be formed into a cluster of roughly 150 finance-themed sender domains.

Observed over three months with a weekly rhythm

Continuing to track the clustered sender domains forward in time, we measured messages matching the activity described every day. The high-volume phase persisted for roughly three months after February 9 and dropped sharply after May 15, 2026. These dates bound the observed use of the specific technique in our telemetry, not the broader campaign, which started earlier without it and continued without it.

Two characteristics stand out:

• A strict weekly cadence. The campaign ran hard on weekdays and went almost completely silent every weekend. Sundays’ volume collapsed to a near-zero and then back to full volume the next day. This on/off pattern is typical of scheduled bulk-sending infrastructure.
• A long, gradual decline. After an intense first phase, with weekday volumes of 1 to 2.37 million messages, peaking on February 26, the numbers stepped down slowly to roughly 80% less per weekday by late March. The high-volume usage of the technique dropped sharply after May 15, with lower residual activity through mid-June and occasional smaller spikes.

After identifying the activity through this technique-specific signal, we connected it to a broader ActiveCampaign-delivered SBA-themed phishing campaign that Fortra had documented earlier. That earlier reporting indicates the campaign predated the adoption of Unicode tag characters; our analysis focuses on the period and messages in which this method was present, not the full lifetime of the broader campaign.

Not instruction smuggling, but filter evasion

Observed obfuscation pattern

When we looked at a sampling of the flagged messages, the surprise was there were no smuggled instructions to an AI assistant. Instead, the invisible tag characters were inserted inside common financial keywords, splitting them apart so that a literal signature or keyword match would fail.

For example, a finance lure term that appeared normal to the recipient could be transmitted with an invisible tag character in the middle:

funding

became:

fun⟨U+E0020⟩ding

Here, ⟨U+E0020⟩ represents the invisible Unicode TAG SPACE inserted between letters. In the messages we examined, the campaign did not encode a hidden ASCII message in the tag block; it used a single invisible tag character as a separator sprinkled inside high-signal words. Strictly speaking, this is invisible-character insertion using a code point from the ASCII-smuggling tag block, rather than full message smuggling.

Why it can affect detection

To a recipient, and to parsing pipelines that drop or normalize these characters, the word still reads as funding. To a detector matching the literal string funding, or a regex that does not account for interleaved invisible code points, the byte sequence no longer contains the contiguous keyword. Whether real-world detectors behave that way depends on their normalization step, which is examined below.

The bigger prize for the attacker, though, is not preventing the literal string matches; it is the ML- and NLP-based models that increasingly drive modern spam and phishing classification. Unless a filtering system takes a picture of a message and does OCR extraction over the visual image, it may miss this type of attack. A standard email classifier may not reason over whole words exactly as a human sees them; for efficiency, they can first split text into tokens or sub-word pieces. A clean lure term such as funding may be represented as a familiar token or a familiar sequence of sub-tokens. Insert an invisible U+E0020 into the middle, however, and the tokenizer may no longer see that same familiar unit. It might split the text into fun, an unexpected tag character, and ding; it might emit rare or unknown sub-tokens; or, if normalization runs first, it simply removes the U+E0020 character, leaving funding.

Why it can help defenders

There is also a defensive opportunity. Since this kind of manipulation appears so seldom in normal traffic, its presence becomes a high-confidence signal. A technique meant to make messages look more benign to ML models can instead give defenders a low-false-positive indicator to detect on.

What is known and what is new

Inserting invisible or look-alike characters to break keyword and signature matching is a long-standing evasion technique used in spam and phishing: defenders have for years seen zero-width spaces (U+200B), zero-width non-joiners, the no-break space (U+00A0), soft hyphens, and homoglyph substitutions used to fracture words so naive string matchers fail.

What is new is the specific characters and scale of the campaign:

• The character choice. Instead of the usual zero-width space or NBSP, this campaign reached for the Unicode Tags block. That block went from forgotten to famous over the past year because of AI security research into ASCII smuggling and prompt injections.
• The scale and discipline. At its peak in Microsoft telemetry, the campaign generated multi-million message daily volume.
• A possible detection blind spot. Because the Unicode Tags block is less commonly abused than zero-width spaces or NBSP, defenders should verify that normalization and tokenization pipelines handle tag characters consistently.

Financially themed sending domains

The campaign ran on hundreds of disposable, finance-themed sender domains with lures that resembled business loan, line-of-credit, and advance-funding phishing patterns often associated with fraud or credential-harvesting funnels. This pattern accounted for roughly 96% of the volume flagged by the hunting signature. The signature also fired on other domains, but those were unrelated senders – chiefly email-security gateways and personal mailbox providers – not part of the campaign.

A partial sample of sender domains counts from February 9, 2026 alone illustrates both the naming pattern and the per-domain volume:

Table 1. Top 20 (by signature hits) of the 148 finance-themed campaign sender domains seen on February 9, 2026, illustrating the naming convention and per-domain volume.

Every domain is just a recombination of the same small vocabulary. The 20 domains above are built from only 28 word-tokens:

advance · boost · business · capital · catalyst · choice · digital · direct · elevate · express · finance · funding · growth · guardian · harbor · loan · loans · loc · online · our · pulse · rocket · rush · the · united · wave · way · your

Sent through a legitimate email-marketing platform

The finance-themed domains in Table 1 are the brand (header / P2) domains the recipient sees, but the actual mail was relayed through infrastructure associated with the legitimate email-marketing platform ActiveCampaign. The platform, which is used widely for marketing, rewrites every outbound link in the message body to route through its own click-tracking domains (acemlnd[.]com and activehosted[.]com), so the URLs the recipient clicks do not point at the brand domain at all – they look like:

hxxps://＜account-id＞.acemlnd[.]com/＜tracking-token＞
hxxps://＜brand-subdomain＞.activehosted[.]com/＜tracking-token＞

Most of the flagged messages carried links associated with the platform’s tracking domains rather than direct links that point directly to the sender-branded domains. The envelope (P1) senders were platform subdomains of the form em-<id>.<brand-domain>.

ActiveCampaign response

Before we published this information, we shared our findings with ActiveCampaign to help them with this abuse, and they wanted us to share the following statement on their work to detect it:

“We appreciate Microsoft’s research and welcome collaboration with the security community to combat this activity. We take abuse, fraud, and security extremely seriously. We tested the specific technique described in this research against our content-moderation systems: messages containing invisible Unicode characters receive the same moderation verdicts as their unobfuscated equivalents, and heavy use of the technique is itself treated as a suspicious signal. We continually invest in improving our detection and prevention capabilities, including expanding our use of AI and machine learning to identify abusive sending behavior earlier in the account lifecycle.” — ActiveCampaign spokesperson

As with any shared sending service, attacker abuse of customer accounts or workflows can complicate reputation-based filtering. By originating from a reputable marketing platform with established IP reputation and authentication, the activity may appear more similar to legitimate marketing traffic and can complicate reputation-based filtering.

Most observed volume also originated from cloud-hosting ranges consistent with the platform’s outbound infrastructure, with the vast majority coming froma single network block, 173.236.20[.]0/24. This indicator helped us cluster the campaign more precisely but note that this is a legitimate segment that belongs to the abused service, and not an IOC on its own.

Identifying the campaign

Content and infrastructure remained consistent for a long time span, providing an effective way to easily fingerprint this phase of the campaign:

• Unicode content (primary). Invisible Unicode tag characters in the range U+E0000-U+E007F – specifically U+E0020 – spliced inside keywords. Legitimate mail rarely ever carries these code points: the one routine exception, the England/Scotland/Wales flag emojis, is easily excluded.
• Lure and brand pattern. Sender (header / P2) domains assembled from a small finance vocabulary – capital, fund/funding, loan, loc, lend, finance, business, express, growth, solutions, choice, hedge, pillar – recombined into fresh, disposable domains and rotated.
• Envelope (P1) pattern. The bulk of mail is relayed through a single email-marketing platform, recognizable by envelope shape rather than any one name:
  • per-account subdomains shaped em-<digits>.<brand-domain> (regex em-\d+\.), where a small set of reused account numbers fans out across hundreds of brand domains; and
  • the platform’s shared sending pool, shaped acems<N>[.]com and emsd<N>[.]com (e.g. emsd4[.]com, s9.acems10[.]com). Across the measured activity, ~98.5% of messages matched this envelope pattern, and ~99.8% matched the envelope pattern or the platform’s tracking-URL pattern (below).
• Tracking-URL pattern. Click/tracking links on the platform’s domains activehosted[.]com and acemlnd[.]com.
• Sending-origin pattern. The bulk of daily volume – about 92% across two measured weeks – originated from a single /24 network block, 173.236.20[.]0/24.

For a high-precision rule, look for the Unicode content pattern combined with the finance-brand pattern, using the sender infrastructure patterns as corroboration.

However, this is just a phase in a long-running broader campaign, that keeps adapting and evolving. The campaign was observed months earlier following a different set of behaviors and continued even after the usage of the specific technique was dropped. During these shifts in behavior, one signature may no longer describe the campaign, while another still matches.

Is there a detection gap?

The potential gap for mail-defense pipelines is whether Unicode tag characters are normalized or flagged before content detections run. In Defender, our filter stack can take a picture of message contents, extract visible text through OCR, and run analysis over that extracted text to avoid these types of tricks. Implementations vary, so defenders should test how these characters are handled in their own pipelines. For MDO protection, over 99% of messages were flagged by layers that did not depend on catching the tag characters directly, including sender, IP, URL and domain reputations, ML spam/phishing classification, brand-impersonation detection, authentication checks and more.

Emerging techniques don’t stay in one domain

ASCII smuggling earned its reputation as an AI attack, hiding instructions from people while leaving them visible to models. This campaign shows the same technique being repurposed for a different objective: obscuring phishing content from detection systems while remaining readable to the intended target.

The broader lesson is that security techniques rarely stay confined to a single domain. As AI-era attack methods become better understood, threat actors may adapt them for use in more traditional threats such as phishing and spam. This case illustrates how techniques that emerge in AI security research can quickly cross over into established attack ecosystems, reinforcing the need for defenders to view emerging threats through a cross-domain lens.

Mitigation and protection guidance

The core defensive principle is simple: normalize before you match. Any content that will be evaluated by keyword, signature, or regex logic should first have invisible and non-rendering Unicode code points stripped or folded, so that splicing them into a word no longer defeats the match.

Recommended controls

• Strip or normalize Unicode tag characters (U+E0000-U+E007F) – and other zero-width / invisible code points – from email subject and body text before applying spam and phishing content signatures.
• Treat the presence of tag-block characters as a strong anomaly signal. Outside known legitimate tag-sequence uses such as certain subdivision flag emojis, these code points are rare in ordinary mail and can be a high-value anomaly signal.
• Look for the behavioral fingerprint. The observed activity had a distinctive shape: bulk volume from churning, finance-themed disposable domains, on a strict weekday-on / weekend-off schedule. A sudden spike of tag-block characters concentrated on finance-themed senders, switching on and off weekly, is a high-confidence campaign indicator.
• Apply the same normalization upstream of AI ingestion. The same control that defeats this evasion also reduces XPIA / ASCII-smuggling exposure for AI assistants that ingest email content.

Microsoft protections

Microsoft Defender for Office 365 has heuristic detections in place to flag these the tactics employed in this type of campaign. The detection that first surfaced the spike continues to flag messages carrying Unicode tag-block characters, and the financially themed sending domains are being tracked and blocked as they rotate. Microsoft uses layered email protections, including standard and OCR content analysis, sender and domain reputation, URL detonation and reputation, bulk-mail detection, and anti-phishing models, to reduce reliance on any single signal that an attacker can try to evade.

Microsoft Defender for Office 365 prompt injection protection further helps protect against emails that contain prompt injection attempts, including cases where invisible characters are used to hide instructions from users while exposing them to AI systems. The same normalization and detection principles that reduce ASCII-smuggling-based prompt injection risk also help blunt this email-borne reuse of the technique for phishing evasion. Investments in AI security and traditional email security increasingly reinforce one another.

Coverage depends on product licensing, configuration, and telemetry.

Advanced hunting

These queries run against the EmailEvents Advanced Hunting table (and EmailUrlInfo for URL joins). They hunt the campaign by its infrastructure fingerprint – the finance-vocabulary brand senders and the marketing-platform envelope shape – rather than by the invisible tag characters, as the mail body is not exposed through the table’s columns. These queries are starting points and may require environment-specific tuning. The proactive defense is implemented with multiple layers of the enterprise mail-filtering pipeline.

1. Infrastructure pattern – finance-vocabulary senders relayed with the campaign’s envelope shape. Combines the brand-domain pattern (a header sender built from three or more adjacent finance/brand keywords, e.g. digital+capital+boost) with the envelope (MAIL FROM) shape em-<digits> / acems<digits> / emsd<digits> – the durable fingerprint that held across the entire period we measured.

// Finance/brand vocabulary the operator recombines into disposable domains.
let kwds = @"(capital|fund|hedge|express|solutions|choice|lend|growth|loan|loc|finance|business|pillar|advance|boost|catalyst|digital|direct|elevate|guardian|harbor|online|pulse|rocket|rush|united|wave|way|surge|swift|elite)";
EmailEvents
| where Timestamp > ago(30d)
| where EmailDirection == "Inbound"
// Header sender domain made of 3 or more adjacent finance/brand tokens.
| where SenderFromDomain matches regex strcat("(?i)", kwds, kwds, kwds)
// Envelope (MAIL FROM) shape: em-[digits] | acems[digits] | emsd[digits].
| where SenderMailFromDomain matches regex @"(?i)(em-|acems|emsd)\d"
| sort by Timestamp desc

For extra corroboration you can scope to the single dominant /24 that carried the bulk of this campaign’s volume, 173.236.20[.]0/24, by adding | where ipv4_is_in_range(SenderIPv4, “173.236.20.0/24”). Like the tracking URLs, that network block is shared platform space (it also carries unrelated legitimate newsletters), so use it to scope, never as a standalone filter.

2. Pivot on the platform tracking URLs. Start from the click/tracking links and join back to the mail events. Useful for scoping, but treat it as corroboration, not a verdict: the tracking domains activehosted[.]com and acemlnd[.]com are shared by every legitimate customer of the same marketing platform, so the URL on its own is not a malicious indicator. The finance-brand filter is what keeps this on the campaign; drop it only if you deliberately want a wider search.

let kwds = @"(capital|fund|hedge|express|solutions|choice|lend|growth|loan|loc|finance|business|pillar|advance|boost|catalyst|digital|direct|elevate|guardian|harbor|online|pulse|rocket|rush|united|wave|way|surge|swift|elite)";
EmailEvents
| where Timestamp > ago(30d)
| where EmailDirection == "Inbound"
| where SenderFromDomain matches regex strcat("(?i)", kwds, kwds, kwds)
| join kind=inner (
    EmailUrlInfo
    | where Timestamp > ago(30d)
    | where UrlDomain endswith "activehosted.com" or UrlDomain endswith "acemlnd.com"
    | distinct NetworkMessageId
  ) on NetworkMessageId 
| sort by Timestamp desc

3. Filter for prompt injection detection in emails

The feature used in the query below is available for Microsoft Defender for Office 365 Plan 2 or Microsoft 365 E5 customers.

EmailEvents
| where DetectionMethods has "Prompt Injection Protection"

MITRE ATT&CK techniques observed

This campaign exhibits the following MITRE ATT&CK® techniques. The table includes MITRE ATT&CK for phishing/evasion behavior and MITRE ATLAS for the AI-security technique class related to prompt obfuscation.

Indicators and hunting pivots

References

• Unicode Standard Annex #45 – Tags (U+E0000-U+E007F) – the deprecated Unicode tags block abused for ASCII smuggling
• ASCII Smuggler Tool: Crafting Invisible Text and Decoding Hidden Codes | Embrace The Red – public write-up showing how Unicode tag characters can hide text from users while remaining present to language models
• LLM Prompt Obfuscation | MITRE ATLAS™ – the AI-security technique class this evasion crossed over from
• Attackers exploit ActiveCampaign to deliver thousands of AI-generated SBA phish | Fortra – earlier reporting on the broader ActiveCampaign-delivered SBA-themed phishing campaign, before the Unicode tag-character evolution examined here

Learn More

For the latest security research from the Microsoft Threat Intelligence community, check out the Microsoft Threat Intelligence Blog.

To get notified about new publications and to join discussions on social media, follow us on LinkedIn, X (formerly Twitter), and Bluesky.

To hear stories and insights from the Microsoft Threat Intelligence community about the ever-evolving threat landscape, listen to the Microsoft Threat Intelligence podcast.

Review our documentation to learn more about our real-time protection capabilities and see how to enable them within your organization.  

• Learn more about securing Copilot Studio agents with Microsoft Defender 
• Evaluate your AI readiness with our latest Zero Trust for AI workshop.
• Microsoft 365 Copilot AI security documentation
• How Microsoft discovers and mitigates evolving attacks against AI guardrails

• Manipulating AI memory for profit: The rise of AI Recommendation Poisoning | Microsoft Security Blog – a related example of an AI-era technique observed in email traffic
• Defending the inbox against prompt injection attacks | Microsoft Defender for Office 365 Blog – feature announcement introducing prompt injection protection in Microsoft Defender for Office 365.
• Prompt injection protection in Microsoft Defender for Office 365 – official documentation of the prompt injection protection in Microsoft Defender for Office 365.
• How Microsoft discovers and mitigates evolving attacks against AI guardrails | Microsoft Security Blog

The post ASCII smuggling crosses over from AI prompt injection to phishing evasion appeared first on Microsoft Security Blog.

Unit 42 details how attackers exploit enterprise collaboration tools for identity phishing and credential theft. Discover key defense strategies.

The post Identity Abuse Through Trusted Communication Channels appeared first on Unit 42.

Identity-based attacks drive 90% of incidents. Learn how modern attackers exploit identities and what SOC leaders can do to respond.

The post Inside the Modern SOC: The Identity Front Door appeared first on Unit 42.

Mac users have historically trusted their operating system to keep them safe. That peace of mind mostly comes from Apple’s strict control over its ecosystem, and the fact that macOS has historically faced fewer mass attacks than Windows. However, that doesn’t mean Macs are invulnerable: threats do exist, and new ones emerge all the time. Over just the past few weeks, security researchers have published reports on at least two new campaigns that target Apple devices.

The malware used in one of the campaigns has been dubbed CrashStealer, while the other is known as ClickLock. Both rely on different tricks to force users into entering their Mac password, which attackers then use to steal account credentials, crypto assets, documents, and much more. In today’s post, we take a close look at how CrashStealer operates — and how to avoid falling victim to it.

A videoconferencing app with CrashStealer inside

It was back in May 2026 that researchers spotted the first signs this malware was being developed, and by early July, they caught it operating in the wild. The malware earned its name because of its core mechanism: it disguises itself as the macOS built-in crash reporting tool (CrashReporter) while functioning as an infostealer designed to hijack sensitive data.

Researchers managed to trace one of the websites users visited to download the malware. The site poses as a legitimate platform for distributing the video conferencing tool Werkbit.

According to researchers, this is the site victims used to download Werkbit, which secretly contained the CrashStealer malware loader. Source

However, you can’t just visit the site and download the software. Before downloading, visitors are asked to enter a special meeting PIN. This setup likely allows the attackers to limit the distribution scope by targeting only specific, pre-selected victims. Exactly how the cybercriminals choose their targets and deliver the PIN remains unknown.

The “lucky” users with a code end up installing the initial malicious payload — named Werkbit Setup. Interestingly, it carries a valid Apple developer certificate and has successfully passed Apple’s notarization process — meaning it cleared the automated prescan for malicious code. As a result, the attackers manage to bypass the operating system’s built-in Gatekeeper defense. This allows the payload to launch without triggering the usual untrusted software warnings.

The Werkbit Setup installer is signed with a valid Apple developer certificate and has passed notarization. Source

Once launched, Werkbit Setup first reaches out to GitHub. Researchers believe using this popular platform helps attackers blend in by making these initial network requests look far less suspicious to security tools. After retrieving instructions from a GitHub repository, the program connects directly to the attackers’ server to fetch CrashStealer itself.

The loader then saves the malware to a temporary macOS folder, launches it, and wipes most of the intermediate setup files. As a result, a fully functional infostealer is up and running within seconds of Werkbit Setup starting. By the way, the user never gets any videoconferencing app.

How CrashStealer works

Unlike the Werkbit Setup loader, the CrashStealer malware itself isn’t signed with an Apple developer certificate. To keep users from suspecting anything, the malware disguises itself as the built-in macOS crash reporting tool, CrashReporter, by using the exact same name, app identifier, and a similar icon.

Once launched, CrashStealer completes a sequence of steps to gain access to sensitive data, establish persistence in the system, and cover its tracks:

1. Remove metadata — including the attribute that flags the app as an internet download.
2. Display a fake system prompt asking for the user’s macOS password.
3. Use the previously captured credentials to gain access to Keychain, the built-in macOS password manager.
4. Check the computer for installed security tools and malware analysis software.
5. Collect saved browser passwords, cookies, Keychain contents, and data from other password managers and crypto wallets.
6. Encrypt the data it stole and prepare it for forwarding to the attackers’ server.
7. Create a copy of itself and establish persistence to launch automatically every time macOS boots.
8. Delete temporary files and other installation traces to make detection much harder.

Step two deserves a closer look. The password prompt that the user sees looks extremely convincing. What’s more, the malware immediately verifies whether the credentials are correct: if you make a typo and enter an invalid password, CrashStealer will pop the window right back up to ask you again.

Once launched, CrashStealer displays a pop-up that mimics the standard macOS password request. Source

What data is CrashStealer after?

CrashStealer’s hit list is massive. First and foremost, its operators target Keychain: the built-in macOS password manager where the system stores account credentials, cryptographic keys, certificates, tokens, and more.

Users of third-party password managers aren’t safe either: the malware steals data from 14 of these services, including 1Password, Bitwarden, LastPass, Dashlane, Keeper, KeePassXC, NordPass, Enpass, and RoboForm.

In addition, the malware collects all credentials and cookies stored in Chromium-based browsers — Chrome, Brave, Edge, Opera, Opera GX, Vivaldi, Chromium, and NAVER Whale — as well as Firefox. The attackers clearly have a strong interest in crypto assets: CrashStealer specifically targets data from 80 different crypto wallet extensions, including MetaMask, Phantom, Coinbase Wallet, Trust Wallet, Rabby, Exodus, Keplr, and Solflare.

Finally, the malware scans the Documents and Downloads folders to pick files that might be of interest to the cybercriminals. CrashStealer encrypts all the stolen data with the AES-256-GCM algorithm, ZIP’s it up, and sends it to the attackers’ server.

How to protect your device

The spike in attacks on macOS is a clear wake-up call: Apple users need to get proactive about their security. We recommend:

• Researching apps online before installing them
• Sticking to utilities from official app stores whenever possible
• Using a reliable security solution that blocks malicious websites and stops malware activity on your device
• Keeping all your credentials and banking details in a secure password manager. One option is Kaspersky Password Manager— which, notably, wasn’t listed among the apps targeted by CrashStealer

Kaspersky security solutions detect the malware described in this post and assigns to it the verdicts HEUR:Trojan-Downloader.OSX.Agent.gen and HEUR:Trojan-PSW.OSX.Agent.gen.