In an industrial network, malicious activity can be hard to tell apart from normal data exchange between devices. Malicious actors routinely rely on legitimate tools and the Living-off-the-Land technique, so a single network connection may show no known signs of an attack: the device uses a familiar protocol and transmits ordinary data. An anomaly can hide in subtle details: the data source, the time of access, or the volume of transferred data may be atypical for a particular device. However, to detect this kind of anomaly, you need to understand the roles of the devices at both ends of the connection and know which network activity patterns are typical for them.

But an organization’s asset inventory data is often incomplete, which makes it harder to catch unauthorized connections in time. Suppose monitoring detects a host that regularly exchanges data with programmable logic controllers (PLCs). Its IP address is known, but the inventory does not specify what role it plays: is it an engineering workstation, a process data server, or a piece of contractor’s equipment? The host may also have been on the industrial network for a long time, with its record going out of date after a network upgrade.

In the joint OT asset inventory guidance released by CISA and its partners in 2025, inventorying devices and classifying them by role are treated as part of OT asset management. For organizations, this is one of the most important areas: according to the SANS 2025 ICS security survey, 50% of respondents had deployed or significantly expanded ICS inventory and visibility tools over the previous 12 months, and 54% named them as investment priorities for the next 12–24 months.

Device inventory in Kaspersky Industrial CyberSecurity for Networks

In Kaspersky Industrial CyberSecurity for Networks, the Asset Discovery technology is the foundation of asset visibility. Passive network traffic analysis detects devices and their interactions, while recognition rules help determine the device category and extract available information about it, such as the vendor, model, or software version. Exactly which attributes the system can obtain depends on the protocol and the traffic it observes. We covered this in more detail at the Kaspersky Industrial Cybersecurity Conference.

The product also supports active polling modules (APMs): where polling is allowed, they can request additional configuration and firmware version details from devices. This kind of inventory is far more detailed than a list of IP addresses.

However, even accurate hardware information doesn’t always explain what function a host performs in a specific industrial facility. Gaps appear for various reasons. Some devices send identifying information only at startup or during configuration, while others rarely communicate. Proprietary protocols can’t always be parsed, and encryption may leave only connection metadata for analysis. If the monitoring system doesn’t receive traffic from every segment of the industrial network, some network activity goes unobserved. Active polling doesn’t suit every device or every operating condition.

When there isn’t enough direct information about a device, its category can be refined by looking at the patterns of its recurring network interactions. Analyzing these interactions is the job of Machine Learning Event Processor (MLEP), a service included in Kaspersky Industrial CyberSecurity for Networks 5.0. This machine-learning-based event processor can both refine device categories and detect anomalies in their network behavior.

What data does MLEP analyze?

To compare device behavior, you don’t need to store every packet as a separate observation (a data object) in the ML model. MLEP represents network sessions as a sequence of short 15-second fragments. For each fragment, MLEP stores information about the endpoints (IP, MAC, and port), the direction of data exchange, the protocol, and the number and size of the packets transmitted. Together these make up an event, which then gets a timestamp. Traffic thus becomes a sequence of events showing which hosts a device communicated with, when, and how communication intensity changed.

Aggregating data into events also sets the method’s limits. An aggregated fragment can’t be used to reconstruct the exact order of individual packets or the content of commands. MLEP can draw conclusions about a new protocol only if another KICS technology has managed to recognize it. That said, comparing stable network connections and changes in exchange volume doesn’t require a detailed record of every packet. These observations form the basis of the device’s behavioral history.

From observations to MLEP results

Each object has its own behavioral history, and one object’s history can’t be applied to another. But the principles of behavior analysis, along with some common rules, work across different objects.

Let’s look at how this works in MLEP, which is built on a spiking neural semantic network (NSN).

Event identification

Neurosemantic networks structure information as a multilayer ordered graph, where the data in each consecutive layer builds on the data in the previous one. The nodes of this graph — that is, the units of information in each layer — act as neurons.The NSN creates dedicated channels to handle different event parameters (port, IP, and so on). Unique parameter values for each channel are stored as neurons in layer 0. The event itself, which consists of a set of parameter values, is stored as a neuron in layer 1 of the network. If an event occurs for the first time, neurogenesis creates a new neuron with input connections to the corresponding parameter neurons. This lets the network learn and operate on the event stream at the same time. If the event has occurred before, the network activates the same neuron that was created when the event first appeared. This way, the network identifies each event precisely. Each neuron that corresponds to an event also collects basic statistics: when it was last activated and how many times it has been activated.
 

Event history

In NSN-based event processing, layer 2 and above store sequences of events over time: recurring patterns and the full history. However, categorization and statistical anomaly detection don’t require storing the full packet sequence or a detailed timeline of every network event. As we noted earlier, events are themselves aggregates, which means the order of network packets has already been averaged out. So MLEP keeps history only temporarily, within each episode (30,000 aggregated events by default), which saves computing resources.

Statistics processing in MLEP

MLEP processes statistics mainly through specialized listener neurons on layer −1 (minus one) of the network. Attention heads and monitors are built on these neurons.

When an event arrives, the corresponding neuron is activated, and the network instantly knows whether that neuron is new or was created earlier. Each attention head subscribes to the events or event features it needs and takes in only the activations of the matching neurons. If an event occurs for the first time, all heads are notified and then decide whether to subscribe (grow a connection to the relevant neuron) and receive information about activations of such neurons from then on.

It is called a spiking network because activation information is a spike, not a number. This spike tells the head that something it’s interested in has happened.

An attention head is configured according to scenarios — the situations that MLEP should send alerts about. The core of a head’s configuration is a query, which filters incoming information and serves as the subscription criterion. Unlike regular database queries, this query runs on the stream, so it can react to an event instantly.

Attention heads work very much like the semantics of ordinary human language. A query has a subject of attention (one or more grammatical subjects) and conditions (the other parts of the sentence). In the infrastructure of an industrial enterprise, the subjects are devices on the network, and the conditions are various aspects of network communication, such as ports, protocol, and average packet size. If a head has many subjects (for example, if it’s interested in every device on an industrial network), it fans out: it creates a child head for each individual value of the subject of attention, that is, each device. The child head then watches a specific device and reports to the parent head.

Attention heads don’t accumulate or process statistics. They only filter event streams. When an attention head receives a relevant event, it activates and sends a spike to the monitor subscribed to that head.

Monitors, in turn, have stacks for accumulating and processing events. They can also have their own queries and post-filter information from the head. For example, they can select only new events from those filtered by the head.

There are several types of monitors:

• Stat monitors track changes in the average count of specific events (for example, device activity) over time intervals typical for those events, or in the average values of numeric event parameters (for example, changes in device traffic volume).
• Monitors work from the event alone: they fire when an event has occurred a certain number of times within a given interval, or has occurred for the first time.
• Similar-behavior monitors work with heads that can generalize events, that is, extract just a few parameters whose combination is shared (generalized) across many events.

MLEP uses these monitor types to implement scenarios for categorizing devices and detecting their anomalous behavior:

1. For categorization, MLEP uses attention heads and monitors that identify common behavior across different assets and group devices accordingly. MLEP then takes information from Asset Discovery about the known categories of some assets and extends those categories to other assets in the same group. Because a device can show different types of activity, it may land in several groups. MLEP then makes a weighted assessment across all relevant groups.
   

   Groups of devices with similar network behavior

   At the final stage, the system verifies that assigning the category to the asset is permissible. This is again done by special rule monitors loaded into the NSN at system startup. The rules rely on asset metadata available from Asset Discovery and APMs.

2. MLEP takes a similar approach to anomaly detection: an attention head and a monitor define an anomaly scenario, often loosely. When a monitor activates on such a scenario, MLEP generates an alert about unusual behavior.

The current version of MLEP ships with preconfigured heads and monitors.

The functional dynamic diagram below shows how events from devices on an industrial network are processed.

MLEP in practice: how behavior helps complete the asset inventory

At a facility with several similar production lines, device information may be more complete on some lines than on others. On one line, devices are identified as PLCs, while on another, after an upgrade, some hosts show up only as addresses: the available traffic didn’t provide enough information to determine their category. Still, these devices communicate with the same types of HMIs and process data collection servers. Their communication directions, frequency, and volumes may be similar.

That’s where MLEP comes in. It takes the categories of already known assets and matches recurring features of their network behavior against those of uncategorized devices. When the observed interactions turn out to be similar, MLEP can add a category for that host to the inventory.

Once the category is refined, the industrial network map provides more context about the equipment on the network. When analyzing network activity, the analyst immediately sees the device’s suggested category and can skip looking up each IP address in the design diagrams. This approach is especially useful where equipment changes faster than the documentation is updated.

The suggested category is also checked against existing device information and defined constraints. This helps filter out some contradictory options, but prediction quality still depends on how much of the traffic is visible, especially if the device communicates only rarely.

Anomalous activity and streaming statistics

Investigations of real-world attacks show that attackers actively use both standard tools and permitted protocols. You don’t always need to know an attacker’s specific technique in advance to spot potentially malicious activity. Signature-based detection helps find known signs of an attack, but it doesn’t assess how typical an observed interaction is for a particular device. MLEP compares current activity with the device’s usual network profile: which hosts it communicates with, which protocols it uses, when and how much data it transmits, its average packet size, and so on. The changes it detects help guide the direction of the investigation. Correlating these changes with other events and the operational context helps determine whether malicious actions could be behind them. When investigating an incident that has already been detected, anomalies help reconstruct the sequence of actions.

The first version of MLEP comes with six preconfigured monitors, one for each type of deviation from the device’s usual network profile:

Anomaly	How it helps with event analysis
New external host	Flags a new external connection that wasn’t previously seen in the device’s observed behavior, even if the IP address isn’t listed among known IoCs. Helps check whether this connection appeared at the same time as other suspicious events.
New combination of destination address and port	Shows that the device has begun using a service it hasn’t used before. Once an incident has been detected, this helps trace new connections after the more critical events have been analyzed.
Unusually large volume of data sent	Highlights data transfers that are atypical for the device and helps correlate their timing and direction with other events.
Atypical activity time	Shows that the device was active at a time that’s unusual for it. In an already detected incident, this helps reconstruct the timeline of actions around the main alert.
New protocol	Helps spot a new method of communication, even when the device is communicating with familiar hosts.
Activity with signs of scanning	Shows that the device has started contacting an unusually large number of neighboring hosts. This signal helps reveal possible reconnaissance ahead of attempts to access equipment.

During an investigation, anomalies help reconstruct the sequence of changes: when a specific host established new connections, switched protocols, increased its data transfer volume, or shifted its operating hours. Matching events by device and time shows an analyst how activity unfolded around an incident that has already been detected.

This is especially useful when some of the attacker’s actions fell outside the monitored scope or didn’t trigger other KICS technologies. Correlating these changes with the device’s role, protocol analysis data, and events from other technologies gives the analyst a more complete timeline. Behavioral anomalies show exactly where to look for the missing links and which network interactions need attention.

In the event list, anomalies detected by MLEP are marked with an AI label. Each event shows its name, source, destination, timestamp, and the ID of the triggered scenario.

Conclusion

In an industrial network, whether behavior is anomalous depends on the host where it occurs and that host’s place in the industrial process. The repetitive nature of many communications makes a change in normal data exchange easier to spot, while knowing a device’s function and criticality helps gauge the possible consequences. Taken together, these observations make network traffic more than a list of connections: it helps assess new events in the context of a specific facility, even without an attack scenario known in advance. Machine learning technologies are well suited to this kind of comprehensive analysis, as they can process and organize huge streams of heterogeneous data. Starting with version 5.0, Kaspersky Industrial CyberSecurity for Networks handles this function with MLEP, which works effectively alongside the product’s existing functionality.

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.

Google said today it worked with industry partners to dismantle a botnet made up of more than 1.3 million Android TV set-top boxes that ship with malicious software which secretly rents the user’s Internet connection out to strangers and conducts advertising fraud. Experts involved in the takedown say the action is likely to sever the botnet’s access to more than 10 million additional compromised systems, including other Internet of Things (IoT) products such as Android-based healthcare devices and karaoke machines.

A report from the Google Threat Intelligence Group (GTIG) said Google has severed ties with Videostrong Technology Co., a TV box manufacturer in China that GTIG and other industry experts concluded was responsible for including malware that enrolled more than 1.3 million set-top boxes in a botnet dubbed “XCPE” (named after one of the malware’s core code components).

The report said the XCPE botnet loaded the user’s device with software that makes their Internet address available for resale by multiple “residential proxy” services, which allow customers to rent Internet addresses as a way to anonymize their web traffic or spoof their true location. As detailed in several stories here over the past year, residential proxy services are a major source of unwanted and often malicious traffic, including account takeover activity, advertising fraud, and mass content scraping for AI projects.

“Videostrong monetized the backdoor by selling access to criminal affiliates and loading those affiliates’ malware modules onto infected devices,” the GTIG post reads, noting that Google took legal action to take down the domains and servers Videostrong used to control the botnet and install additional malware. “A device manufacturer intentionally infecting their users with malware is an unacceptable breach of trust, and represents a serious risk to those users. Without users’ knowledge or consent, infected devices were offered as a distribution mechanism for a wide array of criminal actors.”

The report notes that while TV boxes made up the the majority of the botnet, the same XCPE malware was found in other Android-based IoT products, including telehealth devices, karaoke machines, and even digital signage systems.

Google said it requires all Android device makers to pass compatibility and security tests to ensure their products don’t harm users, but that Videostrong nevertheless found a way to load their malicious software immediately after a user boots up one of these devices for the first time (Google says it has since addressed that weakness).

The GTIG report calls out a number of residential proxy providers whose software development kits were routinely downloaded by devices infected by the XCPE botnet. Those include AppSalt, also known as Honeygain, which is owned by the Vilnius, Lithuania-based Oxylabs. In October 2026, Oxylabs was relaunched under a new brand called Source, after receiving $130 million in funding from private equity firm Warburg Pincus that reportedly valued the business at $3.6 billion. Oxylabs did not respond to a request for comment.

Another provider linked to the botnet was NetNut, a once sprawling residential proxy company operated by the publicly-traded Israeli company Alarum Technologies. In July 2026, the FBI worked with industry partners to seize hundreds of domains associated with NetNut, saying the company was responsible for operating a separate botnet called Popa that enslaved TV boxes as residential proxies. Other proxy providers named in the GTIG report include EarnFM, Traffmonetizer, Jaguar, LINKFOG, BMW/IPWeb and GaGaNode.

Google’s announcement credits a number of industry partners, including Synthient, a startup that tracks proxy providers. In January 2026, Synthient published research into the rapid spread of the Kimwolf botnet, which at the time was hammering networks worldwide with record-smashing DDoS attacks. Synthient documented how Kimwolf infected millions of Android set-top boxes virtually overnight, using a widespread vulnerability in these residential proxy services to compromise other Android-based devices inside the local networks of unsuspecting TV box users.

Synthient’s research caused dozens of residential proxy providers to patch their networks, which helped to fragment the Kimwolf botnet into much smaller pieces that made its incessant and disruptive DDoS attacks far easier for network operators to manage. It also attracted the attention of the Motion Picture Association (MPA), a trade group representing the major Hollywood studios, which partnered with Synthient to purchase and tear apart a number of popular Android TV set-top boxes — including some that were being infected by Kimwolf and other IoT botnets.

In a report published today, Synthient examined a popular, pre-infected TV box sold under the brand MECOOL by a publicly traded company in China called Skyworth Technology, concluding that MECOOL also bundled the malware from Videostrong Technology Co. and likewise installed AppSalt/Honeygain and other proxy components.

Synthient founder Ben Brundage told KrebsOnSecurity that Videostrong’s malware also distributed software for the proxy service Grass. In our November 2025 story, “Is Your Android TV Streaming Box Part of a Botnet?” we quoted a researcher from Censys who found the popular Superbox TV streaming device bundled Grass’s residential proxy software. We also quoted the Grass founder, who denied any affiliation with the TV box maker.

That November 2025 story noted that Superbox and similar Android TV set-top boxes gain new customers primarily by engaging influencers on Tiktok and other social networks who praise the product’s ability to offer hundreds of (pirated) pay TV streaming services for a one-time fee.

Likewise, Synthient’s report observes that the MECOOL set-top boxes exploded in popularity after the product was mentioned in 2023 by the Youtube channel Linus Tech Tips, which has an audience of nearly 17 million subscribers [Linus Tech Tips said in a statement shared with Synthient that it has since updated the video to explicitly frame the product as a scam and have removed all associated affiliate links].

The GTIG report estimates the XCPE botnet takedown should sever access to at least 10 million additional compromised devices, noting that the various proxy and ad fraud affiliates of Videostrong “each have diversified distribution channels for their modules beyond XCPE.”

Synthient’s Brundage echoed that conclusion, calling the 1.3 million number “highly conservative.”

“The scale of affected devices is likely way higher,” he said. “This is probably the largest operation against backdoored TV boxes that we’ve seen.”

Further reading:
Plume Security Labs white paper on the XCPE botnet (PDF)
Black Lotus Labs writeup on LinkedIn
TrendAI’s analysis
Synthient’s list of affected devices, broken down by model number

Introduction

AI agents and associated tooling are becoming increasingly common in corporate infrastructure. Agents can perform a range of actions with the user’s privileges, such as accessing data, running commands, and interacting with external services. This creates new security risks, especially when these tools are used without the security team’s oversight or well-designed policies.

Detecting, tracking, and controlling AI agents is becoming one of the most pressing tasks for security teams today. We closely follow technological developments and continuously update our solutions to respond to new threats in a timely manner amid a rapidly changing technology landscape. This post describes new expertise built into Kaspersky products that helps counter the threats posed by the proliferation of AI agents. We approach this problem from several angles.

First, you need to thoroughly understand the threat you are dealing with. To this end, we added profiles to the Kaspersky Threat Intelligence Portal that describe various AI agents and how attackers exploit them. At their core, AI agents are programs (processes) that run in the operating system on endpoints and can also launch other processes. This means that Endpoint Detection and Response solutions are among the key endpoint controls for this threat. These solutions detect the presence and launch of AI agents, track their activity, and identify potentially dangerous behavior. When needed, they also take action, such as stopping a suspicious process, blocking the activity, or isolating the host, to reduce the likelihood of an incident escalating. For this purpose, we developed specially adapted detection rules.

You can use Kaspersky Endpoint Security, now also available as Kaspersky Next, to control and restrict the launch of AI agents. This solution uses two components to detect AI agents and control how they are used. Application Control categorizes the application processes launched on endpoints, and Web Control manages access to specific categories of web resources for users on endpoints.

Finally, even harmless AI agents can contain malicious instructions (skills), such as those downloaded from unsafe external sources. Kaspersky Scan Engine analyzes installed applications to detect these threats and also checks LLM files for malicious code. Let’s take a closer look at these solutions and technologies.

Popular AI applications and services in organizations

Tools from Anthropic and OpenAI take the top spots in the September 2026 ranking of AI agents by popularity among Kaspersky users. This list is based on telemetry from Kaspersky solutions. It shows the number of users who had the relevant AI vendors’ products installed at the time of the study.

#	AI product
1	Claude
2	Claude Code
3	Codex
4	OpenCode
5	Zed
6	Wispr Flow
7	LM Studio
8	ZCode
9	Chatbox
10	Cherry Studio

The data shows that agentic systems, especially AI agents for software development, are the most popular AI applications. The TOP 10 also includes an application for running large language models locally (LM Studio) and a voice note-taking app (Wispr Flow).

According to open sources, these are the most widely used AI services in 2026:

#	AI service
1	ChatGPT
2	Google Gemini
3	Claude
4	Grammarly
5	DeepSeek

While analyzing the most popular and widely used apps, our experts also noticed a sharp rise in vulnerable versions of AI-related software and services. From 2025 to date, more than 2500 vulnerabilities have been published:

Number of published vulnerabilities in LLMs, AI tools, and plugins with similar functionality, 2025–2026 (download)

* Data for previous quarters was updated and may differ slightly from previously published data.

Application Control and Web Control

To enforce the organization’s security policy for AI agents and other AI applications on the corporate network, Kaspersky products offer application control based on KL categories. These categories classify installed software and give you granular control over which approved applications can run in the organization.

Application Control analyzes the processes launched on endpoints, tracking which AI agents are running in corporate systems at any given moment, on which hosts, and under which accounts. Application Control also shows how long a given program has been in use.

This makes it easier for the Kaspersky Security Center (KSC) administrator to control applications. Administrators can use KL categories to allow or block AI agents inside the company for specific groups of employees.

Here is what this functionality looks like to KSC administrators:

For details on how to configure Application Control in KSC, follow this link.

In Kaspersky Endpoint Security (KES), Application Control can operate in two modes:

• Denylist. In this mode, Application Control lets users run any applications except those blocked by Application Control rules. This is the default mode.
• Allowlist. In this mode, Application Control blocks users from running any applications except those explicitly allowed by Application Control rules.

Below are the steps to configure Application Control to block AI tools.

Configuring Application Control for the AI Tools category in KES

With this setup, when a user tries to launch an AI agent, they get a notification and the agent doesn’t start.

For more on how to configure Application Control in KES, follow this link. Our public portal also lets you explore information on KL categories and the applications that fall under them, in particular details on how AI agents are categorized and how popular they are.

The View applications drop-down list shows products for a specific vendor.

Similarly, Web Control lets you manage user access to web resources. If a user tries to open a denylisted website, KES blocks access and shows a warning.

When configuring access, the administrator can block websites that provide AI as a service.

For more on configuring Web Control, follow this link. The help portal also provides information on website categories that can be assigned to resources to configure access control.

Kaspersky Endpoint Detection and Response

To an EDR solution, an AI agent is a regular process. It has its own executable, a tree of child processes, and network communications, all of which need to be monitored for suspicious activity. An attacker can use an AI agent as an automation tool. An AI agent installed as a legitimate user tool can also be leveraged in a ClickFix attack, where it executes a malicious instruction received from an untrusted source, such as a document, web page, or email. That said, unsafe actions by an agent do not always indicate an attack. There may be no malicious intent in the chain at all. An incident can result from what looks like an ordinary instruction from a user who doesn’t fully understand the consequences of having it executed. The mere fact that an agent was launched does not reveal whether it is being used in an attack. That is why Kaspersky EDR (KEDR) uses both conventional behavioral rules and rules written and adapted to detect AI tools. The latter identify risky agent actions and settings, and help determine which AI tool was involved in suspicious activity.

Conventional KEDR rules detect the vast majority of familiar malicious techniques, including when performed by an AI agent. A suspicious PowerShell launch, persistence via Task Scheduler, or an attempt to steal credentials leaves clear, identifiable traces. These traces look the same no matter what initiated the actions. The rules analyze launch parameters, process relationships, affected files, network activity, and other telemetry events.

For example, in a recent Armored Likho campaign, we found signs that AI had been used to create loader and dropper samples, which were later compiled with NSIS (Nullsoft Scriptable Install System). The key telltale artifacts were icons in the scripts and excessive comments on every function, which is uncharacteristic of human-written code. In addition, the group continues to generate new modules for its BusySnake Stealer and BusySnake RAT samples. For example, the attackers used AI to develop a module that runs multiple checks for virtual environment artifacts and calculates a score. Depending on the score, the sample either runs or exits with code 0. Even though the initial access samples, payloads, and their modules were AI-generated, the commands they execute via rundll32 and PowerShell are detected by the shell_creation_by_rundll32 and suspicious_powershell_cmd_or_script_spawning rules.

So, for detecting an attack like this, it is not important who wrote the loader, the payload, or its modules: they are given away by the commands they execute, process relationships, and other traces of activity in the system. AI can be involved both in preparing the code and in carrying out the attack directly on the device.

In the latter case, the attacker uses the agent to carry out their tasks and lets it choose and run the required commands within the privileges it has. For effective protection, it is critical to link suspicious actions back to the AI tool and the conditions under which it operates. In KEDR, several specialized rules handle this task.

We have created separate rules for popular AI agents, ai_agent_*_access_to_sensitive_file_**, where * is the tool name and ** is the operating system. They generate alerts when an agent accesses files that contain credentials, tokens, or keys.

The ai_agent_dangerous_execution_mode rule detects agents launched with auto-approval of all actions (so-called YOLO mode) or with the sandbox disabled. These settings do not indicate an attack on their own, but they let the agent perform dangerous actions without user confirmation.

The suspicious_mcp_server_artifact rule triggers when MCP server scripts are created or modified in hidden, temporary, or cache directories. Attackers can place an MCP server in these locations to quietly connect it to the agent: through the MCP server, they can pass instructions to the agent, call third-party tools, and access the user’s data.

The llm_api_access_by_unusual_apps rule generates an alert when an application on Windows, Linux, or macOS connects to the API of a known external LLM service, unless this activity falls under predefined exclusions. This helps spot model access by applications that do not normally behave this way.

Some Kaspersky EDR rules tag events for investigation but do not generate alerts. For example, the ai_agent_**_execution rules flag launches of known agents on Windows, Linux, and macOS. The local_ai_model_artifact rule records the creation or modification of AI model files in typical application, runtime, and cache directories. These events reveal AI tools on the device, including tools that run on local models and never call an external API.

Kaspersky Threat Intelligence Portal

AI agent profiles

The Kaspersky Threat Intelligence Portal offers a large number of software profiles, including profiles of AI agents. You can sort them by the ai_ prefix:

The profile of an AI agent such as Claude Desktop includes a detailed description of the software, the privilege level required to run it, supported platforms, and threat actor groups known to use it. It also lists tactics and techniques mapped to the MITRE ATT&CK framework, with descriptions of the procedures the tool can be used for.

The description also lists ways to detect the software through standard system auditing mechanisms:

• Windows: events from standard audit logs and Sysmon events
• Linux: syscall audit events and Sysmon events
• Network auditing: a set of protocols and the traffic generated by the software

The detection section lists the main artifacts the software leaves in the system: command lines, possible launch parameters, names of the services and scheduled tasks it creates, and the key network artifacts.

Hunt Hub

In December 2025, the Kaspersky Threat Intelligence Portal added Hunt Hub (HH), a repository of detection rules. This is a unique Kaspersky project, and we developed it in response to growing market demand.

This functionality is available in the Threat Landscape section of the portal. It gives analysts access to detection rules: their descriptions and their mapping to MITRE ATT&CK tactics and techniques, as well as to the associated cybercriminal groups.

In the Hunt Hub section, customers can explore the detection logic of EDR, NDR, and NGFW products, including the logic for detecting AI agents.

The rule card provides:

• Detailed description
• Rule’s severity level, confidence level, and alert format
• Detection tips
• Description of possible false positives
• Platforms the rule can run on
• Tactics, techniques, and sub-techniques covered by the rule
• Groups that use the technique covered by the rule
• Detection logic that shows what exactly triggers the rule

Kaspersky Scan Engine

Skills are a key component of the AI agent ecosystem. They’re prewritten sets of natural-language instructions that extend agent functionality. We previously covered the risks that malicious skills pose to agent users. A skill can require the usage of additional programs and services, come with helper scripts (for example, written in Python), or contain a malicious command embedded right in the instruction text. This means that adding skills to an originally harmless AI agent opens up several new ways for malicious code to enter the system. To prevent this, any skills obtained from external sources must be scanned. Kaspersky Scan Engine is well suited for this task. This is a cyberthreat detection and response solution that seamlessly integrates with various applications. It can detect malicious code in agent skills, associated scripts, and in files generated by running agents.

Trained LLMs come in many formats. Some of them (PTH, PT, PKL, KERS, and others) also store parameters and weights (training datasets). These files can also contain malicious code, so it’s critical to scan them before running and using them. Kaspersky Scan Engine scans potentially dangerous LLM formats for infected data. It can deserialize and analyze models to detect malicious code regardless of model size.

Conclusion

AI agents are rapidly making their way into corporate environments: they help employees work faster and take on a wide range of tasks, but they also open up new opportunities for attackers. To take advantage of this technology without compromising security, organizations need to build multi-layered protection and minimize risks. A range of Kaspersky solutions can help with this:

• Kaspersky TIP keeps you up to date on current scenarios in which attackers use AI agents, and its Hunt Hub section provides rules for detecting suspicious AI agent activity.
• Kaspersky EDR detects AI agents in the infrastructure and monitors their activity on endpoints. If it detects potentially dangerous actions, the product can log the event, notify the SOC, and take response actions, such as stopping the process or isolating the host.
• Application Control in Kaspersky products can fully restrict the use of AI agents within the company by using application categorization. The Web Control component can restrict access to AIaaS web resources.
• Kaspersky Scan Engine can be integrated into various company processes to detect malicious code in any files, including AI agent skills and LLMs.

When companies combine the tools described above in a comprehensive approach, they can adopt AI agents in an informed way: with an understanding of the associated risks and effective means of minimizing them.

Introduction

AI agents and associated tooling are becoming increasingly common in corporate infrastructure. Agents can perform a range of actions with the user’s privileges, such as accessing data, running commands, and interacting with external services. This creates new security risks, especially when these tools are used without the security team’s oversight or well-designed policies.

Detecting, tracking, and controlling AI agents is becoming one of the most pressing tasks for security teams today. We closely follow technological developments and continuously update our solutions to respond to new threats in a timely manner amid a rapidly changing technology landscape. This post describes new expertise built into Kaspersky products that helps counter the threats posed by the proliferation of AI agents. We approach this problem from several angles.

First, you need to thoroughly understand the threat you are dealing with. To this end, we added profiles to the Kaspersky Threat Intelligence Portal that describe various AI agents and how attackers exploit them. At their core, AI agents are programs (processes) that run in the operating system on endpoints and can also launch other processes. This means that Endpoint Detection and Response solutions are among the key endpoint controls for this threat. These solutions detect the presence and launch of AI agents, track their activity, and identify potentially dangerous behavior. When needed, they also take action, such as stopping a suspicious process, blocking the activity, or isolating the host, to reduce the likelihood of an incident escalating. For this purpose, we developed specially adapted detection rules.

You can use Kaspersky Endpoint Security, now also available as Kaspersky Next, to control and restrict the launch of AI agents. This solution uses two components to detect AI agents and control how they are used. Application Control categorizes the application processes launched on endpoints, and Web Control manages access to specific categories of web resources for users on endpoints.

Finally, even harmless AI agents can contain malicious instructions (skills), such as those downloaded from unsafe external sources. Kaspersky Scan Engine analyzes installed applications to detect these threats and also checks LLM files for malicious code. Let’s take a closer look at these solutions and technologies.

Popular AI applications and services in organizations

Tools from Anthropic and OpenAI take the top spots in the September 2026 ranking of AI agents by popularity among Kaspersky users. This list is based on telemetry from Kaspersky solutions. It shows the number of users who had the relevant AI vendors’ products installed at the time of the study.

#	AI product
1	Claude
2	Claude Code
3	Codex
4	OpenCode
5	Zed
6	Wispr Flow
7	LM Studio
8	ZCode
9	Chatbox
10	Cherry Studio

The data shows that agentic systems, especially AI agents for software development, are the most popular AI applications. The TOP 10 also includes an application for running large language models locally (LM Studio) and a voice note-taking app (Wispr Flow).

According to open sources, these are the most widely used AI services in 2026:

#	AI service
1	ChatGPT
2	Google Gemini
3	Claude
4	Grammarly
5	DeepSeek

While analyzing the most popular and widely used apps, our experts also noticed a sharp rise in vulnerable versions of AI-related software and services. From 2025 to date, more than 2500 vulnerabilities have been published:

Number of published vulnerabilities in LLMs, AI tools, and plugins with similar functionality, 2025–2026 (download)

* Data for previous quarters was updated and may differ slightly from previously published data.

Application Control and Web Control

To enforce the organization’s security policy for AI agents and other AI applications on the corporate network, Kaspersky products offer application control based on KL categories. These categories classify installed software and give you granular control over which approved applications can run in the organization.

Application Control analyzes the processes launched on endpoints, tracking which AI agents are running in corporate systems at any given moment, on which hosts, and under which accounts. Application Control also shows how long a given program has been in use.

This makes it easier for the Kaspersky Security Center (KSC) administrator to control applications. Administrators can use KL categories to allow or block AI agents inside the company for specific groups of employees.

Here is what this functionality looks like to KSC administrators:

For details on how to configure Application Control in KSC, follow this link.

In Kaspersky Endpoint Security (KES), Application Control can operate in two modes:

• Denylist. In this mode, Application Control lets users run any applications except those blocked by Application Control rules. This is the default mode.
• Allowlist. In this mode, Application Control blocks users from running any applications except those explicitly allowed by Application Control rules.

Below are the steps to configure Application Control to block AI tools.

Configuring Application Control for the AI Tools category in KES

With this setup, when a user tries to launch an AI agent, they get a notification and the agent doesn’t start.

For more on how to configure Application Control in KES, follow this link. Our public portal also lets you explore information on KL categories and the applications that fall under them, in particular details on how AI agents are categorized and how popular they are.

The View applications drop-down list shows products for a specific vendor.

Similarly, Web Control lets you manage user access to web resources. If a user tries to open a denylisted website, KES blocks access and shows a warning.

When configuring access, the administrator can block websites that provide AI as a service.

For more on configuring Web Control, follow this link. The help portal also provides information on website categories that can be assigned to resources to configure access control.

Kaspersky Endpoint Detection and Response

To an EDR solution, an AI agent is a regular process. It has its own executable, a tree of child processes, and network communications, all of which need to be monitored for suspicious activity. An attacker can use an AI agent as an automation tool. An AI agent installed as a legitimate user tool can also be leveraged in a ClickFix attack, where it executes a malicious instruction received from an untrusted source, such as a document, web page, or email. That said, unsafe actions by an agent do not always indicate an attack. There may be no malicious intent in the chain at all. An incident can result from what looks like an ordinary instruction from a user who doesn’t fully understand the consequences of having it executed. The mere fact that an agent was launched does not reveal whether it is being used in an attack. That is why Kaspersky EDR (KEDR) uses both conventional behavioral rules and rules written and adapted to detect AI tools. The latter identify risky agent actions and settings, and help determine which AI tool was involved in suspicious activity.

Conventional KEDR rules detect the vast majority of familiar malicious techniques, including when performed by an AI agent. A suspicious PowerShell launch, persistence via Task Scheduler, or an attempt to steal credentials leaves clear, identifiable traces. These traces look the same no matter what initiated the actions. The rules analyze launch parameters, process relationships, affected files, network activity, and other telemetry events.

For example, in a recent Armored Likho campaign, we found signs that AI had been used to create loader and dropper samples, which were later compiled with NSIS (Nullsoft Scriptable Install System). The key telltale artifacts were icons in the scripts and excessive comments on every function, which is uncharacteristic of human-written code. In addition, the group continues to generate new modules for its BusySnake Stealer and BusySnake RAT samples. For example, the attackers used AI to develop a module that runs multiple checks for virtual environment artifacts and calculates a score. Depending on the score, the sample either runs or exits with code 0. Even though the initial access samples, payloads, and their modules were AI-generated, the commands they execute via rundll32 and PowerShell are detected by the shell_creation_by_rundll32 and suspicious_powershell_cmd_or_script_spawning rules.

So, for detecting an attack like this, it is not important who wrote the loader, the payload, or its modules: they are given away by the commands they execute, process relationships, and other traces of activity in the system. AI can be involved both in preparing the code and in carrying out the attack directly on the device.

In the latter case, the attacker uses the agent to carry out their tasks and lets it choose and run the required commands within the privileges it has. For effective protection, it is critical to link suspicious actions back to the AI tool and the conditions under which it operates. In KEDR, several specialized rules handle this task.

We have created separate rules for popular AI agents, ai_agent_*_access_to_sensitive_file_**, where * is the tool name and ** is the operating system. They generate alerts when an agent accesses files that contain credentials, tokens, or keys.

The ai_agent_dangerous_execution_mode rule detects agents launched with auto-approval of all actions (so-called YOLO mode) or with the sandbox disabled. These settings do not indicate an attack on their own, but they let the agent perform dangerous actions without user confirmation.

The suspicious_mcp_server_artifact rule triggers when MCP server scripts are created or modified in hidden, temporary, or cache directories. Attackers can place an MCP server in these locations to quietly connect it to the agent: through the MCP server, they can pass instructions to the agent, call third-party tools, and access the user’s data.

The llm_api_access_by_unusual_apps rule generates an alert when an application on Windows, Linux, or macOS connects to the API of a known external LLM service, unless this activity falls under predefined exclusions. This helps spot model access by applications that do not normally behave this way.

Some Kaspersky EDR rules tag events for investigation but do not generate alerts. For example, the ai_agent_**_execution rules flag launches of known agents on Windows, Linux, and macOS. The local_ai_model_artifact rule records the creation or modification of AI model files in typical application, runtime, and cache directories. These events reveal AI tools on the device, including tools that run on local models and never call an external API.

Kaspersky Threat Intelligence Portal

AI agent profiles

The Kaspersky Threat Intelligence Portal offers a large number of software profiles, including profiles of AI agents. You can sort them by the ai_ prefix:

The profile of an AI agent such as Claude Desktop includes a detailed description of the software, the privilege level required to run it, supported platforms, and threat actor groups known to use it. It also lists tactics and techniques mapped to the MITRE ATT&CK framework, with descriptions of the procedures the tool can be used for.

The description also lists ways to detect the software through standard system auditing mechanisms:

• Windows: events from standard audit logs and Sysmon events
• Linux: syscall audit events and Sysmon events
• Network auditing: a set of protocols and the traffic generated by the software

The detection section lists the main artifacts the software leaves in the system: command lines, possible launch parameters, names of the services and scheduled tasks it creates, and the key network artifacts.

Hunt Hub

In December 2025, the Kaspersky Threat Intelligence Portal added Hunt Hub (HH), a repository of detection rules. This is a unique Kaspersky project, and we developed it in response to growing market demand.

This functionality is available in the Threat Landscape section of the portal. It gives analysts access to detection rules: their descriptions and their mapping to MITRE ATT&CK tactics and techniques, as well as to the associated cybercriminal groups.

In the Hunt Hub section, customers can explore the detection logic of EDR, NDR, and NGFW products, including the logic for detecting AI agents.

The rule card provides:

• Detailed description
• Rule’s severity level, confidence level, and alert format
• Detection tips
• Description of possible false positives
• Platforms the rule can run on
• Tactics, techniques, and sub-techniques covered by the rule
• Groups that use the technique covered by the rule
• Detection logic that shows what exactly triggers the rule

Kaspersky Scan Engine

Skills are a key component of the AI agent ecosystem. They’re prewritten sets of natural-language instructions that extend agent functionality. We previously covered the risks that malicious skills pose to agent users. A skill can require the usage of additional programs and services, come with helper scripts (for example, written in Python), or contain a malicious command embedded right in the instruction text. This means that adding skills to an originally harmless AI agent opens up several new ways for malicious code to enter the system. To prevent this, any skills obtained from external sources must be scanned. Kaspersky Scan Engine is well suited for this task. This is a cyberthreat detection and response solution that seamlessly integrates with various applications. It can detect malicious code in agent skills, associated scripts, and in files generated by running agents.

Trained LLMs come in many formats. Some of them (PTH, PT, PKL, KERS, and others) also store parameters and weights (training datasets). These files can also contain malicious code, so it’s critical to scan them before running and using them. Kaspersky Scan Engine scans potentially dangerous LLM formats for infected data. It can deserialize and analyze models to detect malicious code regardless of model size.

Conclusion

AI agents are rapidly making their way into corporate environments: they help employees work faster and take on a wide range of tasks, but they also open up new opportunities for attackers. To take advantage of this technology without compromising security, organizations need to build multi-layered protection and minimize risks. A range of Kaspersky solutions can help with this:

• Kaspersky TIP keeps you up to date on current scenarios in which attackers use AI agents, and its Hunt Hub section provides rules for detecting suspicious AI agent activity.
• Kaspersky EDR detects AI agents in the infrastructure and monitors their activity on endpoints. If it detects potentially dangerous actions, the product can log the event, notify the SOC, and take response actions, such as stopping the process or isolating the host.
• Application Control in Kaspersky products can fully restrict the use of AI agents within the company by using application categorization. The Web Control component can restrict access to AIaaS web resources.
• Kaspersky Scan Engine can be integrated into various company processes to detect malicious code in any files, including AI agent skills and LLMs.

When companies combine the tools described above in a comprehensive approach, they can adopt AI agents in an informed way: with an understanding of the associated risks and effective means of minimizing them.

We use many technologies to detect email spam. They analyze every part of a message: headers, text, images, and links. But the first thing our email products focus on is the “From” field. If the address contains a domain that’s already on our denylist, it’s an easy call. But spammers know the drill, so they register domains in industrial quantities to make emails, even from a single campaign, arrive from completely different addresses. Clearly, they don’t do that by hand. The process has long been automated, and attackers’ algorithms can register huge numbers of domains in a matter of minutes. But every domain needs a name. Coming up with a unique one for each isn’t feasible. So spammers use domain generation algorithms (DGAs), which create similar names with minor variations, based on date and time, pseudo-random numbers, and so on.

In fact, to block mailings such as these, you don’t need every address on your denylist. We’re good at automating processes, too. For some time now, our email solutions have included a technology that detects domains generated by DGAs, and shuts down entire batch with a single rule. Let me explain how this technology works.

How we detect and block generated domain names in bulk

As soon as any of our technologies flags an email as spam, its address becomes subject to further examination. If it meets a number of criteria, its domain is added to our database of domains we consider “bad”. We built a cascade of classifiers that compares these domains by similarity, groups them into clusters, and creates a short but comprehensive rule that blocks the entire cluster. Our email protection solutions receive this rule with an update. From then on, they block all emails sent from addresses on any domain that matches it — even if those domains have never been used in spam campaigns yet.

Here’s a real-world example. Let’s say the classifier flags several lookalike domains in a collection of “bad” ones it has gathered:

• sxkjcc.asia
• sxkjcd.asia
• sxkjcy.asia

Then, one rule is enough to catch them all: @sxkjc[a-z0-9]\.asia\b

How to protect your company from spam

According to our analysis, 44.9% of emails sent last year were spam. Unwanted, unsolicited emails distract employees, waste working time, and clog inboxes. In some cases, they also contain scam, phishing, or malware content. To protect business customers from this deluge of threats, our solutions for corporate email security have anti-spam technologies that we constantly improve. This includes the technology described above for blocking domains generated by DGAs.

Introduction

Imagine you’ve ordered a small paper unicorn from an online marketplace. You clicked “Buy”, received a cheerful notification, and expect a courier to bring the box tomorrow. If the unicorn is delayed by a day, it will be annoying but not catastrophic: an apology, a discount promo code — and the incident is forgotten.

Now let’s multiply one late unicorn by several hundred thousand orders.

Packages start heading to the wrong cities. The website shows an item is in stock, even though the required bin is empty. Conveyors stop, boxes get stuck between floors, the cargo piles up with no freight manifest. Trucks line up at the gates, and every minute of downtime turns into blown schedules, support tickets, fines, and lost sales.

It is at exactly this scale that a cyberincident in a warehouse becomes a problem for the entire business. A modern warehouse is no longer just a big building with shelving. It is a tightly interconnected hybrid system: web applications, radio channels, and an automated factory. Physical processes here depend on data, and errors in data have very physical consequences. Examples include an outage that affected warehouse management systems at the grocery giant Morrisons, and the 2025 Form 10-K annual report of United Natural Foods, Inc. (UNFI), which confirmed unauthorized activity on information technology systems that impacted order fulfillment. More recently, reports emerged that a cyberattack disrupted eight CEVA Logistics warehouses across Europe and exposed customer delivery data.

This paper follows a single order through a warehouse to show where the operation is vulnerable, the stages it traverses, the systems that drive it, and the points where an attacker can reach them. We then set out the threat model: what is at risk, who is in a position to take it, and how each risk lands on the business. From there, we look at how an attacker gets in, and what reaching the warehouse management system costs in practical terms: orders unshipped, inventory records nobody can trust, and an operation that stops. We close with recommendations on where the controls that matter actually sit.

Tracing the unicorn

A modern warehouse is a sequence of stages involving many machines and people, all working together to keep the business process stable so that, in the end, your order gets delivered. This process consists of eight main stages, shown in the figure below.

So let’s assume we’ve ordered our paper unicorn and are waiting for it at home. Let’s follow our order through all the main stages, from its arrival at the warehouse to the final stop on its journey.

1. Unloading
   First, the unicorn arrives in the unloading zone, packed in a big truck. The driver is assigned a gate, for example, via a tablet with kiosk mode enabled, or an outdoor display board, indicating where the truck should park and where the cargo will be unloaded. Even at this very first step, a digital command (the gate number on the screen) is already triggering a physical action. Therefore, incorrectly assigned gates can create a queue, delay unloading, or send goods into the wrong workflow.
2. Receiving
   At this stage, employees identify the cargo. They scan a barcode using a portable data terminal (PDT) and the central system receives a message indicating that this object arrives at the warehouse. From this moment, the unicorn exists in two forms at once: as a real box and as a record in the information system. If these two versions diverge, the error is inherited by all subsequent processes.
3. Putaway
   Next, the goods are moved into internal storage. Two new devices come into play here. First, a thermal printer, connected to the internal network over Wi-Fi, creates an internal label for each item. Then an employee, forklift, or automated system moves the goods to the assigned bin, often with the help of lifts controlled by PLCs. Both of these devices can be valuable attack entry points: one can let an adversary pivot into the internal network, while the other can give them control over an electric motor.
4. Storage
   Then the unicorn goes into storage. Many distribution centers use multi-level mezzanine structures: several floors of metal shelving inside a huge space. They allow efficient use of the building’s height, but at the same time complicate control over the technical infrastructure. Wi-Fi access points, cameras, switches, cables, and sensors may be mounted high overhead or buried deep inside the rows. A forgotten Ethernet cable or misconfigured networked device (access point or switch) left behind in a dark corner can become a convenient initial access point into critical infrastructure.
5. Picking
   When the customer places an order, picking begins if the goods were already stored in the warehouse or as soon as they are unloaded. A PDT, workstation, light indicator, or voice terminal directs the employee to the required bin where they pick the goods and move them to the next stage. These devices are themselves potential attack entry points: compromising a workstation or PDT could allow an attacker to manipulate picking information, which could misdirect employees to the wrong bin. Here, both the speed and integrity of the information are critical, and any manipulation or disruption can lead to wrong orders, delays, or problems in subsequent stages.
6. Sorting
   At the sorting stage, the warehouse starts to look more like a factory. Boxes move through conveyor belts and pass through different scanners, diverters, and chutes. The sorting plans are usually stored and managed by dedicated servers, while PLCs are responsible for controlling the machinery and executing these plans. For example, when the warehouse management system (WMS) decides that our unicorn should take route A, the control system needs to activate the right diverter at the right moment, sometimes within a fraction of a second.
7. Packing
   During packing, an employee or a robotic arm places the goods into the final box, prints the shipping label, and sometimes measures the dimensions and weight of the package. Safety mechanisms usually prevent robotic arms from leaving the controlled zone and harming nearby operators, but their compromise might inflict physical damage on the packages being processed.
8. Shipment
   Finally, at the shipping stage, the parcel heads to the gates. Automated guided vehicles (AGVs) and autonomous mobile robots (AMRs) move containers and pallets across the warehouse, while the dock management system coordinates the truck that will carry the unicorn onward to a store, a sorting center, or straight to the customer. These robots are themselves a potential target for attackers. Attacks can be conducted against AGVs and AMRs to interfere with their operation, for example by affecting their navigation or preventing them from reaching their intended destination.

All eight stages are coordinated by the WMS. It looks like a large web application, but in its role it’s closer to the operating system of the entire warehouse: it knows which goods exist, where they’re located, who or what should move them, and where they go next, linking employees, inventory levels, storage addresses, tasks, and orders. It also exchanges data externally: with ERP systems, transport platforms, marketplaces, dark stores, and external carriers.

Surrounding the WMS is an even larger digital ecosystem: identity services, databases, APIs, printers, carrier systems, technicians’ laptops, video surveillance, access control, fire and gas alarms, power supply, and a building management system (BMS).

That is why a single “warehouse network” doesn’t really exist. As we’ve seen, there are many overlapping technologies — IT infrastructure, OT systems, radio, cloud services, building security, and automation — layered on top of one another. A lone vulnerability scanner will never tell you whether a warehouse is secure.

Hunting the unicorn

Securing an environment such as a warehouse requires a comprehensive security assessment covering all of its interconnected systems. This involves identifying vulnerabilities across each system and understanding how weaknesses can be combined into attack paths that a real attacker could exploit. But a vulnerability only matters in proportion to what it exposes and who is able to reach it, so before looking at the weaknesses themselves, we need to understand what an attacker in this environment is actually after, because in a warehouse, the blast radius extends well beyond data. Everything we just traced — the systems, the devices, and the people operating them — exists to keep goods moving, and attacks in various parts along that chain can affect the business differently. The chart below shows what is at risk in the warehouse and where those risks land for the business. The segment on the left groups them under the three core security properties: integrity, availability, and confidentiality. The segment on the right maps each property to the business outcomes a failure can cause.

The first area at risk is integrity. Inventory counts, locations, orders, and shipment data must stay in sync with the physical world. Corrupting a record silently desynchronizes the operation, and because the eight stages form a dependency chain, one falsified entry early on is inherited downstream, surfacing later as stockouts and mis-shipments. In its deliberate forms, this lets an attacker redirect goods, release stock, and perform other actions on behalf of the platform’s vendors under cover of legitimate records. Integrity is also a safety concern: when people or machines act on information that no longer reflects the physical environment, a data failure can become a physical incident. This is why integrity is the only property linked to all five outcomes, including harm to people.

The second is availability, the warehouse’s ability to keep goods moving. Halting a conveyor, a sorter, label printing, or the WMS stops orders from shipping, and in a business built on speed, every hour of downtime means delayed deliveries and broken promises. Ransomware can bring the whole warehouse to a standstill. The impact shows up as operational disruption, lost revenue, and reputational damage.

The third is confidentiality. Customer data, business records, and system credentials are valuable targets. Their exposure leads to legal liability and reputational damage, and stolen credentials often open the door to further attacks. Where the WMS connects to financial or ERP processes, the risk extends to theft of funds.
Knowing what is at risk tells us what an attacker stands to gain; the next question is who is in a position to take it. Threat actors differ in the access they already hold. For example, an attacker may be authenticated and privileged, have limited physical or remote access, or have no legitimate access to the environment at all. Based on their relationship with the warehouse and their level of access, threat actors can generally be divided into two categories:

1. External. An attacker with no legitimate relationship with the warehouse or the organization, operating without authorized access. External attackers can be divided into two categories based on their physical proximity to the environment:
   1. Remote. An attacker operating from outside the site’s physical area, reaching the environment through whatever the organization exposes or makes reachable over the internet, including web services, remote access infrastructure, and third-party connections used by suppliers, carriers, and integrators.
   2. Proximate. An attacker with no legitimate access who has nevertheless moved within physical range of the site’s locally reachable technologies. They may, for example, be in a parking lot, a neighboring unit, or a delivery bay. Their proximity may allow them to interact with wireless, radio, Bluetooth, or other short-range interfaces without possessing an account, badge, employment relationship, or other authorized access.
2. Internal. An individual with legitimate access to the warehouse, its systems, or its information, who intentionally or unintentionally creates a security risk. This includes employees, administrators, operators, contractors, and temporary staff.

Combining these threat actors and their access levels with the risks identified earlier, the vulnerabilities present in each system, the resulting attack surfaces, and the potential attack paths between them provides a threat model for the warehouse environment. This allows an organization to understand how different systems and people could be targeted, how individual weaknesses could be combined, and where security controls are most needed.

Cornering the unicorn

Knowing what is at risk and who might come after it still leaves the most practical question open: how they would actually get in, and how far they could go once they did. The diagram below sets out the warehouse’s attack surface: every point where an attacker can get in, organized by the access that reaching each point requires.

On the left are the attacker categories discussed above, distinguished by where they stand and what access they already hold. Each reaches the environment through a different set of entry points, and the impact of a successful attack can land on several systems, from the WMS to OT systems such as robots, sorters, and physical access control system (PACS). The WMS is highlighted because it is the most significant of these: it coordinates the OT systems, so compromising it can affect the entire operation rather than a single process.

Next, we will cover the initial access vectors, the points a threat actor can use to reach the warehouse environment. Following that, we turn to the impact on the logistics processes themselves, focusing on the WMS, since its compromise affects every logistics operation that depends on it.

Initial attack vectors

Internet-facing services

Remote access through internet-facing services, third-party connections, and corporate networks gives an external attacker a foothold on the warehouse network infrastructure. It is a well-covered problem and not specific to this environment, so we note it here as an entry point but will not examine it further.

Wi-Fi, Bluetooth, and other radio channels

Wi-Fi connects handhelds, tablets, printers, cameras, and workstations. Bluetooth serves scanners, headsets, and positioning beacons. Zigbee and similar low-power protocols run lighting, sensors, and building automation, while LoRaWAN carries telemetry over long distances. Cellular provides a backup channel for kiosks and contractor equipment, and proprietary protocols control industrial panels and specialized machinery.

Radio passes through windows, gates, roofing, and thin external panels, so a network built for receiving and processing goods may be reachable from the parking lot and, with a directional antenna, from considerably farther away.

That reach matters in two ways. It gives an external attacker a place to start, as proximity alone is enough to reach the Wi-Fi and the low-power protocols, and from there to gain an initial foothold in the warehouse networks.

However, radio does not only lead into the network. It also connects directly to the field devices and carries the live control traffic of warehouse operations, which makes availability a first-order concern. If hundreds of handhelds lose their connection at once, picking stops. If an AGV loses its control channel, the safe reaction may be to halt in place, and a stopped machine can block an entire route.

Physical network

The scale of the warehouse itself is part of the attack surface. A fulfillment warehouse can run up to tens of thousands of square meters, with racking climbing several stories and walkways threading through it. Structured cabling runs along that racking, cameras and access points are mounted on that, and wall jacks sit behind pallets that have not moved in months. Almost none of it is in anyone’s line of sight.

That gives a person in the warehouse something a normal office does not: unsupervised access to live network infrastructure. Unplugging a ceiling camera leaves an active port. An unused jack behind a shelf leaves another. Neither requires tools, credentials, or any interaction with a system that logs anything. And since the camera or access point sits on the same segment as the rest of the site, the compromised port, not being isolated, opens a path to the network core.

Barcode scanners

Barcode scanners are everywhere in a warehouse. They are the main tool workers use to tell the system what is moving and where, supporting receiving, picking, packing, and inventory management. A typical setup has handheld scanners communicating with a base station, which is in turn connected to a workstation or another business system.

This makes the scanner an entry point into the core network in two ways.

First, through the barcode itself. Since the scanner acts as a keyboard to the connected host, a maliciously crafted barcode or QR code can inject keystrokes when scanned, potentially giving the attacker an initial foothold on the host without directly accessing the network.

Second, through the scanner’s wireless link. During a customer engagement, with the provided test stand of such a device, we discovered that an attacker within range could inject keystrokes into the connected host and potentially achieve remote code execution without user interaction. This attack pattern was observed across multiple vendors, including, for example, Honeywell devices affected by CVE-2026-4272.

In both cases, the result is the same: a foothold on a host inside the warehouse network. The scanner is simply the entry point; the significance of that foothold comes from the host’s trusted position within the operational environment and its connectivity to core systems.

Portable data terminals

Portable data terminals (PDTs) are essentially ordinary Android devices with an embedded barcode scanner, carried by workers to scan items; each scan is passed to a dedicated application that forwards it to a remote server over an API. To keep workers on task, the devices usually run in kiosk mode, locking the user to that single application and preventing interaction with the rest of the Android system.

That lockdown is exactly what an attacker wants to break, and breaking it is what turns the terminal into an initial access vector: escaping the kiosk can expose the Wi-Fi password of the network the terminal is connected to, or allow malicious software to be installed that gives the attacker remote access to the network over the internet.

Printers

Warehouses contain different types of printers. Alongside the usual IT printers used for documents and paperwork, there are specialized thermal printers, small devices that use heated elements on the printhead to create labels, barcodes, and tags. As we mentioned earlier, these are mainly used to print the labels, usually containing barcodes, that are attached to goods during the putaway stage.

Here we focus on mobile thermal printers: small, battery-powered devices that workers can carry or wear. They typically connect to the internal network over Wi-Fi to receive print commands, while also supporting Bluetooth for connecting to nearby devices. This combination makes them a potential entry point into the internal network.

Some models, such as the Zebra ZQ630, support multiple interfaces and can run applications directly on the device. During a security assessment, we discovered that apps can be uploaded and executed over Bluetooth. By exploiting a pairing misconfiguration, an attacker within Bluetooth range could bypass pairing, upload malicious software, and gain an initial foothold through the printer.

Impact on logistics processes (attacks on WMS)

With a foothold in the network, an attacker can pivot to the logistics processes themselves: from the WMS through the warehouse infrastructure to the PLCs and the machinery they drive. As we saw earlier, all OT systems and devices are interconnected and the WMS sits at the head of that chain: it is the system of record every other process reads from and writes to, so compromising it reaches all of them at once. That, and its being specific to this environment, is why it is the focus here.

Warehouse management systems (WMS) are the software platforms that optimize warehouse processes from the entry of goods to their exit. They provide real-time visibility into inventory and orchestrate end-to-end fulfillment by integrating with many warehouse subsystems and pieces of automation equipment, such as conveyors, PLCs, robotics, scanners, and voice systems. A typical WMS includes modules for receiving, inventory control, order management, labor management, shipping, and reporting. Major vendors include SAP EWM, Oracle Warehouse Management, Manhattan Active, Blue Yonder, Infor, Körber/HighJump, Microsoft, and TECSYS.

To picture how central this is, think back to our unicorn. From the moment it was scanned at receiving, it stopped being only a physical box and became a record inside the WMS, a record the system then used to decide where it should be stored, when it should be picked, how it should be sorted, and where it should ship. Every device employed along the way — the PDT at receiving, the printer at putaway, the workstation at picking, the conveyor at sorting — was really just talking to the WMS. It is the thread that ties all eight stages together, which is exactly what makes it such a valuable target.

Because modern WMS platforms are usually web-based and heavily interconnected, they inherit common web and API vulnerabilities alongside flaws specific to the WMS itself. A recent example is CVE-2026-70774 in Oracle Warehouse Management, part of Oracle E-Business Suite. Oracle rated it High and described it as an easily exploitable authorization bypass flaw in which a low-privileged attacker with network access can compromise the application, affecting integrity and availability by allowing unauthorized creation, deletion, or modification of critical data.

A flaw like this may look like a typical web application issue, but its consequences in a warehouse are more serious, because the WMS is the system of record. If an attacker can modify its data, they can alter stock levels, bin locations, receiving records, picking tasks, or shipment information, silently corrupting the digital twin of the warehouse. And since the eight stages form a dependency chain, that corruption doesn’t stay contained: a falsified record at receiving is inherited by putaway, picking, and everything downstream. The physical box and its record diverge, and every later process trusts the record.

The most direct version of this attack comes from an authorization bypass flaw like the Oracle vulnerability above. Here the threat actor can be an external attacker who has reached the application over the network, or an insider such as a low-privileged operator or storekeeper who already holds a legitimate account.

The danger grows when such a flaw is chained with others. An external attacker who combines remote code execution with SQL injection could generate shipment orders to arbitrary addresses together with the documents needed for goods to physically leave the warehouse, fabricate waybills, acceptance or transfer records, and registers, while also exposing customer data such as names and delivery addresses. In effect, the goods are sent out with legitimate-looking paperwork.

This is why ordinary web vulnerabilities carry considerable weight in a WMS. For an external attacker or an insider probing beyond their assigned role, broken access control could open a path into another warehouse zone, allow manipulation of other workers’ tasks, permit unauthorized exception operations, or expose administrative APIs. A server-side request forgery flaw could force the WMS server to reach resources an external attacker cannot touch directly, and if the network is flat or only formally segmented, that server may in turn reach internal management panels, cloud service interfaces, or systems sitting close to the operational network, turning a single web bug into a bridge between IT and OT.

Attacks on a WMS don’t always target the application directly, though. As we saw while tracing the unicorn, the warehouse is full of connected devices that already hold trusted access. A handheld terminal carries authenticated WMS API access as a matter of course, and anyone who gains control of that terminal inherits it (by escaping the kiosk, for example). Depending on the device’s permissions, that might mean confirming picks that never occurred, falsifying receiving operations, adjusting stock counts, or marking orders complete — something warehouse employees themselves could use to avoid their assigned duties.

Where the WMS is connected to control and automation systems, access to the WMS can extend into physical operations, including conveyors, sorting systems, lifts, doors, and pumps.

PLCs translate WMS instructions and sensor inputs into physical actions. Compromising a system that controls them therefore creates an opportunity to manipulate those actions. The impact can be as simple as a sorting diverter sending boxes down the wrong route, a conveyor repeatedly entering an error state and creating a backlog, or a falsified sensor value causing a line to be treated as clear when it is full.

However the WMS is compromised, the business consequence is the same: inventory corruption, unauthorized shipments, and operational disruption, each reaching the customer in the end as a late or missing delivery, the wrong item in the box, or a personal address exposed to a stranger.

Securing the unicorn

We followed our unicorn through web applications and databases, radio channels and PDTs, printers, conveyors, robots, and the loading zone. Because warehouse environments combine IT, OT, wireless, physical, and human components, security controls should be implemented as a single, coordinated security architecture rather than as isolated measures. Network segmentation should be a foundational control, with clear separation between corporate IT, warehouse OT, operational devices, management networks, and externally connected systems. Segmentation should also extend across facilities and customer environments so that each site and customer integration is isolated from the others, limiting the potential impact of a compromise. A zero-trust approach should reinforce this architecture by requiring explicit authentication and authorization for connections between systems, regardless of their network location, and by applying least-privilege access wherever possible. Regular penetration testing of the corporate IT environment, including domain services, identity infrastructure, and endpoints, should be incorporated into the warehouse security program.

Physical and network access controls should be implemented together. Access to warehouse facilities, network rooms, communication cabinets, and operational areas should be restricted according to business need, with appropriate authentication, monitoring, and access logging. Network infrastructure should be hardened through controls such as switch port security, MAC-based access restrictions where appropriate, and IEEE 802.1X with EAP-TLS for strong device and user authentication. Network equipment should also be securely configured and regularly reviewed to ensure that unused interfaces, insecure management protocols, default credentials, and unnecessary services are disabled.

Web applications and their supporting infrastructure should be governed through a secure software development lifecycle (SSDLC), including security requirements, secure coding practices, code and dependency review, and application security assessment before deployment and throughout the application’s lifecycle. Third-party applications and components should be continuously monitored and updated, with a defined vulnerability and patch management process. This should explicitly include warehouse management systems (WMS), warehouse portals, APIs, and other applications that provide operational or customer-facing functionality.

Wireless technologies should be subject to dedicated security assessment and engineering. Radio-frequency surveys should be conducted to establish expected coverage, identify unauthorized or unexpected signals, validate the availability and protection of operational communications, and determine whether wireless deployments create unintended access paths into warehouse networks. Wireless security should be assessed not only from a connectivity perspective but also in terms of authentication, encryption, device provisioning, protocol exposure, interference resilience, and the potential impact of unauthorized devices.

Warehouse operational devices should be incorporated into the organization’s endpoint security and vulnerability management processes. Devices should receive security updates and firmware patches on a defined schedule, with accelerated remediation for critical vulnerabilities. Security assessments should be performed periodically to identify weaknesses in device configuration, applications, exposed services, and authentication mechanisms.

Personnel security should form an additional layer of the warehouse security architecture. In most industrial and ICS environments, getting inside means clearing background checks, holding relevant qualifications, or proving prior experience; the hiring process itself acts as a filter. A warehouse is different. Roles in sorting, packing, and courier work are hired quickly and in volume, and they typically require no prior expertise, certification, or specialized background at all. An ordinary person can walk in off the street, be hired within days, and immediately hold legitimate physical access to the facility. Therefore, a more restrictive and risk-based approach to hiring should be considered, particularly for roles that provide access to sensitive areas, operational systems, network infrastructure, or customer assets.

Ultimately, all of these measures serve the same objective: protecting the continuity and reliability of the warehouse as a whole. This matters because real warehouses handle food, medicines, machine parts, and the millions of ordinary things people and companies depend on every day. Protecting their journey means seeing the warehouse as it has actually become: a single digital system with doors, motors, shelving, and radio signals, and with entirely real consequences when it fails. And if the security program works, the customer never knows any of this happened. The unicorn simply arrives at the scheduled time.

If your warehouse operations rely on interconnected systems like these, we can help identify the gaps before an adversary does. Reach out to discuss a security assessment.

Introduction

Imagine you’ve ordered a small paper unicorn from an online marketplace. You clicked “Buy”, received a cheerful notification, and expect a courier to bring the box tomorrow. If the unicorn is delayed by a day, it will be annoying but not catastrophic: an apology, a discount promo code — and the incident is forgotten.

Now let’s multiply one late unicorn by several hundred thousand orders.

Packages start heading to the wrong cities. The website shows an item is in stock, even though the required bin is empty. Conveyors stop, boxes get stuck between floors, the cargo piles up with no freight manifest. Trucks line up at the gates, and every minute of downtime turns into blown schedules, support tickets, fines, and lost sales.

It is at exactly this scale that a cyberincident in a warehouse becomes a problem for the entire business. A modern warehouse is no longer just a big building with shelving. It is a tightly interconnected hybrid system: web applications, radio channels, and an automated factory. Physical processes here depend on data, and errors in data have very physical consequences. Examples include an outage that affected warehouse management systems at the grocery giant Morrisons, and the 2025 Form 10-K annual report of United Natural Foods, Inc. (UNFI), which confirmed unauthorized activity on information technology systems that impacted order fulfillment. More recently, reports emerged that a cyberattack disrupted eight CEVA Logistics warehouses across Europe and exposed customer delivery data.

This paper follows a single order through a warehouse to show where the operation is vulnerable, the stages it traverses, the systems that drive it, and the points where an attacker can reach them. We then set out the threat model: what is at risk, who is in a position to take it, and how each risk lands on the business. From there, we look at how an attacker gets in, and what reaching the warehouse management system costs in practical terms: orders unshipped, inventory records nobody can trust, and an operation that stops. We close with recommendations on where the controls that matter actually sit.

Tracing the unicorn

A modern warehouse is a sequence of stages involving many machines and people, all working together to keep the business process stable so that, in the end, your order gets delivered. This process consists of eight main stages, shown in the figure below.

So let’s assume we’ve ordered our paper unicorn and are waiting for it at home. Let’s follow our order through all the main stages, from its arrival at the warehouse to the final stop on its journey.

1. Unloading
   First, the unicorn arrives in the unloading zone, packed in a big truck. The driver is assigned a gate, for example, via a tablet with kiosk mode enabled, or an outdoor display board, indicating where the truck should park and where the cargo will be unloaded. Even at this very first step, a digital command (the gate number on the screen) is already triggering a physical action. Therefore, incorrectly assigned gates can create a queue, delay unloading, or send goods into the wrong workflow.
2. Receiving
   At this stage, employees identify the cargo. They scan a barcode using a portable data terminal (PDT) and the central system receives a message indicating that this object arrives at the warehouse. From this moment, the unicorn exists in two forms at once: as a real box and as a record in the information system. If these two versions diverge, the error is inherited by all subsequent processes.
3. Putaway
   Next, the goods are moved into internal storage. Two new devices come into play here. First, a thermal printer, connected to the internal network over Wi-Fi, creates an internal label for each item. Then an employee, forklift, or automated system moves the goods to the assigned bin, often with the help of lifts controlled by PLCs. Both of these devices can be valuable attack entry points: one can let an adversary pivot into the internal network, while the other can give them control over an electric motor.
4. Storage
   Then the unicorn goes into storage. Many distribution centers use multi-level mezzanine structures: several floors of metal shelving inside a huge space. They allow efficient use of the building’s height, but at the same time complicate control over the technical infrastructure. Wi-Fi access points, cameras, switches, cables, and sensors may be mounted high overhead or buried deep inside the rows. A forgotten Ethernet cable or misconfigured networked device (access point or switch) left behind in a dark corner can become a convenient initial access point into critical infrastructure.
5. Picking
   When the customer places an order, picking begins if the goods were already stored in the warehouse or as soon as they are unloaded. A PDT, workstation, light indicator, or voice terminal directs the employee to the required bin where they pick the goods and move them to the next stage. These devices are themselves potential attack entry points: compromising a workstation or PDT could allow an attacker to manipulate picking information, which could misdirect employees to the wrong bin. Here, both the speed and integrity of the information are critical, and any manipulation or disruption can lead to wrong orders, delays, or problems in subsequent stages.
6. Sorting
   At the sorting stage, the warehouse starts to look more like a factory. Boxes move through conveyor belts and pass through different scanners, diverters, and chutes. The sorting plans are usually stored and managed by dedicated servers, while PLCs are responsible for controlling the machinery and executing these plans. For example, when the warehouse management system (WMS) decides that our unicorn should take route A, the control system needs to activate the right diverter at the right moment, sometimes within a fraction of a second.
7. Packing
   During packing, an employee or a robotic arm places the goods into the final box, prints the shipping label, and sometimes measures the dimensions and weight of the package. Safety mechanisms usually prevent robotic arms from leaving the controlled zone and harming nearby operators, but their compromise might inflict physical damage on the packages being processed.
8. Shipment
   Finally, at the shipping stage, the parcel heads to the gates. Automated guided vehicles (AGVs) and autonomous mobile robots (AMRs) move containers and pallets across the warehouse, while the dock management system coordinates the truck that will carry the unicorn onward to a store, a sorting center, or straight to the customer. These robots are themselves a potential target for attackers. Attacks can be conducted against AGVs and AMRs to interfere with their operation, for example by affecting their navigation or preventing them from reaching their intended destination.

All eight stages are coordinated by the WMS. It looks like a large web application, but in its role it’s closer to the operating system of the entire warehouse: it knows which goods exist, where they’re located, who or what should move them, and where they go next, linking employees, inventory levels, storage addresses, tasks, and orders. It also exchanges data externally: with ERP systems, transport platforms, marketplaces, dark stores, and external carriers.

Surrounding the WMS is an even larger digital ecosystem: identity services, databases, APIs, printers, carrier systems, technicians’ laptops, video surveillance, access control, fire and gas alarms, power supply, and a building management system (BMS).

That is why a single “warehouse network” doesn’t really exist. As we’ve seen, there are many overlapping technologies — IT infrastructure, OT systems, radio, cloud services, building security, and automation — layered on top of one another. A lone vulnerability scanner will never tell you whether a warehouse is secure.

Hunting the unicorn

Securing an environment such as a warehouse requires a comprehensive security assessment covering all of its interconnected systems. This involves identifying vulnerabilities across each system and understanding how weaknesses can be combined into attack paths that a real attacker could exploit. But a vulnerability only matters in proportion to what it exposes and who is able to reach it, so before looking at the weaknesses themselves, we need to understand what an attacker in this environment is actually after, because in a warehouse, the blast radius extends well beyond data. Everything we just traced — the systems, the devices, and the people operating them — exists to keep goods moving, and attacks in various parts along that chain can affect the business differently. The chart below shows what is at risk in the warehouse and where those risks land for the business. The segment on the left groups them under the three core security properties: integrity, availability, and confidentiality. The segment on the right maps each property to the business outcomes a failure can cause.

The first area at risk is integrity. Inventory counts, locations, orders, and shipment data must stay in sync with the physical world. Corrupting a record silently desynchronizes the operation, and because the eight stages form a dependency chain, one falsified entry early on is inherited downstream, surfacing later as stockouts and mis-shipments. In its deliberate forms, this lets an attacker redirect goods, release stock, and perform other actions on behalf of the platform’s vendors under cover of legitimate records. Integrity is also a safety concern: when people or machines act on information that no longer reflects the physical environment, a data failure can become a physical incident. This is why integrity is the only property linked to all five outcomes, including harm to people.

The second is availability, the warehouse’s ability to keep goods moving. Halting a conveyor, a sorter, label printing, or the WMS stops orders from shipping, and in a business built on speed, every hour of downtime means delayed deliveries and broken promises. Ransomware can bring the whole warehouse to a standstill. The impact shows up as operational disruption, lost revenue, and reputational damage.

The third is confidentiality. Customer data, business records, and system credentials are valuable targets. Their exposure leads to legal liability and reputational damage, and stolen credentials often open the door to further attacks. Where the WMS connects to financial or ERP processes, the risk extends to theft of funds.
Knowing what is at risk tells us what an attacker stands to gain; the next question is who is in a position to take it. Threat actors differ in the access they already hold. For example, an attacker may be authenticated and privileged, have limited physical or remote access, or have no legitimate access to the environment at all. Based on their relationship with the warehouse and their level of access, threat actors can generally be divided into two categories:

1. External. An attacker with no legitimate relationship with the warehouse or the organization, operating without authorized access. External attackers can be divided into two categories based on their physical proximity to the environment:
   1. Remote. An attacker operating from outside the site’s physical area, reaching the environment through whatever the organization exposes or makes reachable over the internet, including web services, remote access infrastructure, and third-party connections used by suppliers, carriers, and integrators.
   2. Proximate. An attacker with no legitimate access who has nevertheless moved within physical range of the site’s locally reachable technologies. They may, for example, be in a parking lot, a neighboring unit, or a delivery bay. Their proximity may allow them to interact with wireless, radio, Bluetooth, or other short-range interfaces without possessing an account, badge, employment relationship, or other authorized access.
2. Internal. An individual with legitimate access to the warehouse, its systems, or its information, who intentionally or unintentionally creates a security risk. This includes employees, administrators, operators, contractors, and temporary staff.

Combining these threat actors and their access levels with the risks identified earlier, the vulnerabilities present in each system, the resulting attack surfaces, and the potential attack paths between them provides a threat model for the warehouse environment. This allows an organization to understand how different systems and people could be targeted, how individual weaknesses could be combined, and where security controls are most needed.

Cornering the unicorn

Knowing what is at risk and who might come after it still leaves the most practical question open: how they would actually get in, and how far they could go once they did. The diagram below sets out the warehouse’s attack surface: every point where an attacker can get in, organized by the access that reaching each point requires.

On the left are the attacker categories discussed above, distinguished by where they stand and what access they already hold. Each reaches the environment through a different set of entry points, and the impact of a successful attack can land on several systems, from the WMS to OT systems such as robots, sorters, and physical access control system (PACS). The WMS is highlighted because it is the most significant of these: it coordinates the OT systems, so compromising it can affect the entire operation rather than a single process.

Next, we will cover the initial access vectors, the points a threat actor can use to reach the warehouse environment. Following that, we turn to the impact on the logistics processes themselves, focusing on the WMS, since its compromise affects every logistics operation that depends on it.

Initial attack vectors

Internet-facing services

Remote access through internet-facing services, third-party connections, and corporate networks gives an external attacker a foothold on the warehouse network infrastructure. It is a well-covered problem and not specific to this environment, so we note it here as an entry point but will not examine it further.

Wi-Fi, Bluetooth, and other radio channels

Wi-Fi connects handhelds, tablets, printers, cameras, and workstations. Bluetooth serves scanners, headsets, and positioning beacons. Zigbee and similar low-power protocols run lighting, sensors, and building automation, while LoRaWAN carries telemetry over long distances. Cellular provides a backup channel for kiosks and contractor equipment, and proprietary protocols control industrial panels and specialized machinery.

Radio passes through windows, gates, roofing, and thin external panels, so a network built for receiving and processing goods may be reachable from the parking lot and, with a directional antenna, from considerably farther away.

That reach matters in two ways. It gives an external attacker a place to start, as proximity alone is enough to reach the Wi-Fi and the low-power protocols, and from there to gain an initial foothold in the warehouse networks.

However, radio does not only lead into the network. It also connects directly to the field devices and carries the live control traffic of warehouse operations, which makes availability a first-order concern. If hundreds of handhelds lose their connection at once, picking stops. If an AGV loses its control channel, the safe reaction may be to halt in place, and a stopped machine can block an entire route.

Physical network

The scale of the warehouse itself is part of the attack surface. A fulfillment warehouse can run up to tens of thousands of square meters, with racking climbing several stories and walkways threading through it. Structured cabling runs along that racking, cameras and access points are mounted on that, and wall jacks sit behind pallets that have not moved in months. Almost none of it is in anyone’s line of sight.

That gives a person in the warehouse something a normal office does not: unsupervised access to live network infrastructure. Unplugging a ceiling camera leaves an active port. An unused jack behind a shelf leaves another. Neither requires tools, credentials, or any interaction with a system that logs anything. And since the camera or access point sits on the same segment as the rest of the site, the compromised port, not being isolated, opens a path to the network core.

Barcode scanners

Barcode scanners are everywhere in a warehouse. They are the main tool workers use to tell the system what is moving and where, supporting receiving, picking, packing, and inventory management. A typical setup has handheld scanners communicating with a base station, which is in turn connected to a workstation or another business system.

This makes the scanner an entry point into the core network in two ways.

First, through the barcode itself. Since the scanner acts as a keyboard to the connected host, a maliciously crafted barcode or QR code can inject keystrokes when scanned, potentially giving the attacker an initial foothold on the host without directly accessing the network.

Second, through the scanner’s wireless link. During a customer engagement, with the provided test stand of such a device, we discovered that an attacker within range could inject keystrokes into the connected host and potentially achieve remote code execution without user interaction. This attack pattern was observed across multiple vendors, including, for example, Honeywell devices affected by CVE-2026-4272.

In both cases, the result is the same: a foothold on a host inside the warehouse network. The scanner is simply the entry point; the significance of that foothold comes from the host’s trusted position within the operational environment and its connectivity to core systems.

Portable data terminals

Portable data terminals (PDTs) are essentially ordinary Android devices with an embedded barcode scanner, carried by workers to scan items; each scan is passed to a dedicated application that forwards it to a remote server over an API. To keep workers on task, the devices usually run in kiosk mode, locking the user to that single application and preventing interaction with the rest of the Android system.

That lockdown is exactly what an attacker wants to break, and breaking it is what turns the terminal into an initial access vector: escaping the kiosk can expose the Wi-Fi password of the network the terminal is connected to, or allow malicious software to be installed that gives the attacker remote access to the network over the internet.

Printers

Warehouses contain different types of printers. Alongside the usual IT printers used for documents and paperwork, there are specialized thermal printers, small devices that use heated elements on the printhead to create labels, barcodes, and tags. As we mentioned earlier, these are mainly used to print the labels, usually containing barcodes, that are attached to goods during the putaway stage.

Here we focus on mobile thermal printers: small, battery-powered devices that workers can carry or wear. They typically connect to the internal network over Wi-Fi to receive print commands, while also supporting Bluetooth for connecting to nearby devices. This combination makes them a potential entry point into the internal network.

Some models, such as the Zebra ZQ630, support multiple interfaces and can run applications directly on the device. During a security assessment, we discovered that apps can be uploaded and executed over Bluetooth. By exploiting a pairing misconfiguration, an attacker within Bluetooth range could bypass pairing, upload malicious software, and gain an initial foothold through the printer.

Impact on logistics processes (attacks on WMS)

With a foothold in the network, an attacker can pivot to the logistics processes themselves: from the WMS through the warehouse infrastructure to the PLCs and the machinery they drive. As we saw earlier, all OT systems and devices are interconnected and the WMS sits at the head of that chain: it is the system of record every other process reads from and writes to, so compromising it reaches all of them at once. That, and its being specific to this environment, is why it is the focus here.

Warehouse management systems (WMS) are the software platforms that optimize warehouse processes from the entry of goods to their exit. They provide real-time visibility into inventory and orchestrate end-to-end fulfillment by integrating with many warehouse subsystems and pieces of automation equipment, such as conveyors, PLCs, robotics, scanners, and voice systems. A typical WMS includes modules for receiving, inventory control, order management, labor management, shipping, and reporting. Major vendors include SAP EWM, Oracle Warehouse Management, Manhattan Active, Blue Yonder, Infor, Körber/HighJump, Microsoft, and TECSYS.

To picture how central this is, think back to our unicorn. From the moment it was scanned at receiving, it stopped being only a physical box and became a record inside the WMS, a record the system then used to decide where it should be stored, when it should be picked, how it should be sorted, and where it should ship. Every device employed along the way — the PDT at receiving, the printer at putaway, the workstation at picking, the conveyor at sorting — was really just talking to the WMS. It is the thread that ties all eight stages together, which is exactly what makes it such a valuable target.

Because modern WMS platforms are usually web-based and heavily interconnected, they inherit common web and API vulnerabilities alongside flaws specific to the WMS itself. A recent example is CVE-2026-70774 in Oracle Warehouse Management, part of Oracle E-Business Suite. Oracle rated it High and described it as an easily exploitable authorization bypass flaw in which a low-privileged attacker with network access can compromise the application, affecting integrity and availability by allowing unauthorized creation, deletion, or modification of critical data.

A flaw like this may look like a typical web application issue, but its consequences in a warehouse are more serious, because the WMS is the system of record. If an attacker can modify its data, they can alter stock levels, bin locations, receiving records, picking tasks, or shipment information, silently corrupting the digital twin of the warehouse. And since the eight stages form a dependency chain, that corruption doesn’t stay contained: a falsified record at receiving is inherited by putaway, picking, and everything downstream. The physical box and its record diverge, and every later process trusts the record.

The most direct version of this attack comes from an authorization bypass flaw like the Oracle vulnerability above. Here the threat actor can be an external attacker who has reached the application over the network, or an insider such as a low-privileged operator or storekeeper who already holds a legitimate account.

The danger grows when such a flaw is chained with others. An external attacker who combines remote code execution with SQL injection could generate shipment orders to arbitrary addresses together with the documents needed for goods to physically leave the warehouse, fabricate waybills, acceptance or transfer records, and registers, while also exposing customer data such as names and delivery addresses. In effect, the goods are sent out with legitimate-looking paperwork.

This is why ordinary web vulnerabilities carry considerable weight in a WMS. For an external attacker or an insider probing beyond their assigned role, broken access control could open a path into another warehouse zone, allow manipulation of other workers’ tasks, permit unauthorized exception operations, or expose administrative APIs. A server-side request forgery flaw could force the WMS server to reach resources an external attacker cannot touch directly, and if the network is flat or only formally segmented, that server may in turn reach internal management panels, cloud service interfaces, or systems sitting close to the operational network, turning a single web bug into a bridge between IT and OT.

Attacks on a WMS don’t always target the application directly, though. As we saw while tracing the unicorn, the warehouse is full of connected devices that already hold trusted access. A handheld terminal carries authenticated WMS API access as a matter of course, and anyone who gains control of that terminal inherits it (by escaping the kiosk, for example). Depending on the device’s permissions, that might mean confirming picks that never occurred, falsifying receiving operations, adjusting stock counts, or marking orders complete — something warehouse employees themselves could use to avoid their assigned duties.

Where the WMS is connected to control and automation systems, access to the WMS can extend into physical operations, including conveyors, sorting systems, lifts, doors, and pumps.

PLCs translate WMS instructions and sensor inputs into physical actions. Compromising a system that controls them therefore creates an opportunity to manipulate those actions. The impact can be as simple as a sorting diverter sending boxes down the wrong route, a conveyor repeatedly entering an error state and creating a backlog, or a falsified sensor value causing a line to be treated as clear when it is full.

However the WMS is compromised, the business consequence is the same: inventory corruption, unauthorized shipments, and operational disruption, each reaching the customer in the end as a late or missing delivery, the wrong item in the box, or a personal address exposed to a stranger.

Securing the unicorn

We followed our unicorn through web applications and databases, radio channels and PDTs, printers, conveyors, robots, and the loading zone. Because warehouse environments combine IT, OT, wireless, physical, and human components, security controls should be implemented as a single, coordinated security architecture rather than as isolated measures. Network segmentation should be a foundational control, with clear separation between corporate IT, warehouse OT, operational devices, management networks, and externally connected systems. Segmentation should also extend across facilities and customer environments so that each site and customer integration is isolated from the others, limiting the potential impact of a compromise. A zero-trust approach should reinforce this architecture by requiring explicit authentication and authorization for connections between systems, regardless of their network location, and by applying least-privilege access wherever possible. Regular penetration testing of the corporate IT environment, including domain services, identity infrastructure, and endpoints, should be incorporated into the warehouse security program.

Physical and network access controls should be implemented together. Access to warehouse facilities, network rooms, communication cabinets, and operational areas should be restricted according to business need, with appropriate authentication, monitoring, and access logging. Network infrastructure should be hardened through controls such as switch port security, MAC-based access restrictions where appropriate, and IEEE 802.1X with EAP-TLS for strong device and user authentication. Network equipment should also be securely configured and regularly reviewed to ensure that unused interfaces, insecure management protocols, default credentials, and unnecessary services are disabled.

Web applications and their supporting infrastructure should be governed through a secure software development lifecycle (SSDLC), including security requirements, secure coding practices, code and dependency review, and application security assessment before deployment and throughout the application’s lifecycle. Third-party applications and components should be continuously monitored and updated, with a defined vulnerability and patch management process. This should explicitly include warehouse management systems (WMS), warehouse portals, APIs, and other applications that provide operational or customer-facing functionality.

Wireless technologies should be subject to dedicated security assessment and engineering. Radio-frequency surveys should be conducted to establish expected coverage, identify unauthorized or unexpected signals, validate the availability and protection of operational communications, and determine whether wireless deployments create unintended access paths into warehouse networks. Wireless security should be assessed not only from a connectivity perspective but also in terms of authentication, encryption, device provisioning, protocol exposure, interference resilience, and the potential impact of unauthorized devices.

Warehouse operational devices should be incorporated into the organization’s endpoint security and vulnerability management processes. Devices should receive security updates and firmware patches on a defined schedule, with accelerated remediation for critical vulnerabilities. Security assessments should be performed periodically to identify weaknesses in device configuration, applications, exposed services, and authentication mechanisms.

Personnel security should form an additional layer of the warehouse security architecture. In most industrial and ICS environments, getting inside means clearing background checks, holding relevant qualifications, or proving prior experience; the hiring process itself acts as a filter. A warehouse is different. Roles in sorting, packing, and courier work are hired quickly and in volume, and they typically require no prior expertise, certification, or specialized background at all. An ordinary person can walk in off the street, be hired within days, and immediately hold legitimate physical access to the facility. Therefore, a more restrictive and risk-based approach to hiring should be considered, particularly for roles that provide access to sensitive areas, operational systems, network infrastructure, or customer assets.

Ultimately, all of these measures serve the same objective: protecting the continuity and reliability of the warehouse as a whole. This matters because real warehouses handle food, medicines, machine parts, and the millions of ordinary things people and companies depend on every day. Protecting their journey means seeing the warehouse as it has actually become: a single digital system with doors, motors, shelving, and radio signals, and with entirely real consequences when it fails. And if the security program works, the customer never knows any of this happened. The unicorn simply arrives at the scheduled time.

If your warehouse operations rely on interconnected systems like these, we can help identify the gaps before an adversary does. Reach out to discuss a security assessment.

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For more than two decades now, setting up a home router has meant going through the same old routine: pick a Wi-Fi band, name your network, choose a security type, and set a password. After you type in your network name, you often see an option next to it to hide the network name — sometimes labeled hide SSID, or simply hidden or closed network. If you choose this option, the network you create won’t show up automatically in the list of available Wi-Fi networks on any smartphone, computer, or smart-home device — you need to type in the correct network name by hand. Only then will the device be able to find that network and connect to it. At first glance, hiding your wireless network’s name might seem like a smart precaution: keeping neighbors or passersby from hacking into your Wi-Fi since they simply can’t see it. But in fact things work differently: all your devices will give away the hidden network — and not just at home either.

How devices connect to Wi-Fi

Every Wi-Fi access point broadcasts a short data packet roughly 10 times a second so your smartphone or computer can show you a list of nearby networks’ names, like JohnWiFi, TommyNet, CafeDeArts_Guest, or other, creative variants. This packet contains (i) the network’s name (Service Set Identifier, or SSID), (ii) a unique identifier for the access point (Basic Service Set Identifier, or BSSID), and (iii) details about the radio channel the network uses. This info helps devices (i) pick the best network to connect to, and (ii) automatically join networks they already know; accordingly, none of the data in that packet is encrypted. Hidden networks also broadcast these advertising packets; they just leave the SSID field blank. This method of finding networks is called passive because a Wi-Fi client, like, say, your smartphone, just needs to listen to the airwaves to spot nearby access points.

On top of that, the client can scan for Wi-Fi networks on its own, which is known as active discovery. Your smartphone or computer is constantly broadcasting the names of Wi-Fi networks it knows and would like to connect to. If one of those networks happens to be nearby, the access point answers back, and the connection is made.

Active discovery can sometimes help save your smartphone’s battery, and it’s indispensable when you’re working with hidden networks. Discovering a hidden network and connecting to it becomes a two-step process. First, the smartphone “hears” that there’s a Wi-Fi access point nearby with a hidden network name. It then tries to reach that network by broadcasting the names (SSIDs) it already knows — either hidden networks it’s connected to before, or the name the user typed in when first connecting to a hidden network. If the name matches, the router responds, and the connection goes through.

A hidden network name (SSID) doesn’t actually hide your Wi-Fi network

The regular list of available Wi-Fi networks on your smartphone or computer won’t show networks that don’t broadcast a public SSID. But any specialized Wi-Fi scanning software can spot them easily — it just won’t show their names. And hidden networks are still easy to identify and tell apart thanks to each router’s unique BSSID.

So hiding a Wi-Fi network’s name doesn’t make it truly invisible, and it won’t protect you from cybercriminals. It’s worth noting that any large-scale analysis, such as mapping the geographic locations of Wi-Fi networks, relies on BSSIDs, not SSIDs. Besides the huge databases kept by Apple and Google, there are public access-point-mapping projects too, like WiGLE.

If someone wants to break into a network at a specific location, specialized software lets them find out its SSID just by waiting for one of the legitimate clients to connect and listening in on the active discovery session. From there, they can connect to that Wi-Fi network — provided it uses a weak enough security protocol.

Your devices leak hidden Wi-Fi network names and personal data

The main drawback of a hidden SSID is that smartphones and computers have to broadcast it before they can connect to the wireless network. And they do this everywhere — not just near the specific access point in question. As soon as any network with a hidden name shows up within signal range, the client attempts to connect, and during that attempt it broadcasts the names of every hidden network it knows. And this broadcast isn’t encrypted, which means anyone nearby can pick up the data being transmitted.

In 2021, researchers at the University of Hamburg ran an experiment collecting radio signal data on a busy street. In just three one-hour listening sessions, they gathered 252 000 Wi-Fi connection requests from passersby’s smartphones. Of those, 23% contained the SSID of a network the smartphone would have preferred to connect to.

And that’s where a factor few people think about comes into play: the network’s name itself. Twelve percent of the SSIDs found were long numeric strings that looked a lot like passwords. So, once someone knows the SSID and BSSID, they can look up the BSSID in a service like WiGLE and find the access point’s real-world address. Once there, an attacker can try to use such a string as both the SSID and the password to connect — with a good chance of success. The researchers’ sample also included 106 SSIDs containing a first and/or last name, 92 SSIDs that pointed to a home address, and three that revealed an email address.

Given all this, it’s no surprise that Apple and Asus recommend against using the hidden SSID feature.

Other drawbacks of hiding your Wi-Fi network

Beyond the data leak issues, hidden SSID technology — a holdover from early Wi-Fi versions — also hurts the performance of modern versions of the radio protocol. In the 6GHz band, searching for a hidden Wi-Fi network across 59 possible channels becomes noisy and power-hungry: the client has to run through every option and send a lot of requests. As a result, performance drops for all nearby Wi-Fi networks in the 6GHz band, and the client itself burns through battery power sending out all those requests.

Asus also points out that Windows devices may tend to connect to visible networks first — even if a network with a hidden SSID is available and marked as preferred.

When a hidden SSID actually makes sense

There aren’t many scenarios where hiding your network name is genuinely useful. For example, if a router is meant for a small group of people but is installed in a very crowded location, a hidden SSID can cut down on the number of connection attempts and ease the load on the router. But keep in mind that this boosts performance, not security — and only slightly at that.

Another similar case is a technical Wi-Fi network for a limited number of fixed devices — like a network of security cameras or smart home gadgets. These devices always stay in the same place, so whenever they try to connect to their assigned Wi-Fi, they find it right away, and no data leaks out — unless an attacker is actually listening in on the radio signal near the router. At the same time, outsiders are less likely to try connecting to such a network.

How to make your Wi-Fi more secure

To protect your Wi-Fi network from wireless hackers, don’t hide the SSID: instead, use the modern WPA3 security protocol along with a long password. Attackers can intercept data over the air and then try to crack the password with brute force, so a strong Wi-Fi password should be at least 20 characters long. The easiest way to generate and store such long, hard-to-crack passwords is with a password manager that syncs across your devices; that way they’re always at hand — no matter which gadget you’re using.

If some of your older devices don’t support WPA3, it’s fine to use WPA2 with protected management frames (PMF) turned on, which guard against many types of attacks on Wi-Fi networks.

You should also make sure Wi-Fi Protected Setup (WPS) is turned off, since it essentially replaces your password with an easy-to-guess PIN code.

To quickly spot unknown devices connected to your Wi-Fi network, use Smart Home Monitor in Kaspersky Premium.

As for the network name, instead of hiding it, pick one that doesn’t reveal your name, address, or any other personal details. It’s also worth avoiding default network names like ISPName-XXXX or dlink, but for a different reason: generic names like these can confuse you and your guests alike. If you don’t want your access point to show up on Wi-Fi geolocation maps from Apple, Google, and other providers, you can add the suffix _nomap to the end of your network name, but there’s no guarantee it will actually work.

Apart from protecting your Wi-Fi, make sure your router is configured securely overall. First and foremost, that means giving it a long, unique admin password, and keeping it updated with the manufacturer’s latest firmware. And if you have a bit more time, you could even boost your Wi-Fi performance.

How Wi-Fi networks get hacked, and how to protect them:

• How to find a person and recognize their pose using Wi-Fi
• Is your printer informing on you to the police?
• Wi-Fi hacking using PMKID interception
• How to secure your smart home
• Securing home security

In 2026, the owner of an ordinary home router, smart TV, or TV box is a prime target for cybercriminals. These devices are readily recruited into botnets and residential proxy networks, which we’ve covered before.

Criminals “sublease” the infected device by letting outsiders pay to visit any website from the victim’s IP address, so it looks like the device’s owner is doing it. The service is in demand across all kinds of shady schemes: from ad fraud and spam campaigns to password bruteforcing and account hacking. In our previous post, we described how home routers get recruited into these proxy networks; today we take a look at an even more vulnerable, and equally ubiquitous category of devices: smart TVs and TV boxes.

These appeal to criminals for two reasons. First, unlike computers and smartphones, TVs are almost always plugged into the power and have a fast internet connection. A dark screen doesn’t mean the device is switched off. On top of that, the limited user interface and monitoring tools mean suspicious background processes can easily slip unnoticed past users.

The threat is evolving and growing more aggressive. A recent study found that once proxyware turns up on a set-top box, the risks to its owner go well beyond having their traffic siphoned off.

Anatomy of the infection

The researchers focused on a popular TV box by SuperBox, which we already covered earlier in the post Is your TV box renting out your network?

SuperBox’s marketing leans heavily on the promise of providing thousands of TV channels, with no subscription or monthly fees. But out of the box, the device can’t actually do anything of the sort. To get access to pirated content, you need to install the brand’s proprietary app store.

When you launch it and install additional apps, the thousands of TV channels do appear. Meanwhile behind the scenes, with no warnings or permission prompts, the device floods external sites with unauthorized requests, and network traffic spikes sharply.

The researchers uncovered several alarming facts. First, the victim gets enrolled into as many as five botnets at once. The SuperBox runs clients for several proxyware networks simultaneously, which even try to compete with one another by blocking rival software from being installed.

But the real shock was the attacks on the internal network. The TV box’s proxyware places no limits on what its paying clients can actually do on the network. They can reach not only external sites but also devices inside your home network.

Normally, your home router and ISP settings protect you from outside attacks. But proxyware bypasses these barriers because it operates from inside the network. During an experiment, the researchers confirmed that an attacker on the internet can easily leverage the infected set-top box to open the admin panel of a home router, such as a Linksys, which is supposed to be accessible only to its owner when connected to the home Wi-Fi network. This means hackers can try to steal data from other devices in the same household, or even encrypt home network storage (NAS).

At the same time, flaws in SuperBox’s factory firmware let hackers remotely install and run any application with superuser privileges, and the threat is anything but hypothetical. Over three weeks of monitoring, the test set-top box was hit by more than 1300 attacks through the home proxy network. Attackers installed three different types of malware, including a module for launching DDoS attacks.

The malware uses several methods to gain a foothold in the system, and it survives reboots and power failures alike.

How to tell if your set-top box is working for hackers

If you have a cheap TV set-top box or Android TV from a little-known brand, it’s worth checking it for anomalies.

The most reliable method is to review the network traffic. Use the statistics in your router’s control panel, but note that some budget models may lack this feature. This feature is called Traffic Analyzer on Asus routers, Traffic Usage on TP-Link, and Traffic Monitor on Keenetic. Find your TV or set-top box in the device list and check the ratio of downloaded to uploaded data. They should consume a lot of traffic to download video while sending very little. If your box is quietly pushing gigabytes of data out or staying chatty on the network even while idle, that’s a telltale sign it’s infected.

If traffic statistics aren’t available, look for indirect signs:

• The network and memory activity LEDs won’t stop blinking even though no one’s using the device.
• The set-top box’s casing is constantly warm or hot.
• The interface lags behind the remote, and the box freezes at random moments that have nothing to do with heavy video playback.
• Your other devices (computers or phones) are seeing a real drop in internet speed.
• When trying to visit familiar sites over your home Wi-Fi, you’re constantly hit with a CAPTCHA — a sign your network may be compromised and flagged as a spam source.
• Your ISP’s tech support calls you asking about suspicious network loads.

What to do with an infected device

The best solution is to disconnect the device from the internet and dispose of it. If you have to keep using it, follow these steps to minimize the risks:

1. Do a factory (hard) reset. Restore the set-top box to its original state.
2. Take it offline before setting up again. The first time you turn it on after a reset, skip the Wi-Fi setup step and don’t plug in the Ethernet cable.
3. Block app installation. Go to Android settings and disable installation of apps from unknown sources. Be sure to turn off any debugging features if enabled: USB debugging, Wireless debugging, and ADB. Menu item names may differ depending on your Android version and device manufacturer.
4. Isolate it on the network. Set up a guest Wi-Fi network on your router and connect the TV box to it. Enable Client Isolation in your router’s settings, if it has it. This will stop the box from seeing other devices on your home network, which protects your computers and network storage.
5. Check for updates. Once the box is connected to the guest network, check for official firmware updates: manufacturers sometimes patch known vulnerabilities, though with lesser-known brands, you shouldn’t count on it.

Your most reliable safeguards

Buying cheap devices with pirate streaming features and installing software from shady sources is a surefire way to compromise your home network. Your IP address will become a source of malicious activity, which at best gets you blocked by your ISP and at worst puts you on law enforcement’s radar. What’s more, hackers can use the box as a launchpad to attack your home computers and NAS, which can lead to personal data theft or a ransomware attack.

The best protection is to buy devices from trusted brands and pay for legal content. And to rule out someone hijacking control of your network, make sure your router’s admin panel and other home devices are protected with unique, strong passwords. To avoid having to remember them all, use a reliable password manager such as Kaspersky Password Manager. Additionally, the Smart Home Monitor feature included in Kaspersky Premium lets you keep all your devices fully under control.

There may be more things you don’t know about your smart home devices:

• Is your TV box renting out your network?
• Are your TV, smartphone, and smart speakers eavesdropping on you?
• Five rules to stop IP cameras from spying on you
• The hidden risks of cheap Android devices
• Is your router secretly working for foreign intelligence?

Picture your computer keeping a diary — the kind kids used to keep logging their daily activities, or the kind travelers, merchants, and nobles kept centuries before that. Here’s what it might look like.

Thursday, September 10.

• 8:00 AM. User read the news on cnn.com and bbc.co.uk (Safari).
• 8:10 AM. User replied to John Kaufman’s email about delivery dates, then sent the same question to the logistics team (Gmail).
• 8:20 AM. User checked his to-do list, reviewed recent updates, and planned out the day’s priorities (Todo).

We’re not exaggerating. This is exactly what your Mac’s new diary looks like. It’s a set of text files that any app on your computer can read — as can anyone sitting at the keyboard. The feature responsible is called Computer History, and it was released by OpenAI in mid-August. At the moment, it only works in the ChatGPT app for Mac, requires a Pro subscription or higher, and is disabled by default. According to its developers, the idea behind History is to let the AI assistant quickly get up to speed on your work context, so it can answer even vague requests, like “send a reminder to whoever I was emailing about last week’s deliveries”. Of course, that convenience comes at a price, ranging from your data being processed on OpenAI’s servers, to the risk of it leaking off your Mac — whether to a nosy family member or straight into the hands of cybercriminals.

How ChatGPT Computer History works

If this sounds a lot like Microsoft’s similar Recall feature, or Chronicle, which OpenAI itself announced earlier, that’s no coincidence. Both were built with the same goal in mind, just using different methods. Having watched Recall turn into the PR disaster it was, the team behind Computer History decided against constantly taking and analyzing screenshots. Instead, ChatGPT relies on macOS’s Accessibility features to read the text on your screen and track your actions: mouse clicks, text typed into fields, switching between apps, and the like. All of this gets stored as a stream of raw activity data inside the ChatGPT container (app group), which only the ChatGPT app itself, or other apps you’ve specifically given permission, can access. In tests run by digital forensics researchers, just two hours of normal computer use generated around 3500 of these logged events.

That raw data remains on your computer for up to 48 hours. During this time, the ChatGPT app periodically runs hidden chat sessions behind the scenes, where it asks the AI to summarize related events into a single recap. Essentially diary entries, these summaries get saved as plain text files in a folder that any app on your Mac can open: ~/.codex/memories/extensions/skysight/.

When you chat with the assistant, or when certain automated features kick in, these notes can be pulled into the conversation for more context-aware responses. OpenAI is especially keen on pointing out that the assistant can spot patterns in your workflows and offer to automate them, creating what it calls “skills” that replicate your actions for you.

Once a summary is ready, the raw activity data behind it is deleted. OpenAI says it won’t keep those hidden background chats or use them to train its AI, except where required by law. The diary-style summaries that get pulled into your conversations, though, are stored on OpenAI’s servers under the exact same rules as your regular chats and the assistant’s memory, so your preexisting privacy settings will apply to them too.

What Computer History can see, and what you can turn off

Since the whole feature runs on accessibility tools, ChatGPT only picks up what your operating system is already willing to share with those tools. That includes window titles, blocks of text, image captions in your browser, text you’re typing into a field as you type it, and the names of interface elements you click on. Watching a video? The AI can’t see the video itself, but it can read the window title, the site name, and the video description. Browsing photos? It sees file and folder names, not the actual images. Audio is never recorded or saved in any situation. All that gets logged from a video call is the meeting window title, unless the call itself has live captions turned on. Password fields that are masked (shown as dots) aren’t read at all and, according to OpenAI, private browsing windows are never scanned.

One thing that sets this apart from Microsoft Recall is the clearly spelled out step-by-step procedure the user needs to go through to turn it on. Computer History never activates itself. You have to navigate to the settings menu, then Integrations → Computer History and turn it on manually after reading through the warnings and explanations. You’ll also need access to the agent’s memory enabled for the feature to work. From there, you choose which apps and websites you want it to track, and confirm a series of macOS permission prompts.

For companies using a business subscription, this requires approval at two levels: an administrator first has to enable Computer History for the whole organization, and then each employee has to turn it on individually.

At any point, you can go to Computer History → Permissions and either list specific apps and sites to exclude from tracking, or, conversely, set ChatGPT to only watch a limited list you choose (Include only these apps, Include only these websites).

To see what your own computer’s diary looks like, check Computer History → History. From there, you can delete individual entries or disable the tracking of specific apps directly from the list.

The risks of using ChatGPT Computer History

We covered the risks of centralized storage of someone’s entire activity in our earlier piece on Microsoft Recall, and most of what we said there applies to ChatGPT Computer History.

• You might assume your chats and emails are private, protected by passwords and encryption. But a copy of the most important parts of those conversations is sitting in plain text files that anyone with access to your computer, and plenty of other apps, can open.
• The people you’re messaging have no idea this extra copy exists. If they’ve turned on, say, a self-destructing chat feature, but the messaging app’s window itself isn’t marked as private, that precaution does nothing to protect them.
• Even if you trust OpenAI completely and take its privacy promises at face value, your data can still be stolen directly off your computer by an infostealer. Mac-targeting infostealers have exploded over the past two years, so this isn’t a hypothetical risk. And the people who write this type of malware move fast, quickly updating it to grab whatever new type of data looks promising. OpenAI’s files are a boon for them: everything you’ve done, already summarized by AI into something easy to read. While there are no passwords or credit card numbers in there, the available details are likely enough to craft a convincing targeted urgent email from the boss.
• And if a nosy coworker or family member gets their hands on your computer, reading through the diary of your work and life would take them all of five minutes.

Computer History: turn it off, or just configure it?

We’d recommend limiting or fully disabling Computer History if your work involves sensitive information. This especially applies to doctors, lawyers, and anyone else bound by professional confidentiality, or handling personal or medical data. The same goes for anyone living with an overly controlling family member, or working in an office with nosy coworkers.

If none of that applies to you and you’d like to try out how useful the AI’s search and auto-generated skills actually are, here’s how to set things up safely.

• Lock down your Mac’s login. Turn off auto-login, and set your Mac to require a password immediately after the screensaver kicks in or it wakes from sleep. Use a strong password along with Touch ID, and make sure FileVault disk encryption is turned on.
• Set up your exclusions. Manually add every messaging app you use for private conversations, along with any finance apps or sites, and anywhere else you keep sensitive information, to Computer History’s exclusion list.
• Check in regularly. At least a couple of times a week, visit Computer History settings to see which apps and sites it’s been tracking. This helps you catch and remove any sources of private information you forgot to exclude earlier.

No matter how you configure Computer History, the two most common ways your data could end up in the wrong hands — on any Mac — are a macOS infostealer snatching it directly from your computer, and typing your information into a fake website. To guard against both, make sure you’re running full-featured security software on your Mac, such as Kaspersky Premium for macOS.

AI can create privacy risks you might never think to look for. You can read about more of them in our other posts:

• Attacks on Gemini: the possibilities are endless
• Why AI agents need an iron curtain
• Knives, kinks, and shooters: what AI toys are really saying to kids
• AI and the new reality of sextortion

And if you’d like to know how to turn off AI features that activate on your computer or smartphone without your knowledge, check out our guide, Unplugged: how to disable AI on your computer and smartphone.

Threat actors are constantly developing new attack schemes — from OAuth token theft to attacks on AI agents — but the classics never quite leave their playbook. On any given day, an employee may receive malware attached directly to an email, or lying in wait behind a link embedded in an email. Making these attacks succeed still requires a degree of creativity, and in recent years attackers have increasingly adopted exotic file formats. Users don’t perceive these formats as dangerous; more importantly, many EDR and email security solutions skip scanning them entirely. Yet they’ve proven highly effective for deploying malware or harvesting credentials.

Below are the file types that may be blind spots in your organization’s detection strategy — despite being observed as used in real-world attacks.

Disk images

This is the broadest and most dangerous attachment category. A disk image can contain a fully prepared, correctly structured set of files tailored to a wide range of attack scenarios. It mounts as a separate volume (standalone virtual disk), and when doing so often uses tools built into the operating system. The victim doesn’t need to extract anything or create suspicious folders on disk.

The most versatile format is ISO, which dates back to the heyday of multimedia CDs. ISO images mount natively on every major OS without additional utilities, whether through simple tools like File Explorer or via the command line interface. This is precisely why threat actors have no hesitation in sending ISO files as email attachments.

Somewhat less common are IMG containers, which also mount without additional tooling on macOS or Linux, but require installing a small, free utility such as OSFMount on Windows, or using WSL2 (Windows Subsystem for Linux), which is built into recent Windows versions. A file with identical content may also carry a DD extension.

Threat actors also make use of VHD, a virtual disk format native to Windows, which generally works on Linux machines as well (via qemu-nbd or guestmount). On macOS, an additional utility — again, qemu-nbd is a common choice — is required to mount it. Note that on unpatched Windows systems, the victim doesn’t need to take any further action after the disk mounts: CVE-2025-24993 — a vulnerability actively exploited in real-world attacks — triggers attacker code execution immediately upon mounting the image.

For completeness’ sake, VMDK, the disk image format used by VMware, is also worth detecting, though it’s largely ineffective in attacks on regular users since every popular OS requires additional software to mount it.

All of these image formats can be viewed and unpacked not only with specialized tools, but with standard archive utilities like 7-Zip or RAR. As a result, the odds that the user will fall for the bait are higher than one might hope.

In addition to not being a well-known potentially dangerous format, image files give attackers one further advantage: files extracted from disk images on Windows frequently lack the Mark of the Web flag, and therefore can be launched without triggering additional system warnings.

There’s one more reason why EPP/EDR solutions must scan virtual machines and disk images: these formats are also used during later stages of an attack. To mask active file-system activity from monitoring tools, ransomware gangs sometimes run the entire encryption process inside a virtual machine deployed on the victim’s computer. The RagnarLocker ransomware strain has used VirtualBox for this purpose, while the CRON#TRAP campaign relied on a QEMU environment.

MS Office files

No, this isn’t about macro-laced DOC files. Microsoft has grown weary enough of this classic attack vector that macros in downloaded documents are now blocked outright, and enabling them requires considerable effort from the user. But Office relies on dozens of file formats, and some of the less common ones still occasionally give attackers an edge.

Less familiar to both users and security solutions are OneNote files (.one). These are essentially simple notes, but they can carry embedded attachments, including not just images and links, but VBS/WSF/BAT scripts as well. Within the note, an attachment normally displays as an icon, but attackers overlay it with a picture showing a message with a “View” button.

Another Office format popular with attackers is the Excel add-in. These files use the .xll extension but are, in effect, full-fledged Windows dynamic-link libraries (DLLs), and therefore may have extensive functionality. Victims receive a malicious email with what looks like an invoice; the icon suggests that the file will open in Excel. Ignoring the security warning may result in an infostealer infection.

SVG “graphics”

The description of the SVG (Scalable Vector Graphics) format sounds entirely innocuous. Indeed, 99% of SVG files are logos and interface icons. But unlike PNG or JPG, SVG files are fundamentally XML documents, and beyond points, lines and fills they can contain JavaScript. In terms of malicious potential, SVG is not far behind a full web page. In real-world attacks, SVG files can redirect a user to a phishing page immediately upon opening, or render the phishing page directly by unpacking a Base64-encoded block into full HTML.

SVG files pose a distinct risk to corporate portals that accept user file uploads. If a portal’s Content Security Policy (CSP) isn’t sufficiently restrictive, and lets someone upload a malicious SVG file, the embedded JavaScript can steal cookies from the page, inject unauthorized content, and carry out similar attacks.

Polyglot files

Polyglot file formats are less exotic in and of themselves, but the files carry more than meets the eye. They’re called polyglots because attackers construct a file that can be validly interpreted in multiple ways: for example, it can be a valid ZIP archive and a valid executable at the same time. The purpose is purely to evade detection by security solutions and deceive the user. We’ve covered polyglots in detail in a separate blog post.

The clear standout in our roundup is a malicious campaign distributing the IcedID infostealer. Victims receive emails with a ZIP attachment, which unpacks into an ISO disk image. That further unpacks into a CHM file (the Windows Help format) that’s also correctly processed as an mshta (Microsoft HTML Application host) application, which then downloads the primary malware payload.

Defending against exotic files

These attacks rely on the assumption that security solutions either don’t scan unusual file formats, or scan them only superficially. Preventing the threat starts with not operating on that assumption with regard to your own infrastructure:

• Configure EDR, web, and email filters to scan all of the file formats described above.
• Process archives and disk images with the same rigor as you would apply to executables.
• Detonate potentially dangerous file types in a sandbox, and configure recursive unpacking of nested formats through to the end of the chain: ZIP → ISO → CHM.
• Where feasible, quarantine all file formats anomalous for email — disk images in particular.
• Build detection rules for anomalous usage patterns that involve exotic files. For example, unusually small disk images are rare and are therefore worth investigating.
• Review the technical documentation of your security solutions to confirm that complex formats are detected through deep structural parsing rather than magic-byte checks alone.
• Deploy behavioral detection capable of flagging anomalous execution patterns and legitimate utilities launched from unexpected contexts. The usual suspects here include mshta, git, wscript, hh, and rundll32.
• SVG files warrant a dedicated policy. Treat SVGs as active content at the email and web perimeter. On corporate portals, enforce a strict CSP, and serve user-uploaded files from a separate subdomain so that a malicious script embedded in an SVG can’t access the session on the main portal.

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© Photograph: Marina Demidiuk/Alamy

© Photograph: Marina Demidiuk/Alamy

© Photograph: Marina Demidiuk/Alamy

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