Network-connected surveillance cameras are now widespread in both home and enterprise environments, so securing them is essential to safeguarding physical spaces and sensitive assets. As these devices continue to gain new features and network connectivity capabilities, vulnerabilities in their implementation can have significant security implications.
During our research, we discovered a denial-of-service vulnerability that affects Intelbras IP cameras systems. The flaw resides in a firmware update endpoint that is accessible without authentication and allows anyone with access to the local Wi-Fi network to knock cameras offline. A single request disrupts the camera for only about a minute, but sending periodic requests keeps the device offline indefinitely. We reported the issue to Intelbras, monitored the remediation process, validated the fix in the new firmware, and will now demonstrate the exploitation procedure step-by-step.
Following our notification, Intelbras patched the vulnerability and released updated firmware (R.260804) along with update instructions. A candidate ID of CAN-2026-2035970 has been assigned by MITRE following submission, pending the final CVE assignment. Additionally, the Intelbras team recently informed us that MITRE has classified the vulnerability as Medium, assigning it a score of 5.1 under the CVSS 4.0 framework. The issue discussed in this article is no longer present in the latest firmware version.
Firmware
Intelbras provides firmware files on its official website. While users can manually install these updates via an SD card, the cameras also natively support firmware upgrades over the local network via their web interface and mobile app. We obtained the firmware version v240927 for the iM3B-C (IM3 C) camera, which was the latest available version at the time of our research. According to Intelbras, the same firmware is also compatible with the iMX C, iM4 C, iM5 SC, iME 360 C, and iM7+ FC models. Below, we can view the firmware version and the camera model used as an example.
To provide context, we will explain how the camera’s authentication mechanism works and why it is relevant to accessing the camera’s endpoints. The main firmware component is an ARM binary named sonia, responsible for controlling the kernel drivers used by the camera hardware, authentication, and livestream recording, among other things.
How authentication works
The global.login RPC method handles authentication at the /RPC2_Login endpoint. First, the client, such as the mobile app or a custom script, sends the username and an empty password. The camera responds with an authentication challenge (a set of values to verify the password without transmitting it). This response returns a temporary session, the encryption type in the encryption field, a random value, and a realm (authentication scope). At this stage, a false response is normal and does not indicate a failed login, as the authentication process is not yet complete.
Next, the client calculates the response password from the authentication challenge. It first generates an MD5 (username:realm:original_password) hash as the base. It then calculates a second MD5 (username:random:first_md5) hash, where first_md5 is the result of the previous step. To complete the authentication, the client sends this final result back to /RPC2_Login in a second global.login call, passing the username, calculated password, and temporary session ID.
If the login is accepted, the camera returns true and a new, authenticated session. This session must be used for all subsequent RPC calls, including the camera’s other endpoints, for example, the one that reboots the device or those that expose sensitive information.
Affected endpoint
The affected endpoint is the firmware upgrade route http://<ip_address>/firmwareUpgrade_service/Demo, which lacks authentication checks; furthermore, it fails to verify whether the firmware is actually valid before initiating the upgrade process. This creates a serious issue because the upgrade requires the camera to reboot, and that is precisely when the problem occurs: the camera enters reboot mode and eventually shuts down, appearing offline to the user and anyone who has the camera registered on their device.
PoC: vulnerability exploitation
Now that we have outlined how the vulnerability works, we will use a Python script on an iM3B-C camera to exploit it for demonstration purposes. The exploit contacts the camera’s endpoint to upload firmware. Below, we can see sonia creating the endpoint responsible for the firmware upgrade.
Next, the exploit will hit the endpoint and cause the camera to reboot. Consequently, mobile phones running the Intelbras app will lose connection with the registered device. Below, we can see the camera operating normally: the green light is on and the app is showing it is available for viewing.
The camera is online
After the exploit executes, we can see the effects: the camera goes offline and loses connection to the registered devices.
The camera is offline
The exploit is quite simple: it sends an HTTP POST request with the firmware, which causes the camera to accept the request and initiate the reboot process. It does not matter whether the firmware is original; the reboot process will start regardless.
New firmware version
After we notified Intelbras about this flaw in the endpoint implementation, the company contacted the OEM vendor. As a result, a new firmware version was released that requires authentication to initiate an update through this endpoint.
Below, we can compare the new firmware, which validates cookies and session data, with the old firmware, which performed no authentication checks and proceeded directly to the update routine.
Real risk
An attacker can remotely trigger the camera firmware upgrade and reboot process without authenticating or supplying a valid firmware image. This may cause the device to go offline, which results in a denial-of-service condition preventing legitimate users from accessing live monitoring or managing the camera.
The potential impact is significant due to the number of affected users. The Intelbras Mibo Smart app, which the company provides to connect and manage the devices mentioned in this report, has over 4,000,000 downloads. Brazil represents the largest user base, followed by Paraguay, Ecuador, Colombia, Argentina, Chile, Peru, Bolivia, Uruguay, and Venezuela. As a result, exploitation of this issue affects a large number of users.
Takeaways
This is a medium-severity vulnerability that Intelbras is already aware of. The issue has been addressed in a newer firmware release (R.260804 along with update instructions), which mitigates this attack vector. Because the vulnerability directly affects the availability and normal operation of the affected cameras, it is highly recommended that users verify the firmware version currently installed on their devices and update to the latest available version as soon as possible. Keeping the firmware up to date is an essential security practice, especially for internet-connected surveillance devices, as outdated versions may remain exposed to known vulnerabilities.
Network-connected surveillance cameras are now widespread in both home and enterprise environments, so securing them is essential to safeguarding physical spaces and sensitive assets. As these devices continue to gain new features and network connectivity capabilities, vulnerabilities in their implementation can have significant security implications.
During our research, we discovered a denial-of-service vulnerability that affects Intelbras IP cameras systems. The flaw resides in a firmware update endpoint that is accessible without authentication and allows anyone with access to the local Wi-Fi network to knock cameras offline. A single request disrupts the camera for only about a minute, but sending periodic requests keeps the device offline indefinitely. We reported the issue to Intelbras, monitored the remediation process, validated the fix in the new firmware, and will now demonstrate the exploitation procedure step-by-step.
Following our notification, Intelbras patched the vulnerability and released updated firmware (R.260804) along with update instructions. A candidate ID of CAN-2026-2035970 has been assigned by MITRE following submission, pending the final CVE assignment. Additionally, the Intelbras team recently informed us that MITRE has classified the vulnerability as Medium, assigning it a score of 5.1 under the CVSS 4.0 framework. The issue discussed in this article is no longer present in the latest firmware version.
Firmware
Intelbras provides firmware files on its official website. While users can manually install these updates via an SD card, the cameras also natively support firmware upgrades over the local network via their web interface and mobile app. We obtained the firmware version v240927 for the iM3B-C camera, which was the latest available version at the time of our research. According to Intelbras, the same firmware is also compatible with the iMX C, iM4 C, iM5 SC, iME 360 C, and iM7+ FC models. Below, we can view the firmware version and the camera model used as an example.
To provide context, we will explain how the camera’s authentication mechanism works and why it is relevant to accessing the camera’s endpoints. The main firmware component is an ARM binary named sonia, responsible for controlling the kernel drivers used by the camera hardware, authentication, and livestream recording, among other things.
How authentication works
The global.login RPC method handles authentication at the /RPC2_Login endpoint. First, the client, such as the mobile app or a custom script, sends the username and an empty password. The camera responds with an authentication challenge (a set of values to verify the password without transmitting it). This response returns a temporary session, the encryption type in the encryption field, a random value, and a realm (authentication scope). At this stage, a false response is normal and does not indicate a failed login, as the authentication process is not yet complete.
Next, the client calculates the response password from the authentication challenge. It first generates an MD5 (username:realm:original_password) hash as the base. It then calculates a second MD5 (username:random:first_md5) hash, where first_md5 is the result of the previous step. To complete the authentication, the client sends this final result back to /RPC2_Login in a second global.login call, passing the username, calculated password, and temporary session ID.
If the login is accepted, the camera returns true and a new, authenticated session. This session must be used for all subsequent RPC calls, including the camera’s other endpoints, for example, the one that reboots the device or those that expose sensitive information.
Affected endpoint
The affected endpoint is the firmware upgrade route http://<ip_address>/firmwareUpgrade_service/Demo, which lacks authentication checks; furthermore, it fails to verify whether the firmware is actually valid before initiating the upgrade process. This creates a serious issue because the upgrade requires the camera to reboot, and that is precisely when the problem occurs: the camera enters reboot mode and eventually shuts down, appearing offline to the user and anyone who has the camera registered on their device.
PoC: vulnerability exploitation
Now that we have outlined how the vulnerability works, we will use a Python script on an iM3B-C camera to exploit it for demonstration purposes. The exploit contacts the camera’s endpoint to upload firmware. Below, we can see sonia creating the endpoint responsible for the firmware upgrade.
Next, the exploit will hit the endpoint and cause the camera to reboot. Consequently, mobile phones running the Intelbras app will lose connection with the registered device. Below, we can see the camera operating normally: the green light is on and the app is showing it is available for viewing.
The camera is online
After the exploit executes, we can see the effects: the camera goes offline and loses connection to the registered devices.
The camera is offline
The exploit is quite simple: it sends an HTTP POST request with the firmware, which causes the camera to accept the request and initiate the reboot process. It does not matter whether the firmware is original; the reboot process will start regardless.
New firmware version
After we notified Intelbras about this flaw in the endpoint implementation, the company contacted the OEM vendor. As a result, a new firmware version was released that requires authentication to initiate an update through this endpoint.
Below, we can compare the new firmware, which validates cookies and session data, with the old firmware, which performed no authentication checks and proceeded directly to the update routine.
Real risk
An attacker can remotely trigger the camera firmware upgrade and reboot process without authenticating or supplying a valid firmware image. This may cause the device to go offline, which results in a denial-of-service condition preventing legitimate users from accessing live monitoring or managing the camera.
The potential impact is significant due to the number of affected users. The Intelbras Mibo Smart app, which the company provides to connect and manage the devices mentioned in this report, has over 4,000,000 downloads. Brazil represents the largest user base, followed by Paraguay, Ecuador, Colombia, Argentina, Chile, Peru, Bolivia, Uruguay, and Venezuela. As a result, exploitation of this issue affects a large number of users.
Takeaways
This is a medium-severity vulnerability that Intelbras is already aware of. The issue has been addressed in a newer firmware release (R.260804 along with update instructions), which mitigates this attack vector. Because the vulnerability directly affects the availability and normal operation of the affected cameras, it is highly recommended that users verify the firmware version currently installed on their devices and update to the latest available version as soon as possible. Keeping the firmware up to date is an essential security practice, especially for internet-connected surveillance devices, as outdated versions may remain exposed to known vulnerabilities.
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.
Stages of order fulfillment
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.
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.
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.
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.
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.
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.
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.
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.
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.
Warehouse risks
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:
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:
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.
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.
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.
Warehouse attack surface
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.
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.
Stages of order fulfillment
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.
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.
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.
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.
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.
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.
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.
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.
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.
Warehouse risks
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:
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:
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.
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.
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.
Warehouse attack surface
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.
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.
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:
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.
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:
Do a factory (hard) reset. Restore the set-top box to its original state.
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.
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.
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.
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:
Security researchers have found a way to hack a Boeing 737 with a device no bigger than a coin. The 737 is one of the most widely used airliners in the world, so chances are you’ve flown on one yourself at least once.
The attack requires installing a small device in a port that’s accessible through a hatch on the outside of the plane. According to the researchers, this port is regularly within reach not just of maintenance crews, but of other airport and airline staff too. And installing the device takes less than 60 seconds. So let’s talk about what this mysterious little device actually is, what it can do, and whether it’s still safe to fly on a Boeing 737.
Why hacking a plane is harder than hacking a car
Most of us have gotten used to the idea that our computers and phones can be hacked. It’s probably the most common topic on this blog. But we’ve also covered attacks on less common targets: cars, robot lawnmowers, automated card shufflers, bikes, and even mattresses. In today’s world, all of these (and many other) devices contain tiny (or not-so-tiny) computers.
Airplanes are no exception. A modern airliner is packed with computers — some of which handle critical functions: from calculating takeoff parameters to controlling the flight itself. But there’s one key difference: airliner manufacturers, unlike the makers of most smart home gadgets, take security very seriously, which makes it far harder to reach these systems from the outside.
Maybe that’s precisely why the idea of hacking a real plane has been tempting the most inquisitive security researchers for years. A team at the University of California San Diego and Oberlin College — the researchers who finally pulled it off — spent more than a decade wrestling with the problem. As mentioned above, the main challenge in hacking a plane is that its critical systems aren’t connected to the external internet. Because of that, studying plane-related attack vectors wasn’t seen as especially promising within the cybersecurity research community: reaching isolated systems remotely is extremely difficult, and the odds of an attacker gaining physical access were considered very low.
Two other successful projects changed that assumption for UC San Diego researchers Kirill Levchenko and Aaron Schulman: a hack of the Chevrolet Impala, and research into payment card skimmers. The car hack led their team to buy secondhand Boeing 737 computer components and build their own test rig. The skimmer research gave them the idea for a small physical device that could plug directly into the plane’s internal systems.
The port sits in the electronics bay beneath the cockpit. From outside the plane, it’s covered only by an unlocked hatch. Source
A “bus driver” at the controls
On their test rig, the researchers checked what would happen if they connected to the ARINC 429 bus through this port and sent electrical signals more powerful than the ones the Boeing’s components normally exchange with one another. It turned out that doing so let them override legitimate commands with their own. From there, all that was left was building a device small enough to fit into the port they’d found.
Step by step, the team built a device compact enough to slot into the unprotected port. The finished version set them back less than US$100, and appears to have been built largely from off-the-shelf parts: a small microcontroller, electronics for communicating with the bus, and a Wi-Fi module. That last part could potentially help the device connect to the plane’s public in-flight Wi-Fi network, giving the attacker access over the internet, and eventually letting them control the device remotely. And the whole thing takes less than a minute to install into the port.
The device the researchers built to connect to Boeing 737 systems. A coin sits next to it for scale. Source
The researchers dubbed their attack “Bus Driver”, apparently a play on words: “bus” refers to both a passenger vehicle and the data channel that lets a system’s components talk to each other. The attack lets someone step into that channel and swap out the commands being sent — effectively controlling the bus.
What the improvised Boeing 737 hacking device can actually do
An unprotected port, a coin-sized device, access to the bus: that’s all and well and good, but what did the researchers actually manage to pull off? What kind of damage can this tiny implant do? As it turns out, quite a lot — potentially even fatal. By gaining the ability to tamper with the data flowing between the flight management computer and the control and display unit, the researchers found they could interfere with several critical aspects of how the plane operates.
Researchers testing Bus Driver on real Boeing 737 avionics hardware. Their attack interface is running on the MCDU screen. Source
First, the implant can alter the data used to calculate takeoff and landing parameters. For example, it can change the plane’s recorded zero-fuel weight, which throws off the calculation of the speeds needed for a safe takeoff.
The device can also manipulate the assumed temperature value pilots use to determine how much engine thrust is needed at takeoff. Feed the system the wrong numbers, and under certain conditions the plane could end up with insufficient thrust to take off safely — potentially even going off the end of the runway. Faulty calculations during landing carry similarly serious risks.
The implant can also change the route the autopilot is following. On a long flight, a deviation of just a few degrees — subtle enough to go unnoticed — could be enough to send the plane badly off course so it runs out of fuel somewhere over open ocean.
Beyond that, an attacker could use the device to steer the plane into the airspace of a country that isn’t part of its approved flight path. That kind of deviation can also have deadly consequences: the history of civil aviation has seen more than one case of a passenger plane being mistakenly shot down by the military after straying into restricted airspace.
Of course, the odds of a deviation like this slipping past both pilots and air traffic control are very low, but disasters tend to happen exactly when several human errors line up.
Once installed, the implant is nearly invisible inside the port and very hard to spot unless you know to look for it. Source
Finally, the device doesn’t just let someone change flight parameters, it lets them hide those changes from the pilots. For instance, an attacker could load a new route into the flight computer while simultaneously altering what’s shown on the MCDU display, so the pilots keep seeing the original route.
That said, the researchers note that in most cases an attentive crew would catch the discrepancy. The correct data still shows up on other cockpit displays, and switching to manual control lets pilots disengage the autopilot and take back control of the plane.
So, should we all switch to trains?
To wrap up, I’ll try to put the longtime aerophobes (and anyone this article has just turned into one) at ease. The researchers themselves say that despite everything they’ve learned, they still fly on Boeing 737s. And to avoid causing any real-world trouble, they deliberately left out exactly which port they used in their experiments.
The researchers also reached out to Boeing back in 2020 to share their findings. Boeing took the information seriously and worked on addressing the issue.
Specifically, the researchers suggested Boeing either remove the vulnerable port or seal it with epoxy resin to physically rule out the possibility of someone plugging in an unauthorized device. There are more involved options too, like redesigning the internal bus’s electrical protections so a rogue device can’t override the real signals, and adding systems that can detect this kind of attack in the first place.
Longer term, the researchers recommend that Boeing, along with other aircraft manufacturers, adopt cryptographic authentication for the messages passed between avionics components.
Whether Boeing acted on this specific advice or came up with its own fixes isn’t publicly known. When Wired reporters asked the company about it, the response was fairly vague:
“Our technical experts are confident that the layers of protection in place on the airplane, including within the system design and the operating environment, provide sufficient mitigation to significantly limit the feasibility and risk of real-world attacks.”
So no, there’s apparently no need to swear off Boeing 737 flights just yet. That said, I happen to have a lot of respect for trains, so I won’t try to talk you out of traveling by rail or using other ways of getting around either. I’ve actually written about train hacking on this blog before, though in that case the goal was constructive, not destructive.
Cars, lawnmowers, card shufflers, bikes, smart mattresses — it’s not even an exhaustive list of the devices that hackers have successfully tampered with. What else was hacked? Find out in our other posts:
Mexico is one of the host countries for the 2026 FIFA World Cup, with matches to be played in three major cities: Mexico City, Monterrey, and Guadalajara. These locations are expected to see a large influx of international visitors, increasing the potential security risks. Many of those risks arise from users connecting to public wireless networks.
To better understand the wireless environments that visitors may encounter, we at Kaspersky GReAT conducted a wardriving assessment in the three host cities. The aim of the study was to analyze characteristics, deployment patterns, security configurations and potential exposure risks of public Wi-Fi infrastructure in urban wireless environments.
The information collected during the assessment was used exclusively for passive observation and infrastructure analysis. No attempts were made to authenticate, intercept communications, exploit systems or interact with the detected wireless networks beyond the publicly broadcast management information.
During processing of the collected data, one step involved filtering out networks belonging to cars or cell phones categorized as mobile hotspots because they do not represent networks that can be considered part of the assessment.
Research scope
The cities included in the study have high population density and extensive wireless infrastructure deployments. We chose areas with the most prominent wireless network activity and highly concentrated public access points. We carried out wardriving research in Monterrey back in 2008, but the city’s hotspot landscape has changed since then.
We chose the following analysis areas for each of the cities:
Mexico City: México City Stadium, Mexico City International Airport, Zócalo, Paseo de la Reforma, Colonia Roma, La Condesa, Polanco, and Coyoacán.
Guadalajara: Guadalajara Stadium, Guadalajara International Airport, the city center, Zapopan, Providencia, Avenida Chapultepec, Colonia Americana, Tlaquepaque, and the area around Andares.
Monterrey: Monterrey Stadium, Monterrey International Airport, Fundidora Park, Cintermex Monterrey, the downtown area, Barrio Antiguo, MacroPlaza, and the San Pedro financial district.
The wireless information was collected using passive wireless reconnaissance techniques. The collected information included:
SSID analysis and information exposure, including BSSID-derived SSIDs
Default router configurations and ISP deployments
Frequency and signal characteristics
Channel congestion and spectrum usage
Wireless security configurations, including:
Open and insecure wireless networks
WPS-enabled networks
Secure networks (WPA2/WPA3) with WPS enabled
We performed a wireless infrastructure analysis in Mexico City, Guadalajara, and Monterrey. We drove through the areas surrounding the World Cup stadiums, tourist zones, and other places where fan concentrations are likely to be largest. Our goal was to evaluate the security status, deployment characteristics and operational exposure of detected wireless networks.
In total, we recorded 84,588 signals with 69,473 unique Service Set Identifiers (SSIDs) in busy locations and World Cup zones across the three cities. Mexico City accounted for 61.4% of the signals, Guadalajara for 23.6%, and Monterrey for 14.8%. Approximately 82% of the signals had a single SSID (81.9%, 81.34%, and 84% respectively). Notably, they all operate under the IEEE 802.11 standard protocol.
Particular attention was given to identifying standard deployment patterns, legacy configurations, default vendor settings and information disclosure through publicly broadcast wireless identifiers.
The following sections present the results that were obtained by analyzing wireless infrastructure across the three locations.
Our findings
SSID analysis and information exposure
SSID analysis was conducted to evaluate naming conventions, deployment standardization and potential information exposure.
Only a few networks (0.0047%) have an invisible SSID, meaning the names of these networks are not broadcast. Some users prefer to hide the SSID for various reasons, such as the network’s purpose, the profile of its users, internal policies, etc. In contrast, the rest of the networks maintained active SSID broadcasting.
SSID structures may unintentionally disclose operational details about internet service providers (ISPs), device manufacturers, deployment practices, organizational ownership or user identity. The repeated presence of default SSID naming patterns across the analyzed locations indicates a significant degree of infrastructure homogeneity and reuse of default wireless configurations. It may also facilitate passive infrastructure profiling by revealing standard characteristics in use.
Approximately 34% of the detected networks retained the default SSID naming conventions provided by the manufacturer or ISP, while 66% used customized identifiers.
Distribution of SSID naming conventions (download)
Several recurring SSID naming conventions associated with ISP-provided deployments were identified in the three cities. The most frequently observed patterns include identifiers such as “Club_Totalplay_WiFi”, “izzi WiFi”, and “Megacable WiFi”, which suggests extensive standardization of wireless infrastructure deployment. Additionally, we observed distinctive location-specific SSIDs in each area of analysis, such as “XXXX-Internet para Todos-CDMX” or “RED JALISCO”.
Sequential SSID naming structures were also identified during the analysis. Patterns such as “INFINITUMXX” and “IZZI-XX” suggest automated ISP deployment and large-scale deployment strategies.
We identified 33 unique sequential naming structures among the 137 sequential SSIDs in total, representing approximately 0.16% of the detected wireless networks.
The following graph shows the top five sequential SSID patterns found in the largest number of networks:
Five most frequently observed sequential patterns (download)
Several customized SSIDs contained personal or organizational identifiers, including family names, professions, addresses or internal department references. Although personalized SSIDs may simplify local network identification for users, they may also expose sensitive information that could be useful for social engineering, physical targeting, or organizational profiling.
BSSID-derived SSID
During the analysis, multiple networks were identified that used the physical MAC address of a Wi-Fi access point (BSSID) as the visible SSID. This practice exposes hardware-level information that could facilitate vendor fingerprinting and targeted reconnaissance activities.
The organizationally unique identifier (OUI) contained in the first bytes of the BSSID identifies the equipment manufacturer. Threat actors can correlate exposed manufacturers with device-specific vulnerabilities.
Notably, we found that more than 30% of networks in all three cities reuse the MAC address as the SSID.
Default router configurations and ISP deployments
We performed wireless infrastructure profiling to identify the most common wireless equipment manufacturers and ISP deployments across the three locations.
Large-scale ISP deployments frequently use standardized wireless configurations and vendor-specific hardware platforms. Identifying dominant manufacturers and ISP naming conventions can provide insight into infrastructure and deployment practices facilitating the mapping of standardized attack surfaces.
The following figure shows the distribution of the most commonly used manufacturers.
Most frequently observed wireless equipment manufacturers (download)
The manufacturer analysis revealed a strong concentration of wireless infrastructure among a limited number of vendors. Across the three locations, Huawei Technologies, MediaTek-based devices, and other manufacturers’ equipment that is distributed through ISP channels represented a significant portion of the detected deployments. Mexico City had the most diverse infrastructure, while Monterrey and Guadalajara had a greater concentration of wireless equipment known as SOHO (small office/home office) or residential-grade hardware. The widespread presence of standard vendor platforms may facilitate infrastructure fingerprinting and large-scale targeting of known device-specific vulnerabilities.
Most frequently observed wireless equipment manufacturers across the three cities (download)
ISP deployments frequently exhibited standardized configuration patterns and recurring manufacturer identifiers. Our ISP deployment analysis revealed a high concentration of access points associated with major residential internet providers. Deployments associated with Infinitum, Totalplay and Izzi represented a substantial portion of the detected wireless infrastructure across all locations. These findings suggest a high degree of deployment standardization across networks associated with major residential internet providers. This observation was supported by the repeated presence of ISP-associated SSIDs such as “Infinitum”, “Totalplay”, and “Izzi”, combined with manufacturer identifiers frequently associated with consumer equipment, including Huawei, ZTE and other residential wireless equipment vendors.
It is important to note that, for this analysis, ISPs were primarily inferred from SSID naming conventions and manufacturer fingerprint data. A significant portion of the detected wireless networks fell into the “UNKNOWN/CUSTOM” category. This classification includes custom hotspots and networks whose naming conventions did not expose identifiable ISP-associated patterns. The findings suggest that many users and organizations (as we saw previously, approximately 66%) use custom network names, limiting direct provider attribution.
The following figure illustrates the distribution of ISP-associated wireless deployments in general.
To better understand this distribution, we took the most frequently observed ISPs by city.
Most frequently observed ISPs across the three cities (download)
Frequency and signal characteristics
We also analyzed wireless signal characteristics to evaluate coverage quality, signal strength, and frequency band utilization in the three cities. In dense urban environments, signal quality and frequency spectrum distribution can affect wireless reliability, client connectivity, roaming performance, and overall network efficiency.
Signal quality analysis revealed that a substantial portion of the detected access points operated under weak or very weak signal conditions. Monterrey had the highest percentage of very weak signals, with approximately 50% of detected deployments. Similar patterns were observed in Guadalajara and Mexico City, suggesting high-density wireless environments with overlapping coverage areas. Only a limited percentage of networks were classified within the very good or excellent signal categories across the three locations.
Signal stability analysis revealed that most detected wireless deployments exhibited stable beacon transmission behavior. More than 96% of the detected access points across all locations were classified as stable, while only a small percentage exhibited unstable or indeterminate signal behavior.
These findings imply that the majority of the wireless infrastructure observed during the assessment corresponded to permanently deployed access points rather than transient or intermittent wireless devices.
Frequency band analysis revealed the strong prevalence of 2.4 GHz wireless deployments across the three locations. More than 95% of the detected wireless networks operated within the 2.4 GHz spectrum, while only a small percentage of deployments were classified under the unknown or non-standard frequency categories. This uneven distribution reflects the continued prevalence of legacy-compatible wireless infrastructure and SOHO deployments.
These findings are consistent with dense urban wireless environments with large numbers of access points in restricted spectrum allocations.
Channel congestion and spectrum usage
Next, we analyzed wireless channel utilization to evaluate frequency spectrum congestion and channel allocation patterns across the three cities. Our analysis focused on the 2.4 GHz spectrum, where channel overlap and high access point density commonly produce interference and degraded wireless performance. In densely populated wireless environments, an excessive concentration of access points on a limited number of channels can lead to co-channel interference, packet collisions, reduced throughput, and degraded network stability.
Spectrum congestion analysis revealed that the 2.4 GHz band consistently experienced elevated congestion levels across the three cities. The detailed results showed a strong concentration of deployments on channels 11, 6 and 1, which are traditionally recommended as non-overlapping channels within the 2.4 GHz spectrum. Channel 11 was the most utilized channel, accounting for 25.2% of the detected access points, followed by channel 6 with 22.5% and channel 1 with 19.5%. This distribution indicates that most wireless deployments adhere to standard channel allocation practices for 2.4 GHz Wi-Fi environments.
The following figure illustrates the overall distribution of the most frequently utilized wireless channels.
To further assess wireless spectrum saturation, the detected access points were grouped according to channel congestion levels: VERY_HIGH, HIGH, UNKNOWN, MEDIUM, LOW and NONE.
Mexico City had the highest proportion of heavily congested wireless channels, with approximately 7% of detected access points operating under HIGH congestion conditions. Guadalajara followed with nearly 5% of deployments categorized as HIGH congestion, while Monterrey had the lowest percentage at approximately 3.29%.
These findings suggest that wireless spectrum saturation increases proportionally with urban infrastructure density and access point concentration. Despite the presence of congested deployments, most detected access points were categorized as LOW or MEDIUM congestion, suggesting severe spectrum saturation was localized rather than uniformly distributed.
A thorough analysis of individual channel utilization revealed that channels 11, 6 and 1 consistently experienced the highest congestion levels across the three cities, which correlates with our previous findings. These channels accounted for the majority of VERY_HIGH congestion classifications, particularly within the 2.4 GHz band.
In Mexico City, channel 11 alone accounted for more than 25% of detected deployments and consistently exhibited VERY_HIGH congestion levels.
This behavior reflects the limited availability of non-overlapping channels within the 2.4 GHz spectrum and the widespread reliance on default wireless configurations.
Overall, the channel utilization analysis showed that wireless deployments are concentrated heavily within the traditional, non-overlapping 2.4 GHz channels. While this strategy reduces adjacent-channel interference, excessive access point density on the same channels can still produce significant co-channel contention and poor wireless performance in high-density urban environments.
Wireless security configurations
The next thing we evaluated was the security posture of the detected wireless networks. We analyzed the wireless security configurations advertised by access points in each of the locations.
Overall security configuration distribution
The analysis revealed that WPA2 was the dominant wireless authentication mechanism across the three cities. Mexico City had the highest WPA2 adoption rate at 81.19%, followed by Monterrey at 79.19% and Guadalajara at 77.59%.
The study found that every 6th open access point (17%) was unsafe, namely 16.5% in Mexico City, 18.5% in Guadalajara, and 17.2% in Monterrey. Open wireless deployments were consistently present across all locations, ranging between 10% and 12% of detected access points. These findings show that despite the widespread deployment of modern wireless security standards, encryption adoption remains incomplete.
Distribution of wireless authentication mechanisms across the three locations (download)
To simplify the interpretation of wireless security posture, we grouped detected networks into four categories:
Secure (WPA2/WPA3)
Insecure (Open/WEP)
Weak (WPA)
Unknown
Across the three locations, secure networks comprised most of detected deployments, accounting for approximately 82% of all access points. However, insecure open networks still account for between 10% and 12% of detected wireless infrastructure, consistent with our previous findings. It is important to mention that networks within the unknown category are not considered secure.
Mexico City had the highest percentage of secure deployments at 83.54%, while Guadalajara had the highest percentage of insecure open networks at 12.46%. Although Monterrey had the lowest percentage of insecure networks, open deployments still accounted for more than 10% of the detected access points.
Wireless security posture grouping across the three locations (download)
Although modern WPA2/WPA3 encryption standards dominate current wireless deployments, the continued presence of open and legacy WPA deployments indicates that insecure wireless configurations remain relevant from an operational standpoint. These networks may expose users to passive traffic interception, unauthorized monitoring, rogue access point attacks, and credential harvesting techniques.
WPS-enabled networks
We also analyzed Wi-Fi Protected Setup (WPS) in all the locations to evaluate additional attack surfaces. WPS is a standard feature on wireless routers that enables devices such as printers, repeaters or mobile phones to connect to a secure Wi-Fi network without manually entering a long password, typically through a PIN-based enrolled mechanism. Although WPA2 and WPA3 provide strong encryption mechanisms, the presence of WPS can introduce security weaknesses due to inherently vulnerable PIN-based enrollment methods.
By combining detections from the three locations, we found that 55% of all detected access points did not advertise WPS capabilities, leaving 45% of deployments vulnerable to WPS-based abuse. These results suggest that, despite the adoption of modern encryption standards, a significant portion of wireless infrastructure continues to expose legacy convenience features.
During the analysis, we found that Mexico City had the highest proportion of WPS-enabled networks, with 46.61% of the detected access points advertising WPS capabilities. Guadalajara was second with 43.45%, while Monterrey had the lowest proportion at 40.93%.
The percentage of detected access points advertising WPS capabilities across the three locations (download)
Almost half of the detected wireless networks in each city continued to advertise WPS, indicating that WPS prevalence is consistently high across the three cities.
Secure networks with WPS enabled
In many cases, networks classified as secure because of WPA2/WPA3 encryption still had WPS functionality enabled, which effectively increased the available attack surface.
To further assess the relationship between encryption strength and WPS exposure, we conducted a secondary analysis of secure networks (WPA2/WPA3) only. The results showed that around half of all secure deployments still exposed WPS, with the following breakdown for each city:
Mexico City: 53.7%
Guadalajara: 50.9%
Monterrey: 47.5%
The proportion of secure networks with WPS enabled across the three locations (download)
These findings indicate that encryption strength alone is not enough to evaluate wireless security posture because additional protocol features, such as WPS, may still expose exploitable attack vectors.
Additional security considerations
Overall, travelers operating within dense public environments are exposed not only to insecure wireless infrastructure but also to various risks associated with digital interactions. These risks include many threats, from public USB charging systems and phishing QR codes to proximity-based protocols and exposure to shared public devices, such as interactive totems or kiosks. One particular point that should be taken into account in light of our research is the issue of rogue wireless deployments.
Rogue access points are not necessarily malicious; they may be set up accidentally by misconfiguring router settings. An entry point for potential compromise might be caused by various misconfigurations, from a weak password to an insecure protocol. However, attackers deploy such unauthorized hotspots with malicious intent to infiltrate a network. Threat actors may deploy rogue access points posing as legitimate public wireless networks in airports, hotels, cafés and tourist areas. These deployments are called “evil twins” and can trick users into connecting to attacker-controlled infrastructure capable of intercepting traffic, harvesting credentials, or performing man-in-the-middle attacks. Further risk lies in the potential compromise of local network devices or even malware distribution. Such threats complement our findings, underscoring the importance of implementing traffic encryption, using a security solution and exercising extreme caution while browsing via public networks.
Conclusion
The wardriving assessment conducted in Mexico City, Guadalajara, and Monterrey revealed that modern wireless infrastructure continues to present multiple forms of operational exposure despite the widespread adoption of WPA2 and WPA3 security standards. The analysis demonstrated that wireless environments are highly standardized in all the locations, with recurring ISP deployments, default SSID naming conventions, homogeneous manufacturer distribution, and predictable channel allocation practices observed in all three cities.
Although most of the detected networks were classified as secure under WPA2/WPA3 authentication mechanisms, a significant proportion were exposing additional attack surfaces through enabled WPS functionality, default configurations, sequential SSID structures, and infrastructure metadata disclosure. This demonstrates that encryption strength alone is insufficient for evaluating the overall security posture of wireless infrastructure. Additionally, the prevalence of open networks and legacy wireless configurations indicates that insecure deployments are still operationally relevant in all the locations.
The results also showed that wireless infrastructure is heavily concentrated within the 2.4 GHz spectrum, particularly around channels 11, 6, and 1. This leads to elevated congestion and increased co-channel interference in densely populated urban environments.
SSID analysis further revealed that publicly broadcast wireless identifiers frequently expose valuable operational information about ISPs, equipment manufacturers, deployment templates, organizational ownership, and user-defined naming practices. The identification of default ISP naming conventions, sequential SSID structures, and BSSID-derived SSIDs demonstrated that many deployments prioritize operational convenience and simplicity over exposure minimization and privacy.
The scope of the threats stemming from vulnerable wireless configurations poses serious digital exposure risks for users. The widespread presence of standard deployments, predictable SSID naming and publicly exposed infrastructure identifiers can facilitate passive reconnaissance, infrastructure fingerprinting and opportunistic targeting.
Recommendations
To minimize the risks of wireless-based exposure and the attack surface related to hotspot infrastructure, we recommend taking the following measures:
Disable WPS functionality on wireless routers whenever possible, particularly within WPA2/WPA3 deployments.
Avoid using default SSID naming conventions that disclose ISP providers, router manufacturers, or deployment templates.
Refrain from using personal, organizational, or location-based identifiers in wireless network names.
Avoid configuring SSID using BSSID or naming conventions derived from MAC addresses, as these may expose hardware fingerprinting information.
Promote migration toward modern WPA3-capable infrastructure while removing legacy wireless protocols when operationally feasible.
Reduce wireless congestion by optimizing channel allocation strategies and minimizing excessive dependence on the 2.4 GHz spectrum.
Encourage adoption of 5 GHz and newer wireless technologies to reduce interference and improve spectrum efficiency.
The findings presented in this assessment emphasize the importance of combining strong wireless encryption standards, secure deployment practices, exposure minimization strategies, and user awareness to enhance the overall security posture of wireless environments.
Mexico is one of the host countries for the 2026 FIFA World Cup, with matches to be played in three major cities: Mexico City, Monterrey, and Guadalajara. These locations are expected to see a large influx of international visitors, increasing the potential security risks. Many of those risks arise from users connecting to public wireless networks.
To better understand the wireless environments that visitors may encounter, we at Kaspersky GReAT conducted a wardriving assessment in the three host cities. The aim of the study was to analyze characteristics, deployment patterns, security configurations and potential exposure risks of public Wi-Fi infrastructure in urban wireless environments.
The information collected during the assessment was used exclusively for passive observation and infrastructure analysis. No attempts were made to authenticate, intercept communications, exploit systems or interact with the detected wireless networks beyond the publicly broadcast management information.
During processing of the collected data, one step involved filtering out networks belonging to cars or cell phones categorized as mobile hotspots because they do not represent networks that can be considered part of the assessment.
Research scope
The cities included in the study have high population density and extensive wireless infrastructure deployments. We chose areas with the most prominent wireless network activity and highly concentrated public access points. We carried out wardriving research in Monterrey back in 2008, but the city’s hotspot landscape has changed since then.
We chose the following analysis areas for each of the cities:
Mexico City: México City Stadium, Mexico City International Airport, Zócalo, Paseo de la Reforma, Colonia Roma, La Condesa, Polanco, and Coyoacán.
Guadalajara: Guadalajara Stadium, Guadalajara International Airport, the city center, Zapopan, Providencia, Avenida Chapultepec, Colonia Americana, Tlaquepaque, and the area around Andares.
Monterrey: Monterrey Stadium, Monterrey International Airport, Fundidora Park, Cintermex Monterrey, the downtown area, Barrio Antiguo, MacroPlaza, and the San Pedro financial district.
The wireless information was collected using passive wireless reconnaissance techniques. The collected information included:
SSID analysis and information exposure, including BSSID-derived SSIDs
Default router configurations and ISP deployments
Frequency and signal characteristics
Channel congestion and spectrum usage
Wireless security configurations, including:
Open and insecure wireless networks
WPS-enabled networks
Secure networks (WPA2/WPA3) with WPS enabled
We performed a wireless infrastructure analysis in Mexico City, Guadalajara, and Monterrey. We drove through the areas surrounding the World Cup stadiums, tourist zones, and other places where fan concentrations are likely to be largest. Our goal was to evaluate the security status, deployment characteristics and operational exposure of detected wireless networks.
In total, we recorded 84,588 signals with 69,473 unique Service Set Identifiers (SSIDs) in busy locations and World Cup zones across the three cities. Mexico City accounted for 61.4% of the signals, Guadalajara for 23.6%, and Monterrey for 14.8%. Approximately 82% of the signals had a single SSID (81.9%, 81.34%, and 84% respectively). Notably, they all operate under the IEEE 802.11 standard protocol.
Particular attention was given to identifying standard deployment patterns, legacy configurations, default vendor settings and information disclosure through publicly broadcast wireless identifiers.
The following sections present the results that were obtained by analyzing wireless infrastructure across the three locations.
Our findings
SSID analysis and information exposure
SSID analysis was conducted to evaluate naming conventions, deployment standardization and potential information exposure.
Only a few networks (0.0047%) have an invisible SSID, meaning the names of these networks are not broadcast. Some users prefer to hide the SSID for various reasons, such as the network’s purpose, the profile of its users, internal policies, etc. In contrast, the rest of the networks maintained active SSID broadcasting.
SSID structures may unintentionally disclose operational details about internet service providers (ISPs), device manufacturers, deployment practices, organizational ownership or user identity. The repeated presence of default SSID naming patterns across the analyzed locations indicates a significant degree of infrastructure homogeneity and reuse of default wireless configurations. It may also facilitate passive infrastructure profiling by revealing standard characteristics in use.
Approximately 34% of the detected networks retained the default SSID naming conventions provided by the manufacturer or ISP, while 66% used customized identifiers.
Distribution of SSID naming conventions (download)
Several recurring SSID naming conventions associated with ISP-provided deployments were identified in the three cities. The most frequently observed patterns include identifiers such as “Club_Totalplay_WiFi”, “izzi WiFi”, and “Megacable WiFi”, which suggests extensive standardization of wireless infrastructure deployment. Additionally, we observed distinctive location-specific SSIDs in each area of analysis, such as “XXXX-Internet para Todos-CDMX” or “RED JALISCO”.
Sequential SSID naming structures were also identified during the analysis. Patterns such as “INFINITUMXX” and “IZZI-XX” suggest automated ISP deployment and large-scale deployment strategies.
We identified 33 unique sequential naming structures among the 137 sequential SSIDs in total, representing approximately 0.16% of the detected wireless networks.
The following graph shows the top five sequential SSID patterns found in the largest number of networks:
Five most frequently observed sequential patterns (download)
Several customized SSIDs contained personal or organizational identifiers, including family names, professions, addresses or internal department references. Although personalized SSIDs may simplify local network identification for users, they may also expose sensitive information that could be useful for social engineering, physical targeting, or organizational profiling.
BSSID-derived SSID
During the analysis, multiple networks were identified that used the physical MAC address of a Wi-Fi access point (BSSID) as the visible SSID. This practice exposes hardware-level information that could facilitate vendor fingerprinting and targeted reconnaissance activities.
The organizationally unique identifier (OUI) contained in the first bytes of the BSSID identifies the equipment manufacturer. Threat actors can correlate exposed manufacturers with device-specific vulnerabilities.
Notably, we found that more than 30% of networks in all three cities reuse the MAC address as the SSID.
Default router configurations and ISP deployments
We performed wireless infrastructure profiling to identify the most common wireless equipment manufacturers and ISP deployments across the three locations.
Large-scale ISP deployments frequently use standardized wireless configurations and vendor-specific hardware platforms. Identifying dominant manufacturers and ISP naming conventions can provide insight into infrastructure and deployment practices facilitating the mapping of standardized attack surfaces.
The following figure shows the distribution of the most commonly used manufacturers.
Most frequently observed wireless equipment manufacturers (download)
The manufacturer analysis revealed a strong concentration of wireless infrastructure among a limited number of vendors. Across the three locations, Huawei Technologies, MediaTek-based devices, and other manufacturers’ equipment that is distributed through ISP channels represented a significant portion of the detected deployments. Mexico City had the most diverse infrastructure, while Monterrey and Guadalajara had a greater concentration of wireless equipment known as SOHO (small office/home office) or residential-grade hardware. The widespread presence of standard vendor platforms may facilitate infrastructure fingerprinting and large-scale targeting of known device-specific vulnerabilities.
Most frequently observed wireless equipment manufacturers across the three cities (download)
ISP deployments frequently exhibited standardized configuration patterns and recurring manufacturer identifiers. Our ISP deployment analysis revealed a high concentration of access points associated with major residential internet providers. Deployments associated with Infinitum, Totalplay and Izzi represented a substantial portion of the detected wireless infrastructure across all locations. These findings suggest a high degree of deployment standardization across networks associated with major residential internet providers. This observation was supported by the repeated presence of ISP-associated SSIDs such as “Infinitum”, “Totalplay”, and “Izzi”, combined with manufacturer identifiers frequently associated with consumer equipment, including Huawei, ZTE and other residential wireless equipment vendors.
It is important to note that, for this analysis, ISPs were primarily inferred from SSID naming conventions and manufacturer fingerprint data. A significant portion of the detected wireless networks fell into the “UNKNOWN/CUSTOM” category. This classification includes custom hotspots and networks whose naming conventions did not expose identifiable ISP-associated patterns. The findings suggest that many users and organizations (as we saw previously, approximately 66%) use custom network names, limiting direct provider attribution.
The following figure illustrates the distribution of ISP-associated wireless deployments in general.
To better understand this distribution, we took the most frequently observed ISPs by city.
Most frequently observed ISPs across the three cities (download)
Frequency and signal characteristics
We also analyzed wireless signal characteristics to evaluate coverage quality, signal strength, and frequency band utilization in the three cities. In dense urban environments, signal quality and frequency spectrum distribution can affect wireless reliability, client connectivity, roaming performance, and overall network efficiency.
Signal quality analysis revealed that a substantial portion of the detected access points operated under weak or very weak signal conditions. Monterrey had the highest percentage of very weak signals, with approximately 50% of detected deployments. Similar patterns were observed in Guadalajara and Mexico City, suggesting high-density wireless environments with overlapping coverage areas. Only a limited percentage of networks were classified within the very good or excellent signal categories across the three locations.
Signal stability analysis revealed that most detected wireless deployments exhibited stable beacon transmission behavior. More than 96% of the detected access points across all locations were classified as stable, while only a small percentage exhibited unstable or indeterminate signal behavior.
These findings imply that the majority of the wireless infrastructure observed during the assessment corresponded to permanently deployed access points rather than transient or intermittent wireless devices.
Frequency band analysis revealed the strong prevalence of 2.4 GHz wireless deployments across the three locations. More than 95% of the detected wireless networks operated within the 2.4 GHz spectrum, while only a small percentage of deployments were classified under the unknown or non-standard frequency categories. This uneven distribution reflects the continued prevalence of legacy-compatible wireless infrastructure and SOHO deployments.
These findings are consistent with dense urban wireless environments with large numbers of access points in restricted spectrum allocations.
Channel congestion and spectrum usage
Next, we analyzed wireless channel utilization to evaluate frequency spectrum congestion and channel allocation patterns across the three cities. Our analysis focused on the 2.4 GHz spectrum, where channel overlap and high access point density commonly produce interference and degraded wireless performance. In densely populated wireless environments, an excessive concentration of access points on a limited number of channels can lead to co-channel interference, packet collisions, reduced throughput, and degraded network stability.
Spectrum congestion analysis revealed that the 2.4 GHz band consistently experienced elevated congestion levels across the three cities. The detailed results showed a strong concentration of deployments on channels 11, 6 and 1, which are traditionally recommended as non-overlapping channels within the 2.4 GHz spectrum. Channel 11 was the most utilized channel, accounting for 25.2% of the detected access points, followed by channel 6 with 22.5% and channel 1 with 19.5%. This distribution indicates that most wireless deployments adhere to standard channel allocation practices for 2.4 GHz Wi-Fi environments.
The following figure illustrates the overall distribution of the most frequently utilized wireless channels.
To further assess wireless spectrum saturation, the detected access points were grouped according to channel congestion levels: VERY_HIGH, HIGH, UNKNOWN, MEDIUM, LOW and NONE.
Mexico City had the highest proportion of heavily congested wireless channels, with approximately 7% of detected access points operating under HIGH congestion conditions. Guadalajara followed with nearly 5% of deployments categorized as HIGH congestion, while Monterrey had the lowest percentage at approximately 3.29%.
These findings suggest that wireless spectrum saturation increases proportionally with urban infrastructure density and access point concentration. Despite the presence of congested deployments, most detected access points were categorized as LOW or MEDIUM congestion, suggesting severe spectrum saturation was localized rather than uniformly distributed.
A thorough analysis of individual channel utilization revealed that channels 11, 6 and 1 consistently experienced the highest congestion levels across the three cities, which correlates with our previous findings. These channels accounted for the majority of VERY_HIGH congestion classifications, particularly within the 2.4 GHz band.
In Mexico City, channel 11 alone accounted for more than 25% of detected deployments and consistently exhibited VERY_HIGH congestion levels.
This behavior reflects the limited availability of non-overlapping channels within the 2.4 GHz spectrum and the widespread reliance on default wireless configurations.
Overall, the channel utilization analysis showed that wireless deployments are concentrated heavily within the traditional, non-overlapping 2.4 GHz channels. While this strategy reduces adjacent-channel interference, excessive access point density on the same channels can still produce significant co-channel contention and poor wireless performance in high-density urban environments.
Wireless security configurations
The next thing we evaluated was the security posture of the detected wireless networks. We analyzed the wireless security configurations advertised by access points in each of the locations.
Overall security configuration distribution
The analysis revealed that WPA2 was the dominant wireless authentication mechanism across the three cities. Mexico City had the highest WPA2 adoption rate at 81.19%, followed by Monterrey at 79.19% and Guadalajara at 77.59%.
The study found that every 6th open access point (17%) was unsafe, namely 16.5% in Mexico City, 18.5% in Guadalajara, and 17.2% in Monterrey. Open wireless deployments were consistently present across all locations, ranging between 10% and 12% of detected access points. These findings show that despite the widespread deployment of modern wireless security standards, encryption adoption remains incomplete.
Distribution of wireless authentication mechanisms across the three locations (download)
To simplify the interpretation of wireless security posture, we grouped detected networks into four categories:
Secure (WPA2/WPA3)
Insecure (Open/WEP)
Weak (WPA)
Unknown
Across the three locations, secure networks comprised most of detected deployments, accounting for approximately 82% of all access points. However, insecure open networks still account for between 10% and 12% of detected wireless infrastructure, consistent with our previous findings. It is important to mention that networks within the unknown category are not considered secure.
Mexico City had the highest percentage of secure deployments at 83.54%, while Guadalajara had the highest percentage of insecure open networks at 12.46%. Although Monterrey had the lowest percentage of insecure networks, open deployments still accounted for more than 10% of the detected access points.
Wireless security posture grouping across the three locations (download)
Although modern WPA2/WPA3 encryption standards dominate current wireless deployments, the continued presence of open and legacy WPA deployments indicates that insecure wireless configurations remain relevant from an operational standpoint. These networks may expose users to passive traffic interception, unauthorized monitoring, rogue access point attacks, and credential harvesting techniques.
WPS-enabled networks
We also analyzed Wi-Fi Protected Setup (WPS) in all the locations to evaluate additional attack surfaces. WPS is a standard feature on wireless routers that enables devices such as printers, repeaters or mobile phones to connect to a secure Wi-Fi network without manually entering a long password, typically through a PIN-based enrolled mechanism. Although WPA2 and WPA3 provide strong encryption mechanisms, the presence of WPS can introduce security weaknesses due to inherently vulnerable PIN-based enrollment methods.
By combining detections from the three locations, we found that 55% of all detected access points did not advertise WPS capabilities, leaving 45% of deployments vulnerable to WPS-based abuse. These results suggest that, despite the adoption of modern encryption standards, a significant portion of wireless infrastructure continues to expose legacy convenience features.
During the analysis, we found that Mexico City had the highest proportion of WPS-enabled networks, with 46.61% of the detected access points advertising WPS capabilities. Guadalajara was second with 43.45%, while Monterrey had the lowest proportion at 40.93%.
The percentage of detected access points advertising WPS capabilities across the three locations (download)
Almost half of the detected wireless networks in each city continued to advertise WPS, indicating that WPS prevalence is consistently high across the three cities.
Secure networks with WPS enabled
In many cases, networks classified as secure because of WPA2/WPA3 encryption still had WPS functionality enabled, which effectively increased the available attack surface.
To further assess the relationship between encryption strength and WPS exposure, we conducted a secondary analysis of secure networks (WPA2/WPA3) only. The results showed that around half of all secure deployments still exposed WPS, with the following breakdown for each city:
Mexico City: 53.7%
Guadalajara: 50.9%
Monterrey: 47.5%
The proportion of secure networks with WPS enabled across the three locations (download)
These findings indicate that encryption strength alone is not enough to evaluate wireless security posture because additional protocol features, such as WPS, may still expose exploitable attack vectors.
Additional security considerations
Overall, travelers operating within dense public environments are exposed not only to insecure wireless infrastructure but also to various risks associated with digital interactions. These risks include many threats, from public USB charging systems and phishing QR codes to proximity-based protocols and exposure to shared public devices, such as interactive totems or kiosks. One particular point that should be taken into account in light of our research is the issue of rogue wireless deployments.
Rogue access points are not necessarily malicious; they may be set up accidentally by misconfiguring router settings. An entry point for potential compromise might be caused by various misconfigurations, from a weak password to an insecure protocol. However, attackers deploy such unauthorized hotspots with malicious intent to infiltrate a network. Threat actors may deploy rogue access points posing as legitimate public wireless networks in airports, hotels, cafés and tourist areas. These deployments are called “evil twins” and can trick users into connecting to attacker-controlled infrastructure capable of intercepting traffic, harvesting credentials, or performing man-in-the-middle attacks. Further risk lies in the potential compromise of local network devices or even malware distribution. Such threats complement our findings, underscoring the importance of implementing traffic encryption, using a security solution and exercising extreme caution while browsing via public networks.
Conclusion
The wardriving assessment conducted in Mexico City, Guadalajara, and Monterrey revealed that modern wireless infrastructure continues to present multiple forms of operational exposure despite the widespread adoption of WPA2 and WPA3 security standards. The analysis demonstrated that wireless environments are highly standardized in all the locations, with recurring ISP deployments, default SSID naming conventions, homogeneous manufacturer distribution, and predictable channel allocation practices observed in all three cities.
Although most of the detected networks were classified as secure under WPA2/WPA3 authentication mechanisms, a significant proportion were exposing additional attack surfaces through enabled WPS functionality, default configurations, sequential SSID structures, and infrastructure metadata disclosure. This demonstrates that encryption strength alone is insufficient for evaluating the overall security posture of wireless infrastructure. Additionally, the prevalence of open networks and legacy wireless configurations indicates that insecure deployments are still operationally relevant in all the locations.
The results also showed that wireless infrastructure is heavily concentrated within the 2.4 GHz spectrum, particularly around channels 11, 6, and 1. This leads to elevated congestion and increased co-channel interference in densely populated urban environments.
SSID analysis further revealed that publicly broadcast wireless identifiers frequently expose valuable operational information about ISPs, equipment manufacturers, deployment templates, organizational ownership, and user-defined naming practices. The identification of default ISP naming conventions, sequential SSID structures, and BSSID-derived SSIDs demonstrated that many deployments prioritize operational convenience and simplicity over exposure minimization and privacy.
The scope of the threats stemming from vulnerable wireless configurations poses serious digital exposure risks for users. The widespread presence of standard deployments, predictable SSID naming and publicly exposed infrastructure identifiers can facilitate passive reconnaissance, infrastructure fingerprinting and opportunistic targeting.
Recommendations
To minimize the risks of wireless-based exposure and the attack surface related to hotspot infrastructure, we recommend taking the following measures:
Disable WPS functionality on wireless routers whenever possible, particularly within WPA2/WPA3 deployments.
Avoid using default SSID naming conventions that disclose ISP providers, router manufacturers, or deployment templates.
Refrain from using personal, organizational, or location-based identifiers in wireless network names.
Avoid configuring SSID using BSSID or naming conventions derived from MAC addresses, as these may expose hardware fingerprinting information.
Promote migration toward modern WPA3-capable infrastructure while removing legacy wireless protocols when operationally feasible.
Reduce wireless congestion by optimizing channel allocation strategies and minimizing excessive dependence on the 2.4 GHz spectrum.
Encourage adoption of 5 GHz and newer wireless technologies to reduce interference and improve spectrum efficiency.
The findings presented in this assessment emphasize the importance of combining strong wireless encryption standards, secure deployment practices, exposure minimization strategies, and user awareness to enhance the overall security posture of wireless environments.
One of the biggest football (soccer) events of this summer is the World Cup 2026. The tournament is co-hosted by three countries: the U.S., Canada, and Mexico. Unfortunately, events of this scale attract not just fans, but also scammers from all over the globe. We’ve already covered how cybercriminals are prepping for the World Cup online, and today we’re talking about digital security for fans on the ground in Mexico.
The country will host 13 matches and welcome millions of tourists. They’ll be staying in hotels, heading to games, checking out restaurants, navigating airports, and visiting popular tourist spots — and everywhere they go, the temptation to connect to public Wi-Fi will be high.
We’ve surveyed more than 84 500 (!) public Wi-Fi access points in Mexico City, Guadalajara, and Monterrey — and we have a lot to share about their security. Spoiler alert: many networks are still using outdated security standards, so you really shouldn’t go on vacation without reliable protection and an eSIM.
What and how we tested
Walking across Mexico looking for public Wi-Fi access points would have been a bit tough, though that’s exactly what we did for a similar Wi-Fi security survey in Paris. You can check out the results of that in our post, How safe is Wi-Fi in Paris?
This time the mission was far more demanding: mapping the wireless landscape of three major metropolises. That’s why we went wardriving — scanning for and logging wireless networks from a moving vehicle while equipped with a smartphone or laptop. It’s similar to searching for Wi-Fi on your phone, where the device constantly listens for nearby networks. Except instead of connecting to them, we just collect data about them.
All information was used strictly for passive observation and infrastructure analysis. Beyond receiving publicly broadcast service information, the experts of Kaspersky’s Global Research and Analysis Team (GReAT) didn’t attempt to authenticate, intercept traffic, exploit systems, or otherwise interact with the wireless networks they discovered. Mobile access points deployed in cars and on mobile devices were excluded from the sample.
Our main target was Mexico City — the capital and one of the most densely populated cities in Latin America. We took a drive through popular tourist spots: Mexico City Stadium, Mexico City International Airport, Zócalo, Paseo de la Reforma, Colonia Roma, La Condesa, Polanco, Coyoacán.
In Guadalajara and Monterrey, we drove similar routes: stadiums, main avenues, airports, and popular neighborhoods. Below you can see a heatmap of the areas we covered, ranging from red for areas with the highest density of public access points, through yellow and green, to blue for the lowest concentration.
Heatmap showing the locations of all Wi-Fi access points we covered in Mexico City
Heatmap showing the locations of all Wi-Fi access points we covered in Guadalajara
Heatmap showing the locations of all Wi-Fi access points we covered in Monterrey
We used passive radio reconnaissance to log 84 500 signals and 69 500 unique network identifiers across these three cities. The majority of the signals were caught in Mexico City (61.4%), followed by Guadalajara (23.6%) and Monterrey (14.8%).
What we analyzed:
Wireless network identifiers (SSIDs): the names that show up in your list of available Wi-Fi networks
Information that can be gleaned from these identifiers
Default router configurations and how ISPs deploy their networks
Frequencies used and signal characteristics
Channel load and radio frequency spectrum usage
Wireless network security configurations:
Open and insecure networks
Networks with WPS enabled
Secure networks (WPA2/WPA3) with WPS activated
You can find the full version of the study on the Securelist blog.
Telltale public Wi-Fi access point names
Network names (SSIDs) can tell you a lot by unintentionally revealing information about hardware manufacturers, ISPs, deployment methods, and whether an access point belongs to a business or a private user.
About 34% of the public Wi-Fi networks we logged didn’t bother changing their names at all, either sticking with the factory SSIDs from the router manufacturers or using standard naming conventions from their ISPs. For attackers, this can be a pretty solid hint, since this kind of network name lets them know which provider owns a given access point, what hardware is being used, and how it’s likely configured by default.
Another troubling nuance is the large number of Wi-Fi networks (over 30%) that use the access point’s MAC address (BSSID) as the visible network name. The first few bytes of a BSSID contain an Organizationally Unique Identifier (OUI), which gives away the router’s manufacturer. This is a useful lead for bad actors: they can find out who made the hardware and test for vulnerabilities specific to that brand’s models.
Is Mexican Wi-Fi well-protected?
An access point secured with WPA2/WPA3 can be considered more or less safe. All other authentication mechanisms yield much weaker results. We grouped the public Wi-Fi networks into four categories:
Secure (WPA2/WPA3)
Unsecured (open/WEP)
Weak (WPA)
Undetermined
The results are roughly the same across all three cities: about 82% of all analyzed access points are protected by secure standards. The outdated and insecure WPA protocol was practically nonexistent. However, more than 10% of the access points turned out to be completely unsecured. Connecting to these networks carries the risk of traffic interception and hidden surveillance.
But security isn’t evaluated by WPA protocols alone. We also checked for the presence of WPS, the infamous feature for quickly connecting to a network without entering a password, which is highly vulnerable to attacks. It turned out that WPS is enabled on nearly half (47%) of the access points in Mexico City, 43% in Guadalajara, and 41% in Monterrey. On average, 45% of the access points are potentially vulnerable to WPS-related attacks — sacrificing security for the sake of convenience.
What’s more, this feature frequently remained active even on seemingly secure WPA2/WPA3 networks — about half of them utilized WPS. This shows that having WPA2/WPA3 is still not enough to consider a Wi-Fi access point safe, as additional features like WPS can still leave the door open to attacks.
What else every tourist needs to know
Digital risks on a trip aren’t limited to public Wi-Fi alone, especially now that many are shifting away from public Wi-Fi to an eSIM. There are still plenty of threats in crowded places: public USB chargers, QR codes with swapped links, NFC and Bluetooth attacks, and, of course, social engineering tactics. Let’s break it all down.
Charging stations. Public USB chargers can also be dangerous: bad actors could potentially gain access to the data on your device or try to install malware. We covered these attacks in detail in our post, Data theft during smartphone charging.
Dangerous QR codes. Criminals can plant phishing QR codes in popular tourist spots. The pretexts can vary wildly; for instance, ads for team-specific fan “events”, or links supposedly offering discounts or restaurant menus. In reality, any QR code posted on the street can be considered insecure by default, and you shouldn’t scan them with your smartphone unless you have a QR code threat analyzer installed.
Fake broadcasts, tickets, and betting pools. Earlier, we described cases where bad actors were distributing malware via fake IPTV apps to capitalize on the WC26 hype. Remember, even if you plan to watch the tournament from home, you still need to stay alert and not trust the first sites that pop up advertising free broadcasts, offering betting pools, or promising unbelievably generous payouts.
Despite the prevalence of secure WPA2/WPA3 public Wi-Fi access points in Mexico City, Guadalajara, and Monterrey, our study shows that public Wi-Fi networks remain vulnerable. It’s also important to remember that attackers can create fake networks — so-called evil twins — disguised as legitimate public Wi-Fi in airports, hotels, cafés, and tourist spots.
For the average user, it’s practically impossible to tell how safe a specific access point is when trying to connect. That’s why the safest option is to use cellular data to access the internet — completely eliminating the need for Wi-Fi. Besides, there’s no need to research the nuances of local laws, rates, and other cellular details for every country you plan to visit; you can just buy a global eSIM online in two clicks. We explained how to make the entire process hassle-free in our post, Internet on the go with Kaspersky eSIM Store.
If you still plan on connecting to public Wi-Fi, always use a VPN to secure your device and data when connecting to unfamiliar — especially unsecured — Wi-Fi networks. This creates an encrypted tunnel between your device and the VPN server, making it impossible to intercept your data along the way. Haven’t picked a VPN yet? Try Kaspersky VPN Secure Connection, which is included with both Kaspersky Premium and Kaspersky Plus subscriptions.
Now, if you still plan to attend the World Cup without any cybersecurity solution, at least follow these basic rules of digital hygiene:
Don’t use public USB chargers
Don’t send sensitive information over connections that aren’t secure
Don’t log in to banking, email, or social media accounts over unsecured Wi-Fi
Turn off Bluetooth and NFC while walking around in crowded places
Don’t trust QR codes posted on the street
Connect to public Wi-Fi only when absolutely necessary
What else to read to make sure cheering for your favorite team isn’t only exciting, but also safe:
Unlike previous Wi-Fi attacks, AirSnitch exploits core features in Layers 1 and 2 and the failure to bind and synchronize a client across these and higher layers, other nodes, and other network names such as SSIDs (Service Set Identifiers). This cross-layer identity desynchronization is the key driver of AirSnitch attacks.
The most powerful such attack is a full, bidirectional machine-in-the-middle (MitM) attack, meaning the attacker can view and modify data before it makes its way to the intended recipient. The attacker can be on the same SSID, a separate one, or even a separate network segment tied to the same AP. It works against small Wi-Fi networks in both homes and offices and large networks in enterprises.
With the ability to intercept all link-layer traffic (that is, the traffic as it passes between Layers 1 and 2), an attacker can perform other attacks on higher layers. The most dire consequence occurs when an Internet connection isn’t encrypted—something that Google recently estimated occurred when as much as 6 percent and 20 percent of pages loaded on Windows and Linux, respectively. In these cases, the attacker can view and modify all traffic in the clear and steal authentication cookies, passwords, payment card details, and any other sensitive data. Since many company intranets are sent in plaintext, traffic from them can also be intercepted.
Even when HTTPS is in place, an attacker can still intercept domain look-up traffic and use DNS cache poisoning to corrupt tables stored by the target’s operating system. The AirSnitch MitM also puts the attacker in the position to wage attacks against vulnerabilities that may not be patched. Attackers can also see the external IP addresses hosting webpages being visited and often correlate them with the precise URL.
In the world of cybersecurity, it’s important to understand what attack surfaces exist. The best way to understand something is by first doing it. Whether you’re an aspiring penetration tester, […]
Ethan Robish // In this series of posts, I’ll discuss how I segmented my home network using VLANs and how I moved away from using a risky consumer-grade router at […]
David Fletcher// My wife and I recently purchased a 2016 Ford Flex to replace an aging version of the same make and model that met an untimely fate. During the […]
Jordan Drysdale & Kent Ickler // Jordan and Kent demonstrate why there is only ONE correct way to configure your wireless networks. They also talk about the use of a […]
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