ExifTool is a widely adopted utility for reading and writing metadata in image, PDF, audio, and video files. It is available both as a standalone command-line application and as a library that can be embedded in other software. In this article, we break down CVE-2026-3102, an ExifTool vulnerability discovered by Kaspersky’s Global Research and Analysis Team (GReAT) in February 2026 and patched by the developers within the same month. Affecting macOS systems with ExifTool version 13.49 and earlier, this flaw could let an attacker run arbitrary commands by hiding instructions inside an image file’s metadata.
This investigation originated from revisiting an n-day vulnerability I first examined years ago: CVE-2021-22204. That flaw exploited weak regex-based sanitization before feeding user input into an eval sink. By auditing adjacent input validation routines across ExifTool codebase for similar oversights, I discovered CVE-2026-3102. Successful exploitation of CVE-2026-3102 enables an attacker to execute arbitrary shell commands with the privileges of the user invoking ExifTool, potentially leading to full system compromise.
Technical details
Disclaimer
Exploiting CVE-2026-3102 requires the -n (also known as -printConv) flag and outputs machine-readable data without additional processing.
Tracing the vulnerable sink
Taint analysis (aka tainted data analysis) allows for the detection of “dirty” data that reaches dangerous locations without validation. In this context, a “sink” is a point or function in a program where data or a parameter marked as “tainted” or originating from an untrusted source (e.g., user input) can affect the program’s behavior. In ExifTool, these functions are eval and system, both of which are capable of executing system commands. While CVE-2021-22204 exploited an eval function as a sink, this vulnerability (CVE-2026-3102) targets the system function. Knowing the vulnerable sink, we needed to trace how user-controlled data reaches it. Below, we break down the details.
Finding an unsanitized date value
The screenshot above shows where the system() sink resides within the SetMacOSTags function. Tracing backward from system(), we identified the $cmd variable as the source of the executed command. This variable is assembled from three inputs: $file (properly sanitized), $setTags (processed iteratively), and $val (user-controlled and, crucially, left unsanitized in the vulnerable branch).
In ExifTool, a tag is a named metadata field. When parsing an image, the utility extracts date and time values from standard EXIF records or macOS filesystem attributes. To handle file creation dates on macOS, ExifTool relies on the Spotlight system attribute MDItemFSCreationDate. Within the program code, this attribute maps to the internal alias $FileCreateDate. These two identifiers govern how the file creation date is stored and applied.
This creates a critical link to the vulnerability: when parsing an image, ExifTool iterates through the discovered tags. The current tag’s name is assigned to the $tag variable, while its text content (e.g., a date string) is assigned to $val. The vulnerable code path is triggered only when $tag matches MDItemFSCreationDate or $FileCreateDate. At this point, the tag’s content flows into $val and is passed to the SetMacOSTags function. As shown in the screenshot below, the filename parameter is properly escaped, but the date value ($val) is not. Because the date is extracted directly from file metadata, an attacker can inject quotes into this field. This breaks the command structure and allows the payload to execute via the system() sink.
The following screenshots show some of the tags that can be modified. With the vulnerable parameter identified, the next challenge was delivery: how to place our payload into FileCreateDate without triggering early validation? We found the answer in the official documentation.
Planning the payload delivery
Let’s refer to the documentation to understand how ExifTool handles tag operations and identify a legitimate feature that can be repurposed for exploitation. Specifically, we need to find a way to deliver our payload into the vulnerable FileCreateDate parameter. When looking for macOS-related tags as well as FileCreateDate, we can find the following information:
To write or delete metadata, tag values are assigned using –TAG=[VALUE], and/or the -geotag, -csv= or -json=
To copy or move metadata, the -tagsFromFile feature is used.
To trigger the vulnerability, we need to copy a string (date format: MM/DD/YYYY) using the -tagsFromFile feature, as this operation invokes the SetMacOSTags function where the unsanitized $val parameter reaches the system() sink.
Why copy instead of writing directly? Because the vulnerable code path (SetMacOSTags) is only triggered when metadata is copied into FileCreateDate — not when it is written directly. By using -tagsFromFile, we can prepare a “source” tag (e.g., DateTimeOriginal) that accepts arbitrary values and copy that value into FileCreateDate, thereby invoking the vulnerable function with our controlled input.
Furthermore, we want to introduce single quotes (since they are not being escaped in $val). For starters, we can look for date-time tag and copy via -tagsFromFile by searching the EXIF tag table. Direct assignment to FileCreateDate is heavily validated, so we looked for a source tag that accepts raw values and can be copied into the target field. The following snippet shows the beginning of said table.
When doing the analysis, I made use of DateTimeOriginal though I believe you can also use CreateDate which is 0x9004 (see the following screenshot). Initial attempts to inject malformed dates failed: ExifTool’s built-in filter rejected the input. To bypass this, we examined how the tool handles raw metadata.
Bypassing the filter
To confirm that the PrintConvInv filter rejects invalid dates when written directly, I ran the following command, where evil_benign.jpg is a normal JPG with an invalid date time format. We are greeted with the error message: Invalid date/time. This requires the time as well. The next screenshot confirms that direct exploitation fails: ExifTool’s date validation detects the malformed input and rejects the change, activating the internal PrintConvInv filter.
That said, it is possible to ignore the formatting and use the -n flag which accepts raw values instead of human-readable value. The -n flag skips the PrintConvInv conversion step, which is exactly where input sanitization occurs. This confirmed we could park unsanitized data in a source tag. The final step was to trigger the vulnerable code path by copying that data into FileCreateDate. This means we should now be able to modify the DateTimeOriginal tag with the invalid date time format with an -n flag. Examining the EXIF metadata tag, we can confirm that we can store a raw value without a proper human readable format that ExifTool accepts:
Triggering the exploit
To inject commands, we have to revisit the single quote injection into this datetime related tag.
The following screenshot shows that we have successfully set the datetime metadata with the single quote. With the payload safely stored in a source tag, the next step was to copy it into FileCreateDate, triggering the vulnerable system() call.
The next step now is to copy the datetime tag to a file which invokes SetMacOSTags. According to the documentation, this is how we can copy the data from the SRC tag to the FileCreateDate tag as seen in the SetMacOSTags with the -tagsFromFile feature.
Here, we confirm that the payload has been executed! Note that when copying tags in MacOS (Darwin), the /usr/bin/setfile command is used. To view the full $cmd value before the injection, I have added the debugging statement to displaying the actual command that is executed within the system function.
Upon injection, we can see that our command gets executed via command substitution. The single quotes that we added helped to make the entire command syntactically valid. The following shows a more detailed labelling and their roles in making this command line injection successful:
Such an image can appear completely benign and easily find its way into a newsroom or any organization that processes photos on macOS using ExifTool. Once processed, an attacker could silently deploy a Trojan for covert data exfiltration, drop additional malware, or use the compromised machine as a foothold to expand the attack within the victim’s network.
Patch analysis
After verifying successful exploitation, we examined how the maintainer addressed the flaw in version 13.50. In the vulnerable version of ExifTool, commands were sanitized before being concatenated together. This means that it is possible to concatenate single quotes which led to the exploitation. However, by abstracting the system call into a dedicated wrapper and requiring a list of arguments instead of concatenated string, the fix removes the need for any manual escaping altogether.
1. Replacing string form to argument list form:
#### BEFORE
$cmd = "/usr/bin/setfile -d '${val}' '${f}'";
system $cmd;
#### AFTER
system('/usr/bin/setfile', '-d', $val, $file);
2. Create new System() wrapper. In version 13.49, the output is piped to /dev/null . To maintain that logic, the wrapper would temporarily redirect STDOUT/STDERR to /dev/null and restore them after the call.
# Call system command, redirecting all I/O to /dev/null
# Inputs: system arguments
# Returns: system return code
sub System
{
open(my $oldout, ">&STDOUT");
open(my $olderr, ">&STDERR");
open(STDOUT, '>', '/dev/null');
open(STDERR, '>', '/dev/null');
my $result = system(@_);
open(STDOUT, ">&", $oldout);
open(STDERR, ">&", $olderr);
return $result;
}
How to protect against ExifTool vulnerability
It’s critical to ensure that all photo processing workflows are using the updated version. You should verify that all asset management platforms, photo organization apps, and any bulk image processing scripts running on Macs are calling ExifTool version 13.50 or later, and don’t contain an embedded older copy of the ExifTool library.
ExifTool, like any software, may contain additional vulnerabilities of this class. To harden defenses, I recommend using Kaspersky Open Source Software Threats Data Feed for continuous monitoring of open-source components in your software supply chain, and Kaspersky for macOS as comprehensive endpoint protection. Additionally, isolate processing of untrusted files on dedicated machines or virtual environments with strictly limited network and storage access. If you work with freelancers, contractors, or allow BYOD, enforce a policy that only devices with an active macOS security solution can access your corporate network.
Conclusions
CVE-2026-3102 highlights the risks of inconsistent input sanitization in tools that bridge high-level metadata parsing with platform-specific utilities. While exploitation requires explicit flag usage (-n) and is restricted to macOS, the vulnerability underscores the danger of manual escaping routines in evolving codebases. The transition to list-form system execution provides a robust, architecture-level fix that eliminates shell interpretation risks entirely. This case reinforces a core security principle: replacing fragile string concatenation with secure, list-based API calls remains the most reliable mitigation against command injection.
ExifTool is a widely adopted utility for reading and writing metadata in image, PDF, audio, and video files. It is available both as a standalone command-line application and as a library that can be embedded in other software. In this article, we break down CVE-2026-3102, an ExifTool vulnerability discovered by Kaspersky’s Global Research and Analysis Team (GReAT) in February 2026 and patched by the developers within the same month. Affecting macOS systems with ExifTool version 13.49 and earlier, this flaw could let an attacker run arbitrary commands by hiding instructions inside an image file’s metadata.
This investigation originated from revisiting an n-day vulnerability I first examined years ago: CVE-2021-22204. That flaw exploited weak regex-based sanitization before feeding user input into an eval sink. By auditing adjacent input validation routines across ExifTool codebase for similar oversights, I discovered CVE-2026-3102. Successful exploitation of CVE-2026-3102 enables an attacker to execute arbitrary shell commands with the privileges of the user invoking ExifTool, potentially leading to full system compromise.
Technical details
Disclaimer
Exploiting CVE-2026-3102 requires the -n (also known as -printConv) flag and outputs machine-readable data without additional processing.
Tracing the vulnerable sink
Taint analysis (aka tainted data analysis) allows for the detection of “dirty” data that reaches dangerous locations without validation. In this context, a “sink” is a point or function in a program where data or a parameter marked as “tainted” or originating from an untrusted source (e.g., user input) can affect the program’s behavior. In ExifTool, these functions are eval and system, both of which are capable of executing system commands. While CVE-2021-22204 exploited an eval function as a sink, this vulnerability (CVE-2026-3102) targets the system function. Knowing the vulnerable sink, we needed to trace how user-controlled data reaches it. Below, we break down the details.
Finding an unsanitized date value
The screenshot above shows where the system() sink resides within the SetMacOSTags function. Tracing backward from system(), we identified the $cmd variable as the source of the executed command. This variable is assembled from three inputs: $file (properly sanitized), $setTags (processed iteratively), and $val (user-controlled and, crucially, left unsanitized in the vulnerable branch).
In ExifTool, a tag is a named metadata field. When parsing an image, the utility extracts date and time values from standard EXIF records or macOS filesystem attributes. To handle file creation dates on macOS, ExifTool relies on the Spotlight system attribute MDItemFSCreationDate. Within the program code, this attribute maps to the internal alias $FileCreateDate. These two identifiers govern how the file creation date is stored and applied.
This creates a critical link to the vulnerability: when parsing an image, ExifTool iterates through the discovered tags. The current tag’s name is assigned to the $tag variable, while its text content (e.g., a date string) is assigned to $val. The vulnerable code path is triggered only when $tag matches MDItemFSCreationDate or $FileCreateDate. At this point, the tag’s content flows into $val and is passed to the SetMacOSTags function. As shown in the screenshot below, the filename parameter is properly escaped, but the date value ($val) is not. Because the date is extracted directly from file metadata, an attacker can inject quotes into this field. This breaks the command structure and allows the payload to execute via the system() sink.
The following screenshots show some of the tags that can be modified. With the vulnerable parameter identified, the next challenge was delivery: how to place our payload into FileCreateDate without triggering early validation? We found the answer in the official documentation.
Planning the payload delivery
Let’s refer to the documentation to understand how ExifTool handles tag operations and identify a legitimate feature that can be repurposed for exploitation. Specifically, we need to find a way to deliver our payload into the vulnerable FileCreateDate parameter. When looking for macOS-related tags as well as FileCreateDate, we can find the following information:
To write or delete metadata, tag values are assigned using –TAG=[VALUE], and/or the -geotag, -csv= or -json=
To copy or move metadata, the -tagsFromFile feature is used.
To trigger the vulnerability, we need to copy a string (date format: MM/DD/YYYY) using the -tagsFromFile feature, as this operation invokes the SetMacOSTags function where the unsanitized $val parameter reaches the system() sink.
Why copy instead of writing directly? Because the vulnerable code path (SetMacOSTags) is only triggered when metadata is copied into FileCreateDate — not when it is written directly. By using -tagsFromFile, we can prepare a “source” tag (e.g., DateTimeOriginal) that accepts arbitrary values and copy that value into FileCreateDate, thereby invoking the vulnerable function with our controlled input.
Furthermore, we want to introduce single quotes (since they are not being escaped in $val). For starters, we can look for date-time tag and copy via -tagsFromFile by searching the EXIF tag table. Direct assignment to FileCreateDate is heavily validated, so we looked for a source tag that accepts raw values and can be copied into the target field. The following snippet shows the beginning of said table.
When doing the analysis, I made use of DateTimeOriginal though I believe you can also use CreateDate which is 0x9004 (see the following screenshot). Initial attempts to inject malformed dates failed: ExifTool’s built-in filter rejected the input. To bypass this, we examined how the tool handles raw metadata.
Bypassing the filter
To confirm that the PrintConvInv filter rejects invalid dates when written directly, I ran the following command, where evil_benign.jpg is a normal JPG with an invalid date time format. We are greeted with the error message: Invalid date/time. This requires the time as well. The next screenshot confirms that direct exploitation fails: ExifTool’s date validation detects the malformed input and rejects the change, activating the internal PrintConvInv filter.
That said, it is possible to ignore the formatting and use the -n flag which accepts raw values instead of human-readable value. The -n flag skips the PrintConvInv conversion step, which is exactly where input sanitization occurs. This confirmed we could park unsanitized data in a source tag. The final step was to trigger the vulnerable code path by copying that data into FileCreateDate. This means we should now be able to modify the DateTimeOriginal tag with the invalid date time format with an -n flag. Examining the EXIF metadata tag, we can confirm that we can store a raw value without a proper human readable format that ExifTool accepts:
Triggering the exploit
To inject commands, we have to revisit the single quote injection into this datetime related tag.
The following screenshot shows that we have successfully set the datetime metadata with the single quote. With the payload safely stored in a source tag, the next step was to copy it into FileCreateDate, triggering the vulnerable system() call.
The next step now is to copy the datetime tag to a file which invokes SetMacOSTags. According to the documentation, this is how we can copy the data from the SRC tag to the FileCreateDate tag as seen in the SetMacOSTags with the -tagsFromFile feature.
Here, we confirm that the payload has been executed! Note that when copying tags in MacOS (Darwin), the /usr/bin/setfile command is used. To view the full $cmd value before the injection, I have added the debugging statement to displaying the actual command that is executed within the system function.
Upon injection, we can see that our command gets executed via command substitution. The single quotes that we added helped to make the entire command syntactically valid. The following shows a more detailed labelling and their roles in making this command line injection successful:
Such an image can appear completely benign and easily find its way into a newsroom or any organization that processes photos on macOS using ExifTool. Once processed, an attacker could silently deploy a Trojan for covert data exfiltration, drop additional malware, or use the compromised machine as a foothold to expand the attack within the victim’s network.
Patch analysis
After verifying successful exploitation, we examined how the maintainer addressed the flaw in version 13.50. In the vulnerable version of ExifTool, commands were sanitized before being concatenated together. This means that it is possible to concatenate single quotes which led to the exploitation. However, by abstracting the system call into a dedicated wrapper and requiring a list of arguments instead of concatenated string, the fix removes the need for any manual escaping altogether.
1. Replacing string form to argument list form:
#### BEFORE
$cmd = "/usr/bin/setfile -d '${val}' '${f}'";
system $cmd;
#### AFTER
system('/usr/bin/setfile', '-d', $val, $file);
2. Create new System() wrapper. In version 13.49, the output is piped to /dev/null . To maintain that logic, the wrapper would temporarily redirect STDOUT/STDERR to /dev/null and restore them after the call.
# Call system command, redirecting all I/O to /dev/null
# Inputs: system arguments
# Returns: system return code
sub System
{
open(my $oldout, ">&STDOUT");
open(my $olderr, ">&STDERR");
open(STDOUT, '>', '/dev/null');
open(STDERR, '>', '/dev/null');
my $result = system(@_);
open(STDOUT, ">&", $oldout);
open(STDERR, ">&", $olderr);
return $result;
}
How to protect against ExifTool vulnerability
It’s critical to ensure that all photo processing workflows are using the updated version. You should verify that all asset management platforms, photo organization apps, and any bulk image processing scripts running on Macs are calling ExifTool version 13.50 or later, and don’t contain an embedded older copy of the ExifTool library.
ExifTool, like any software, may contain additional vulnerabilities of this class. To harden defenses, I recommend using Kaspersky Open Source Software Threats Data Feed for continuous monitoring of open-source components in your software supply chain, and Kaspersky for macOS as comprehensive endpoint protection. Additionally, isolate processing of untrusted files on dedicated machines or virtual environments with strictly limited network and storage access. If you work with freelancers, contractors, or allow BYOD, enforce a policy that only devices with an active macOS security solution can access your corporate network.
Conclusions
CVE-2026-3102 highlights the risks of inconsistent input sanitization in tools that bridge high-level metadata parsing with platform-specific utilities. While exploitation requires explicit flag usage (-n) and is restricted to macOS, the vulnerability underscores the danger of manual escaping routines in evolving codebases. The transition to list-form system execution provides a robust, architecture-level fix that eliminates shell interpretation risks entirely. This case reinforces a core security principle: replacing fragile string concatenation with secure, list-based API calls remains the most reliable mitigation against command injection.
Attackers are increasingly abusing Microsoft’s decades-old MSHTA utility to stealthily deliver stealers, loaders, and persistent malware through phishing, fake software downloads, and LOLBIN-based attack chains.
In the third quarter of 2025, we updated the methodology for calculating statistical indicators based on the Kaspersky Security Network. These changes affected all sections of the report except for the statistics on installation packages, which remained unchanged.
To illustrate the differences between the reporting periods, we have also recalculated data for the previous quarters. Consequently, these figures may significantly differ from the previously published ones. However, subsequent reports will employ this new methodology, enabling precise comparisons with the data presented in this post.
The Kaspersky Security Network (KSN) is a global network for analyzing anonymized threat information, voluntarily shared by users of Kaspersky solutions. The statistics in this report are based on KSN data unless explicitly stated otherwise.
The quarter in numbers
According to Kaspersky Security Network, in Q1 2026:
More than 2.67 million attacks utilizing malware, adware, or unwanted mobile software were prevented.
The Trojan-Banker category was the prevalent mobile malware threat with a 52.96% share of total detected applications.
More than 306,000 malicious installation packages were discovered, including:
162,275 packages related to mobile banking Trojans;
439 packages related to mobile ransomware Trojans.
Quarterly highlights
The number of malware, adware, or unwanted software attacks on mobile devices decreased to 2,676,328 in Q1, down from 3,239,244 in the previous quarter.
Attacks on users of Kaspersky mobile solutions, Q3 2024 — Q1 2026 (download)
The overall drop in attack volume stems primarily from a reduction in adware and RiskTool detections. Nonetheless, this trend does not equate to a lower risk for mobile users. As shown later in this report, the number of unique users targeted by these threats remained relatively stable.
In Q1, Synthient researchers identified a link between the notorious Kimwolf botnet and the IPIDEA proxy network. This network was later taken down in cooperation with GTIG.
In early 2026, we discovered several apps on Google Play and the App Store that contained a new version of the SparkCat crypto stealer.
The Trojan code, meticulously concealed, was embedded into the infected Android apps. The obfuscated malicious Rust library was decrypted using a Dalvik-like virtual machine custom-built by the attackers. The iOS version of the malware also underwent several changes; specifically, the attackers began leveraging Apple’s proprietary Vision framework for optical character recognition (OCR).
Mobile threat statistics
The number of Android malware samples saw a slight increase compared to Q4 2025, reaching a total of 306,070.
The detected installation packages were distributed by type as follows:
Detected mobile apps by type, Q4 2025* — Q1 2026 (download)
* Data for the previous quarter may differ slightly from previously published figures due to certain verdicts being retrospectively revised.
Threat actors once again ramped up the production of new banking Trojans; as a result, this category overtook all others in volume, accounting for more than half of all installation packages.
Share* of users attacked by the given type of malicious or potentially unwanted app out of all targeted users of Kaspersky mobile products, Q4 2025 — Q1 2026 (download)
* The total percentage may exceed 100% if the same users encountered multiple attack types.
Following the surge in banking Trojan installation packages, the number of associated attacks also rose, causing Trojan-Banker apps to climb one spot in terms of their share of targeted users. Mamont variants emerged as the most prevalent banking Trojans, accounting for 73.5% of detections, with the rest of the users encountering Faketoken, Rewardsteal, Creduz, and other families.
Yet banking Trojans were still outpaced by adware and RiskTool-type unwanted apps when measured by the total number of affected users. Despite a decrease in their share of installation packages, these two app types retained their positions as the top two threats by attack volume. The most common adware detections involved HiddenAd (44.9%) and MobiDash (38.1%), while most frequently seen RiskTool apps were Revpn (67%) and SpyLoan (20.5%).
TOP 20 most frequently detected types of mobile malware
Note that the malware rankings below exclude riskware or potentially unwanted software, such as RiskTool or adware.
Verdict
%* Q4 2025
%* Q1 2026
Difference in p.p.
Change in ranking
Backdoor.AndroidOS.Triada.ag
2.62
7.09
+4.48
+10
DangerousObject.Multi.Generic.
6.75
5.84
-0.92
-1
DangerousObject.AndroidOS.GenericML.
3.52
5.51
+1.99
+6
Trojan-Banker.AndroidOS.Mamont.jo
0.00
5.28
+5.28
Trojan.AndroidOS.Fakemoney.v
5.40
3.44
-1.96
-1
Trojan-Downloader.AndroidOS.Keenadu.l
0.00
3.35
+3.35
Trojan-Banker.AndroidOS.Mamont.jx
0.00
3.09
+3.09
Backdoor.AndroidOS.Triada.z
4.87
3.08
-1.79
-2
Trojan.AndroidOS.Triada.fe
5.01
2.98
-2.02
-4
Backdoor.AndroidOS.Keenadu.a
2.07
2.73
+0.66
+6
Trojan-Banker.AndroidOS.Mamont.jg
0.34
2.37
+2.03
Trojan.AndroidOS.Triada.hf
2.15
2.23
+0.07
+3
Trojan.AndroidOS.Boogr.gsh
2.35
2.15
-0.20
0
Trojan.AndroidOS.Triada.ii
5.68
2.07
-3.60
-11
Backdoor.AndroidOS.Triada.ae
1.91
1.76
-0.16
+3
Backdoor.AndroidOS.Triada.ab
1.79
1.72
-0.08
+3
Trojan.AndroidOS.Triada.gn
2.38
1.58
-0.80
-5
Trojan-Banker.AndroidOS.Mamont.gg
1.56
1.50
-0.06
+2
Trojan.AndroidOS.Triada.ga
1.48
1.50
+0.01
+4
Backdoor.AndroidOS.Triada.ad
0.53
1.40
+0.87
+44
* Unique users who encountered this malware as a percentage of all attacked users of Kaspersky mobile solutions.
The pre-installed Triada.ag backdoor rose to the top spot; it is similar to the older Triada.z version we documented previously. Because the same variant was pre-installed across a wide range of devices, the total number of affected users is aggregated. Consequently, Triada outpaced even Mamont, as users encountered a variety of Mamont variants, causing the share of that banking Trojan to spread across multiple rows. Other pre-installed Triada variants (Triada.z, Triada.ae, Triada.ab, and Triada.ad) also made the rankings. Furthermore, we observed increasing activity from the Keenadu.a backdoor, while diverse variants of the embedded Triada Trojan remained in the rankings.
Mobile banking Trojans
Q1 2026 saw a characteristic rise in mobile banking Trojan activity, with the number of packages totaling 162,275, a 50% increase compared to the prior quarter.
Number of installation packages for mobile banking Trojans detected by Kaspersky, Q1 2025 — Q1 2026 (download)
We saw a similar growth in the previous quarter, with banking Trojan volumes rising by 50% during that period as well. Various Mamont variants accounted for the absolute majority of packages and represented nearly every entry in the rankings of most frequent banking Trojans by affected user count.
TOP 10 mobile bankers
Verdict
%* Q4 2025
%* Q1 2026
Difference in p.p.
Change in ranking
Trojan-Banker.AndroidOS.Mamont.jo
0.00
15.75
+15.75
Trojan-Banker.AndroidOS.Mamont.jx
0.00
9.22
+9.22
Trojan-Banker.AndroidOS.Mamont.jg
1.47
7.08
+5.61
+24
Trojan-Banker.AndroidOS.Mamont.gg
6.79
4.48
-2.32
-3
Trojan-Banker.AndroidOS.Mamont.ks
0.00
3.98
+3.98
Trojan-Banker.AndroidOS.Agent.ws
6.03
3.78
-2.25
-2
Trojan-Banker.AndroidOS.Mamont.hl
4.30
3.27
-1.03
+1
Trojan-Banker.AndroidOS.Mamont.iv
6.00
3.08
-2.92
-3
Trojan-Banker.AndroidOS.Mamont.jb
3.93
3.07
-0.86
+1
Trojan-Banker.AndroidOS.Mamont.jv
0.00
2.79
+2.79
* Unique users who encountered this malware as a percentage of all users of Kaspersky mobile security solutions who encountered banking threats.
The statistics in this report are based on detection verdicts returned by Kaspersky products unless otherwise stated. The information was provided by Kaspersky users who consented to sharing statistical data.
Quarterly figures
In Q1 2026:
Kaspersky products blocked more than 343 million attacks that originated with various online resources.
Web Anti-Virus responded to 50 million unique links.
File Anti-Virus blocked nearly 15 million malicious and potentially unwanted objects.
2938 new ransomware variants were detected.
More than 77,000 users experienced ransomware attacks.
14% of all ransomware victims whose data was published on threat actors’ data leak sites (DLS) were victims of Clop.
More than 260,000 users were targeted by miners.
Ransomware
Quarterly trends and highlights
Law enforcement success
In January 2026, it was reported that the FBI had seized the domains of the RAMP cybercrime forum, a major platform used extensively by ransomware developers to advertise their RaaS programs and to recruit affiliates. There has been no official statement from the FBI, nor is it clear if RAMP servers were seized. In a post on an external website, a RAMP moderator mentioned law enforcement agencies gaining control over the forum. The takedown disrupted a key element of the RaaS ecosystem, creating ripple effects for ransomware operators, affiliates, and initial access brokers.
A man suspected of links to the Phobos group was apprehended in Poland. He was charged with the creation, acquisition, and distribution of software designed for unlawfully obtaining information, including data that facilitates unauthorized access to information stored within a computer system.
In March, a Phobos ransomware administrator pleaded guilty to the creation and distribution of the Trojan, which had been used in international attacks dating back to at least November 2020.
In March, the U.S. Department of Justice charged a man who had acted as a negotiator for ransomware groups. The company he worked for specializes in cyberincident investigations. The prosecution alleges the suspect colluded with the BlackCat threat actor to share privileged insights into the ongoing progress of negotiations. Additionally, the suspect is alleged to have had a prior direct role in BlackCat attacks, serving as an affiliate for the RaaS operation.
In a separate development this March, a U.S. court sentenced an initial access broker associated with the Yanluowang ransomware group to 81 months of imprisonment. According to the U.S. Department of Justice, the convict facilitated dozens of ransomware attacks across the United States, resulting in over $9 million in actual loss and more than $24 million in intended loss.
Vulnerabilities and attacks
The Interlock group has been heavily exploiting the CVE-2026-20131 zero-day vulnerability in Cisco Secure FMC firewall management software since at least January 26, 2026. The vulnerability enabled arbitrary Java code execution with root privileges on the affected device. This campaign demonstrates the ongoing reliance on zero-day vulnerabilities for initial access, a focus on network appliances as high-value entry points, and the rapid weaponization of new vulnerabilities within the ransomware ecosystem.
The most prolific groups
This section highlights the most prolific ransomware gangs by number of victims added to each group’s DLS. This quarter, the Clop ransomware (14.42%) returned to the top of the rankings, displacing Qilin (12.34%), which had held the leading position in the previous reporting period. Following closely is a new threat actor, The Gentlemen (9.25%). Emerging no later than July 2025, the group had already surpassed the activity levels of mainstays such as Akira (7.25%) and INC Ransom (6.13%).
Number of each group’s victims according to its DLS as a percentage of all groups’ victims published on all the DLSs under review during the reporting period (download)
Number of new variants
In Q1 2026, Kaspersky solutions detected six new ransomware families and 2938 new modifications. Volumes have returned to Q3 2025 levels following a surge in Q4 2025.
Number of new ransomware modifications, Q1 2025 — Q1 2026 (download)
Number of users attacked by ransomware Trojans
Throughout Q1, our solutions protected 77,319 unique users from ransomware. Ransomware activity was highest in March, with 35,056 unique users encountering such attacks during the month.
Number of unique users attacked by ransomware Trojans, Q1 2026 (download)
Attack geography
TOP 10 countries and territories attacked by ransomware Trojans
Country/territory*
%**
1
Pakistan
0.79
2
South Korea
0.64
3
China
0.52
4
Tajikistan
0.40
5
Libya
0.38
6
Turkmenistan
0.36
7
Iraq
0.35
8
Bangladesh
0.33
9
Rwanda
0.30
10
Cameroon
0.28
* Excluded are countries and territories with relatively few (under 50,000) Kaspersky users.
** Unique users whose computers were attacked by ransomware Trojans as a percentage of all unique users of Kaspersky products in the country/territory.
TOP 10 most common families of ransomware Trojans
Name
Verdict
%*
1
(generic verdict)
Trojan-Ransom.Win32.Gen
33.90
2
(generic verdict)
Trojan-Ransom.Win32.Crypren
6.38
3
WannaCry
Trojan-Ransom.Win32.Wanna
5.87
4
(generic verdict)
Trojan-Ransom.Win32.Encoder
4.68
5
(generic verdict)
Trojan-Ransom.Win32.Agent
3.80
6
LockBit
Trojan-Ransom.Win32.Lockbit
2.80
7
(generic verdict)
Trojan-Ransom.Win32.Phny
1.99
8
(generic verdict)
Trojan-Ransom.MSIL.Agent
1.96
9
(generic verdict)
Trojan-Ransom.Python.Agent
1.93
10
(generic verdict)
Trojan-Ransom.Win32.Crypmod
1.89
* Unique Kaspersky users attacked by the specific ransomware Trojan family as a percentage of all unique users attacked by this type of threat.
Miners
Number of new variants
In Q1 2026, Kaspersky solutions detected 3485 new modifications of miners.
Number of new miner modifications, Q1 2026 (download)
Number of users attacked by miners
In Q1, we detected attacks using miner programs on the computers of 260,588 unique Kaspersky users worldwide.
Number of unique users attacked by miners, Q1 2026 (download)
Attack geography
TOP 10 countries and territories attacked by miners
Country/territory*
%**
1
Senegal
3.19
2
Turkmenistan
3.06
3
Mali
2.63
4
Tanzania
1.62
5
Bangladesh
1.06
6
Ethiopia
0.95
7
Panama
0.88
8
Afghanistan
0.79
9
Kazakhstan
0.77
10
Bolivia
0.75
* Excluded are countries and territories with relatively few (under 50,000) Kaspersky users.
** Unique users whose computers were attacked by miners as a percentage of all unique users of Kaspersky products in the country/territory.
Attacks on macOS
In Q1 2026, Google uncovered a new cryptocurrency theft campaign. The scammers directed victims to a fraudulent video call, prompting them to execute malicious scripts under the guise of technical support fixes for connection problems.
In March, researchers with GTIG and iVerify reported the discovery of an in-the-wild exploit chain targeting both iOS and macOS devices. The exploit kit was apparently marketed on the dark web, providing threat actors with a suite of spyware capabilities alongside specialized cryptocurrency exfiltration modules. The exploit was delivered via drive-by downloads when victims visited various compromised websites. Our analysis confirmed that the toolkit included an updated version of a component previously identified in the Operation Triangulation attack chain.
Devices running macOS were similarly impacted by the high-profile supply chain attack targeting the Axios npm package, a widely used HTTP client for JavaScript. The installation of the infected package led to the deployment of a backdoor on macOS devices.
TOP 20 threats to macOS
Unique users* who encountered this malware as a percentage of all attacked users of Kaspersky security solutions for macOS (download)
* Data for the previous quarter may differ slightly from previously published data due to some verdicts being retrospectively revised.
The share of PasivRobber spyware attacks is beginning to decline, giving way to more traditional adware and Monitor-class software capable of tracking user activity. The popular Amos stealer also maintains its presence within the TOP 20.
Geography of threats to macOS
TOP 10 countries and territories by share of attacked users
Country/territory
%* Q4 2025
%* Q1 2026
China
1.28
1.97
France
1.18
1.07
Brazil
1.13
0.98
Mexico
0.72
0.52
Germany
0.71
0.45
The Netherlands
0.62
0.75
Hong Kong
0.49
0.53
India
0.42
0.48
Russian Federation
0.34
0.37
Thailand
0.24
0.27
* Unique users who encountered threats to macOS as a percentage of all unique Kaspersky users in the country/territory.
IoT threat statistics
This section presents statistics on attacks targeting Kaspersky IoT honeypots. The geographic data on attack sources is based on the IP addresses of attacking devices.
In Q1 2026, the share of devices attacking Kaspersky honeypots via the SSH protocol saw a significant increase compared to the previous reporting period.
Distribution of attacked services by number of unique IP addresses of attacking devices (download)
The distribution of attacks between Telnet and SSH maintained the ratio observed in Q4 2025.
Distribution of attackers’ sessions in Kaspersky honeypots (download)
TOP 10 threats delivered to IoT devices
Share of each threat delivered to an infected device as a result of a successful attack, out of the total number of threats delivered (download)
The primary shifts in the IoT threat distribution are linked to the activity of various Mirai botnet variants, although members of this family continue to account for the majority of the list. Furthermore, a new variant, Mirai.kl, surfaced in the rankings. We also observed a significant decline in NyaDrop botnet activity during Q1.
Attacks on IoT honeypots
The United States, the Netherlands, and Germany accounted for the highest proportions of SSH-based attacks during this period.
Country/territory
Q4 2025
Q1 2026
United States
16.10%
23.74%
The Netherlands
15.78%
17.57%
Germany
12.07%
10.34%
Panama
7.72%
6.34%
India
5.32%
6.05%
Romania
4.05%
5.82%
Australia
1.62%
4.61%
Vietnam
4.21%
3.50%
Russian Federation
3.79%
2.35%
Sweden
2.25%
2.09%
China continues to account for the largest proportion of Telnet attacks, though there was a marked increase in activity originating from Pakistan.
Country/territory
Q4 2025
Q1 2026
China
53.64%
39.54%
Pakistan
14.27%
27.31%
Russian Federation
8.20%
8.25%
Indonesia
8.58%
6.71%
India
4.85%
4.66%
Brazil
0.06%
3.30%
Argentina
0.02%
2.51%
Nigeria
1.22%
1.38%
Thailand
0.01%
0.55%
Sweden
0.54%
0.55%
Attacks via web resources
The statistics in this section are based on detection verdicts by Web Anti-Virus, which protects users when suspicious objects are downloaded from malicious or infected web pages. These malicious pages are purposefully created by cybercriminals. Websites that host user-generated content, such as message boards, as well as compromised legitimate sites, can become infected.
TOP 10 countries and territories that served as sources of web-based attacks
The following statistics show the distribution by country/territory of the sources of internet attacks blocked by Kaspersky products on user computers (web pages redirecting to exploits, sites containing exploits and other malicious programs, botnet C&C centers, and so on). One or more web-based attacks could originate from each unique host.
To determine the geographic source of web attacks, we matched the domain name with the real IP address where the domain is hosted, then identified the geographic location of that IP address (GeoIP).
In Q1 2026, Kaspersky solutions blocked 343,823,407 attacks launched from internet resources worldwide. Web Anti-Virus was triggered by 49,983,611 unique URLs.
Web-based attacks by country/territory, Q1 2026 (download)
Countries and territories where users faced the greatest risk of online infection
To assess the risk of malware infection via the internet for users’ computers in different countries and territories, we calculated the share of Kaspersky users in each location on whose computers Web Anti-Virus was triggered during the reporting period. The resulting data provides an indication of the aggressiveness of the environment in which computers operate in different countries and territories.
This ranked list includes only attacks by malicious objects classified as Malware. Our calculations leave out Web Anti-Virus detections of potentially dangerous or unwanted programs, such as RiskTool or adware.
Country/territory*
%**
1
Venezuela
9.33
2
Hungary
8.16
3
Italy
7.58
4
Tajikistan
7.48
5
India
7.21
6
Greece
7.13
7
Portugal
7.10
8
France
7.05
9
Belgium
6.83
10
Slovakia
6.80
11
Vietnam
6.62
12
Bosnia and Herzegovina
6.57
13
Canada
6.56
14
Serbia
6.50
15
Tunisia
6.36
16
Qatar
6.01
17
Spain
5.95
18
Germany
5.95
19
Sri Lanka
5.89
20
Brazil
5.88
* Excluded are countries and territories with relatively few (under 10,000) Kaspersky users.
** Unique users targeted by web-based Malware attacks as a percentage of all unique users of Kaspersky products in the country/territory.
On average during the quarter, 4.73% of users’ computers worldwide were subjected to at least one Malware web attack.
Local threats
Statistics on local infections of user computers are an important indicator. They include objects that penetrated the target computer by infecting files or removable media, or initially made their way onto the computer in non-open form. Examples of the latter are programs in complex installers and encrypted files.
Data in this section is based on analyzing statistics produced by anti-virus scans of files on the hard drive at the moment they were created or accessed, and the results of scanning removable storage media. The statistics are based on detection verdicts from the On-Access Scan (OAS) and On-Demand Scan (ODS) modules of File Anti-Virus and include detections of malicious programs located on user computers or removable media connected to the computers, such as flash drives, camera memory cards, phones, or external hard drives.
In Q1 2026, our File Anti-Virus detected 15,831,319 malicious and potentially unwanted objects.
Countries and territories where users faced the highest risk of local infection
For each country and territory, we calculated the percentage of Kaspersky users whose computers had the File Anti-Virus triggered at least once during the reporting period. This statistic reflects the level of personal computer infection in different countries and territories around the world.
Note that this ranked list includes only attacks by malicious objects classified as Malware. Our calculations leave out File Anti-Virus detections of potentially dangerous or unwanted programs, such as RiskTool or adware.
Country/territory*
%**
1
Turkmenistan
47.96
2
Tajikistan
31.48
3
Cuba
31.03
4
Yemen
29.59
5
Afghanistan
28.47
6
Burundi
26.93
7
Uzbekistan
24.81
8
Syria
23.08
9
Nicaragua
21.97
10
Cameroon
21.60
11
China
21.09
12
Mozambique
21.02
13
Algeria
20.64
14
Democratic Republic of the Congo
20.63
15
Bangladesh
20.44
16
Mali
20.35
17
Republic of the Congo
20.23
18
Madagascar
20.00
19
Belarus
19.78
20
Tanzania
19.52
* Excluded are countries and territories with relatively few (under 10,000) Kaspersky users.
** Unique users on whose computers local Malware threats were blocked, as a percentage of all unique users of Kaspersky products in the country/territory.
On average worldwide, Malware local threats were detected at least once on 11.55% of users’ computers during Q1.
Over the past few months, we have conducted an in-depth analysis of specific activity clusters of Kimsuky (aka APT43, Ruby Sleet, Black Banshee, Sparkling Pisces, Velvet Chollima, and Springtail), a prolific Korean-speaking threat actor. Our research revealed notable tactical shifts throughout multiple phases of the group’s latest campaigns.
Kimsuky has continuously introduced new malware variants based on the PebbleDash platform, a tool historically leveraged by the Lazarus Group but appropriated by Kimsuky since at least 2021. Our monitoring indicates various strategic updates to the group’s arsenal, including the use of VSCode Tunneling, Cloudflare Quick Tunnels, DWAgent, large language models (LLMs), and the Rust programming language. This expanding set of tools underscores the group’s ongoing adaptation and evolution.
Specifically, Kimsuky leveraged legitimate VSCode tunneling mechanisms to establish persistence and distributed the open-source DWAgent remote monitoring and management tool for post-exploitation activities. These activities affected various sectors in South Korea, impacting both public and private entities.
This article covers both previously undocumented attacks and a deeper technical analysis of incidents within this campaign that have been reported before — offering new insight beyond what has already been published.
Executive summary
Kimsuky obtains initial access to target systems by delivering spear-phishing emails containing malicious attachments disguised as documents. They also contact targets via messengers in some cases.
Kimsuky uses a variety of droppers in different formats, such as JSE, PIF, SCR, EXE, etc.
The droppers deliver malware mainly belonging to two big clusters: PebbleDash and AppleSeed. These clusters are considered the most technically advanced in the group’s toolset. The report covers the following PebbleDash malware: HelloDoor, httpMalice, MemLoad, httpTroy. It also covers AppleSeed and HappyDoor from AppleSeed cluster.
For post-exploitation activities Kimsuky uses legitimate tools Visual Studio Code (VSCode) and DWAgent. For VSCode, the attacker uses GitHub authentication method.
For hosting C2 infrastructure the group mainly uses domains registered at a free South Korean hosting provider. It also occasionally relies on hacked South Korean websites and tunneling tools, such as Ngrok or VSCode.
Kimsuky mainly targets South Korean entities. However, PebbleDash attacks were also seen in Brazil and Germany. This malware cluster focuses on defense sector, while AppleSeed most often targets government organizations.
Background
First identified by Kaspersky in 2013, Kimsuky has been active for over 10 years and is considered less technically proficient compared to other Korean-speaking APT groups. The group has targeted a wide range of entities and demonstrated capability in creating tailored spear-phishing emails. The group’s arsenal includes proprietary malware such as PebbleDash, BabyShark, AppleSeed, and RandomQuery, as well as open-source RATs like xRAT, XenoRAT, and TutRAT. This blog post examines the evolving PebbleDash-based malware (referred to as the PebbleDash cluster) and its connections to the AppleSeed-based malware (referred to as the AppleSeed cluster).
The PebbleDash and AppleSeed clusters are considered the most technically advanced in Kimsuky’s toolset. Since at least 2019, these clusters have masqueraded as legitimate documents and application installers, manifesting as JSE droppers or executables with .EXE, .SCR and .PIF extensions. Both are particularly adept at establishing backdoors and stealing information, and ongoing development of their variants has been observed. They even occasionally utilize stolen legitimate certificates from South Korean organizations to avoid detection.
Timeline of the AppleSeed and PebbleDash malware families
AppleSeed and PebbleDash have primarily targeted the public and private sectors in South Korea. The PebbleDash cluster has shown a particular interest in the medical, military and defense industries worldwide. The PebbleDash cluster compromised Brazilian and South Korean defense organizations throughout the past several years, as well as a German defense firm. In 2024, the South Korean government released a security advisory regarding the AppleSeed cluster, detailing how the malware was distributed by replacing a security software installer required to access a construction entity’s website.
Initial access
Kimsuky meticulously crafts and delivers spear-phishing emails to its targets in an attempt to entice them into opening attachments. According to recent research, the group also occasionally approaches targets by contacting them via messengers. In all cases, the initial contact leads to the delivery of a malicious attachment disguised as a document. These attachments often consist of compressed files containing droppers in formats such as .JSE, .EXE, .PIF, or .SCR. The filenames are consistent with the message content and are meant to convince the recipient to open the attachment. The malicious files are often disguised as product quotations, job offers, information guides, surveys, government documents, and personal photos.
Appendix Form No. 8 – Request for Access, Correction, Deletion, and Suspension of Processing of Personal Information (PIPA Enforcement Rules).hwp.jse
August 28, 2025
995a0a49ae4b244928b3f67e2bfd7a6e
HelloDoor
2
2026년 상반기 국내대학원 석사야간과정 위탁교육생 선발관련 서류.hwpx.jse
Documents for the Selection of Commissioned Students for Domestic Graduate School Master’s Evening Programs (H1 2026).hwpx.jse
December 14, 2025
52f1ff082e981cbdfd1f045c6021c63f
httpMalice
3
security_20260126.scr
–
January 26, 2026
65fc9f06de5603e2c1af9b4f288bb22c
Reger Dropper, MemLoad, httpTroy
4
노현정님.pdf.jse
Ms. Noh Hyun-jung.pdf.jse
January 28, 2026
8e15c4d4f71bdd9dbc48cd2cabc87806
AppleSeed chain
5
대국민서비스관리운영체계현장점검증적(초안).pif
On-site Inspection Evidence for the Public Service Management System (Draft).pif
February 5, 2026
8983ffa6da23e0b99ccc58c17b9788c7
Pidoc Dropper, HappyDoor
JSE droppers contain a minimum of two Base64-encoded blobs: one serving as a benign lure file and one or more containing malicious code. Additional blobs may exist within the dropper, but they are unused. The two blobs are decoded using JScript and stored in an arbitrary location on disk, such as C:\ProgramData, with the malicious filenames randomly generated according to the scheme [random]{7}.[random]{4}. The lure file is opened immediately. The malicious payload leverages powershell.exe -windowstyle hidden certutil -decode [src path] [dst path] for the second Base64 decoding before execution. Ultimately, the malicious payload is executed via command-line instructions such as regsvr32.exe /s [file path] or rundll32.exe [file path] [export function].
Reger Dropper (.SCR) and Pidoc Dropper (.PIF) also contain benign lure files and malicious payloads that, in both cases, are encrypted using XOR operations. Specifically, Reger Dropper employs a hard-coded key #RsfsetraW#@EsfesgsgAJOPj4eml;, while Pidoc Dropper utilizes single-byte XOR with 0xFF to decrypt the internal data for execution. Pidoc Dropper is fully obfuscated using dummy data and encrypted strings. Both droppers deploy files in specific directories such as %temp% or C:\ProgramData before executing the malware using regsvr32.exe.
In addition to these droppers, Kimsuky employed a variety of executable droppers, including those crafted in Go or packaged with Inno Setup.
Deployed malware
In this section, we describe several malware families recently dropped by the droppers discussed above.
HelloDoor: first Rust-based PebbleDash variant
Written in Rust, a programming language rarely used by Kimsuky, HelloDoor is a DLL-based backdoor first identified in August 2025. It is deployed via a malicious JSE dropper. Since it has limited capabilities and a simplistic communication mechanism, the backdoor is most probably in the early stages of development. Nevertheless, it is noteworthy that HelloDoor employs a C2 server hosted through TryCloudflare, a temporary tunneling service provided by Cloudflare. This service allows users to expose a local web service to the internet with no setup or account, making the infrastructure behind it difficult to trace.
HelloDoor establishes persistence upon execution by registering itself to the HKCU\Software\Microsoft\Windows\CurrentVersion\Run key with the value name tdll and the command regsvr32.exe /s [current file path].
The implant communicates with the C2 server (hxxp://female-disorder-beta-metropolitan.trycloudflare[.]com/index.php) over the HTTP protocol. Depending on whether the process is executing with an elevated token, it binds to a specific local port: 5555 if the token is elevated, or 5554 if not. Before initiating communication, it generates a unique identifier by collecting device information, such as the MAC address, computer name, and the string “windows”, then computes a hash value from this information.
The malware then constructs a query string in the format aaaaaaaaaa=2&bbbbbbbbbb=[the unique identifier]&cccccccccc=1, which is a traditional format used across the PebbleDash cluster. Subsequent server responses are Base64-decoded and then decrypted using RC4 with the key fwr3errsettwererfs. The decrypted content contains command strings. Possible commands are:
Command
Description
“mcd”
Set the current directory
“msleep”
Sleep for the provided time
“install”
Register the regsvr32.exe /s [the provided file path] command to the HKCU\Software\Microsoft\Windows\CurrentVersion\Run autorun registry using the install value name
[command]
Execute the provided command using chcp 65001 > nul & cmd /U /C [command]
Though interesting, it is no longer surprising that we found comments in the code that appear to have been generated by an LLM service rather than a human developer. This is based on traces that include emojis used for logging debugging messages.
✅ Port is now listening (no accepting)
❌ Port is already in use
🔍 regsvr32.exe detected as parent. Attempting to terminate...
This is a common trait of LLM services that provides users with better visibility. We previously observed similar comments in the PowerShell-based stealer suite used by BlueNoroff. HelloDoor’s simple structure and the fact that no other Rust-based malware from the group has been discovered yet support our claim.
Even though the code is believed to have been developed using an LLM service, we still found some typos and grammatical errors, such as:
result send fail (grammatically incorrect text)
server request fail (grammatically incorrect text)
It is likely that the flawed comments were added manually before or after AI was used.
httpMalice: latest backdoor variant of PebbleDash
The latest PebbleDash-based backdoor, httpMalice, emerged no later than December 2025 and is deployed by the JSE Dropper. Although we found limited direct connections to both the AppleSeed and PebbleDash clusters, the malware is closer to PebbleDash. The following shared characteristics have been identified:
(PebbleDash cluster) Ability to run commands received from the C2 server with the S-1-12-12288 SID, indicating a high integrity level – a feature also observed in PebbleDash and httpTroy.
(PebbleDash cluster) Unique identifier generated by combining the volume serial number of the root directory with the elevation status of the current token, mirroring a technique used since the appearance of NikiDoor.
(PebbleDash cluster) Communication with its C2 server utilizing three HTTP parameters, consistent with other PebbleDash-based families.
(PebbleDash cluster) Core command set more closely aligned with PebbleDash than with AppleSeed-based malware.
(AppleSeed cluster) Use of the m= parameter in C2 communication.
(AppleSeed cluster) Gathering system details using PowerShell and Windows commands similar to those found in AppleSeed and Troll Stealer.
Our analysis revealed two distinct versions of httpMalice based on their C2 communications: version 1.9 communicates over HTTP and version 1.8 uses Dropbox. The latter, the older variant, leverages the Dropbox API by utilizing pre-defined application credentials. Unlike its predecessor, the HTTP variant employs HTTP/HTTPS protocols to interact with its C2 server and maintains persistent access to the victim device through a Windows service named CacheDB. This mirrors tactics observed in similar threats, such as httpSpy.
The more recent variant gathers critical information from the compromised system, such as the current directory path, volume serial numbers, user privileges, username, local IP address, and the name and size of the currently executed httpMalice DLL file. It then combines the root drive’s volume serial number with the user’s access token privilege level to create a unique identifier for each infected system, formatted as [volume serial]{8}_[elevation status].
Value of elevation status
Description
0
Running under the SYSTEM account with an elevated token
1
Running under an elevated administrator account
2
Running without elevation
Depending on the token privilege, the backdoor then establishes persistence by either creating a service or registering itself to autostart at user logon. If the token is elevated, a service named CacheDB is created that executes the command cmd.exe /c “rundll32.exe [current DLL path], load”. The service’s display name is set to Administrator, and its description is defined as CacheDB Service. If the token is not elevated, the backdoor registers the same command under the registry key HKCU\Software\Microsoft\Windows\CurrentVersion\Run with the value name Everything 1.9a-[filesize]. The older version used Everything 1.8a-[filesize] as a value name.
The latest version can execute a combination of Windows commands by default to perform host profiling, while the older version fetches the command set from Dropbox. In httpMalice, commands are mostly executed using the format cmd.exe /c chcp 949 [command] > [temporary filename], which redirects the output to separate files, with the consistent prefix 2Ato6478s added to their names. The chcp 949 command changes the code page to 949, indicating that the malware targets users of the Korean language (EUC-KR charset).
Windows commands used to gather system details
httpMalice transmits the result of host profiling to its C2 server as a URL parameter, using the POST method over the HTTP/HTTPS protocol, with the header x-www-form-urlencoded. The URL includes two or three parameters: operation mode, unique identifier (referred to as UID), and data. The operation mode, or parameter m, supports the following values:
Value
Description
1
Send the session identifier (parameter s) along with the current state (parameter a)
2
Request command
3
Send result after executing the command (parameter d)
8
Request directory to be archived and sent
9
Send the archived directory
10
Send a message like “.cmd” or “.tmp” (parameter d)
11
Send ping
12
Send the captured screenshot (parameter d)
13
Send the infected device information (parameter d)
As shown in the table above, the mode is set to 13 at the host profiling stage. The UID is formatted as [volume serial]{8}_[elevation status], and the data contains the ChaCha20-encrypted and Base64-encoded output of the command set stored in the temporary file. The resulting URL format is: m=13&u=[volume serial]{8}_[elevation status]&d=[Chacha20 encrypted + Base64-encoded data to be sent].
The key and nonce used for ChaCha20 encryption are derived from the pointer address of the buffer, resulting in nearly randomized keys. To ensure proper decryption on the attacker side, the nonce and key values are appended after the encrypted data, and the combined blob is then Base64-encoded. The counter is initialized to 0. The following figure illustrates how the encrypted data is structured after performing Base64 decoding.
Structure of the ChaCha20-encrypted data blob
After sending the host profiling data, the backdoor continuously transmits a screen capture with mode 12 and a ping message with mode 11. Finally, it sends a session identifier, which is a combination of the current username and local IP address separated by an ‘@’ symbol. In this case, the mode is set to 1 and the a parameter (current state) is set to 0, indicating that the C2 operation has been activated. The following table provides other possible values of the a parameter:
Value
Description
0
httpMalice has been activated
1
httpMalice has been inactivated (upon command 9)
2
httpMalice has been removed (upon command 8)
The whole process from sending the host profile to the backdoor activation repeats every two minutes until the C2 server returns a “success!” message.
C2 communication sequence of httpMalice
When the backdoor receives the message from the C2 server, it creates two threads dedicated to processing commands and sending the current state, including the session identifier. The first thread receives a command from the C2 server. It requests a command by sending mode 2 and, if successful, immediately sends mode 10 along with the string “.cmd” in the d parameter.
The commands supported by httpMalice are as follows:
Command
Description
0
Do nothing
1
Execute the command with EUC-KR encoding
2
Download and extract the file to the infected device
3
Upload a directory to the C2 server after it has been archived
5
Get the current directory
6
Set the current directory
7
Execute the command without setting a EUC-KR character set
8
Remove its persistence traces and exit the process
9
Hibernate
10
Execute the command using the provided session ID
12
Capture the screen
13
Load the downloaded payload into memory
MemLoad downloads httpTroy
Since early 2025, we have observed several versions of MemLoad; specifically, MemLoad V2 emerged in March, and V3 appeared by September. The payload that began being deployed through the Reger Dropper this year has been identified as an updated variant of MemLoad, slightly modified from the V3 version (referred to internally as MemLoader.dll).
Kimsuky leverages MemLoad to evade detection of its final backdoor and to carefully assess the value of targeted systems through anti-VM checks and reconnaissance. Upon installation, it requests an additional payload from the C2 server, executing it reflectively in memory if deemed suitable. Notably, all versions of MemLoad V2 and later use the same RC4 key.
Below are the key operations of MemLoad:
Creates a flag file. Creates a file containing a random eight-character string from the set 0123456789abcdefABCDEF with another random eight-character string as the name and “.dat.cfg” extension at the current file path.
Generates an ID. Generates an ID value by adding either ‘A-‘ or ‘U-‘ to the beginning of the random bytes. The choice of symbol is determined by attempting to create a random file in the C:\Windows\system32 directory. If successful, the ID starts with ‘A-‘ (indicating administrative privileges); otherwise, it starts with ‘U-‘.
Persistence via a scheduled task. Checks for the existence of the .dat.cfg file, and if confirmed, a scheduled task is set up for persistence. The task name is determined by whether the process is running with elevated privileges. If elevated, the task is named ChromeCheck, and the command
schtasks/create/tn<task name>/tr"regsvr32 /s <current file path>"/sc minute/mo1/rl highest/f is executed. Otherwise, the task is named EdgeCheck, and the command
schtasks/create/tn<task name>/tr"regsvr32 /s <current file path>"/sc minute/mo1/f is executed.
C2 communication and payload download. Requests an additional payload from its C2 server, with the header Authorization: Bearer {ID} or X-Browser-Validation: {ID} for authentication. The ID is set to the previously generated ID value.
Payload decryption and execution. Once the download is successful, the payload is decrypted using the RC4 algorithm with the key #RsfsetraW#@EsfesgsgAJOPj4eml;. The decrypted payload is then reflectively loaded into memory, and its hello export function is invoked.
The payload downloaded and executed by MemLoad is identified as the httpTroy backdoor. This backdoor serves as the primary role for long-term access and data exfiltration. Similar to MemLoad, it employs stealth techniques by creating a flag file and writing eight random bytes to it. However, in this case the file is created at [current file path]:HUI in the ADS (Alternative Data Stream) area. The backdoor then checks its privileges to determine if it is elevated and assigns an ID value in the format A-[random-8-chars] or U-[random-8-chars].
Since Gen Digital covers httpTroy’s features and functionality in detail elsewhere, we will not provide a thorough explanation here to avoid redundancy. Instead, we will simply note that it communicates with the C2 server at hxxps://file.bigcloud.n-e[.]kr/index.php.
AppleSeed
AppleSeed first appeared in 2019 and reached version 3.0. However, we now only see version 2.1. It originally consisted of two components: a dropper and the main AppleSeed. Since 2022, the updated AppleSeed chain has involved two droppers, an additional component referred to as the installer, and the main payload. It is mostly delivered through JSE Dropper.
Updated AppleSeed infection chain
There are two versions of the main AppleSeed: Dropper and Spy. The Dropper variant is responsible for downloading additional malware and executing commands received from its C2 server, while the Spy version gathers sensitive information such as documents, screenshots, keystrokes, and lists of USB drives. A notable change in version 2.1 is the inclusion, since 2022, of collecting the C:\GPKI directory – functionality that is also implemented in Troll Stealer. This directory contains a digital certificate used by the South Korean government to securely authenticate public officials and government systems.
HappyDoor
HappyDoor, an AppleSeed-based backdoor malware disclosed by AhnLab in 2024, is less visible than AppleSeed. HappyDoor shares several features with AppleSeed, including the same string obfuscation algorithm, the data types it collects, and the use of RSA encryption. Given these similarities, we assess with medium confidence that HappyDoor is an advanced variant evolved from AppleSeed.
Post-exploitation
We observed interesting post-exploitation activities involving VSCode and DWAgent. All of the observed VSCode droppers used the same lure files as the PebbleDash malware cluster. While we are unsure of the exact reason for this strategy, we suspect that the actor prepared both PebbleDash and VSCode droppers in anticipation of the PebbleDash infection chain being detected by security products because of its backdoor capabilities. In contrast, the use of VSCode is designed to have fewer detection points.
VSCode (launched by the JSE dropper)
Since last year, Kimsuky has been leveraging the legitimate Visual Studio Code Remote Tunneling feature to establish covert remote access to the victim’s device, bypassing detection designed for traditional malware-based C2 channels (first described by Darktrace researchers). In these attacks, instead of dropping malware, the JSE dropper downloads a legitimate Visual Studio Code (VSCode) CLI onto the infected device. The script establishes persistence by creating a tunnel via the application, with the tunnel name “bizeugene”, using the command below.
The Remote Tunneling feature in VSCode supports establishing a tunnel using either a Microsoft or GitHub account. When the code tunnel command is executed, the CLI initiates an authentication flow and returns a login URL along with a device code. The user must then navigate to the URL, enter the device code, and authenticate with their account. Once authentication is successful, the tunnel is created and the CLI outputs a URL for tunneling that enables browser-based access to the remote host.
The GitHub authentication method is selected in this instance because GitHub is configured as the default provider in non-interactive execution contexts. By using echo |, the script injects a \r\n (Carriage Return and Line Feed) into the standard input stream, effectively confirming the default prompt selection without manual interaction. As a result, the CLI automatically initiates the GitHub authentication flow. Next, all CLI output that includes a login URL and a device code is saved to out.txt.
Out.txt content
The JScript code in the JSE dropper monitors the out.txt file for a URL that begins with hxxps://vscode[.]dev/tunnel. This URL contains the full address of the established tunnel. Once detected, the file content containing the URL and the device code is sent to a compromised legitimate South Korean website (hxxps://www.yespp.co[.]kr/common/include/code/out[.]php) using the HTTP POST method. The request contains the file contents in the application/x-www-form-urlencoded header data formatted as out=URLencoded{result of the command}&token=URLencoded{"bizeugene"}. After authentication is complete, the attacker can access the compromised host externally through a web browser by authenticating with their own GitHub account.
VSCode (launched by VSCode installer)
While searching our telemetry for artifacts related to a different infection, we identified a new VSCode tunnel installer written in Go. A previous version of this installer was implemented using JScript and was limited to secure channels because of its reliance on a specific tunnel name. The new variant, named vscode_payload by the developer based on the embedded Go path, is fully operational and supports every tunnel on each targeted device. It includes features that are nearly identical to those of the previous version, such as downloading, unarchiving, and executing the VSCode CLI.
After the VSCode CLI file has been successfully downloaded, it is unzipped into the C:\Users\Public directory, and the extracted code.exe is executed with the tunnel command.
This is how the installer works:
Executes code.exe tunnel.
Searches for the “Microsoft Account” string in the stdout.
Sends the 0x1B 0x5B 0x42 (Down Arrow) and 0x0A (Enter) escape sequence to the pseudo-terminal, which enables tunnel creation via a GitHub account.
Searches for the “use code” string in the stdout.
Sends the printed code for authentication, prepended with the “hxxps://github[.]com/login/device” => prefix. The attacker authorizes Visual Studio Code with the logged-in GitHub account using the printed code.
Searches for the “What would you like to call this machine?” string in the stdout.
Sends the 0x0A escape sequence to the pseudo-terminal to use the current machine name as the identifier.
Searches for the “https://vscode.dev/tunnel/” string in the stdout.
Sends the printed URL for tunneling to the Slack WebHook.
The following figure illustrates the sequence for creating a tunnel using the VSCode CLI. Red boxes highlight the strings that the installer searches for. Yellow boxes indicate standard input operations sent from the installer using escape sequences. Sky blue boxes represent the values that are necessary to create the tunnel on the attacker’s side. (The “Microsoft Account” string in the second step is not shown in this figure because the second “GitHub Account” was already selected during the process.)
Creating a tunnel using VSCode CLI
Once the process is complete, the attacker can access the targeted host through the tunnel on their remote machine using their GitHub account via a browser or VSCode. The targeted device then begins communicating with Microsoft-owned servers without the user realizing that the communication is from an attacker.
An interesting feature of this variant is that it sends debugging messages and necessary values to a Slack channel via a WebHook. Upon execution, it sends "+++ I am started +++", as well as a heartbeat message "~~~ I am alive ~~~" approximately every second during tunneling authentication.
DWAgent
DWAgent is a remote administration tool that is frequently exploited by threat actors, including ransomware and APT groups, to easily access compromised endpoints with minimal risk of detection. Kimsuky is one of the threat actors that uses this tool in its operations.
We observed that the group delivered DWAgent in at least two ways. The first involved delivering a compressed file containing DWAgent, along with separate commands, to a host infected with httpMalice for installation. The second method involved creating a separate installer.
This installer is very similar to the Reger Dropper. It uses the same RC4 key and has a similar code structure. It includes an archived binary and a legitimate unrar.exe binary, both encrypted with RC4. When executed, the installer decrypts the archived binary and saves it as 1.zip in the C:\ProgramData directory. It also creates an unrar.exe file in the same location using the decrypted unrar.exe binary. The dropper then uses the command C:\programdata\unrar.exe x C:\programdata\1.zip C:\programdata\ to extract the contents of the ZIP file. Finally, it executes the commands necessary to install DWService as a service on the target host:
The compressed file contains a pre-packaged, ready-to-use DWAgent, as well as a predefined config file. The actor deployed the agent with a config.json file linked to their own account to covertly control the device. As a result, the remote session is immediately activated by the above command, granting the attacker control.
The predefined config file is as follows. Note that the servers are legitimate DWAgent relay servers.
For years, Kimsuky has relied heavily on the South Korea-based free domain hosting service 내도메인[.]한국 (pronounced as “naedomain[.]hankook) to mimic legitimate sites with domains like .p-e.kr, .o-r.kr, .n-e.kr, .r-e.kr, and .kro.kr. This service has been utilized to create C2 servers for PebbleDash and AppleSeed clusters, and the background infrastructures have been mostly resolved to the virtual private servers belonging to InterServer. It has also been noted that many other malicious actors have exploited this free domain hosting service, so it alone cannot be considered proof of a connection to Kimsuky.
The actor also occasionally exploits South Korean websites as C2 servers to evade network-IoC-based detection and increase the success rate of attacks. Furthermore, they actively leverage tunneling services such as Cloudflare Quick Tunnels, VSCode Tunneling, and Ngrok to hide their infrastructure. These traits are mostly observed across the PebbleDash cluster.
Victims
We identified multiple infection logs uploaded to the Dropbox storage used for httpMalice’s C2 server. They were analyzed as having been stolen from infected systems across various organizations or individuals in South Korea. Notably, each victim’s folder contained a user.txt file with detailed information such as target details, the presence of something named “http” (possibly a backdoor, such as httpTroy or httpMalice), DWAgent existence, and relationships between infected devices and targets. While we could not verify the exact creation process of these files, they were likely created manually by attackers to manage victims using Korean words.
Below you can see an example of this type of file content. In this context, “장악” means “take over” and “있음” means “exists”.
While both clusters have mainly focused on targeting the private and public sectors in South Korea, the AppleSeed malware cluster shows more interest in government entities. The PebbleDash cluster has also shown particular interest in the defense sector worldwide.
Attribution
Over the past few years, we have observed two clusters using overlapping distribution methods – JSE, EXE, SCR, and PIF droppers. The targets are also increasingly aligning. Furthermore, we noted that several samples from both malware clusters were signed with the same stolen certificate and used identical mutex patterns. These findings suggest that a single actor is likely controlling both clusters and has the capability to modify code as needed. This concept was also described in another research paper at the Virus Bulletin conference.
Since its emergence, AppleSeed has been linked to Kimsuky operations, with each variant showing ties to the group. Since 2021, PebbleDash has been found exclusively in Kimsuky attacks. Based on our analysis of targets, infrastructure, and malware characteristics, we assess with medium-high confidence that attacks associated with these malware families are conducted by Kimsuky-affiliated clusters.
These two clusters share technical links to the threat actor known as Ruby Sleet, one of the names Microsoft uses for Kimsuky activity. In previous reports, Mandiant also referred to these clusters as Cerium, but now they appear to consider them part of the broader APT43 designation – another name for Kimsuky.
Conclusion
Our analysis shows that the actor retains access to the original source code of the malware clusters and the ability to modify it. Over time, malware undergoes updates and modifications, sometimes being repurposed or reused by other actors. Although analyzing malware may seem repetitive and time-consuming, understanding how these tools evolve helps us grasp the threat actor’s changing tactics.
Two clusters have overlapping target sectors that span the defense, military, government, medical, machinery, and energy industries. The AppleSeed cluster is shifting its focus to data exfiltration, and GPKI certificate extraction has become a signature capability. Meanwhile, the PebbleDash cluster demonstrates advanced remote control capabilities and an expanding set of targets.
Although AI may offer full automation for some attacks, many groups stick with the tools and strategies they have used for years. Structuring a fully automated attack is not trivial. Despite ongoing changes, we will continue to track advanced threat actors by comprehensively considering malware, initial vectors, targets, post-exploitation activities, and ultimate goals.
In the third quarter of 2025, we updated the methodology for calculating statistical indicators based on the Kaspersky Security Network. These changes affected all sections of the report except for the statistics on installation packages, which remained unchanged.
To illustrate the differences between the reporting periods, we have also recalculated data for the previous quarters. Consequently, these figures may significantly differ from the previously published ones. However, subsequent reports will employ this new methodology, enabling precise comparisons with the data presented in this post.
The Kaspersky Security Network (KSN) is a global network for analyzing anonymized threat information, voluntarily shared by users of Kaspersky solutions. The statistics in this report are based on KSN data unless explicitly stated otherwise.
The quarter in numbers
According to Kaspersky Security Network, in Q1 2026:
More than 2.67 million attacks utilizing malware, adware, or unwanted mobile software were prevented.
The Trojan-Banker category was the prevalent mobile malware threat with a 52.96% share of total detected applications.
More than 306,000 malicious installation packages were discovered, including:
162,275 packages related to mobile banking Trojans;
439 packages related to mobile ransomware Trojans.
Quarterly highlights
The number of malware, adware, or unwanted software attacks on mobile devices decreased to 2,676,328 in Q1, down from 3,239,244 in the previous quarter.
Attacks on users of Kaspersky mobile solutions, Q3 2024 — Q1 2026 (download)
The overall drop in attack volume stems primarily from a reduction in adware and RiskTool detections. Nonetheless, this trend does not equate to a lower risk for mobile users. As shown later in this report, the number of unique users targeted by these threats remained relatively stable.
In Q1, Synthient researchers identified a link between the notorious Kimwolf botnet and the IPIDEA proxy network. This network was later taken down in cooperation with GTIG.
In early 2026, we discovered several apps on Google Play and the App Store that contained a new version of the SparkCat crypto stealer.
The Trojan code, meticulously concealed, was embedded into the infected Android apps. The obfuscated malicious Rust library was decrypted using a Dalvik-like virtual machine custom-built by the attackers. The iOS version of the malware also underwent several changes; specifically, the attackers began leveraging Apple’s proprietary Vision framework for optical character recognition (OCR).
Mobile threat statistics
The number of Android malware samples saw a slight increase compared to Q4 2025, reaching a total of 306,070.
The detected installation packages were distributed by type as follows:
Detected mobile apps by type, Q4 2025* — Q1 2026 (download)
* Data for the previous quarter may differ slightly from previously published figures due to certain verdicts being retrospectively revised.
Threat actors once again ramped up the production of new banking Trojans; as a result, this category overtook all others in volume, accounting for more than half of all installation packages.
Share* of users attacked by the given type of malicious or potentially unwanted app out of all targeted users of Kaspersky mobile products, Q4 2025 — Q1 2026 (download)
* The total percentage may exceed 100% if the same users encountered multiple attack types.
Following the surge in banking Trojan installation packages, the number of associated attacks also rose, causing Trojan-Banker apps to climb one spot in terms of their share of targeted users. Mamont variants emerged as the most prevalent banking Trojans, accounting for 73.5% of detections, with the rest of the users encountering Faketoken, Rewardsteal, Creduz, and other families.
Yet banking Trojans were still outpaced by adware and RiskTool-type unwanted apps when measured by the total number of affected users. Despite a decrease in their share of installation packages, these two app types retained their positions as the top two threats by attack volume. The most common adware detections involved HiddenAd (44.9%) and MobiDash (38.1%), while most frequently seen RiskTool apps were Revpn (67%) and SpyLoan (20.5%).
TOP 20 most frequently detected types of mobile malware
Note that the malware rankings below exclude riskware or potentially unwanted software, such as RiskTool or adware.
Verdict
%* Q4 2025
%* Q1 2026
Difference in p.p.
Change in ranking
Backdoor.AndroidOS.Triada.ag
2.62
7.09
+4.48
+10
DangerousObject.Multi.Generic.
6.75
5.84
-0.92
-1
DangerousObject.AndroidOS.GenericML.
3.52
5.51
+1.99
+6
Trojan-Banker.AndroidOS.Mamont.jo
0.00
5.28
+5.28
Trojan.AndroidOS.Fakemoney.v
5.40
3.44
-1.96
-1
Trojan-Downloader.AndroidOS.Keenadu.l
0.00
3.35
+3.35
Trojan-Banker.AndroidOS.Mamont.jx
0.00
3.09
+3.09
Backdoor.AndroidOS.Triada.z
4.87
3.08
-1.79
-2
Trojan.AndroidOS.Triada.fe
5.01
2.98
-2.02
-4
Backdoor.AndroidOS.Keenadu.a
2.07
2.73
+0.66
+6
Trojan-Banker.AndroidOS.Mamont.jg
0.34
2.37
+2.03
Trojan.AndroidOS.Triada.hf
2.15
2.23
+0.07
+3
Trojan.AndroidOS.Boogr.gsh
2.35
2.15
-0.20
0
Trojan.AndroidOS.Triada.ii
5.68
2.07
-3.60
-11
Backdoor.AndroidOS.Triada.ae
1.91
1.76
-0.16
+3
Backdoor.AndroidOS.Triada.ab
1.79
1.72
-0.08
+3
Trojan.AndroidOS.Triada.gn
2.38
1.58
-0.80
-5
Trojan-Banker.AndroidOS.Mamont.gg
1.56
1.50
-0.06
+2
Trojan.AndroidOS.Triada.ga
1.48
1.50
+0.01
+4
Backdoor.AndroidOS.Triada.ad
0.53
1.40
+0.87
+44
* Unique users who encountered this malware as a percentage of all attacked users of Kaspersky mobile solutions.
The pre-installed Triada.ag backdoor rose to the top spot; it is similar to the older Triada.z version we documented previously. Because the same variant was pre-installed across a wide range of devices, the total number of affected users is aggregated. Consequently, Triada outpaced even Mamont, as users encountered a variety of Mamont variants, causing the share of that banking Trojan to spread across multiple rows. Other pre-installed Triada variants (Triada.z, Triada.ae, Triada.ab, and Triada.ad) also made the rankings. Furthermore, we observed increasing activity from the Keenadu.a backdoor, while diverse variants of the embedded Triada Trojan remained in the rankings.
Mobile banking Trojans
Q1 2026 saw a characteristic rise in mobile banking Trojan activity, with the number of packages totaling 162,275, a 50% increase compared to the prior quarter.
Number of installation packages for mobile banking Trojans detected by Kaspersky, Q1 2025 — Q1 2026 (download)
We saw a similar growth in the previous quarter, with banking Trojan volumes rising by 50% during that period as well. Various Mamont variants accounted for the absolute majority of packages and represented nearly every entry in the rankings of most frequent banking Trojans by affected user count.
TOP 10 mobile bankers
Verdict
%* Q4 2025
%* Q1 2026
Difference in p.p.
Change in ranking
Trojan-Banker.AndroidOS.Mamont.jo
0.00
15.75
+15.75
Trojan-Banker.AndroidOS.Mamont.jx
0.00
9.22
+9.22
Trojan-Banker.AndroidOS.Mamont.jg
1.47
7.08
+5.61
+24
Trojan-Banker.AndroidOS.Mamont.gg
6.79
4.48
-2.32
-3
Trojan-Banker.AndroidOS.Mamont.ks
0.00
3.98
+3.98
Trojan-Banker.AndroidOS.Agent.ws
6.03
3.78
-2.25
-2
Trojan-Banker.AndroidOS.Mamont.hl
4.30
3.27
-1.03
+1
Trojan-Banker.AndroidOS.Mamont.iv
6.00
3.08
-2.92
-3
Trojan-Banker.AndroidOS.Mamont.jb
3.93
3.07
-0.86
+1
Trojan-Banker.AndroidOS.Mamont.jv
0.00
2.79
+2.79
* Unique users who encountered this malware as a percentage of all users of Kaspersky mobile security solutions who encountered banking threats.
The statistics in this report are based on detection verdicts returned by Kaspersky products unless otherwise stated. The information was provided by Kaspersky users who consented to sharing statistical data.
Quarterly figures
In Q1 2026:
Kaspersky products blocked more than 343 million attacks that originated with various online resources.
Web Anti-Virus responded to 50 million unique links.
File Anti-Virus blocked nearly 15 million malicious and potentially unwanted objects.
2938 new ransomware variants were detected.
More than 77,000 users experienced ransomware attacks.
14% of all ransomware victims whose data was published on threat actors’ data leak sites (DLS) were victims of Clop.
More than 260,000 users were targeted by miners.
Ransomware
Quarterly trends and highlights
Law enforcement success
In January 2026, it was reported that the FBI had seized the domains of the RAMP cybercrime forum, a major platform used extensively by ransomware developers to advertise their RaaS programs and to recruit affiliates. There has been no official statement from the FBI, nor is it clear if RAMP servers were seized. In a post on an external website, a RAMP moderator mentioned law enforcement agencies gaining control over the forum. The takedown disrupted a key element of the RaaS ecosystem, creating ripple effects for ransomware operators, affiliates, and initial access brokers.
A man suspected of links to the Phobos group was apprehended in Poland. He was charged with the creation, acquisition, and distribution of software designed for unlawfully obtaining information, including data that facilitates unauthorized access to information stored within a computer system.
In March, a Phobos ransomware administrator pleaded guilty to the creation and distribution of the Trojan, which had been used in international attacks dating back to at least November 2020.
In March, the U.S. Department of Justice charged a man who had acted as a negotiator for ransomware groups. The company he worked for specializes in cyberincident investigations. The prosecution alleges the suspect colluded with the BlackCat threat actor to share privileged insights into the ongoing progress of negotiations. Additionally, the suspect is alleged to have had a prior direct role in BlackCat attacks, serving as an affiliate for the RaaS operation.
In a separate development this March, a U.S. court sentenced an initial access broker associated with the Yanluowang ransomware group to 81 months of imprisonment. According to the U.S. Department of Justice, the convict facilitated dozens of ransomware attacks across the United States, resulting in over $9 million in actual loss and more than $24 million in intended loss.
Vulnerabilities and attacks
The Interlock group has been heavily exploiting the CVE-2026-20131 zero-day vulnerability in Cisco Secure FMC firewall management software since at least January 26, 2026. The vulnerability enabled arbitrary Java code execution with root privileges on the affected device. This campaign demonstrates the ongoing reliance on zero-day vulnerabilities for initial access, a focus on network appliances as high-value entry points, and the rapid weaponization of new vulnerabilities within the ransomware ecosystem.
The most prolific groups
This section highlights the most prolific ransomware gangs by number of victims added to each group’s DLS. This quarter, the Clop ransomware (14.42%) returned to the top of the rankings, displacing Qilin (12.34%), which had held the leading position in the previous reporting period. Following closely is a new threat actor, The Gentlemen (9.25%). Emerging no later than July 2025, the group had already surpassed the activity levels of mainstays such as Akira (7.25%) and INC Ransom (6.13%).
Number of each group’s victims according to its DLS as a percentage of all groups’ victims published on all the DLSs under review during the reporting period (download)
Number of new variants
In Q1 2026, Kaspersky solutions detected six new ransomware families and 2938 new modifications. Volumes have returned to Q3 2025 levels following a surge in Q4 2025.
Number of new ransomware modifications, Q1 2025 — Q1 2026 (download)
Number of users attacked by ransomware Trojans
Throughout Q1, our solutions protected 77,319 unique users from ransomware. Ransomware activity was highest in March, with 35,056 unique users encountering such attacks during the month.
Number of unique users attacked by ransomware Trojans, Q1 2026 (download)
Attack geography
TOP 10 countries and territories attacked by ransomware Trojans
Country/territory*
%**
1
Pakistan
0.79
2
South Korea
0.64
3
China
0.52
4
Tajikistan
0.40
5
Libya
0.38
6
Turkmenistan
0.36
7
Iraq
0.35
8
Bangladesh
0.33
9
Rwanda
0.30
10
Cameroon
0.28
* Excluded are countries and territories with relatively few (under 50,000) Kaspersky users.
** Unique users whose computers were attacked by ransomware Trojans as a percentage of all unique users of Kaspersky products in the country/territory.
TOP 10 most common families of ransomware Trojans
Name
Verdict
%*
1
(generic verdict)
Trojan-Ransom.Win32.Gen
33.90
2
(generic verdict)
Trojan-Ransom.Win32.Crypren
6.38
3
WannaCry
Trojan-Ransom.Win32.Wanna
5.87
4
(generic verdict)
Trojan-Ransom.Win32.Encoder
4.68
5
(generic verdict)
Trojan-Ransom.Win32.Agent
3.80
6
LockBit
Trojan-Ransom.Win32.Lockbit
2.80
7
(generic verdict)
Trojan-Ransom.Win32.Phny
1.99
8
(generic verdict)
Trojan-Ransom.MSIL.Agent
1.96
9
(generic verdict)
Trojan-Ransom.Python.Agent
1.93
10
(generic verdict)
Trojan-Ransom.Win32.Crypmod
1.89
* Unique Kaspersky users attacked by the specific ransomware Trojan family as a percentage of all unique users attacked by this type of threat.
Miners
Number of new variants
In Q1 2026, Kaspersky solutions detected 3485 new modifications of miners.
Number of new miner modifications, Q1 2026 (download)
Number of users attacked by miners
In Q1, we detected attacks using miner programs on the computers of 260,588 unique Kaspersky users worldwide.
Number of unique users attacked by miners, Q1 2026 (download)
Attack geography
TOP 10 countries and territories attacked by miners
Country/territory*
%**
1
Senegal
3.19
2
Turkmenistan
3.06
3
Mali
2.63
4
Tanzania
1.62
5
Bangladesh
1.06
6
Ethiopia
0.95
7
Panama
0.88
8
Afghanistan
0.79
9
Kazakhstan
0.77
10
Bolivia
0.75
* Excluded are countries and territories with relatively few (under 50,000) Kaspersky users.
** Unique users whose computers were attacked by miners as a percentage of all unique users of Kaspersky products in the country/territory.
Attacks on macOS
In Q1 2026, Google uncovered a new cryptocurrency theft campaign. The scammers directed victims to a fraudulent video call, prompting them to execute malicious scripts under the guise of technical support fixes for connection problems.
In March, researchers with GTIG and iVerify reported the discovery of an in-the-wild exploit chain targeting both iOS and macOS devices. The exploit kit was apparently marketed on the dark web, providing threat actors with a suite of spyware capabilities alongside specialized cryptocurrency exfiltration modules. The exploit was delivered via drive-by downloads when victims visited various compromised websites. Our analysis confirmed that the toolkit included an updated version of a component previously identified in the Operation Triangulation attack chain.
Devices running macOS were similarly impacted by the high-profile supply chain attack targeting the Axios npm package, a widely used HTTP client for JavaScript. The installation of the infected package led to the deployment of a backdoor on macOS devices.
TOP 20 threats to macOS
Unique users* who encountered this malware as a percentage of all attacked users of Kaspersky security solutions for macOS (download)
* Data for the previous quarter may differ slightly from previously published data due to some verdicts being retrospectively revised.
The share of PasivRobber spyware attacks is beginning to decline, giving way to more traditional adware and Monitor-class software capable of tracking user activity. The popular Amos stealer also maintains its presence within the TOP 20.
Geography of threats to macOS
TOP 10 countries and territories by share of attacked users
Country/territory
%* Q4 2025
%* Q1 2026
China
1.28
1.97
France
1.18
1.07
Brazil
1.13
0.98
Mexico
0.72
0.52
Germany
0.71
0.45
The Netherlands
0.62
0.75
Hong Kong
0.49
0.53
India
0.42
0.48
Russian Federation
0.34
0.37
Thailand
0.24
0.27
* Unique users who encountered threats to macOS as a percentage of all unique Kaspersky users in the country/territory.
IoT threat statistics
This section presents statistics on attacks targeting Kaspersky IoT honeypots. The geographic data on attack sources is based on the IP addresses of attacking devices.
In Q1 2026, the share of devices attacking Kaspersky honeypots via the SSH protocol saw a significant increase compared to the previous reporting period.
Distribution of attacked services by number of unique IP addresses of attacking devices (download)
The distribution of attacks between Telnet and SSH maintained the ratio observed in Q4 2025.
Distribution of attackers’ sessions in Kaspersky honeypots (download)
TOP 10 threats delivered to IoT devices
Share of each threat delivered to an infected device as a result of a successful attack, out of the total number of threats delivered (download)
The primary shifts in the IoT threat distribution are linked to the activity of various Mirai botnet variants, although members of this family continue to account for the majority of the list. Furthermore, a new variant, Mirai.kl, surfaced in the rankings. We also observed a significant decline in NyaDrop botnet activity during Q1.
Attacks on IoT honeypots
The United States, the Netherlands, and Germany accounted for the highest proportions of SSH-based attacks during this period.
Country/territory
Q4 2025
Q1 2026
United States
16.10%
23.74%
The Netherlands
15.78%
17.57%
Germany
12.07%
10.34%
Panama
7.72%
6.34%
India
5.32%
6.05%
Romania
4.05%
5.82%
Australia
1.62%
4.61%
Vietnam
4.21%
3.50%
Russian Federation
3.79%
2.35%
Sweden
2.25%
2.09%
China continues to account for the largest proportion of Telnet attacks, though there was a marked increase in activity originating from Pakistan.
Country/territory
Q4 2025
Q1 2026
China
53.64%
39.54%
Pakistan
14.27%
27.31%
Russian Federation
8.20%
8.25%
Indonesia
8.58%
6.71%
India
4.85%
4.66%
Brazil
0.06%
3.30%
Argentina
0.02%
2.51%
Nigeria
1.22%
1.38%
Thailand
0.01%
0.55%
Sweden
0.54%
0.55%
Attacks via web resources
The statistics in this section are based on detection verdicts by Web Anti-Virus, which protects users when suspicious objects are downloaded from malicious or infected web pages. These malicious pages are purposefully created by cybercriminals. Websites that host user-generated content, such as message boards, as well as compromised legitimate sites, can become infected.
TOP 10 countries and territories that served as sources of web-based attacks
The following statistics show the distribution by country/territory of the sources of internet attacks blocked by Kaspersky products on user computers (web pages redirecting to exploits, sites containing exploits and other malicious programs, botnet C&C centers, and so on). One or more web-based attacks could originate from each unique host.
To determine the geographic source of web attacks, we matched the domain name with the real IP address where the domain is hosted, then identified the geographic location of that IP address (GeoIP).
In Q1 2026, Kaspersky solutions blocked 343,823,407 attacks launched from internet resources worldwide. Web Anti-Virus was triggered by 49,983,611 unique URLs.
Web-based attacks by country/territory, Q1 2026 (download)
Countries and territories where users faced the greatest risk of online infection
To assess the risk of malware infection via the internet for users’ computers in different countries and territories, we calculated the share of Kaspersky users in each location on whose computers Web Anti-Virus was triggered during the reporting period. The resulting data provides an indication of the aggressiveness of the environment in which computers operate in different countries and territories.
This ranked list includes only attacks by malicious objects classified as Malware. Our calculations leave out Web Anti-Virus detections of potentially dangerous or unwanted programs, such as RiskTool or adware.
Country/territory*
%**
1
Venezuela
9.33
2
Hungary
8.16
3
Italy
7.58
4
Tajikistan
7.48
5
India
7.21
6
Greece
7.13
7
Portugal
7.10
8
France
7.05
9
Belgium
6.83
10
Slovakia
6.80
11
Vietnam
6.62
12
Bosnia and Herzegovina
6.57
13
Canada
6.56
14
Serbia
6.50
15
Tunisia
6.36
16
Qatar
6.01
17
Spain
5.95
18
Germany
5.95
19
Sri Lanka
5.89
20
Brazil
5.88
* Excluded are countries and territories with relatively few (under 10,000) Kaspersky users.
** Unique users targeted by web-based Malware attacks as a percentage of all unique users of Kaspersky products in the country/territory.
On average during the quarter, 4.73% of users’ computers worldwide were subjected to at least one Malware web attack.
Local threats
Statistics on local infections of user computers are an important indicator. They include objects that penetrated the target computer by infecting files or removable media, or initially made their way onto the computer in non-open form. Examples of the latter are programs in complex installers and encrypted files.
Data in this section is based on analyzing statistics produced by anti-virus scans of files on the hard drive at the moment they were created or accessed, and the results of scanning removable storage media. The statistics are based on detection verdicts from the On-Access Scan (OAS) and On-Demand Scan (ODS) modules of File Anti-Virus and include detections of malicious programs located on user computers or removable media connected to the computers, such as flash drives, camera memory cards, phones, or external hard drives.
In Q1 2026, our File Anti-Virus detected 15,831,319 malicious and potentially unwanted objects.
Countries and territories where users faced the highest risk of local infection
For each country and territory, we calculated the percentage of Kaspersky users whose computers had the File Anti-Virus triggered at least once during the reporting period. This statistic reflects the level of personal computer infection in different countries and territories around the world.
Note that this ranked list includes only attacks by malicious objects classified as Malware. Our calculations leave out File Anti-Virus detections of potentially dangerous or unwanted programs, such as RiskTool or adware.
Country/territory*
%**
1
Turkmenistan
47.96
2
Tajikistan
31.48
3
Cuba
31.03
4
Yemen
29.59
5
Afghanistan
28.47
6
Burundi
26.93
7
Uzbekistan
24.81
8
Syria
23.08
9
Nicaragua
21.97
10
Cameroon
21.60
11
China
21.09
12
Mozambique
21.02
13
Algeria
20.64
14
Democratic Republic of the Congo
20.63
15
Bangladesh
20.44
16
Mali
20.35
17
Republic of the Congo
20.23
18
Madagascar
20.00
19
Belarus
19.78
20
Tanzania
19.52
* Excluded are countries and territories with relatively few (under 10,000) Kaspersky users.
** Unique users on whose computers local Malware threats were blocked, as a percentage of all unique users of Kaspersky products in the country/territory.
On average worldwide, Malware local threats were detected at least once on 11.55% of users’ computers during Q1.
Over the past few months, we have conducted an in-depth analysis of specific activity clusters of Kimsuky (aka APT43, Ruby Sleet, Black Banshee, Sparkling Pisces, Velvet Chollima, and Springtail), a prolific Korean-speaking threat actor. Our research revealed notable tactical shifts throughout multiple phases of the group’s latest campaigns.
Kimsuky has continuously introduced new malware variants based on the PebbleDash platform, a tool historically leveraged by the Lazarus Group but appropriated by Kimsuky since at least 2021. Our monitoring indicates various strategic updates to the group’s arsenal, including the use of VSCode Tunneling, Cloudflare Quick Tunnels, DWAgent, large language models (LLMs), and the Rust programming language. This expanding set of tools underscores the group’s ongoing adaptation and evolution.
Specifically, Kimsuky leveraged legitimate VSCode tunneling mechanisms to establish persistence and distributed the open-source DWAgent remote monitoring and management tool for post-exploitation activities. These activities affected various sectors in South Korea, impacting both public and private entities.
This article covers both previously undocumented attacks and a deeper technical analysis of incidents within this campaign that have been reported before — offering new insight beyond what has already been published.
Executive summary
Kimsuky obtains initial access to target systems by delivering spear-phishing emails containing malicious attachments disguised as documents. They also contact targets via messengers in some cases.
Kimsuky uses a variety of droppers in different formats, such as JSE, PIF, SCR, EXE, etc.
The droppers deliver malware mainly belonging to two big clusters: PebbleDash and AppleSeed. These clusters are considered the most technically advanced in the group’s toolset. The report covers the following PebbleDash malware: HelloDoor, httpMalice, MemLoad, httpTroy. It also covers AppleSeed and HappyDoor from AppleSeed cluster.
For post-exploitation activities Kimsuky uses legitimate tools Visual Studio Code (VSCode) and DWAgent. For VSCode, the attacker uses GitHub authentication method.
For hosting C2 infrastructure the group mainly uses domains registered at a free South Korean hosting provider. It also occasionally relies on hacked South Korean websites and tunneling tools, such as Ngrok or VSCode.
Kimsuky mainly targets South Korean entities. However, PebbleDash attacks were also seen in Brazil and Germany. This malware cluster focuses on defense sector, while AppleSeed most often targets government organizations.
Background
First identified by Kaspersky in 2013, Kimsuky has been active for over 10 years and is considered less technically proficient compared to other Korean-speaking APT groups. The group has targeted a wide range of entities and demonstrated capability in creating tailored spear-phishing emails. The group’s arsenal includes proprietary malware such as PebbleDash, BabyShark, AppleSeed, and RandomQuery, as well as open-source RATs like xRAT, XenoRAT, and TutRAT. This blog post examines the evolving PebbleDash-based malware (referred to as the PebbleDash cluster) and its connections to the AppleSeed-based malware (referred to as the AppleSeed cluster).
The PebbleDash and AppleSeed clusters are considered the most technically advanced in Kimsuky’s toolset. Since at least 2019, these clusters have masqueraded as legitimate documents and application installers, manifesting as JSE droppers or executables with .EXE, .SCR and .PIF extensions. Both are particularly adept at establishing backdoors and stealing information, and ongoing development of their variants has been observed. They even occasionally utilize stolen legitimate certificates from South Korean organizations to avoid detection.
Timeline of the AppleSeed and PebbleDash malware families
AppleSeed and PebbleDash have primarily targeted the public and private sectors in South Korea. The PebbleDash cluster has shown a particular interest in the medical, military and defense industries worldwide. The PebbleDash cluster compromised Brazilian and South Korean defense organizations throughout the past several years, as well as a German defense firm. In 2024, the South Korean government released a security advisory regarding the AppleSeed cluster, detailing how the malware was distributed by replacing a security software installer required to access a construction entity’s website.
Initial access
Kimsuky meticulously crafts and delivers spear-phishing emails to its targets in an attempt to entice them into opening attachments. According to recent research, the group also occasionally approaches targets by contacting them via messengers. In all cases, the initial contact leads to the delivery of a malicious attachment disguised as a document. These attachments often consist of compressed files containing droppers in formats such as .JSE, .EXE, .PIF, or .SCR. The filenames are consistent with the message content and are meant to convince the recipient to open the attachment. The malicious files are often disguised as product quotations, job offers, information guides, surveys, government documents, and personal photos.
Appendix Form No. 8 – Request for Access, Correction, Deletion, and Suspension of Processing of Personal Information (PIPA Enforcement Rules).hwp.jse
August 28, 2025
995a0a49ae4b244928b3f67e2bfd7a6e
HelloDoor
2
2026년 상반기 국내대학원 석사야간과정 위탁교육생 선발관련 서류.hwpx.jse
Documents for the Selection of Commissioned Students for Domestic Graduate School Master’s Evening Programs (H1 2026).hwpx.jse
December 14, 2025
52f1ff082e981cbdfd1f045c6021c63f
httpMalice
3
security_20260126.scr
–
January 26, 2026
65fc9f06de5603e2c1af9b4f288bb22c
Reger Dropper, MemLoad, httpTroy
4
노현정님.pdf.jse
Ms. Noh Hyun-jung.pdf.jse
January 28, 2026
8e15c4d4f71bdd9dbc48cd2cabc87806
AppleSeed chain
5
대국민서비스관리운영체계현장점검증적(초안).pif
On-site Inspection Evidence for the Public Service Management System (Draft).pif
February 5, 2026
8983ffa6da23e0b99ccc58c17b9788c7
Pidoc Dropper, HappyDoor
JSE droppers contain a minimum of two Base64-encoded blobs: one serving as a benign lure file and one or more containing malicious code. Additional blobs may exist within the dropper, but they are unused. The two blobs are decoded using JScript and stored in an arbitrary location on disk, such as C:\ProgramData, with the malicious filenames randomly generated according to the scheme [random]{7}.[random]{4}. The lure file is opened immediately. The malicious payload leverages powershell.exe -windowstyle hidden certutil -decode [src path] [dst path] for the second Base64 decoding before execution. Ultimately, the malicious payload is executed via command-line instructions such as regsvr32.exe /s [file path] or rundll32.exe [file path] [export function].
Reger Dropper (.SCR) and Pidoc Dropper (.PIF) also contain benign lure files and malicious payloads that, in both cases, are encrypted using XOR operations. Specifically, Reger Dropper employs a hard-coded key #RsfsetraW#@EsfesgsgAJOPj4eml;, while Pidoc Dropper utilizes single-byte XOR with 0xFF to decrypt the internal data for execution. Pidoc Dropper is fully obfuscated using dummy data and encrypted strings. Both droppers deploy files in specific directories such as %temp% or C:\ProgramData before executing the malware using regsvr32.exe.
In addition to these droppers, Kimsuky employed a variety of executable droppers, including those crafted in Go or packaged with Inno Setup.
Deployed malware
In this section, we describe several malware families recently dropped by the droppers discussed above.
HelloDoor: first Rust-based PebbleDash variant
Written in Rust, a programming language rarely used by Kimsuky, HelloDoor is a DLL-based backdoor first identified in August 2025. It is deployed via a malicious JSE dropper. Since it has limited capabilities and a simplistic communication mechanism, the backdoor is most probably in the early stages of development. Nevertheless, it is noteworthy that HelloDoor employs a C2 server hosted through TryCloudflare, a temporary tunneling service provided by Cloudflare. This service allows users to expose a local web service to the internet with no setup or account, making the infrastructure behind it difficult to trace.
HelloDoor establishes persistence upon execution by registering itself to the HKCU\Software\Microsoft\Windows\CurrentVersion\Run key with the value name tdll and the command regsvr32.exe /s [current file path].
The implant communicates with the C2 server (hxxp://female-disorder-beta-metropolitan.trycloudflare[.]com/index.php) over the HTTP protocol. Depending on whether the process is executing with an elevated token, it binds to a specific local port: 5555 if the token is elevated, or 5554 if not. Before initiating communication, it generates a unique identifier by collecting device information, such as the MAC address, computer name, and the string “windows”, then computes a hash value from this information.
The malware then constructs a query string in the format aaaaaaaaaa=2&bbbbbbbbbb=[the unique identifier]&cccccccccc=1, which is a traditional format used across the PebbleDash cluster. Subsequent server responses are Base64-decoded and then decrypted using RC4 with the key fwr3errsettwererfs. The decrypted content contains command strings. Possible commands are:
Command
Description
“mcd”
Set the current directory
“msleep”
Sleep for the provided time
“install”
Register the regsvr32.exe /s [the provided file path] command to the HKCU\Software\Microsoft\Windows\CurrentVersion\Run autorun registry using the install value name
[command]
Execute the provided command using chcp 65001 > nul & cmd /U /C [command]
Though interesting, it is no longer surprising that we found comments in the code that appear to have been generated by an LLM service rather than a human developer. This is based on traces that include emojis used for logging debugging messages.
✅ Port is now listening (no accepting)
❌ Port is already in use
🔍 regsvr32.exe detected as parent. Attempting to terminate...
This is a common trait of LLM services that provides users with better visibility. We previously observed similar comments in the PowerShell-based stealer suite used by BlueNoroff. HelloDoor’s simple structure and the fact that no other Rust-based malware from the group has been discovered yet support our claim.
Even though the code is believed to have been developed using an LLM service, we still found some typos and grammatical errors, such as:
result send fail (grammatically incorrect text)
server request fail (grammatically incorrect text)
It is likely that the flawed comments were added manually before or after AI was used.
httpMalice: latest backdoor variant of PebbleDash
The latest PebbleDash-based backdoor, httpMalice, emerged no later than December 2025 and is deployed by the JSE Dropper. Although we found limited direct connections to both the AppleSeed and PebbleDash clusters, the malware is closer to PebbleDash. The following shared characteristics have been identified:
(PebbleDash cluster) Ability to run commands received from the C2 server with the S-1-12-12288 SID, indicating a high integrity level – a feature also observed in PebbleDash and httpTroy.
(PebbleDash cluster) Unique identifier generated by combining the volume serial number of the root directory with the elevation status of the current token, mirroring a technique used since the appearance of NikiDoor.
(PebbleDash cluster) Communication with its C2 server utilizing three HTTP parameters, consistent with other PebbleDash-based families.
(PebbleDash cluster) Core command set more closely aligned with PebbleDash than with AppleSeed-based malware.
(AppleSeed cluster) Use of the m= parameter in C2 communication.
(AppleSeed cluster) Gathering system details using PowerShell and Windows commands similar to those found in AppleSeed and Troll Stealer.
Our analysis revealed two distinct versions of httpMalice based on their C2 communications: version 1.9 communicates over HTTP and version 1.8 uses Dropbox. The latter, the older variant, leverages the Dropbox API by utilizing pre-defined application credentials. Unlike its predecessor, the HTTP variant employs HTTP/HTTPS protocols to interact with its C2 server and maintains persistent access to the victim device through a Windows service named CacheDB. This mirrors tactics observed in similar threats, such as httpSpy.
The more recent variant gathers critical information from the compromised system, such as the current directory path, volume serial numbers, user privileges, username, local IP address, and the name and size of the currently executed httpMalice DLL file. It then combines the root drive’s volume serial number with the user’s access token privilege level to create a unique identifier for each infected system, formatted as [volume serial]{8}_[elevation status].
Value of elevation status
Description
0
Running under the SYSTEM account with an elevated token
1
Running under an elevated administrator account
2
Running without elevation
Depending on the token privilege, the backdoor then establishes persistence by either creating a service or registering itself to autostart at user logon. If the token is elevated, a service named CacheDB is created that executes the command cmd.exe /c “rundll32.exe [current DLL path], load”. The service’s display name is set to Administrator, and its description is defined as CacheDB Service. If the token is not elevated, the backdoor registers the same command under the registry key HKCU\Software\Microsoft\Windows\CurrentVersion\Run with the value name Everything 1.9a-[filesize]. The older version used Everything 1.8a-[filesize] as a value name.
The latest version can execute a combination of Windows commands by default to perform host profiling, while the older version fetches the command set from Dropbox. In httpMalice, commands are mostly executed using the format cmd.exe /c chcp 949 [command] > [temporary filename], which redirects the output to separate files, with the consistent prefix 2Ato6478s added to their names. The chcp 949 command changes the code page to 949, indicating that the malware targets users of the Korean language (EUC-KR charset).
Windows commands used to gather system details
httpMalice transmits the result of host profiling to its C2 server as a URL parameter, using the POST method over the HTTP/HTTPS protocol, with the header x-www-form-urlencoded. The URL includes two or three parameters: operation mode, unique identifier (referred to as UID), and data. The operation mode, or parameter m, supports the following values:
Value
Description
1
Send the session identifier (parameter s) along with the current state (parameter a)
2
Request command
3
Send result after executing the command (parameter d)
8
Request directory to be archived and sent
9
Send the archived directory
10
Send a message like “.cmd” or “.tmp” (parameter d)
11
Send ping
12
Send the captured screenshot (parameter d)
13
Send the infected device information (parameter d)
As shown in the table above, the mode is set to 13 at the host profiling stage. The UID is formatted as [volume serial]{8}_[elevation status], and the data contains the ChaCha20-encrypted and Base64-encoded output of the command set stored in the temporary file. The resulting URL format is: m=13&u=[volume serial]{8}_[elevation status]&d=[Chacha20 encrypted + Base64-encoded data to be sent].
The key and nonce used for ChaCha20 encryption are derived from the pointer address of the buffer, resulting in nearly randomized keys. To ensure proper decryption on the attacker side, the nonce and key values are appended after the encrypted data, and the combined blob is then Base64-encoded. The counter is initialized to 0. The following figure illustrates how the encrypted data is structured after performing Base64 decoding.
Structure of the ChaCha20-encrypted data blob
After sending the host profiling data, the backdoor continuously transmits a screen capture with mode 12 and a ping message with mode 11. Finally, it sends a session identifier, which is a combination of the current username and local IP address separated by an ‘@’ symbol. In this case, the mode is set to 1 and the a parameter (current state) is set to 0, indicating that the C2 operation has been activated. The following table provides other possible values of the a parameter:
Value
Description
0
httpMalice has been activated
1
httpMalice has been inactivated (upon command 9)
2
httpMalice has been removed (upon command 8)
The whole process from sending the host profile to the backdoor activation repeats every two minutes until the C2 server returns a “success!” message.
C2 communication sequence of httpMalice
When the backdoor receives the message from the C2 server, it creates two threads dedicated to processing commands and sending the current state, including the session identifier. The first thread receives a command from the C2 server. It requests a command by sending mode 2 and, if successful, immediately sends mode 10 along with the string “.cmd” in the d parameter.
The commands supported by httpMalice are as follows:
Command
Description
0
Do nothing
1
Execute the command with EUC-KR encoding
2
Download and extract the file to the infected device
3
Upload a directory to the C2 server after it has been archived
5
Get the current directory
6
Set the current directory
7
Execute the command without setting a EUC-KR character set
8
Remove its persistence traces and exit the process
9
Hibernate
10
Execute the command using the provided session ID
12
Capture the screen
13
Load the downloaded payload into memory
MemLoad downloads httpTroy
Since early 2025, we have observed several versions of MemLoad; specifically, MemLoad V2 emerged in March, and V3 appeared by September. The payload that began being deployed through the Reger Dropper this year has been identified as an updated variant of MemLoad, slightly modified from the V3 version (referred to internally as MemLoader.dll).
Kimsuky leverages MemLoad to evade detection of its final backdoor and to carefully assess the value of targeted systems through anti-VM checks and reconnaissance. Upon installation, it requests an additional payload from the C2 server, executing it reflectively in memory if deemed suitable. Notably, all versions of MemLoad V2 and later use the same RC4 key.
Below are the key operations of MemLoad:
Creates a flag file. Creates a file containing a random eight-character string from the set 0123456789abcdefABCDEF with another random eight-character string as the name and “.dat.cfg” extension at the current file path.
Generates an ID. Generates an ID value by adding either ‘A-‘ or ‘U-‘ to the beginning of the random bytes. The choice of symbol is determined by attempting to create a random file in the C:\Windows\system32 directory. If successful, the ID starts with ‘A-‘ (indicating administrative privileges); otherwise, it starts with ‘U-‘.
Persistence via a scheduled task. Checks for the existence of the .dat.cfg file, and if confirmed, a scheduled task is set up for persistence. The task name is determined by whether the process is running with elevated privileges. If elevated, the task is named ChromeCheck, and the command
schtasks/create/tn<task name>/tr"regsvr32 /s <current file path>"/sc minute/mo1/rl highest/f is executed. Otherwise, the task is named EdgeCheck, and the command
schtasks/create/tn<task name>/tr"regsvr32 /s <current file path>"/sc minute/mo1/f is executed.
C2 communication and payload download. Requests an additional payload from its C2 server, with the header Authorization: Bearer {ID} or X-Browser-Validation: {ID} for authentication. The ID is set to the previously generated ID value.
Payload decryption and execution. Once the download is successful, the payload is decrypted using the RC4 algorithm with the key #RsfsetraW#@EsfesgsgAJOPj4eml;. The decrypted payload is then reflectively loaded into memory, and its hello export function is invoked.
The payload downloaded and executed by MemLoad is identified as the httpTroy backdoor. This backdoor serves as the primary role for long-term access and data exfiltration. Similar to MemLoad, it employs stealth techniques by creating a flag file and writing eight random bytes to it. However, in this case the file is created at [current file path]:HUI in the ADS (Alternative Data Stream) area. The backdoor then checks its privileges to determine if it is elevated and assigns an ID value in the format A-[random-8-chars] or U-[random-8-chars].
Since Gen Digital covers httpTroy’s features and functionality in detail elsewhere, we will not provide a thorough explanation here to avoid redundancy. Instead, we will simply note that it communicates with the C2 server at hxxps://file.bigcloud.n-e[.]kr/index.php.
AppleSeed
AppleSeed first appeared in 2019 and reached version 3.0. However, we now only see version 2.1. It originally consisted of two components: a dropper and the main AppleSeed. Since 2022, the updated AppleSeed chain has involved two droppers, an additional component referred to as the installer, and the main payload. It is mostly delivered through JSE Dropper.
Updated AppleSeed infection chain
There are two versions of the main AppleSeed: Dropper and Spy. The Dropper variant is responsible for downloading additional malware and executing commands received from its C2 server, while the Spy version gathers sensitive information such as documents, screenshots, keystrokes, and lists of USB drives. A notable change in version 2.1 is the inclusion, since 2022, of collecting the C:\GPKI directory – functionality that is also implemented in Troll Stealer. This directory contains a digital certificate used by the South Korean government to securely authenticate public officials and government systems.
HappyDoor
HappyDoor, an AppleSeed-based backdoor malware disclosed by AhnLab in 2024, is less visible than AppleSeed. HappyDoor shares several features with AppleSeed, including the same string obfuscation algorithm, the data types it collects, and the use of RSA encryption. Given these similarities, we assess with medium confidence that HappyDoor is an advanced variant evolved from AppleSeed.
Post-exploitation
We observed interesting post-exploitation activities involving VSCode and DWAgent. All of the observed VSCode droppers used the same lure files as the PebbleDash malware cluster. While we are unsure of the exact reason for this strategy, we suspect that the actor prepared both PebbleDash and VSCode droppers in anticipation of the PebbleDash infection chain being detected by security products because of its backdoor capabilities. In contrast, the use of VSCode is designed to have fewer detection points.
VSCode (launched by the JSE dropper)
Since last year, Kimsuky has been leveraging the legitimate Visual Studio Code Remote Tunneling feature to establish covert remote access to the victim’s device, bypassing detection designed for traditional malware-based C2 channels (first described by Darktrace researchers). In these attacks, instead of dropping malware, the JSE dropper downloads a legitimate Visual Studio Code (VSCode) CLI onto the infected device. The script establishes persistence by creating a tunnel via the application, with the tunnel name “bizeugene”, using the command below.
The Remote Tunneling feature in VSCode supports establishing a tunnel using either a Microsoft or GitHub account. When the code tunnel command is executed, the CLI initiates an authentication flow and returns a login URL along with a device code. The user must then navigate to the URL, enter the device code, and authenticate with their account. Once authentication is successful, the tunnel is created and the CLI outputs a URL for tunneling that enables browser-based access to the remote host.
The GitHub authentication method is selected in this instance because GitHub is configured as the default provider in non-interactive execution contexts. By using echo |, the script injects a \r\n (Carriage Return and Line Feed) into the standard input stream, effectively confirming the default prompt selection without manual interaction. As a result, the CLI automatically initiates the GitHub authentication flow. Next, all CLI output that includes a login URL and a device code is saved to out.txt.
Out.txt content
The JScript code in the JSE dropper monitors the out.txt file for a URL that begins with hxxps://vscode[.]dev/tunnel. This URL contains the full address of the established tunnel. Once detected, the file content containing the URL and the device code is sent to a compromised legitimate South Korean website (hxxps://www.yespp.co[.]kr/common/include/code/out[.]php) using the HTTP POST method. The request contains the file contents in the application/x-www-form-urlencoded header data formatted as out=URLencoded{result of the command}&token=URLencoded{"bizeugene"}. After authentication is complete, the attacker can access the compromised host externally through a web browser by authenticating with their own GitHub account.
VSCode (launched by VSCode installer)
While searching our telemetry for artifacts related to a different infection, we identified a new VSCode tunnel installer written in Go. A previous version of this installer was implemented using JScript and was limited to secure channels because of its reliance on a specific tunnel name. The new variant, named vscode_payload by the developer based on the embedded Go path, is fully operational and supports every tunnel on each targeted device. It includes features that are nearly identical to those of the previous version, such as downloading, unarchiving, and executing the VSCode CLI.
After the VSCode CLI file has been successfully downloaded, it is unzipped into the C:\Users\Public directory, and the extracted code.exe is executed with the tunnel command.
This is how the installer works:
Executes code.exe tunnel.
Searches for the “Microsoft Account” string in the stdout.
Sends the 0x1B 0x5B 0x42 (Down Arrow) and 0x0A (Enter) escape sequence to the pseudo-terminal, which enables tunnel creation via a GitHub account.
Searches for the “use code” string in the stdout.
Sends the printed code for authentication, prepended with the “hxxps://github[.]com/login/device” => prefix. The attacker authorizes Visual Studio Code with the logged-in GitHub account using the printed code.
Searches for the “What would you like to call this machine?” string in the stdout.
Sends the 0x0A escape sequence to the pseudo-terminal to use the current machine name as the identifier.
Searches for the “https://vscode.dev/tunnel/” string in the stdout.
Sends the printed URL for tunneling to the Slack WebHook.
The following figure illustrates the sequence for creating a tunnel using the VSCode CLI. Red boxes highlight the strings that the installer searches for. Yellow boxes indicate standard input operations sent from the installer using escape sequences. Sky blue boxes represent the values that are necessary to create the tunnel on the attacker’s side. (The “Microsoft Account” string in the second step is not shown in this figure because the second “GitHub Account” was already selected during the process.)
Creating a tunnel using VSCode CLI
Once the process is complete, the attacker can access the targeted host through the tunnel on their remote machine using their GitHub account via a browser or VSCode. The targeted device then begins communicating with Microsoft-owned servers without the user realizing that the communication is from an attacker.
An interesting feature of this variant is that it sends debugging messages and necessary values to a Slack channel via a WebHook. Upon execution, it sends "+++ I am started +++", as well as a heartbeat message "~~~ I am alive ~~~" approximately every second during tunneling authentication.
DWAgent
DWAgent is a remote administration tool that is frequently exploited by threat actors, including ransomware and APT groups, to easily access compromised endpoints with minimal risk of detection. Kimsuky is one of the threat actors that uses this tool in its operations.
We observed that the group delivered DWAgent in at least two ways. The first involved delivering a compressed file containing DWAgent, along with separate commands, to a host infected with httpMalice for installation. The second method involved creating a separate installer.
This installer is very similar to the Reger Dropper. It uses the same RC4 key and has a similar code structure. It includes an archived binary and a legitimate unrar.exe binary, both encrypted with RC4. When executed, the installer decrypts the archived binary and saves it as 1.zip in the C:\ProgramData directory. It also creates an unrar.exe file in the same location using the decrypted unrar.exe binary. The dropper then uses the command C:\programdata\unrar.exe x C:\programdata\1.zip C:\programdata\ to extract the contents of the ZIP file. Finally, it executes the commands necessary to install DWService as a service on the target host:
The compressed file contains a pre-packaged, ready-to-use DWAgent, as well as a predefined config file. The actor deployed the agent with a config.json file linked to their own account to covertly control the device. As a result, the remote session is immediately activated by the above command, granting the attacker control.
The predefined config file is as follows. Note that the servers are legitimate DWAgent relay servers.
For years, Kimsuky has relied heavily on the South Korea-based free domain hosting service 내도메인[.]한국 (pronounced as “naedomain[.]hankook) to mimic legitimate sites with domains like .p-e.kr, .o-r.kr, .n-e.kr, .r-e.kr, and .kro.kr. This service has been utilized to create C2 servers for PebbleDash and AppleSeed clusters, and the background infrastructures have been mostly resolved to the virtual private servers belonging to InterServer. It has also been noted that many other malicious actors have exploited this free domain hosting service, so it alone cannot be considered proof of a connection to Kimsuky.
The actor also occasionally exploits South Korean websites as C2 servers to evade network-IoC-based detection and increase the success rate of attacks. Furthermore, they actively leverage tunneling services such as Cloudflare Quick Tunnels, VSCode Tunneling, and Ngrok to hide their infrastructure. These traits are mostly observed across the PebbleDash cluster.
Victims
We identified multiple infection logs uploaded to the Dropbox storage used for httpMalice’s C2 server. They were analyzed as having been stolen from infected systems across various organizations or individuals in South Korea. Notably, each victim’s folder contained a user.txt file with detailed information such as target details, the presence of something named “http” (possibly a backdoor, such as httpTroy or httpMalice), DWAgent existence, and relationships between infected devices and targets. While we could not verify the exact creation process of these files, they were likely created manually by attackers to manage victims using Korean words.
Below you can see an example of this type of file content. In this context, “장악” means “take over” and “있음” means “exists”.
While both clusters have mainly focused on targeting the private and public sectors in South Korea, the AppleSeed malware cluster shows more interest in government entities. The PebbleDash cluster has also shown particular interest in the defense sector worldwide.
Attribution
Over the past few years, we have observed two clusters using overlapping distribution methods – JSE, EXE, SCR, and PIF droppers. The targets are also increasingly aligning. Furthermore, we noted that several samples from both malware clusters were signed with the same stolen certificate and used identical mutex patterns. These findings suggest that a single actor is likely controlling both clusters and has the capability to modify code as needed. This concept was also described in another research paper at the Virus Bulletin conference.
Since its emergence, AppleSeed has been linked to Kimsuky operations, with each variant showing ties to the group. Since 2021, PebbleDash has been found exclusively in Kimsuky attacks. Based on our analysis of targets, infrastructure, and malware characteristics, we assess with medium-high confidence that attacks associated with these malware families are conducted by Kimsuky-affiliated clusters.
These two clusters share technical links to the threat actor known as Ruby Sleet, one of the names Microsoft uses for Kimsuky activity. In previous reports, Mandiant also referred to these clusters as Cerium, but now they appear to consider them part of the broader APT43 designation – another name for Kimsuky.
Conclusion
Our analysis shows that the actor retains access to the original source code of the malware clusters and the ability to modify it. Over time, malware undergoes updates and modifications, sometimes being repurposed or reused by other actors. Although analyzing malware may seem repetitive and time-consuming, understanding how these tools evolve helps us grasp the threat actor’s changing tactics.
Two clusters have overlapping target sectors that span the defense, military, government, medical, machinery, and energy industries. The AppleSeed cluster is shifting its focus to data exfiltration, and GPKI certificate extraction has become a signature capability. Meanwhile, the PebbleDash cluster demonstrates advanced remote control capabilities and an expanding set of targets.
Although AI may offer full automation for some attacks, many groups stick with the tools and strategies they have used for years. Structuring a fully automated attack is not trivial. Despite ongoing changes, we will continue to track advanced threat actors by comprehensively considering malware, initial vectors, targets, post-exploitation activities, and ultimate goals.
During Q1 2026, the exploit kits leveraged by threat actors to target user systems expanded once again, incorporating new exploits for the Microsoft Office platform, as well as Windows and Linux operating systems.
In this report, we dive into the statistics on published vulnerabilities and exploits, as well as the known vulnerabilities leveraged by popular C2 frameworks throughout Q1 2026.
Statistics on registered vulnerabilities
This section provides statistical data on registered vulnerabilities. The data is sourced from cve.org.
We examine the number of registered CVEs for each month starting from January 2022. The total volume of vulnerabilities continues rising and, according to current reports, the use of AI agents for discovering security issues is expected to further reinforce this upward trend.
Total published vulnerabilities per month from 2022 through 2026 (download)
Next, we analyze the number of new critical vulnerabilities (CVSS > 8.9) over the same period.
Total critical vulnerabilities published per month from 2022 through 2026 (download)
The graph indicates that while the volume of critical vulnerabilities slightly decreased compared to previous years, an upward trend remained clearly visible. At present, we attribute this to the fact that the end of last year was marked by the disclosure of several severe vulnerabilities in web frameworks. The current growth is driven by high-profile issues like React2Shell, the release of exploit frameworks for mobile platforms, and the uncovering of secondary vulnerabilities during the remediation of previously discovered ones. We will be able to test this hypothesis in the next quarter; if correct, the second quarter will show a significant decline, similar to the pattern observed in the previous year.
Exploitation statistics
This section presents statistics on vulnerability exploitation for Q1 2026. The data draws on open sources and our telemetry.
Windows and Linux vulnerability exploitation
In Q1 2026, threat actor toolsets were updated with exploits for new, recently registered vulnerabilities. However, we first examine the list of veteran vulnerabilities that consistently account for the largest share of detections:
CVE-2018-0802: a remote code execution (RCE) vulnerability in the Equation Editor component
CVE-2017-11882: another RCE vulnerability also affecting Equation Editor
CVE-2017-0199: a vulnerability in Microsoft Office and WordPad that allows an attacker to gain control over the system
CVE-2023-38831: a vulnerability resulting from the improper handling of objects contained within an archive
CVE-2025-6218: a vulnerability allowing the specification of relative paths to extract files into arbitrary directories, potentially leading to malicious command execution
CVE-2025-8088: a directory traversal bypass vulnerability during file extraction utilizing NTFS Streams
Among the newcomers, we have observed exploits targeting the Microsoft Office platform and Windows OS components. Notably, these new vulnerabilities exploit logic flaws arising from the interaction between multiple systems, making them technically difficult to isolate within a specific file or library. A list of these vulnerabilities is provided below:
CVE-2026-21509 and CVE-2026-21514: security feature bypass vulnerabilities: despite Protected View being enabled, a specially crafted file can still execute malicious code without the user’s knowledge. Malicious commands are executed on the victim’s system with the privileges of the user who opened the file.
CVE-2026-21513: a vulnerability in the Internet Explorer MSHTML engine, which is used to open websites and render HTML markup. The vulnerability involves bypassing rules that restrict the execution of files from untrusted network sources. Interestingly, the data provider for this vulnerability was an LNK file.
These three vulnerabilities were utilized together in a single chain during attacks on Windows-based user systems. While this combination is noteworthy, we believe the widespread use of the entire chain as a unified exploit will likely decline due to its instability. We anticipate that these vulnerabilities will eventually be applied individually as initial entry vectors in phishing campaigns.
Below is the trend of exploit detections on user Windows systems starting from Q1 2025.
Dynamics of the number of Windows users encountering exploits, Q1 2025 – Q1 2026. The number of users who encountered exploits in Q1 2025 is taken as 100% (download)
The vulnerabilities listed here can be leveraged to gain initial access to a vulnerable system and for privilege escalation. This underscores the critical importance of timely software updates.
On Linux devices, exploits for the following vulnerabilities were detected most frequently:
CVE-2022-0847: a vulnerability known as Dirty Pipe, which enables privilege escalation and the hijacking of running applications
CVE-2019-13272: a vulnerability caused by improper handling of privilege inheritance, which can be exploited to achieve privilege escalation
CVE-2021-22555: a heap out-of-bounds write vulnerability in the Netfilter kernel subsystem
CVE-2023-32233: a vulnerability in the Netfilter subsystem that allows for Use-After-Free conditions and privilege escalation through the improper processing of network requests
Dynamics of the number of Linux users encountering exploits, Q1 2025 – Q1 2026. The number of users who encountered exploits in Q1 2025 is taken as 100% (download)
In the first quarter of 2026, we observed a decrease in the number of detected exploits; however, the detection rates are on the rise relative to the same period last year. For the Linux operating system, the installation of security patches remains critical.
Most common published exploits
The distribution of published exploits by software type in Q1 2026 features an updated set of categories; once again, we see exploits targeting operating systems and Microsoft Office suites.
Distribution of published exploits by platform, Q1 2026 (download)
Vulnerability exploitation in APT attacks
We analyzed which vulnerabilities were utilized in APT attacks during Q1 2026. The ranking provided below includes data based on our telemetry, research, and open sources.
TOP 10 vulnerabilities exploited in APT attacks, Q1 2026 (download)
In Q1 2026, threat actors continued to utilize high-profile vulnerabilities registered in the previous year for APT attacks. The hypothesis we previously proposed has been confirmed: security flaws affecting web applications remain heavily exploited in real-world attacks. However, we are also observing a partial refresh of attacker toolsets. Specifically, during the first quarter of the year, APT campaigns leveraged recently discovered vulnerabilities in Microsoft Office products, edge networking device software, and remote access management systems. Although the most recent vulnerabilities are being exploited most heavily, their general characteristics continue to reinforce established trends regarding the categories of vulnerable software. Consequently, we strongly recommend applying the security patches provided by vendors.
C2 frameworks
In this section, we examine the most popular C2 frameworks used by threat actors and analyze the vulnerabilities targeted by the exploits that interacted with C2 agents in APT attacks.
The chart below shows the frequency of known C2 framework usage in attacks against users during Q1 2026, according to open sources.
TOP 10 C2 frameworks used by APTs to compromise user systems, Q1 2026 (download)
Metasploit has returned to the top of the list of the most common C2 frameworks, displacing Sliver, which now shares the second position with Havoc. These are followed by Covenant and Mythic, the latter of which previously saw greater popularity. After studying open sources and analyzing samples of malicious C2 agents that contained exploits, we determined that the following vulnerabilities were utilized in APT attacks involving the C2 frameworks mentioned above:
CVE-2023-46604: an insecure deserialization vulnerability allowing for arbitrary code execution within the server process context if the Apache ActiveMQ service is running
CVE-2024-12356 and CVE-2026-1731: command injection vulnerabilities in BeyondTrust software that allow an attacker to send malicious commands even without system authentication
CVE-2023-36884: a vulnerability in the Windows Search component that enables command execution on the system, bypassing security mechanisms built into Microsoft Office applications
CVE-2025-53770: an insecure deserialization vulnerability in Microsoft SharePoint that allows for unauthenticated command execution on the server
CVE-2025-8088 and CVE-2025-6218: similar directory traversal vulnerabilities that allow files to be extracted from an archive to a predefined path, potentially without the archiving utility displaying any alerts to the user
The nature of the described vulnerabilities indicates that they were exploited to gain initial access to the system. Notably, the majority of these security issues are targeted to bypass authentication mechanisms. This is likely due to the fact that C2 agents are being detected effectively, prompting threat actors to reduce the probability of discovery by utilizing bypass exploits.
Notable vulnerabilities
This section highlights the most significant vulnerabilities published in Q1 2026 that have publicly available descriptions.
At the core of this vulnerability is a Type Confusion flaw. By attempting to access a resource within the Desktop Window Manager subsystem, an attacker can achieve privilege escalation. A necessary condition for exploiting this issue is existing authorization on the system.
It is worth noting that the DWM subsystem has been under close scrutiny by threat actors for quite some time. Historically, the primary attack vector involves interacting with the NtDComposition* function set.
RegPwn (CVE-2026-21533): a system settings access control vulnerability
CVE-2026-21533 is essentially a logic vulnerability that enables privilege escalation. It stems from the improper handling of privileges within Remote Desktop Services (RDS) components. By modifying service parameters in the registry and replacing the configuration with a custom key, an attacker can elevate privileges to the SYSTEM level. This vulnerability is likely to remain a fixture in threat actor toolsets as a method for establishing persistence and gaining high-level privileges.
CVE-2026-21514: a Microsoft Office vulnerability
This vulnerability was discovered in the wild during attacks on user systems. Notably, an LNK file is used to initiate the exploitation process. CVE-2026-21514 is also a logic issue that allows for bypassing OLE technology restrictions on malicious code execution and the transmission of NetNTLM authentication requests when processing untrusted input.
Clawdbot (CVE-2026-25253): an OpenClaw vulnerability
This vulnerability in the AI agent leaks credentials (authentication tokens) when queried via the WebSocket protocol. It can lead to the compromise of the infrastructure where the agent is installed: researchers have confirmed the ability to access local system data and execute commands with elevated privileges. The danger of CVE-2026-25253 is further compounded by the fact that its exploitation has generated numerous attack scenarios, including the use of prompt injections and ClickFix techniques to install stealers on vulnerable systems.
CVE-2026-34070: LangChain framework vulnerability
LangChain is an open-source framework designed for building applications powered by large language models (LLMs). A directory traversal vulnerability allowed attackers to access arbitrary files within the infrastructure where the framework was deployed. The core of CVE-2026-34070 lies in the fact that certain functions within langchain_core/prompts/loading.py handled configuration files insecurely. This could potentially lead to the processing of files containing malicious data, which could be leveraged to execute commands and expose critical system information or other sensitive files.
CVE-2026-22812: an OpenCode vulnerability
CVE-2026-22812 is another vulnerability identified in AI-assisted coding software. By default, the OpenCode agent provided local access for launching authorized applications via an HTTP server that did not require authentication. Consequently, attackers could execute malicious commands on a vulnerable device with the privileges of the current user.
Conclusion and advice
We observe that the registration of vulnerabilities is steadily gaining momentum in Q1 2026, a trend driven by the widespread development of AI tools designed to identify security flaws across various software types. This trajectory is likely to result not only in a higher volume of registered vulnerabilities but also in an increase in exploit-driven attacks, further reinforcing the critical necessity of timely security patch deployment. Additionally, organizations must prioritize vulnerability management and implement effective defensive technologies to mitigate the risks associated with potential exploitation.
To ensure the rapid detection of threats involving exploit utilization and to prevent their escalation, it is essential to deploy a reliable security solution. Key features of such a tool include continuous infrastructure monitoring, proactive protection, and vulnerability prioritization based on real-world relevance. These mechanisms are integrated into Kaspersky Next, which also provides endpoint security and protection against cyberattacks of any complexity.
During Q1 2026, the exploit kits leveraged by threat actors to target user systems expanded once again, incorporating new exploits for the Microsoft Office platform, as well as Windows and Linux operating systems.
In this report, we dive into the statistics on published vulnerabilities and exploits, as well as the known vulnerabilities leveraged by popular C2 frameworks throughout Q1 2026.
Statistics on registered vulnerabilities
This section provides statistical data on registered vulnerabilities. The data is sourced from cve.org.
We examine the number of registered CVEs for each month starting from January 2022. The total volume of vulnerabilities continues rising and, according to current reports, the use of AI agents for discovering security issues is expected to further reinforce this upward trend.
Total published vulnerabilities per month from 2022 through 2026 (download)
Next, we analyze the number of new critical vulnerabilities (CVSS > 8.9) over the same period.
Total critical vulnerabilities published per month from 2022 through 2026 (download)
The graph indicates that while the volume of critical vulnerabilities slightly decreased compared to previous years, an upward trend remained clearly visible. At present, we attribute this to the fact that the end of last year was marked by the disclosure of several severe vulnerabilities in web frameworks. The current growth is driven by high-profile issues like React2Shell, the release of exploit frameworks for mobile platforms, and the uncovering of secondary vulnerabilities during the remediation of previously discovered ones. We will be able to test this hypothesis in the next quarter; if correct, the second quarter will show a significant decline, similar to the pattern observed in the previous year.
Exploitation statistics
This section presents statistics on vulnerability exploitation for Q1 2026. The data draws on open sources and our telemetry.
Windows and Linux vulnerability exploitation
In Q1 2026, threat actor toolsets were updated with exploits for new, recently registered vulnerabilities. However, we first examine the list of veteran vulnerabilities that consistently account for the largest share of detections:
CVE-2018-0802: a remote code execution (RCE) vulnerability in the Equation Editor component
CVE-2017-11882: another RCE vulnerability also affecting Equation Editor
CVE-2017-0199: a vulnerability in Microsoft Office and WordPad that allows an attacker to gain control over the system
CVE-2023-38831: a vulnerability resulting from the improper handling of objects contained within an archive
CVE-2025-6218: a vulnerability allowing the specification of relative paths to extract files into arbitrary directories, potentially leading to malicious command execution
CVE-2025-8088: a directory traversal bypass vulnerability during file extraction utilizing NTFS Streams
Among the newcomers, we have observed exploits targeting the Microsoft Office platform and Windows OS components. Notably, these new vulnerabilities exploit logic flaws arising from the interaction between multiple systems, making them technically difficult to isolate within a specific file or library. A list of these vulnerabilities is provided below:
CVE-2026-21509 and CVE-2026-21514: security feature bypass vulnerabilities: despite Protected View being enabled, a specially crafted file can still execute malicious code without the user’s knowledge. Malicious commands are executed on the victim’s system with the privileges of the user who opened the file.
CVE-2026-21513: a vulnerability in the Internet Explorer MSHTML engine, which is used to open websites and render HTML markup. The vulnerability involves bypassing rules that restrict the execution of files from untrusted network sources. Interestingly, the data provider for this vulnerability was an LNK file.
These three vulnerabilities were utilized together in a single chain during attacks on Windows-based user systems. While this combination is noteworthy, we believe the widespread use of the entire chain as a unified exploit will likely decline due to its instability. We anticipate that these vulnerabilities will eventually be applied individually as initial entry vectors in phishing campaigns.
Below is the trend of exploit detections on user Windows systems starting from Q1 2025.
Dynamics of the number of Windows users encountering exploits, Q1 2025 – Q1 2026. The number of users who encountered exploits in Q1 2025 is taken as 100% (download)
The vulnerabilities listed here can be leveraged to gain initial access to a vulnerable system and for privilege escalation. This underscores the critical importance of timely software updates.
On Linux devices, exploits for the following vulnerabilities were detected most frequently:
CVE-2022-0847: a vulnerability known as Dirty Pipe, which enables privilege escalation and the hijacking of running applications
CVE-2019-13272: a vulnerability caused by improper handling of privilege inheritance, which can be exploited to achieve privilege escalation
CVE-2021-22555: a heap out-of-bounds write vulnerability in the Netfilter kernel subsystem
CVE-2023-32233: a vulnerability in the Netfilter subsystem that allows for Use-After-Free conditions and privilege escalation through the improper processing of network requests
Dynamics of the number of Linux users encountering exploits, Q1 2025 – Q1 2026. The number of users who encountered exploits in Q1 2025 is taken as 100% (download)
In the first quarter of 2026, we observed a decrease in the number of detected exploits; however, the detection rates are on the rise relative to the same period last year. For the Linux operating system, the installation of security patches remains critical.
Most common published exploits
The distribution of published exploits by software type in Q1 2026 features an updated set of categories; once again, we see exploits targeting operating systems and Microsoft Office suites.
Distribution of published exploits by platform, Q1 2026 (download)
Vulnerability exploitation in APT attacks
We analyzed which vulnerabilities were utilized in APT attacks during Q1 2026. The ranking provided below includes data based on our telemetry, research, and open sources.
TOP 10 vulnerabilities exploited in APT attacks, Q1 2026 (download)
In Q1 2026, threat actors continued to utilize high-profile vulnerabilities registered in the previous year for APT attacks. The hypothesis we previously proposed has been confirmed: security flaws affecting web applications remain heavily exploited in real-world attacks. However, we are also observing a partial refresh of attacker toolsets. Specifically, during the first quarter of the year, APT campaigns leveraged recently discovered vulnerabilities in Microsoft Office products, edge networking device software, and remote access management systems. Although the most recent vulnerabilities are being exploited most heavily, their general characteristics continue to reinforce established trends regarding the categories of vulnerable software. Consequently, we strongly recommend applying the security patches provided by vendors.
C2 frameworks
In this section, we examine the most popular C2 frameworks used by threat actors and analyze the vulnerabilities targeted by the exploits that interacted with C2 agents in APT attacks.
The chart below shows the frequency of known C2 framework usage in attacks against users during Q1 2026, according to open sources.
TOP 10 C2 frameworks used by APTs to compromise user systems, Q1 2026 (download)
Metasploit has returned to the top of the list of the most common C2 frameworks, displacing Sliver, which now shares the second position with Havoc. These are followed by Covenant and Mythic, the latter of which previously saw greater popularity. After studying open sources and analyzing samples of malicious C2 agents that contained exploits, we determined that the following vulnerabilities were utilized in APT attacks involving the C2 frameworks mentioned above:
CVE-2023-46604: an insecure deserialization vulnerability allowing for arbitrary code execution within the server process context if the Apache ActiveMQ service is running
CVE-2024-12356 and CVE-2026-1731: command injection vulnerabilities in BeyondTrust software that allow an attacker to send malicious commands even without system authentication
CVE-2023-36884: a vulnerability in the Windows Search component that enables command execution on the system, bypassing security mechanisms built into Microsoft Office applications
CVE-2025-53770: an insecure deserialization vulnerability in Microsoft SharePoint that allows for unauthenticated command execution on the server
CVE-2025-8088 and CVE-2025-6218: similar directory traversal vulnerabilities that allow files to be extracted from an archive to a predefined path, potentially without the archiving utility displaying any alerts to the user
The nature of the described vulnerabilities indicates that they were exploited to gain initial access to the system. Notably, the majority of these security issues are targeted to bypass authentication mechanisms. This is likely due to the fact that C2 agents are being detected effectively, prompting threat actors to reduce the probability of discovery by utilizing bypass exploits.
Notable vulnerabilities
This section highlights the most significant vulnerabilities published in Q1 2026 that have publicly available descriptions.
At the core of this vulnerability is a Type Confusion flaw. By attempting to access a resource within the Desktop Window Manager subsystem, an attacker can achieve privilege escalation. A necessary condition for exploiting this issue is existing authorization on the system.
It is worth noting that the DWM subsystem has been under close scrutiny by threat actors for quite some time. Historically, the primary attack vector involves interacting with the NtDComposition* function set.
RegPwn (CVE-2026-21533): a system settings access control vulnerability
CVE-2026-21533 is essentially a logic vulnerability that enables privilege escalation. It stems from the improper handling of privileges within Remote Desktop Services (RDS) components. By modifying service parameters in the registry and replacing the configuration with a custom key, an attacker can elevate privileges to the SYSTEM level. This vulnerability is likely to remain a fixture in threat actor toolsets as a method for establishing persistence and gaining high-level privileges.
CVE-2026-21514: a Microsoft Office vulnerability
This vulnerability was discovered in the wild during attacks on user systems. Notably, an LNK file is used to initiate the exploitation process. CVE-2026-21514 is also a logic issue that allows for bypassing OLE technology restrictions on malicious code execution and the transmission of NetNTLM authentication requests when processing untrusted input.
Clawdbot (CVE-2026-25253): an OpenClaw vulnerability
This vulnerability in the AI agent leaks credentials (authentication tokens) when queried via the WebSocket protocol. It can lead to the compromise of the infrastructure where the agent is installed: researchers have confirmed the ability to access local system data and execute commands with elevated privileges. The danger of CVE-2026-25253 is further compounded by the fact that its exploitation has generated numerous attack scenarios, including the use of prompt injections and ClickFix techniques to install stealers on vulnerable systems.
CVE-2026-34070: LangChain framework vulnerability
LangChain is an open-source framework designed for building applications powered by large language models (LLMs). A directory traversal vulnerability allowed attackers to access arbitrary files within the infrastructure where the framework was deployed. The core of CVE-2026-34070 lies in the fact that certain functions within langchain_core/prompts/loading.py handled configuration files insecurely. This could potentially lead to the processing of files containing malicious data, which could be leveraged to execute commands and expose critical system information or other sensitive files.
CVE-2026-22812: an OpenCode vulnerability
CVE-2026-22812 is another vulnerability identified in AI-assisted coding software. By default, the OpenCode agent provided local access for launching authorized applications via an HTTP server that did not require authentication. Consequently, attackers could execute malicious commands on a vulnerable device with the privileges of the current user.
Conclusion and advice
We observe that the registration of vulnerabilities is steadily gaining momentum in Q1 2026, a trend driven by the widespread development of AI tools designed to identify security flaws across various software types. This trajectory is likely to result not only in a higher volume of registered vulnerabilities but also in an increase in exploit-driven attacks, further reinforcing the critical necessity of timely security patch deployment. Additionally, organizations must prioritize vulnerability management and implement effective defensive technologies to mitigate the risks associated with potential exploitation.
To ensure the rapid detection of threats involving exploit utilization and to prevent their escalation, it is essential to deploy a reliable security solution. Key features of such a tool include continuous infrastructure monitoring, proactive protection, and vulnerability prioritization based on real-world relevance. These mechanisms are integrated into Kaspersky Next, which also provides endpoint security and protection against cyberattacks of any complexity.
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.
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.
Our experts have discovered a large-scale supply chain attack via DAEMON Tools – software for emulating optical drives. The attackers managed to inject malicious code into the software installers, and all trojanized executable files are signed with a valid digital signature of AVB Disc Soft – the developer of DAEMON Tools. The malicious version of the program has been circulating since April 8, 2026. At the time of writing, the attack is still ongoing. Researchers at Kaspersky believe this is a targeted attack.
What are the risks of installing the malicious version of DAEMON Tools?
After the Trojanized software is installed on the victim’s computer, a malicious file is launched every time the system starts up – sending a request to a command-and-control server. In response, the server may send a command to download and execute additional malicious payloads.
First, the attackers deploy an information gatherer that collects the MAC address, hostname, DNS domain name, lists of running processes and installed software, and language settings. The malware then sends this information to the command-and-control server.
In some cases, in response to the collected information, the command server sends a minimalistic backdoor to the victim’s machine. It’s capable of downloading additional malicious payloads, executing shell commands, and running shellcode modules in memory.
The backdoor can be used to deploy a more sophisticated implant dubbed as QUIC RAT. It supports multiple communication protocols with the command-and-control server, and is capable of injecting malicious payloads into the notepad.exe and conhost.exe processes.
Since early April, several thousand attempts to install additional malicious payloads via infected DAEMON Tools software have been detected. Most of the infected devices belonged to home users, but approximately 10% of installation attempts were detected on systems running in organizations. Geographically, the victims were spread across around a hundred different countries and territories. Most victims were located in Russia, Brazil, Turkey, Spain, Germany, France, Italy, and China.
Most often, the attack was limited to installing an information collector. The backdoor infected only a dozen machines in government, scientific, and manufacturing organizations, as well as in retail businesses in Russia, Belarus, and Thailand.
What exactly was infected
The malicious code was detected in DAEMON Tools versions ranging from 12.5.0.2421 to 12.5.0.2434. The attackers compromised the files DTHelper.exe, DiscSoftBusServiceLite.exe, and DTShellHlp.exe, which are installed in the main DAEMON Tools directory.
Updated on March 6: Following disclosure, the vendor acknowledged the issue and published a new version of the software to address it. The updated DAEMON Tools version 12.6.0.2445 no longer shows the malicious behavior described in this article.
How to stay safe?
If DAEMON Tools software is used on your computer (or elsewhere in your organization), our experts recommend thoroughly checking the computers on which it is installed for any unusual activity starting from April 8.
In addition, we recommend using reliable security solutions on all home and corporate computers used to access the internet. Our solutions successfully protect users from all malware used in the supply chain attack via DAEMON Tools.
DarkSword is a sophisticated piece of malware—probably government designed—that targets iOS.
Google Threat Intelligence Group (GTIG) has identified a new iOS full-chain exploit that leveraged multiple zero-day vulnerabilities to fully compromise devices. Based on toolmarks in recovered payloads, we believe the exploit chain to be called DarkSword. Since at least November 2025, GTIG has observed multiple commercial surveillance vendors and suspected state-sponsored actors utilizing DarkSword in distinct campaigns. These threat actors have deployed the exploit chain against targets in Saudi Arabia, Turkey, Malaysia, and Ukraine.
DarkSword supports iOS versions 18.4 through 18.7 and utilizes six different vulnerabilities to deploy final-stage payloads. GTIG has identified three distinct malware families deployed following a successful DarkSword compromise: GHOSTBLADE, GHOSTKNIFE, and GHOSTSABER. The proliferation of this single exploit chain across disparate threat actors mirrors the previously discovered Coruna iOS exploit kit. Notably, UNC6353, a suspected Russian espionage group previously observed using Coruna, has recently incorporated DarkSword into their watering hole campaigns.
A week after it was identified, a version of it leaked onto the internet, where it is being used more broadly.
This news is a month old. Your devices are safe, assuming you patch regularly.
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