Introduction

As part of proactive threat monitoring, Kaspersky’s Cyber Threat Intelligence team identified a malicious campaign involving C2 agents controlled by artificial intelligence. Confirmed victims of the campaign included organizations in the education sector in several countries across Africa and Latin America. CyberStrikeAI played a key role in the post-exploitation phase. This is an open-source framework in which an LLM orchestrator on the command-and-control server plans actions, selects tools, and generates commands, while an implant executes them on compromised hosts. Using CyberStrikeAI, the attackers progressed from reconnaissance to full domain compromise.

According to our data, this is one of the first public descriptions of a compromise involving an LLM in the wild. The campaign we discovered clearly illustrates how the role of AI in attackers’ arsenals is evolving: until recently, language models were used primarily during preparatory stages – for writing phishing emails, debugging code, or searching for vulnerabilities. Now, however, they are being embedded into the very core of the attack, automating actions at every stage: payload execution, persistence on the host, discovery, lateral movement, and, finally, data collection and exfiltration. Post-exploitation – the stage that traditionally required manual work by an experienced operator – plays a particularly significant role here.

A key feature of the scheme using the CyberStrikeAI framework is that the implant ceases to be a simple tool for executing the operator’s commands. Decisions are distributed across two levels: the operator defines the task in general terms, while the model determines how to execute it – specifically, which commands to send to the host and in what sequence. Thus, the strategic planning remains with the human, while the choice of technical steps is delegated to the model, and the implant executes the model’s decisions.

To understand how this scheme works in practice, let’s examine the chain of actions taken by the attackers, as reconstructed by our analysts during the incident investigation.

Timeline of the attack

Let’s examine the timeline of the attack using an example from one incident within the detected campaign.

The first phase follows a standard approach: a human operator gains access to the victim’s infrastructure via a compromised SQL Server (version 16.0.1000), then collects basic information about the host, deploys tools, and conducts network reconnaissance. All of this activity takes nearly a day, followed by a two-week pause.

The second phase is fundamentally different. The first step involves deploying the CyberStrikeAI C2 agent, which receives commands from a server-based LLM orchestrator. During the second phase, the agent performs the following:

• establishing persistence and conducting local reconnaissance within 2 minutes;
• conducting domain reconnaissance over the next 5 hours;
• after a brief pause of several days, moving on to the final stage – lateral movement via SMB and WMI, mass credential dumping on hosts, and database export – with the entire operation taking no more than one hour.

We’ll discuss these stages in more detail in the next section.

Technical details

CyberStrikeAI overview

CyberStrikeAI is an open-source, modular platform written in Go for automating penetration testing. Its key feature is combining LLM-based attack orchestration and a built-in C2 server with agents (beacons) within a single server process.

The entire “intelligent” layer is isolated on the control server. C2 functions (calling reconnaissance and exploitation utilities, managing webshell connections, assigning tasks to agents, and reading results) are available to the model as MCP tools. The implant is a classic C2 agent with no AI functionality – no API keys, no calls to AI service APIs, and no autonomous logic. It periodically polls the listener server via HTTP/S, TCP, or WebSocket, executes shell commands, and interacts with the file system. To the victim, this activity looks like a typical remote-control session, where a server-side LLM orchestrator queues tasks instead of a live operator. The LLM influences how decisions are made, but not how the corresponding commands are delivered to the host.

Although CyberStrikeAI is publicly available, specific instances of the framework can be linked to specific attackers or campaigns. Detection and attribution are possible based on the following artifacts:

• Control panel: Upon launch, a self-signed TLS certificate is generated, with the framework’s name appearing in the issuer and subject fields. The certificate is created in memory at each startup, so the not_before field effectively records the time the process was launched. Accordingly, as with Cobalt Strike, the chronology of restarts can be reconstructed from the scan history.
  

  CyberStrikeAI control panel’s self-signed TLS certificate

• Implant: The build metadata (go version -m) contains a path to the main package, which includes data pointing to the built-in CyberStrikeAI generator – a temporary directory with the build identifier. This path is identical for all payloads, making it a reliable signature. Additionally, the build info field contains version control system metadata, which can be used to compare samples against one another.
• Authentication: When communicating with the server, the implant sends an X-Implant-Token header with a fixed value in the HTTP request. The token is unique to each C2 server and is embedded into the malicious payload during the build phase; therefore, it serves as both a network indicator and a sign that the samples belong to the same server.

It should be noted that the implant version does not reflect the server version. The implant can operate for months while the operator updates the server-side components: listener configurations and keys are preserved during updates. In the incidents we detected, we observed the use of a single agent version throughout the entire incident. In such cases, the sample’s build date may indicate the earliest possible deployment date of the CyberStrikeAI infrastructure, but it still does not provide an estimate of the compromise duration.

Execution & Privilege Escalation

The first malicious commands on the victims’ systems in the campaign under investigation originated from the sqlservr.exe process, indicating the likely initial access vector and the context in which the attackers gained the ability to execute code. It is important to note that the commands marked as having been executed prior to agent installation were executed manually by the attacker specifically within the context of this process. At this stage, we are not yet dealing with automated implant activity, but rather with manual actions by the operator.

Before launching the agent on the machines, the attackers checked for its presence using a PowerShell script, from which we can deduce its default deployment paths: C:\Windows\Temp\, C:\Windows\, C:\ProgramData\, C:\Temp. At the same time, the attacker can disguise the agent’s name as that of a legitimate system process. In the example below, it mimics svchost.exe (svchosts.exe)

Like most C2 agents for Windows, CyberStrikeAI uses PowerShell to execute its payload. When a request is received from the operator, the commands to be executed are additionally encoded in Base64 to bypass keyword-based detection.

Decoded form of the command:

When administrative privileges were available (for example, a domain administrator account), instead of executing commands directly, the agent on remote hosts often created scheduled tasks so that the malicious payload would run with SYSTEM privileges:

An alternative method that does not use schtasks utility is to create a task via the Schedule.Service COM object:

The payload (p1.ps1) from the example above is a reverse shell that connects to the server at 185.186.245[.]144:5501:

The p1.ps1 payload and similar scripts were distributed across the network using shared network administrative folders and the WMI protocol, which we will describe in more detail in the Lateral Movement section.

Stealth

In addition to encoding commands in Base64 and obfuscating SQL/LDAP queries – which we will describe in the Collection & Exfiltration and Discovery sections, respectively – the attackers split the malicious payload into multiple files. It is noteworthy that the scripts were transmitted in this manner even before the agent was installed – that is, at a stage where human intervention is typically required – yet they clearly bear the hallmarks of being generated by an LLM.

Below is a sequential record of several script fragments written to the file d3.b64.

Subsequently, the attackers used the certutil utility to decode the resulting file and executed it with the following command:

Once assembled and decoded, the script’s contents looked like this:

The script is a tool for decrypting DPAPI secrets from the file C:\ProgramData\dsn_line.txt. This file, in turn, is populated by the following command, which searches for a DSN string containing connection settings in the SQL Server Reporting Services configuration file rsreportserver.config and copies its contents to dsn_line.txt.

It is worth noting that some of the scripts delivered and executed as described above contain comments in Chinese (##  –  researcher’s comments).

Persistence

If the CyberStrikeAI implant was successfully delivered, the first step was to establish persistence on the target system. To do this, the agent created a scheduled task named SystemUpdate, which specified the path to the implant as the action to be performed:

Analysis of the framework’s source code revealed an alternative method of persistence: by adding a value to the registry key HKCU\Software\Microsoft\Windows\CurrentVersion\Run, where the name of the created value matches the task name (SystemUpdate).

Agent persistence on Linux systems is also implemented in two ways – by adding the entry @reboot <path_to_beacon> & to the user’s crontab (in which case the process runs at system startup), or by adding the line (nohup <path_to_beacon> &>/dev/null &) to ~/.bashrc (in which case the agent starts when an interactive bash shell is opened).

It is worth noting that the choice of a specific persistence method is delegated to the LLM connected to CyberStrikeAI. However, thanks to the human-in-the-loop (HITL) mechanism, the operator retains full control over the process: commands generated by the model may require manual approval or correction before they are executed.

Discovery

Local Discovery

Before installing the agent on a compromised host, the attackers manually gathered basic system information using a fairly trivial set of commands:

The attackers also manually checked for internet access and obtained the external IP addresses of the compromised machines using the Chinese service dnslog[.]cn. The service generates a random third-level domain, after which the attackers attempt to resolve it on the compromised machine.

If the machine has internet access, the request succeeds, and the attackers obtain the external IP address of the compromised device on the service’s page.

For reconnaissance purposes, the agent generates a more complex script designed to map out a path for further privilege escalation by compromising SCCM or exploiting msiexec (the AlwaysInstallElevated key):

Additionally, to find ways to elevate privileges locally, the agent executed scripts to inventory services running under domain accounts and tasks running under the SYSTEM account:

During their activity, the attackers periodically checked for Microsoft Defender detections:

Remote Discovery

After gathering information about the compromised host, the attackers proceeded to conduct reconnaissance within the network. During the preparatory phase of the attack, they manually collected additional information about remote hosts using the net.exe utility.

During the main phase, the agent used PowerShell’s built-in functionality to detect accessible hosts without relying on additional utilities (such as nmap or fscan):

Additionally, relevant LDAP queries to Active Directory objects helped build a network map:

Furthermore, the presence of the installed agent on remote hosts was verified using WMI:

Domain Discovery

The next step was to gather information about the victims’ domains. To this end, before installing the agent, the attackers used standard OS utilities:

However, later on, using the CyberStrikeAI implant, reconnaissance was carried out via generated PowerShell scripts:

In addition to basic domain information, the agent performed LDAP queries to gather information for Kerberoasting and AS-REP Roasting attacks:

Finally, the agent was used to check the domain for the presence of LAPS and Microsoft Azure:

To detect signs of Microsoft Azure usage within the infrastructure, the attackers also searched for the .azcopy directory – created by the AzCopy (Microsoft Azure Storage Copy) utility – in users’ home directories:

Credential Access

After successfully conducting reconnaissance and mapping the network, the attackers moved on to the next stage – dumping credentials. For this purpose, the agent used a classic set of LOLBINs and well-known third-party tools.

The agent dumped registry hives containing credentials using the reg.exe utility:

The agent dumped the lsass.exe process using the MiniDump function of the comsvcs.dll library. In doing so, the target process’s PID was used instead of its name to make static detection based on the command line more difficult:

After successfully compromising privileged domain accounts (specifically, the built-in domain administrator), the attackers also carried out DCSync and Golden Ticket attacks via the agent using a renamed version of the mimikatz utility:

Lateral Movement

To spread across the network using compromised accounts, the attackers employed utilities from the Impacket toolkit, specifically WMIExec, as indicated by the utility’s characteristic command line:

Once installed, the agent prefers to use standard OS mechanisms, such as shared network folders.

Another method the agent uses to remotely execute commands is the WMI protocol. In particular, the agent used it to distribute the aforementioned p1.ps1 reverse shell script. This involved using compromised user accounts with local administrator privileges on the target hosts:

A second method of lateral movement via WMI uses the Invoke-CimMethod cmdlet:

Command & Control

To deliver the aforementioned tools (malicious scripts, mimikatz, and the agent itself) to the initial nodes in the infection chain, the attackers used standard OS utilities:

After the agent is installed, it checks the availability of the C2 server’s ports. The implementation mechanisms here are virtually unlimited and depend on the “creativity” of the respective LLM agents connected to the server:

To download additional utilities, the CyberStrikeAI implant also used built-in utilities (LOLBINs). The tools could be hosted on the attackers’ own servers or downloaded directly from GitHub:

Collection & Exfiltration

The data collection phase deserves special attention. It demonstrates yet another capability of LLM orchestration – the automatic generation of SQL and LDAP queries tailored to a specific task.

In one incident during the campaign, the attackers deployed the CyberStrikeAI implant on an educational organization’s SQL server and collected information from the database via PowerShell. Here is an example of a command executed by the agent:

Normalized view of the executed script with comments:

Before accessing the data, the agent checked the privileges of the account under which it connected to the database:

In decoded form, the check looks as follows:

On network resources, file collection was preceded by a recursive directory scan to identify files of interest.

To interact with the file system, the implant provides a built-in interface that supports direct uploading and downloading of files. Files are transferred directly via the main encrypted communication channel with the C2 server (HTTP or TCP), and the data itself is encoded in Base64 and packaged into the agent’s standard JSON responses.

Another built-in capability of the implant is taking screenshots of the infected machine. As with other actions, the implant does not initiate this on its own: the command to capture the screen is issued by the LLM orchestrator, and the agent merely executes it and returns the result via the same control channel.

On Windows, the command for taking screenshots looks like this:

The Linux version looks like this:

In the incidents we detected, data collection was the final stage of the attack: the chain ended there, and we observed no further activity.

Attribution

The key artifact for attribution in this campaign is the implant’s build metadata. The attackers built the malicious payload inside a Git clone of the project, so the Go compiler embedded version control system information into the binary files – the hash and commit time, as well as the vcs.modified=true flag (analogous to the +dirty suffix) – indicating uncommitted local changes in the working copy. The combination of these fields forms a unique fingerprint for a specific build.

Based on this fingerprint, the samples are divided into two independent groups, in each of which the Windows implant and the Linux implant have completely identical build metadata. A match indicates that, within each group, both payloads were built from the same project working copy.

However, these groups do not overlap with one another. They do not share a common C2 infrastructure, and different versions of CyberStrikeAI are deployed on the command-and-control servers. In other words, the build metadata reliably links the samples within each group but does not provide grounds for linking the two groups together.

The available data does not allow us to link the activity to any known group of attackers, nor to state with certainty that the same operator is behind both campaigns. Two factors prevent this. First, the tool’s openness: CyberStrikeAI is publicly available, so different operators could have independently deployed it and assembled malicious payloads under similar conditions. Second, command generation by the model: the “signature” characteristic of a specific operator is largely shaped not by a human but by an LLM. On the one hand, this increases the diversity of commands: the same command, when generated multiple times, can look different. On the other hand, it strips them of their individual characteristics, which reduces the value of behavioral metrics for attribution.

However, the Chinese-language comments found in the attackers’ scripts and the use of Chinese services still suggest a connection between the activity and a Chinese-speaking operator. In addition, analysts noted the timing of the control servers’ restarts: since the self-signed TLS certificate is regenerated each time the process starts, its not_before field records the restart time, and most of these restarts occurred during business hours in the UTC+8 time zone.

The origin of the tool itself also indirectly points in the same direction: CyberStrikeAI was developed within the Chinese cybersecurity community and is well known there.

In summary

CyberStrikeAI is a telling – though not unique – example of a shift occurring within the class of C2 frameworks: the LLM orchestrator is no longer merely an add-on to reconnaissance but has become a control layer over post-exploitation. If HexStrike AI and similar tools cover the “find and break” phase, and Sliver/Mythic/Havoc cover the “establish a foothold and control” phase, CyberStrikeAI completes the kill chain: a single server-side process written in Go executes utilities via MCP, handles webshell connections, and assigns tasks to the implant, while the model analyzes the results and decides what to do next.

The key takeaway is that the introduction of AI into the attack chain does not yet present fundamentally new challenges for detection. All the “intelligent” work remains on the attacker’s side, while the compromised host is subjected to familiar actions: from reconnaissance via Kerberoasting and AS-REP Roasting to LSASS dumps, DCSync, and the Golden Ticket attacks. This is a typical, well-documented set of domain compromise techniques. The novelty of the attack lies not in the methods themselves, but in the speed and ease with which they were executed: AI orchestration has lowered the skill requirements for the operator, but has not taken the attack beyond what defenders already know and are capable of detecting. The timing is also telling: individual key phases of the activity (such as establishing a foothold, lateral movement, and obtaining credentials) took only a few hours, and the entire attack – from agent deployment to the compromise of domain administrators – was completed within 3–4 days.

However, the CyberStrikeAI architecture does not limit users to the scenario described. The same mechanisms allow for the on-demand assembly of more complex chains – including DPAPI secret extraction and attacks on SCCM and Azure IMDS – all of which are within reach of the same group of actors, with a relatively low skill barrier. As the models evolve, the gap between the complexity of orchestration and the level of techniques used will narrow, and the operator’s role will gradually shift from setting tasks to monitoring the model’s actions.

Detection by Kaspersky solutions

It may seem that attacks using AI agents require the creation of fundamentally new detection rules. Indeed, to detect the activity of an LLM agent installed on a host within the infrastructure, it is necessary to create detection logic based on the specific characteristics of the agents. Detection rules for this activity can be found using Hunt Hub with the following filter.

However, in the case of the attack described, a different logic applies. The main difference lies in the architecture of the attack itself: the LLM agent was not located within the corporate network perimeter. It operated remotely via a C2 agent, orchestrating actions, and left no traces of its presence in the infrastructure (such as specific API requests to local LLMs or anomalous patterns of resource consumption by hosts).

The key difference between this activity and an attack by an experienced human operator lies largely in speed. Tactically, technically, and methodologically, AI, for the most part, uses exactly the same tools, utilities, and techniques as a human penetration tester. It does not write unique exploits from scratch, but rather combines known methods and dynamically adapts them to the situation, handling the tools’ errors on its own.

Since the actions performed by an AI agent on the network are indistinguishable from those of a human, classic, well-tuned correlation and behavioral analysis rules are sufficient to detect it. Here are a few examples of how Kaspersky Next EDR Expert successfully detects the attack described:

• To detect the execution of a malicious script via a remote scheduled task, you need to monitor the creation of the schtasks.exe process with the /s argument. EDR Expert detects this activity using the rules create_task_on_remote_host_using_standard_tool, scheduled_task_creation_as_system_via_schtasks, and scheduled_task_with_powershell_execution.
  
• During the attack, the adversary will need access to the victim’s credentials; in the described case, one way to obtain them was through a well-known technique that uses legitimate tools to dump LSASS, specifically by calling the MiniDump function from comsvcs.dll. The EDR Expert solution detects this activity using the rules dumping_memory_with_com_services_dll and suspicious_lsass_memory_access.
  
• A significant part of the attack chain is lateral movement, which helps the attacker navigate within the infrastructure and achieve their desired objectives. In the described case, the WinRM protocol is used to initiate connections directly from PowerShell. This is a standard technique that allows the attacker to execute commands on remote machines without using additional network tunnels. The EDR Expert solution detects this activity using the rules using_powershell_for_remote_execution_via_wmi and winrm_send_command.
  

Kaspersky Anti Targeted Attack (KATA) is used to detect malicious activity in network traffic. Communication between CyberStrikeAI agents and the command-and-control server occurs without accessing AI platforms such as ChatGPT or Claude. Instead, the agent’s C2 server is an infrastructure pre-configured by the attackers, just as in attacks that do not use AI agents. Analysis of decrypted network traffic using NDR-class solutions reveals characteristic communication patterns of CyberStrikeAI agent activity. When these patterns are detected, KATA generates an alert with the verdict Backdoor.CyberStrikeAI.HTTP.C&C.

To detect post-exploitation activity, you can use not only IDS technology but also NAD (Network Anomaly Detection). It analyzes all traffic for suspicious indicators that do not correspond to a node’s typical network activity.

For example, actions performed by CyberStrikeAI during the Domain Discovery phase are detected by an NDR:NAD rule named “Scanning domain records via LDAP queries.”

You can learn more about NDR:NAD technology in our article on Securelist.

You can also visualize the connections between C2 addresses and files used by attackers in the Research Graph section of the Kaspersky Threat Intelligence Portal. This includes tracking the emergence of new CyberStrikeAI implants that communicate with malicious addresses.

In addition, the Threat Lookup service available on the portal allows you to determine whether malicious files belong to CyberStrikeAI using attribution mechanisms based on the executable file’s code.

Kaspersky SIEM detects all stages of an attack by the CyberStrikeAI agent. The product repository contains over 100 rules for detecting the techniques discussed in this article; we will list the most critical ones below.

Execution of various commands from the sqlservr.exe process:

• R300_04_Windows shell started by a web application

Downloading and executing a payload via PowerShell:

• R110_03_PowerShell code downloaded and executed

Creating Windows scheduled tasks:

• R099_04_Suspicious arguments when running schtasks.exe
• R300_03_Windows shell started by the scheduler

Persisting via crontab or bashrc:

• R099_06_Suspicious cron job activity
• R405_02_UNIX shell configuration modification

Rules located at the KUMA Packages/SOC package/Configuration discovery path detect the collection of information about hosts, network settings, and domains using various reconnaissance tools. Rules that aggregate detections by reconnaissance tools:

• R224_14_Windows reconnaissance activity
• R224_09_System information discovery by running multiple utilities within a short period
• R224_04_System information discovery via standard Linux tools

Data collection for Kerberoasting and AS-REP Roasting:

• R034_01_Multiple TGS ticket requests (Kerberoasting)
• R452_18_AS-REP Roasting

Dumping registry hives containing credentials using the reg.exe utility:

• R230_02_Copying data containing credentials from the Windows registry

Dumping the lsass.exe process using the MiniDump function of the comsvcs.dll library:

• R233_04_Process memory dump via comsvcs.dll
• R262_Suspicious access to the LSASS process

DCSync and Golden Ticket attacks using the renamed mimikatz utility:

• R073_DC Sync attack
• R450_41_Golden Ticket Attack
• R100_03_Traces of Mimikatz use via cmd

Lateral movement using WMIExec:

• R460_08_Lateral movement using WMIexec Impacket
• R460_09_Lateral movement using WMIExec and Impacket

For the rules to work, you must configure the SIEM system to receive the relevant events:

• Process launches (Windows security log – 4688, auditd – execve) and PowerShell commands (4104)
• User logins to the system (Windows security log – 4624, 4768, 4769)
• Various object manipulations (Windows security log – 4662, 4663, 4656; auditd – openat)
• Interaction with shared resources (Windows security log – 5145)

Indicators of compromise

File-based indicators:

CyberStrikeAI Windows Beacon
D726A8B4886C636F76CA6D30EF6A6FF4	c:\windows\temp\svchosts.exe
2770562d104391b92b42671c270079bb	c:\windows\temp\svcmon32.exe c:\programdata\svcmon32.exe
CyberStrikeAI Linux Beacon
09e1da2afa1216ecc55648ec8e122d2a
805B9E4AE74C903787416800999B8418
Tools & Scripts
29EFD64DD3C7FE1E2B022B7AD73A1BA5	C:\Windows\Temp\wdiag2.exe (mimikatz)
74F57B3E897A4960DCDD5B7E8A54F9DF	c:\rwx\RogueWinRM.exe
0BBD08AF95F877F1D568B03DE2EB76B4	c:\ProgramData\p1.ps1 (reverse shell PS script)

Network indicators:

185.186.245[.]144	C2 for Windows Beacon
fastcdn[.]bar	C2 for Windows Beacon
media.fastcdn[.]bar	C2 for Linux Beacon
104.234.26[.]163	C2 for Payload Delivery + Linux Beacon

A modern organization protects its information systems with a whole suite of technologies that generate thousands of security alerts every day. Looking at these alerts in bulk, you’ll see that a significant share are false positives. Ideally, these alerts would be filtered out automatically so that analysts could focus on the ones that may point to a real incident. However, writing filters is a time-consuming task in itself. To create a correct filter, an analyst needs to:

• Review the logs
• Identify the source of the activity
• Define filtering boundaries
• Choose filtering criteria
• Write a rule
• Check that the filter is robust

This post looks at how filter creation itself can be automated to reduce analyst workload.

Filter validity criteria

In the context of security alerts, a filter is a combination of event field values that marks a detection as a false positive and not worth the analyst’s attention. Filters can be generated for any combination of event fields, but not every combination yields a valid filter. That’s why we need to define validity criteria: conditions showing that a specific filter actually removes noise and doesn’t suppress useful alerts.

Detection rules in security systems typically contain a set of conditions, and each of them can generate an alert. A specific event triggers a given fragment of a detection rule. The set of events that a given fragment responds to is what we’ll call its context. A good filter should cover the entire context where possible, without going beyond it. Filters that are too broad or too narrow are useless. For example, a filter based solely on PowerShell would exclude too many events, including some that deserve the analyst’s attention. Most of the filtered events would also be unrelated to the rule in question. A filter on a specific ID, on the other hand, would cover only one event, which may never recur.

Problems in filter generation

We can run into a number of problems when creating filters. First, events often contain an excessive number of fields (around 400 on average), and most of them aren’t needed for filtering. At the same time, generating and ranking combinations of these fields is also fairly resource-intensive. So, the first step is to discard the irrelevant fields and keep only those worth using in the filter. For example, out of 400 fields in an event, four may be enough for filtering.

Another problem is string normalization. Field values (for example, the path to the same application on different systems) can vary depending on system settings, user folder names, and so on. For a filter to account for all these variations, the strings must be normalized. For file paths, in particular, you can replace the drive letter and user folders with standard environment variables and convert all paths to a single case.

Finally, if a filter completely mirrors the query of a detection rule, it effectively disables that rule. To fix this, remove the event fields used in the detection rule from the filter. For example, if a rule triggers on a file path, there are two ways to filter out unwanted alerts:

• In the filter, use fields other than the file path.
• Use a part of the file path that the rule doesn’t use. For example, for the rule file_path: ".../something.exe", you can set up a filter that excludes events with the user/unique_files/ directory in the file path.

Filtering security alerts with an algorithm

Given the problems described above, automatically generating filters for security alerts should consist of the following steps:

1. An analyst receives a security alert that they assess as a false positive and uses the filter generator.
2. The generator takes the 400-field event from the alert, along with the rule that triggered on it, and determines the context.
3. The generator selects the fields that are relevant to the filter.
4. The generator normalizes the strings.
5. The generator builds different combinations of the selected fields. To do this, we applied the FP-Growth (frequent pattern growth) algorithm to the context determined earlier. FP Growth builds a tree of frequently occurring items (fields) from a dataset (in our case, the context) and discards items that aren’t frequent enough. This reduces the number of combinations to check, which lowers the cost of finding the most relevant ones.
6. The generator ranks the resulting combinations and offers the analyst the top three to choose from.

For example, consider an event described by the following JSON:

After generating and ranking the combinations, we got three filters based on file paths:

• processcmdline:("nodejs\\node.exe" AND "*****\\appdata\\roaming\\npm\/node_modules\/pi-coding-agent\/dist\/cli.js")

• file_path:"*****/.pi\\agent\\bin\\fd.exe"

• processcmdline: "programfiles\\nodejs\\node.exe"

The last filter turned out to be too broad, but the analyst marked the other two as valid.

Pros and cons of the algorithmic approach

The algorithmic approach to filter generation has several advantages. First, it’s simple and deterministic: the same alert always produces the same result. Second, it’s interpretable, meaning we can see why a given combination ranked near the top of the list and why a particular field didn’t make it into the list of candidate fields at all. Third, the stages of the process are independent of each other, so individual steps can be improved and tuned without the risk of breaking the algorithm elsewhere. Finally, it has no external dependencies or models, which makes it easy to deploy in any customer environment.

However, it also has drawbacks. The first is its dependence on context. If there’s no context for some reason — for example, the rule’s first-ever trigger — a filter can’t be created. The second problem is string normalization. There’s a high chance of running into atypical cases that the normalization rules don’t cover. Often such cases can’t be described correctly at all, because they contradict each other.

These problems can be solved with large language models (LLMs). When an LLM generates the filters, no context is needed, because the LLM doesn’t iterate over or rank combinations. The LLM also takes a more flexible approach to string normalization, including non-standard cases. That said, an LLM doesn’t have to be involved at every stage of the generation process. We decided to keep the algorithmic solution where it works well. For example, a simple algorithm selects suitable fields without errors, so we didn’t move this stage to the model.

Filter generation with an LLM

The LLM-based filter generation looks like this:

The input is still an alert containing an event with 400 fields, plus the rule that triggered on that event. Then an algorithm selects the most suitable fields for the filter. Next, the LLM processes the input — the event, the selected fields, and the detection rule — and generates three filter options along with an explanation for them. The explanation helps the analyst understand why these particular filters were suggested and make an informed choice.

Consider the following event in JSON format:

The LLM suggested three filter options that all contain the same command line fragment:

• file_path: "\\WindowsPowerShell\\powershell.exe" AND filecmdline: "Get-VM -Name kafka\*"

• filecmdline: "Get-VM -Name kafka\*"

• filecmdline: "kafka*"

As in the previous example, two of the three output filters were valid. The last one was too broad to be usable. The analyst chose the first filter with minor edits:

file_path: "powershell.exe" AND filecmdline: "Get-VM -Name kafka\*"

Pros and cons of generating filters with an LLM

Using an LLM solved the problems of purely algorithmic filter generation: dependence on context and errors in string normalization. However, the single-request approach had its own drawbacks. First, it had no output validation. The LLM returned filters as-is, with no syntax check and no way to correct errors. Second, unlike in the algorithmic version, all filter generation tasks were handled by a single system prompt. As a result, a minor fix in one place could break the logic in another.

Using multiple AI agents

To address the problems of generating filters with a single model and a single system prompt, we tried distributing the tasks across different LLM agents. In this version, we brought in agents as early as the stage of selecting relevant fields. The chain includes four agents in total:

• Selector: selects the relevant fields.
• Generator: generates filters.
• Validator: evaluates the generated filters. If the filters are found to be invalid, it returns the task to the generator. If the selected fields are found to be irrelevant, it starts a new field selection cycle. If the filters pass validation, or the limit on regeneration attempts is reached, the valid filters (or the best ones available) are passed to the analyst.
• Commentator: analyzes the output filters and explains which field values were used and why.

For the following JSON event, the agents produce three valid filters based on the file path.

The filters differ in the additional parameters that support the detection:

• file_path:"sysvol\\domain\\scripts\\logon.bat"

• file_path:"sysvol\\domain\\scripts\\logon.bat" AND file_md5:"0x7..6E"

• autorun_entry: "Group Policy\\State" AND file_path: "SYSVOL\\domain\\scripts\\logon.bat"

Pros and cons of the agent-based approach

LLM agents give us a system that can validate and correct filters. Its modular architecture also lets us tune and improve different stages of generation independently. However, this approach makes more LLM calls than the previous ones, so the system is slower. It also depends on access to a model, which may not be available in the organization’s isolated environment.

Evaluating the filter generation system

To evaluate how well the filter generation system works, we use two main metrics:

• Approval rate. For each security alert, the system suggests three filters. If the analyst selects at least one of them, we count the generation as successful. For example, in the case below, the approval rate is 2/3.
  
• False positive reduction rate (FP drop). This metric shows how much a filter helps reduce the number of false positives. It can only be measured over time, because we need to compare the number of false positive alerts received during a set period before and after the filter is deployed.

Below is a comparison of the three filter generation approaches by these metrics:

As the results show, LLMs significantly improve the efficiency of filter generation. At the same time, the LLM agentless and agent-based approaches perform almost identically. So we chose the simpler and faster option: an LLM with a single system prompt. As a result, we cut false positives by 99%, increased the number of filters generated per day by 28%, and made analysts’ alert handling 17% more efficient.

Takeaways

Practice has shown that filter generation can be automated effectively. We’ve looked at several options for automatically generating filters for false-positive security alerts: a purely algorithmic approach, an LLM-based approach, and an approach built on a group of LLM agents. All of them can generate valid filters and make the analyst’s work much easier, but each has its own pros and cons.

The algorithmic approach scored lower on the evaluation metrics, but in two-thirds of cases, it still produces filters that satisfy the analyst. It works especially well when there’s already a set of similar events to filter out, meaning enough context has accumulated. The algorithmic generator can also be deployed in any environment. Both standalone LLM and multi-agent LLM approaches show high accuracy, and their results differ only slightly, so the slower agent-based approach looks excessive at this stage. If an LLM is available, the best option appears to be a single model that handles several stages of the generation cycle.

The vulnerability landscape shifted significantly in Q2 2026. First, the number of registered CVEs reached an unprecedented level. This is driven primarily by the widespread adoption of AI, both for application development and search for security flaws. This resulted in entire new classes of vulnerabilities emerging, particularly in the Linux networking subsystem.

Second, security researchers have been publishing exploits for unpatched vulnerabilities more frequently. Publications like these can generate significant fallout, since they potentially open the door for attackers to target unprotected systems.

Statistics on registered vulnerabilities

This section provides statistical data on registered vulnerabilities. The data comes from Kaspersky’s vulnerability knowledge base, which draws on the CVE database as well as the Russian BDU database and GitHub Advisory (GHSA). As a result, the figures for previous reporting periods may differ from those published in earlier reports.

We examine the number of registered vulnerabilities for each month over the last five years. As the chart below shows, this number continues to surge, a trend reflected across all the databases we track. It’s driven primarily by the widespread adoption of AI tools: as we predicted in our previous report, these tools have played a major role in the discovery of vulnerabilities in third-party software. Meanwhile, these tools often contain security issues of their own. For example, OpenClaw, a popular AI project, ranked 12th among those with the highest number of vulnerabilities discovered and published in Q2, with over 200 CVEs registered during the reporting period. Finally, AI development tools are also contributing to the vulnerability landscape, since the quality of the code they produce can vary widely. Therefore, the rate at which new vulnerabilities are discovered will inevitably keep growing.

Total published vulnerabilities per month from 2022 through 2026 (download)

Next, we analyze the number of new critical vulnerabilities (CVSS > 9.0) over the same period.

Total critical vulnerabilities published per month from 2022 through 2026 (download)

As the chart shows, the number of published critical vulnerabilities jumped sharply in Q2. This is because using AI for vulnerability research makes it possible to analyze massive amounts of previously unexamined code, uncover new attack surfaces, and identify entire classes of vulnerabilities that have gone unnoticed for decades. In particular, AI was used to find a series of Dirty Frag vulnerabilities in the Linux kernel.

Exploitation statistics

This section presents statistics on vulnerability exploitation for Q2 2026. The data draws on open sources and our telemetry.

Windows and Linux vulnerability exploitation

Q2 2026 saw a new precedent in the publication of vulnerabilities in Windows components and exploits for these: researchers no longer waiting for CVE registration, let alone patches. A case in point: a researcher who goes by Nightmare Eclipse (also known as Chaotic Eclipse) published a list of new “named” vulnerabilities across various Windows subsystems. At the time the technical details were published, none of the vulnerabilities had been assigned a CVE identifier:

• BlueHammer: a local privilege escalation vulnerability in Windows Defender. During signature database updates, a time-of-check to time-of-use (TOCTOU) race condition occurs, allowing an attacker to substitute the directory where temporary update files are written. The researcher published a fully functional exploit for the vulnerability.
• RedSun: another logical vulnerability in Windows Defender with a working exploit. Suspicious and malicious files marked as “cloud” can be overwritten or restored to their original directory with elevated privileges. The exploit incorporates fragments of algorithms that make it possible to leverage various logical vulnerabilities in Windows, effectively combining a large number of popular exploitation techniques.
• YellowKey: a vulnerability that lets the user bypass BitLocker full-disk encryption and access system data through the Windows Recovery Environment (WinRE). A fully functional exploit was also published.
• GreenPlasma: a vulnerability that enables system object injection via the CTF loader for the Collaborative Translation Framework (CTFMON) service in Windows. The original publication included an exploit with limited functionality.
• RoguePlanet: yet another Windows Defender vulnerability that, like BlueHammer, stems from a TOCTOU issue, this time in the engine responsible for real-time system scanning. The published exploit uses the vulnerability to overwrite the system file wermgr.exe with a malicious one.
• UnDefend: another vulnerability in the Windows Defender service. This time, the exploit causes a denial of service and blocks updates.

Even though such cases remain isolated for now, we believe they’ll grow into a full-fledged trend. Early publication of exploits gives attackers an advantage over software developers, who are left with no time to fix the issues.

Veteran vulnerabilities in Windows software also remain relevant. These are the ones our solutions most frequently detect exploits for:

• 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 in WinRAR that involves improper handling of objects within an archive
• CVE-2025-6218 (formerly ZDI-CAN-27198): another WinRAR vulnerability allowing the specification of relative paths to extract files into arbitrary directories, potentially leading to malicious command execution
• CVE-2025-8088: a vulnerability similar in exploitation method to CVE-2025-6218. The attackers used NTFS Streams to circumvent controls on the directory into which files are being unpacked

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.

That said, the number of Windows users who encountered exploits declined slightly in Q2, hitting an 18-month low.

Dynamics of the number of Windows users encountering exploits, Q1 2025 – Q2 2026. The number of users who encountered exploits in Q1 2025 is taken as 100% (download)

Linux also hit a rough patch in Q2 2026. Specifically, the period saw the disclosure of the Dirty Frag family of vulnerabilities, which lets an attacker reliably escalate privileges within the operating system.

All the vulnerabilities published in Q2 2026 were, in one way or another, related to the Linux caching subsystem. Here are the ones being most actively exploited:

• CVE-2026-31431 (Copy Fail): a local privilege escalation vulnerability in the Linux kernel that lets an unprivileged user modify the page cache and gain root privileges. Especially dangerous for cloud and containerized environments
• CVE-2026-43284, CVE-2026-43500 (Dirty Frag): a family of vulnerabilities in the Linux networking subsystem (IPsec ESP and RxRPC) that lets a local user overwrite the page cache and escalate privileges to root
• CVE-2026-46300 (Fragnesia): a local privilege escalation vulnerability in the Linux kernel related to packet fragment handling and the page cache mechanism. It lets an unprivileged user gain root privileges and is also classified as part of the Dirty Frag family
• CVE-2026-31635 (DirtyDecrypt): a Linux kernel vulnerability that lets a local attacker escalate privileges due to improper handling of decryption operations and page cache data modification
• CVE-2026-43494 (PinTheft): a Linux kernel vulnerability that lets a local user gain elevated privileges due to errors in the memory page pinning mechanism
• CVE-2026-46331 (pedit COW): a vulnerability in the Linux kernel’s traffic control subsystem (tc-pedit) that exploits a flaw in copy-on-write to modify the page cache and subsequently escalate privileges to root

The vulnerabilities described above were quickly embraced by attackers. At the same time, our solutions continue to detect exploitation attempts targeting older vulnerabilities as well:

• 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: another Netfilter subsystem vulnerability that allows for Use-After-Free conditions and privilege escalation through improper processing of network requests

Dynamics of the number of Linux users encountering exploits, Q1 2025 – Q2 2026. The number of users who encountered exploits in Q1 2025 is taken as 100% (download)

In Q2 2026, the number of Linux users who encountered exploits declined slightly compared to Q1. Given that a significant share of new vulnerabilities are tied to the operating system’s caching subsystem, we recommend installing patches as quickly as possible, or disabling vulnerable kernel modules if patching isn’t an option.

Most common published exploits

The distribution of published exploits by software type in Q2 2026 includes categories that haven’t appeared in the sample for a long time. For instance, we’re once again seeing exploits targeting SharePoint. It’s worth noting that while several vulnerability write-ups for Exchange and SharePoint were published during the quarter, most turned out to be fake, AI-generated research. While the articles and exploit source code themselves look fairly polished, they describe nonexistent problems in the software or its components — often close to genuinely vulnerable mechanisms — in order to mislead researchers. This type of attack is aimed at increasing the time it takes to detect real vulnerabilities. In some cases, the description of a nonexistent vulnerability came bundled with completely unrelated malware.

Distribution of published exploits by platform, Q1 2026 (download)

Distribution of published exploits by platform, Q2 2026 (download)

Vulnerability exploitation in APT attacks

We analyzed which vulnerabilities were exploited in APT attacks during Q2 2026. The rankings provided below include data based on our telemetry, research, and open sources.

TOP 10 vulnerabilities exploited in APT attacks, Q2 2026 (download)

In Q2 2026, a trend emerged in APT attacks toward exploiting new vulnerabilities right from the moment they’re published. As before, we’re also seeing a large number of zero-day vulnerabilities. The Langflow vulnerability deserves particular attention: it’s one of the first cases of an APT group exploiting AI technology, which many organizations are only just beginning to integrate. Because most of this tech is proprietary, it has a considerable number of security blind spots. Therefore, given the growing number of AI-based automation tools, we strongly recommend going beyond the usual patching and developing secure procedures for credential use and sensitive data handling in systems that rely on agents and LLMs.

C2 frameworks

In this section, we examine the most popular C2 frameworks used by APT groups 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 during Q2 2026, according to open sources.

TOP 10 C2 frameworks used by APTs to compromise user systems, Q2 2026 (download)

Sliver, Havoc, AdaptixC2, and Metasploit remain the most widely used C2 frameworks. 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-2026-35273: a vulnerability in Oracle PeopleSoft PeopleTools that security vendors classify as server-side request forgery (SSRF). The details of the vulnerability have never been disclosed, although some research covers the post-exploitation steps
• CVE-2023-46604: an insecure deserialization vulnerability in Apache ActiveMQ that allows arbitrary code execution in the context of the service process
• 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 allows commands to be run on the system, bypassing the mark-of-the-web (MoTW) mechanism
• 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 in WinRAR that allow files to be extracted from an archive to a predetermined path, potentially without the archiving utility displaying any alerts to the user

These vulnerabilities show that attackers used them for initial access and privilege escalation on vulnerable systems, setting the stage for launching a C2 agent. They include both zero-day vulnerabilities and fairly well-known security issues.

LLM/AI tool vulnerabilities

This section analyzes data published in Kaspersky’s vulnerability knowledge base. We reviewed the Q2 2026 version of the knowledge base.

As mentioned above, AI tools, plugins, and technologies have proven fairly effective at automating the search for problematic code and anomalous behavior. The high speed at which new vulnerabilities are being discovered has naturally created a need to fix them just as quickly. AI is often used for this too, which increases the volume of code being generated. However, neither code written without human involvement nor AI-generated advice is always correct.

The chart below covers registered vulnerabilities in AI tools for 2025–2026.

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

As the charts show, AI tools are racking up a substantial number of registered vulnerabilities, and that number keeps growing quarter over quarter. It’s also worth looking at how AI tool vulnerabilities break down by type, according to the CWE system:

Interestingly, vulnerabilities of an undetermined type have ranked first in every quarter since the start of 2025. Traditionally-made software has the same issue, and it doesn’t look like the growing number of AI tools will fix it. It’s also notable that the list includes classes CWE developers themselves don’t recommend using for vulnerability classification, since they lump together a whole range of more specific types. CWE-284 is an example of this.

Looking at the most common classes, the key issues found in AI-related software can be summed up as follows:

• Inadequate access control over critical system objects
• Improper implementation of authentication and authorization mechanisms
• Injections

It’s worth noting that injection-related vulnerabilities were relatively rare before AI agents took off (previously, they mostly affected web apps). Recently, though, these security issues have become relevant again.

Looking back at a year and a half of the AI boom, one conclusion stands out regarding registered vulnerabilities: AI tool developers are more focused on expanding functionality than on security. This is worth keeping in mind when using these tools. Let’s look at the projects and applications that either integrated AI tools or offered them as the core product. Below is a list of the those with the highest number of registered vulnerabilities for 2025–2026.

TOP AI/LLM-related projects by number of published vulnerabilities, 2025–2026 (download)

Notable vulnerabilities

This section highlights the most significant vulnerabilities published in Q2 2026 that have publicly available descriptions. Since the above already covers several significant vulnerabilities published during the reporting period, this section consists mainly of LLM/AI tool vulnerabilities.

CVE-2026-25253: a gatewayUrl vulnerability in OpenClaw

The issue stems from the fact that the OpenClaw user interface trusts the value of the gatewayUrl parameter passed in the URL and automatically establishes a WebSocket connection to the specified address. During this connection process, it sends an authentication token without any additional user confirmation.

The attack algorithm exploiting this vulnerability works as follows:

1. The application obtains a critical connection address from an external source (the gatewayUrl URL parameter), which is controlled by the attacker.
2. There is no validation before use.
3. The client automatically initiates a connection to the address specified in the parameter, which belongs to the attacker.
4. While connected, the application sends credentials (an access token) to the specified address.

If the attacker obtains a valid token, the consequences depend on that token’s level of access within the system. In general, this could lead to:

• User session compromise
• Execution of operations on the user’s behalf
• Modification of the AI agent configuration
• Unauthorized access to tools and resources connected to the agent
• Under certain OpenClaw configurations, further compromise of the host running the agent

It’s worth noting that the risk of exploitation arises from a combination of several factors: the automatic connection and token transmission, the lack of address trust verification, and the high privileges granted to the local AI agent.

CVE-2026-41948: a path traversal vulnerability in the Dify AI platform

The vulnerability lets an authenticated user craft a request that enables the application to escape its permitted tenant and gain access to internal REST APIs that weren’t meant for that user. The root cause is insufficient normalization and validation of the URL path before it’s passed to the internal service.

Depending on the Dify configuration, the consequences can include:

• Unauthorized access to internal service interfaces
• Breach of isolation between workspaces
• Exposure of internal service information
• Conditions favorable to further attacks when combined with other vulnerabilities

The use of Dify in enterprise AI platforms is particularly risky, since internal services there tend to hold elevated privileges.

CVE-2026-45386: an improper access control vulnerability in Open WebUI

In Open WebUI, pin/unpin operations on messages are write operations, since they modify that message’s metadata (is_pinned, pinned_by, pinned_at). In vulnerable versions, however, before performing these actions, the API only checked for read access to the channel (a chat between a user or group and the AI) containing the message, not permission to modify its content. As a result, a user with a role limited to viewing messages could still change a message’s pinned status.

The vulnerability’s mechanism works as follows:

1. The user initiates an action that changes the state of an object.
2. The application treats this action as a regular read request.
3. Only channel view permission is checked.
4. The application performs a write without verifying the required user authorization.

This violates one of the fundamental principles of access control models — namely, that any operation that changes the state of data must be checked for the appropriate write or moderation permissions, regardless of whether the object itself is readable.

Although the vulnerability doesn’t lead to arbitrary code execution or compromise of sensitive data, it can affect data integrity and collaborative workflows. Potential consequences of exploitation include unauthorized pinning or unpinning of messages, disruption of channel moderators’ and administrators’ activities, changes to the display order of important information, and even the potential spread of false or misleading information by altering the channel containing a pinned message.

Open WebUI is widely used as an interface for interacting with local and enterprise LLMs. In these systems, pinned messages often contain important instructions, announcements, or tips for users. The ability to modify them with minimal privileges can disrupt collaborative workflows, cause confusion, and undermine trust in information published by administrators and moderators.

CVE-2026-45501: a vulnerability in Microsoft Exchange

The vulnerability stems from improper neutralization of user input when generating Exchange web pages. As a result, the browser may interpret specially crafted data as active content instead of plain text.

Although Microsoft categorizes the potential impact of exploiting this vulnerability as spoofing, flaws like this can lead to alteration of displayed content, imitation of trusted interfaces, actions on behalf of the user within an active session, and abuse of user trust.

It’s worth noting that issues like this are still relevant in modern software, given that mechanisms like Content Security Policy and various parsers were specifically created to help developers neutralize dangerous parts of user page content.

Conclusion and advice

Q2 brought the first significant results of AI automation adoption in software development and vulnerability hunting tools. This research shows that beyond traditional patch management, organizations now need real-time monitoring of systems and access controls, since infrastructure and everyday applications now contain far more AI functionality that could lead to compromise.

Accordingly, besides quickly detecting infrastructure vulnerabilities and managing security patches, modern enterprise-grade security solutions need to provide a broad range of preventive measures for tracking the overall health of systems and workstations. Kaspersky Next meets these requirements by combining proactive mechanisms with the ability to respond promptly to emerging threats.

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.

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.

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.

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.

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.

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.

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)  ENV MOODLE_URL=<a href="http://0.0.0.0/">http://0.0.0.0</a> MOODLE_ADMIN admin       MOODLE_ADMIN_PASSWORD [removed]      MOODLE_ADMIN_EMAIL admin@example.com MOODLE_DB_HOST     MOODLE_DB_PASSWORD       MOODLE_DB_USER     MOODLE_DB_NAME    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)  HEALTHCHECK &{[""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;     apt-get update &&       apt-get -y install sudo;       echo ""solr ALL=(ALL) NOPASSWD: ALL"" >/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' >> /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 — 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 — 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.