This is a collaborative follow-up to our original post, developed jointly with Emmanuel C., a security researcher not affiliated with LevelBlue, who contributed additional infrastructure and tooling findings based on an analysis of the same GitHub staging account.
A cyber incident affecting a small UK electricity generator in July 2026 resulted in several days of operational unavailability and triggered a government and NCSC response. UK authorities confirmed that the event posed no threat to the wider grid and caused no customer outages. Media reporting described the affected asset as a small gas-fired peaking plant of approximately 15 MW, although the operator, location, technical architecture, and exact attack path remain undisclosed.
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.
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.
This section presents statistics on vulnerability exploitation for Q2 2026. The data draws on open sources and our telemetry.
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:
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:
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:
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:
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.
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)
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.
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:
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.
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:
TOP 6 vulnerability types in products that implement or use AI/LLM logic, 2025–2026
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:
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)
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.
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:
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:
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.
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:
The use of Dify in enterprise AI platforms is particularly risky, since internal services there tend to hold elevated privileges.
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:
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.
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.
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.
In Part 4, we stole every document from every index, planted a rogue superuser account, created credential-independent API keys, and planted three persistence mechanisms that survive password rotations. Everything was logged. Now we answer two final questions: how much worse could it get, and how do we put everything back?
We have access through port 9200. We have code execution through port 5601. Reconnaissance is complete, CVEs have been exploited, and Kibana has been compromised. Over the past three posts, we proved that we could get in. Now we prove what happens after.
In Parts 1 and 2, every command targeted port 9200. Every exploit, every reconnaissance query, every credential test hit the Elasticsearch REST API directly. But Elasticsearch rarely operates alone. Sitting alongside it on most deployments is Kibana, the visualization and management interface, quietly serving dashboards on port 5601 with its own API surface, plugin architecture, and history of critical vulnerabilities.
In July 2026, Kaspersky experts detected a new attack by the Head Mare group. Previously, we classified them as hacktivists, but now we define them as an APT group due to the sophistication of their TTPs and the absence of destructive activity (encryption, wiping) in the targeted infrastructures. In this latest campaign, the attackers exploited a chain of vulnerabilities in the TrueConf video conferencing server and replaced the original TrueConf client installers with infected versions that installed the PhantomCore malware on the system.
An investigation of the compromised server revealed that the attackers used a combination of two new vulnerabilities (assigned the internal identifiers KLCERT-26-057 and KLCERT-26-058), allowing them to execute arbitrary code with the highest privileges.
The attack occurs in several stages:
Entrar…\public\js\locale.php with a web shell, which can be used for subsequent remote control of the compromised server.This web shell was used for the following activities:
The vulnerabilities exploited by the attackers were patched by the vendor in the latest TrueConf Server updates (versions 5.3.9, 5.4.9, and 5.5.5). These updates were released on June 18, 2026.
The PhantomCore backdoor was successfully detected by Kaspersky solutions.
To automatically launch the malware after the system boots, a registry key is created: HKEY_CURRENT_USER\Software\Classes\CLSID\{0340F119-A598-4ed9-B0AC-6F6A12D3E755}\InprocServer32, with the value set to the path to the malicious program’s file.
Using a web shell, in addition to PhantomCore, the attackers load a backdoor that we have named PhantomGraph, consisting of two modules:
SysExcSvc.dll is responsible for receiving commands from the attackers and transmitting the results of their execution. The attackers used an account on Microsoft OneDrive cloud storage as their command-and-control (C2) server.SysReadSvc.dll reads the command transmitted by the first module, executes it, and saves the execution result.To establish persistence on the system, the attackers execute a Base64-encoded PowerShell command that installs SysExcSvc.dll and SysReadSvc.dll as Windows services. We believe the attackers deliberately split this malicious command into two components to make it harder to detect using EDR tools. Additionally, the program’s code partially matches that of PhantomCore, indicating that it belongs to Head Mare’s arsenal.
We also managed to identify the commands executed by the attackers when connecting to the backdoor. The SysReadSvc module executes commands using a BATCH file. Example of execution:
$system32\cmd.exe /c cmd /c ""$temp\cmd_cmd_4488.bat"" 2>&1
Commands detected:
lsass.exe process:
hostname whoami "$system32\WindowsPowerShell\v1.0\powershell.exe" -noexit -command Set-Location -literalPath '$system32\inetsrv'
In addition, we discovered several commands that did not work due to the attackers’ typos and encoding issues.
We are observing several active Head Mare campaigns targeting Russian organizations across various industries: instrument manufacturing, electronics, transportation, energy,
IT, and software development. The attackers distribute their backdoors using various methods, including phishing, exploiting public web servers, or through a subcontractor.
We recommend that all organizations using TrueConf software install the latest server version (versions 5.3.9, 5.4.9, and 5.5.5) in accordance with the vendor’s recommendations.
We also recommend verifying that the client distributions downloaded from the TrueConf server used by your organization have a valid TrueConf digital signature and have not been tampered with. The malicious distributions we detected did not have a valid digital signature. You can also verify authenticity on the vendor’s website.
Important: Even if your organization does not use a TrueConf server, your employees may connect to compromised TrueConf servers belonging to business partners to participate in online meetings and download infected installation packages.
The attack mechanism and the vulnerabilities exploited are described in more detail on the Kaspersky ICS CERT website.
Kaspersky security solutions successfully detect malicious activity associated with the attacks described above.
The malware used in this attack is detected by our solutions with the following detection names:
Let’s take a closer look using Kaspersky Endpoint Detection and Response Expert (KEDR Expert) as an example.
Specifically, activity involving the replacement of the legitimate file …\public\js\locale.php with a web shell, as well as the deletion of entries from TrueConf event logs, is detected by the rule unusual_php_file_creation_from_trueconf_process.
Downloading a file containing the PhantomCore backdoor via the replaced legitimate file …\public\js\locale.php is detected by KEDR Expert with the rule unusual_file_creation_from_trueconf.
Activity related to the installation of an infected TrueConf client installer containing the PhantomCore backdoor is detected by KEDR Expert using the unsigned_trueconf_installer rule.
The Kaspersky Managed Detection and Response service detects the described attack by monitoring the following actions:
lsass.exe process.lsass.exe process using the comsvcs.dll library.lsass.exe process.To protect companies using our Kaspersky SIEM system, a general set of rules is available in the product repository that allows detection of the following techniques:
lsass.exe process using the comsvcs.dll library:lsass.exe process:We also recommend paying attention to the following events when developing your own detection rules or conducting threat hunting:
(DeviceEventClassID = '4657' OR DeviceEventClassID = '13')
AND FileName like '%\Software\Classes\CLSID\{0340F119-A598-4ed9-B0AC-6F6A12D3E755}%' AND DeviceCustomString6 = 'InprocServer32'
SysExcSvc and SysReadSvc services to run executables from temporary directories in the background via cmd:DeviceEventClassID = '4697' AND (DestinationServiceName = 'SysExcSvc' OR DestinationServiceName = 'SysReadSvc') AND match (FileName, '.*cmd\s+\/c.*temp\\cmd_cmd_.*\.bat.*')
(DeviceEventClassID = '4688' OR DeviceEventClassID = '1') AND SourceProcessName LIKE '%\trueconf_windows_update.exe'
For the detection rules to work correctly, ensure that events from Windows systems are received in full, including Security events 4688, 4663, 4657, and 4697 and Sysmon events 1, 7, 11, and 13.
Web shell
4d27b4eb1c5dbb3d8160f29b8119523e locale.php
Infected installer
748c9f8cb1065000616204935f96207f trueconf_windows_update.exe
PhantomCore DLL
c5a460e4e68a088f6e51b2c6474642ec
129462164a7d52e9ea8560b60f0412c5 doc.txt
ec0bf4a2186a88874e9f26f07cfeb532 usocacheddata.txt
b348642146ea34771e5785c5857950f5
c915cb6c2aeb863ee8479238e1644217 doc.txt
0e79996d9483d1e44fea32b0a48c2c19 doc.txt
2bb75c20e778eb5c416965bd4d4259b1 trueconf_windows_client_x64_[redacted].exe
b3a6fee3307f1c26841fd5c603e2b013 usocacheddata.txt
8fcc3e4ccbf1725d9989fb464abf3561 usocacheddata.txt
PhantomGraph
489f43be558b2679284ceabed7adc4f3 sysexcsvc.dll
dd1fd2b459b97b7d59375cb8383cd19a sysreadsvc.dll
0e4541c3153ec5ed01497f19cf4f63d0 sysexcsvc.dll
12d4e8f5295f2ef7e0f9bfc0f4830939 sysexcsvc.dll
7f267006cac10f341c356b62fe493527 sysexcsvc.dll
ee2861d5965e8730708cd1da8a93fa4c sysexcsvc.dll
Backdoor (ELF)
c3a2abe8756910f42582b04a44ea3514
43f435c3c437bc879a2d7d4634f43494
Rootkit
aee9642b45b099cb7f3053b9b680b425
81.177.32[.]12
194.87.239[.]71 ssh
194.87.93[.]153 ssh
38.244.205[.]244
31.59.102[.]61
penzadogshelter[.]site
trendy-market[.]site
bright-deals[.]site
nova-stream[.]site
rinomobile[.]ink
urbanpixel[.]store
flexish[.]shop
media-hub[.]today
cosmetic-deals[.]store
vks.gossopka[.]forum
SysExcSvc
SysReadSvc
C:\Windows\System32\inetsrv\SysExcSvc.dll
C:\Windows\System32\inetsrv\SysReadSvc.dll
C:\Windows\System32\inetsrv\graphi-refresh.dat
C:\Windows\System32\inetsrv\share\input_*.txt
C:\Windows\System32\inetsrv\share\output_*.txt
%TEMP%\cmd_cmd_*.bat
%LOCALAPPDATA%\TrueConf\Client\api-ms-win-crt-time-l1-1-0-2.dll
/etc/systemd/system/omicluster.service
/etc/systemd/system/schedul2-bin.service
/opt/acronis/bin/schedul2-bin
/omi/bin/omicluster
/usr/lib64/libzvbi-tchain.so.2
/var/tmp/cx2
HKEY_CURRENT_USER\Software\Classes\CLSID\{0340F119-A598-4ed9-B0AC-6F6A12D3E755}\InprocServer32
Backdoor.PHP.WebShell.abi
Backdoor.Win64.PhantomCore.dt
Trojan.Win64.Agent.smgvnc
Trojan.Win64.Agent.smgvnb
HEUR:Backdoor.Win64.PhantomCore.gen
HEUR:Backdoor.Linux.Agent.fb
HEUR:Backdoor.Linux.PhantomHook.a
HEUR:Backdoor.Linux.PhantomReact.a
Trojan.Win64.PhantomGraph.gen
UDS:Backdoor.Win64.PhantomCore.a
import "pe"
rule apt_HeadMare_PhantomCore
{
meta:
description = "Rule to detect PhantomCore used by HeadMare"
author = "Kaspersky ICS CERT"
copyright = "Kaspersky ICS CERT"
version = "1.0"
last_modified = "2026-08-02"
hash = "c5a460e4e68a088f6e51b2c6474642ec"
strings:
$a1 = "lying.dll" ascii
$a2 = { 2D 7F 95 4C 2D F4 51 58 }
$a3 = { 4F 81 67 F7 7E 7B 05 14 }
condition:
(uint16(0) == 0x5A4D) and (filesize > 4MB) and (filesize 20MB) and (all of them) and (pe.number_of_signatures == 0)
}
rule apt_HeadMare_FakeConf_installer
{
meta:
description = "Rule to detect any unsigned TrueConf installers"
author = "Kaspersky"
copyright = "Kaspersky"
version = "1.0"
last_modified = "2026-08-02"
hash = "748c9f8cb1065000616204935f96207f"
strings:
$a1 = "TrueConf Setup" wide
$a2 = "This installation was built with Inno Setup." wide
condition:
(uint16(0) == 0x5A4D) and (filesize > 20MB) and (all of them) and (pe.number_of_signatures == 0)
}
rule apt_HeadMare_PhantomCore_exchange
{
meta:
description = "Rule to detect PhantomCore exchange module used by HeadMare"
author = "Kaspersky ICS CERT"
copyright = "Kaspersky ICS CERT"
version = "1.0"
last_modified = "2026-08-02"
hash = "489f43be558b2679284ceabed7adc4f3"
strings:
$a1 = "graphi_exchange.dll" ascii
$a2 = "graphi-client/1.0" ascii
$b1 = "https://graph.microsoft.com/v1.0/me/drive/root:/" ascii
$b2 = ":/children?$select=name,id&$top=200" ascii
$b3 = "offline_access Files.ReadWrite" ascii
$b4 = "GRAPHI_INSECURE" ascii
$b5 = "\"@microsoft.graph.conflictBehavior\":\"replace\"}" ascii
$b6 = "https://login.microsoftonline.com/" ascii
condition:
(uint16(0) == 0x5A4D) and (any of ($a*)) and (3 of ($b*))
}
rule apt_HeadMare_PhantomCore_executor
{
meta:
description = "Rule to detect PhantomCore executor module used by HeadMare"
author = "Kaspersky ICS CERT"
copyright = "Kaspersky ICS CERT"
version = "1.0"
last_modified = "2026-08-02"
hash = "dd1fd2b459b97b7d59375cb8383cd19a"
strings:
$a1 = "graphi_reader.dll" ascii
$a2 = "^input_(.+)\\.txt$" ascii
$b1 = "output_" ascii
$b2 = "cmd_cmd_" ascii
$b3 = "cmd /c \"\"" ascii
$b4 = "error: failed to start cmd process" ascii
$b5 = "share" ascii
$b6 = "SysReadSvc" ascii
condition:
(uint16(0) == 0x5A4D) and (filesize < 4MB) and (any of ($a*)) and (4 of ($b*))
}
rule apt_HeadMare_FakeLocale_webshell
{
meta:
description = "Rule to detect the HeadMare TrueConf web shell"
author = "Kaspersky"
copyright = "Kaspersky"
version = "1.0"
last_modified = "2026-08-04"
hash = "4d27b4eb1c5dbb3d8160f29b8119523e"
strings:
$a1 = "X-Redirect-Bit" ascii wide nocase
$a2 = "tc_vcs_web_db_conn" ascii wide
$a3 = "user=postgres" ascii wide
$b1 = "UPL ok::" ascii wide
$b2 = "DWN fail nexs" ascii wide
$b3 = "DWN fail inv" ascii wide
condition:
(2 of ($a*)) or (2 of ($b*))
}
rule apt_HeadMare_TrueConf_Rootkit
{
meta:
description = "Rule to detect the HeadMare rootkit installed on TrueConf servers"
author = "Kaspersky"
copyright = "Kaspersky"
version = "1.0"
last_modified = "2026-08-06"
hash = "aee9642b45b099cb7f3053b9b680b425"
strings:
$a1 = "PQconnectdb"
$a2 = "obfuscated_data"
$a3 = "install_hook"
condition:
(uint32(0) == 0x464c457f) and (filesize < 400000) and (all of them)
}
rule apt_HeadMare_Github_Backdoor
{
meta:
description = "Rule to detect the HeadMare backdoor with Github C2"
author = "Kaspersky"
copyright = "Kaspersky"
version = "1.0"
last_modified = "2026-08-06"
hash = "43f435c3c437bc879a2d7d4634f43494"
hash = "c3a2abe8756910f42582b04a44ea3514"
strings:
$a1 = "cryptor5crypt"
$a2 = "execraw_task"
$a3 = "jitter_task"
$a4 = "upload_task"
$a5 = "exec_task"
$a6 = "react_comment"
condition:
(uint32(0) == 0x464c457f) and (filesize > 5000000) and (filesize < 10000000) and (4 of them)
}
The LevelBlue OpsCTI Team recently identified a large-scale phishing campaign leveraging a new social engineering method to deploy unauthorized ConnectWise ScreenConnect clients. Rather than relying on conventional phishing pages, the campaign recreates convincing software update and installation alerts by impersonating the Microsoft Store and Apple App Store while reproducing the look and behavior of trusted applications through dynamic modal dialogs and other interactive web elements. The campaign impersonates trusted software and services, including Google Meet, Adobe Acrobat, Microsoft Teams, Zoom, Docusign, Secure Access Manager, Blue Mountain, Paperless Post, and other popular applications.
In Part 1, we went from a single open port to a complete map of the target. Version, topology, indices, secrets, credentials, privilege structure — all of it documented, all of it ready to be weaponized. Reconnaissance is finished. Now we find out what breaks.
You are mid-engagement. Nmap finishes its sweep and port 9200 lights up on a host. Elasticsearch. You know it matters. You know the client's logging pipeline, search infrastructure, or analytics platform probably flow through it. But what do you actually know about this cluster? Right now, nothing. No version, no configuration, no indication of whether it is locked down or wide open.
Frontier AI is reshaping vulnerability discovery. Learn how our NOVA system found 14,000+ unknown vulnerabilities across the open-source software supply chain.
The post The Frontier AI Vulnerability Burst: Industrializing Autonomous Zero-Day Discovery in Open-Source Software appeared first on Unit 42.
Unit 42 details a Chinese speaking threat actor combining autonomous AI scanning across seven vulnerabilities with manual exploitation. Read more.
The post Chinese-Speaking Threat Actor Harnesses AI Models for Autonomous Cyberattacks appeared first on Unit 42.
You have almost certainly interacted with Elasticsearch today. The search bar on your company's internal wiki. The autocomplete on the e-commerce site where you ordered lunch. The log aggregation dashboard your SOC team stares at for eight hours straight. The recommendation engine that just served you this article. Elasticsearch is the invisible infrastructure behind modern search, and it processes some of the most sensitive data an organization possesses, including access logs, customer records, financial transactions, and authentication events. It knows where your users click, what they search for, and when they log in.
Following GreenPlasma, YellowKey and MiniPlasma, as well as RoguePlanet and GreatXML, the Nightmare-Eclipse disclosure actor has published LegacyHive, its latest Windows proof-of-concept (PoC) released shortly after Microsoft's July 2026 Patch Tuesday.
Coordinated vulnerability disclosures operate on a straightforward premise: the affected vendor is notified first, given a defined window to remediate, and public disclosure follows.
Vexed researcher Nightmare-Eclipse (aka Chaotic Eclipse, Dead Eclipse, and MSNightmare) released his ninth unpatched Windows vulnerability called LegacyHive. This latest bug drop is a Local Privilege Escalation (LPE) vulnerability affecting Windows User Profile, a component responsible for loading and unloading Windows user profiles. When exploited, LegacyHive can enable attackers to load other users’ hives and gain access to application data and Windows Explorer history, among others.
A technical analysis of three chained zero-day vulnerabilities in Siemens ROX II OT switches that allow privilege escalation and persistent root access.
The post Three Steps to the Terminal: A Siemens ROX II Zero-Day Trilogy appeared first on Unit 42.
You're browsing a legitimate small business website. Before the page loads, a familiar Cloudflare box appears: "Verify you are human." It asks you to open Terminal, paste a code, and press Enter. You've seen this before. You follow the steps. The page loads normally.
The LevelBlue Managed Threat Research team investigated a security alert in a customer environment involving a malicious ZIP file containing a Windows shortcut (.lnk) used for initial execution. When triggered, the LNK file executes a hidden PowerShell command that downloads a legitimate node.exe binary and deploys a NodeJS-based backdoor. The malware also uses the EtherHiding technique, leveraging the TON blockchain to retrieve its command-and-control (C2) address.
