NIS2 Article 20 makes management bodies answerable for the access controls they approve, and boards are starting to ask for proof that those controls work. NIS2 board accountability gives directors a more direct role in cybersecurity. Under Article 20, the management bodies of essential and important entities must approve the cybersecurity risk-management measures required by the directive, oversee their implementation, and can be held liable for infringements, subject to national law. That obligation is live across most of the EU, with four member states going to the Court of Justice in July for failing to notify full transposition. Access control is one of the required measures. Directors are not expected to decide who gets administrator rights or review individual user accounts. They do, however, need to know that access is being controlled, that someone has responsibility for managing the risk, and that problems are being addressed. An estimated 160,000 entities fall within the scope of NIS2 across the EU. Approval is only part of the job Article 20 requires management bodies to both approve cybersecurity risk-management measures and oversee their implementation. It also requires the members of those bodies to undergo cybersecurity training, and to offer comparable training to employees on a regular basis. The measures themselves are set out in Article 21. They include access control policies and asset management, along with the use of multi-factor authentication or continuous authentication solutions where appropriate. Many organizations have traditionally left these decisions to security and IT teams. Under NIS2, the board still needs to know whether the measures it approved are being implemented. Day-to-day responsibility can remain with the CISO and IT teams. The difficulty comes when the person responsible for a risk does not control the budget, systems, or people needed to address it. Greece’s implementation goes further in defining who carries that responsibility. Its NIS2 law requires essential and important entities to appoint a suitably qualified member of their organization as an Information and Communication Systems Security Officer. The officer coordinates compliance, must have the resources needed to do the job, and operates independently of the company’s data protection officer. Access problems also need to reach the board in terms that it can act on. If an MFA rollout has failed or administrators have more access than they need, the board needs to understand the risk of leaving it unresolved. The CISO may be responsible for the risk without controlling the budget or teams needed to fix it. The board needs evidence Board reports often show what the access control policy requires, rather than whether it is being followed. For example, a company policy will likely require MFA and the removal of accounts when employees leave. However, the board also needs to know about the exceptions, including old accounts that remain active, excessive privileges, MFA exemptions that have never been reviewed, and suppliers whose access has outlasted their contracts. The board needs information about how the controls are working in practice. In a NIS2 access management report from European password and secrets manager Passwork, regulators from two national cybersecurity authorities and independent security leaders describe boards that have absorbed the liability change and now ask for proof that controls work, where they once accepted a summary of the policy. Organizations should be able to show who can access critical systems, why that access was granted, who approved it, how long it is valid, and when it was changed or revoked. The board needs to know whether that evidence exists and what it says about material access risk. Personal liability raises the stakes Implementation is still catching up in parts of Europe. In July 2026, the European Commission referred Ireland, Spain, France, and the Netherlands to the Court of Justice of the European Union for failing to notify full transposition of NIS2 into national law. The Commission also asked the Court to impose financial sanctions. For organizations in countries where NIS2 has been transposed, regulators have significant enforcement powers. For essential entities, member states must set a maximum fine of at least €10,000,000 or 2% of the global annual turnover, whichever is higher. Important entities are subject to a lower maximum threshold, of at least €7,000,000 or 1.4% of their global annual turnover, in a similar capacity. Article 32 also allows competent authorities, in specific circumstances, to seek the temporary prohibition of someone at CEO or legal representative level from exercising managerial functions in an essential entity. This can happen where enforcement action has failed to produce the required response. It is not an automatic consequence of a cyber incident. Article 20 separately requires member states to ensure that management bodies can be held liable for infringements of Article 21, subject to the applicable rules under national law. Approving the policy is not the end of the board’s involvement. It also needs information on whether the policy is being followed and what is happening with known problems. Verizon’s 2026 Data Breach Investigations Report found credential abuse somewhere in the chain of 39% of breaches, more than any other vector. Stolen credentials also circulate at scale, as the exposed database of 24 billion credential records reported in June showed. Directors should expect an answer on which accounts and credentials reach critical systems, who is responsible for them, and whether that access is being controlled. Give access risk an owner with authority CISOs briefing their boards should be able to say who owns access-control risk and whether that person has the authority to address it. The board also needs evidence of what is happening. When an access problem is identified, someone needs to be responsible for getting it fixed. That might mean removing unnecessary privileges or dealing with an MFA exception that has remained in place. If the fix depends on another team’s budget or resources, the board needs to know. Board reporting should show whether the controls it approved are working, flag significant exceptions that remain unresolved, and who is accountable for closing them. Author the author: Kirsten Doyle has spent 27 years writing about technology and has covered the industry through some of its most significant shifts. Her focus areas include cybersecurity, cloud, channel, enterprise IT, and data centre operations. She works across many formats: news, features, white papers, eBooks, and thought leadership. Kirsten has many years of experience in journalism and editing for both print and digital media, and has supported plenty of go-to-market and PR programmes with technically grounded writing that holds up to scrutiny. She is also a regular writer at Bora. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, NIS2)
Customers of the mSecure password manager are being urged to act now to protect the confidential information they store in it, possibly by changing passwords, resetting two-factor authentication (2FA), and removing passkeys. The warning came in an email sent on November 3 in the name of developer mSeven Software. It says the company recently learned of suspicious activity linked to the app but
Binance on Tuesday said the record-setting theft of $6.43 billion in assets from the world's largest cryptocurrency exchange was the culmination of a "sophisticated, multi-stage campaign" that involved the use of a tampered Homebrew installation and recruitment-themed social engineering attack targeting two of its engineers. "It was not a single isolated breach or a simple phishing incident,"
Barracuda has disclosed details of two vulnerabilities affecting its products that, if chained together, could allow an attacker to bypass authorization controls and gain root access to a susceptible device. The vulnerabilities, a pre-authentication authorization bypass vulnerability and a command injection vulnerability, have been assigned the CVE identifiers CVE-2026-116606 (CVSS score: 9.3)
A maximum-severity security flaw impacting Siemens SIMATIC IoT2050 Advanced devices has come under active exploitation, VulnCheck has revealed. The vulnerability, tracked as CVE-2026-58115 (CVSS score: 10.0), is a case of missing authentication for a critical function that could be exploited by an unauthenticated remote attacker to run arbitrary code. "SIMATIC IoT2050 Advanced devices running
Chinese hackers used ARTEX, an open-source, multi-agent AI penetration-testing framework, in attacks against more than 200 organizations across 14 countries. The operation began on October 17 and targeted government ministries, telecommunications providers, and organizations in the pharmaceutical, semiconductor, defense, nuclear, and financial sectors. The threat actor compromised at least nine organizations and carried out password spraying attacks and credential scanning activity against at least 12 other high-value targets. According to researchers at CrowdStrike, the "campaign marks an escalation in China-nexus use of AI agents, from single intrusions to multiple simultaneous engagements directed by a human operator." Confirmed victims include telecommunications providers in the United States and Belgium, government organizations in Greece, the Middle East, and Latin America, and pharmaceutical firms in the U.S.
The maintainer of BTCPay Server, an open-source cryptocurrency payment processor, has urged users to install the latest version as hackers have been draining “hot wallets” on instances running older versions. Operators were initially advised to shut down their servers pending a full investigation into the unauthorized Bitcoin and Litecoin payments. The BTCPay Server project provides businesses with a self-hosted payment processing solution that allows them to accept Bitcoin and other digital assets directly into their own wallets, eliminating payment intermediaries or paying platform processing fees. It handles checkout invoices and payment verification, including payments over the Lightning Network. Early Monday, the maintainer said it had “identified an attack vector targeting instances running version 2.4.1 (July 23, 2026) or earlier” and strongly urged users to update to version 2.4.7.
Chainalysis suffered a cyberattack exposing customer names, emails and user IDs, while investigation data and passwords remain safe. Blockchain analytics firm Chainalysis says it was the target of a cybersecurity incident. A threat actor accessed its systems and internal tools and stole some non-sensitive customer data. “This month, Chainalysis detected an external actor using offensive agentic techniques to gain unauthorized access to Chainalysis systems and internal tools.” reads the statement published by the company, which also includes the full email sent to affected customers. Chainalysis says it discovered the attacker this month and described the methods used as offensive techniques involving AI agents. However, it did not explain how the attack was carried out. The company also warned that AI-powered attacks are becoming more common across the industry and said it is taking a company-wide approach to dealing with this growing threat. The data the attacker reached matters most. According to Chainalysis, attackers gained access to user IDs, full names, email addresses and organization role labels for users on the platform. The company is still working out which records belong to which customer and promises a detailed breakdown when it finishes. What the attacker didn’t reach is just as important. So far, Chainalysis has found no sign of access to investigation content such as graphs, notes or cases, or to transaction and query results. It also found no clear-text passwords, MFA secrets or user session tokens in the attacker’s hands. “Based on the evidence available to date, we have found no indication that these incidents resulted in the unauthorized access of investigation content (such as graphs, notes, or cases), transaction or query results, passwords in clear text, MFA secrets, or user session tokens.” continues the press release. Some non-sensitive customer data, such as user IDs, full names, email addresses, and organization role labels for users provisioned on our platform were accessed, including such information relating to your organization and users. We are in the process of determining the records for your organization that were accessed and will provide a detailed breakdown as soon as that analysis is complete.“ The company says it’s patching vulnerabilities and hardening its systems around the clock. It has hired outside cybersecurity experts and is using large language models to support a wider review that goes beyond the flaws the attacker used. It has already told all its customers and will keep them updated. Chainalysis advises customers to watch out for unexpected password reset requests or login approval prompts. The company also strongly recommends enabling two-factor authentication (2FA) for all organizations that have not already done so. Since the exposed data includes names and email addresses, attackers could use it to launch phishing campaigns. The company also clarified that the security incident has nothing to do with the recent theft of more than $6 billion from Binance. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, Chainalysis)
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. HTTP header parameters sent by the implant to the 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) Checking for the presence of the CyberStrikeAI agent on the system Delivery and execution of the CyberStrikeAI agent 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. Identical build metadata for the Windows and Linux implant samples, indicating a shared build environment 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. Differences in CyberStrikeAI versions on the command-and-control servers 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. Alert regarding the detection of CyberStrikeAI communication with a C2 server 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.” NDR:NAD alert: reconnaissance via LDAP queries detected 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. Visualization via the Research Graph on the Kaspersky Threat Intelligence Portal 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. Demonstration of attributing a malicious implant to the CyberStrikeAI framework 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
Cybersecurity researchers have disclosed details of a cyber espionage campaign targeting Israel and the Kurdistan Region of Iraq by an Iran state-aligned threat actor between August and October 2026. According to a report published by Dream Research Labs, the campaign has led to the compromise of a high-profile individual in Israel, along with additional compromises affecting a government cloud
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