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 discovered a new Windows infostealer malware called VOLTA-AUTODOXER (aka VOLTA) that makes use of a large language model (LLM) to rank a victim's stolen data by their monetization value. Like other typical stealer families, VOLTA is equipped to harvest passwords and cookies from Chromium-based web browsers; data from Outlook profiles and Slack workspaces;
The FBI has confirmed that North Korean state-sponsored hackers known as TraderTraitor were behind the $6.3 billion crypto-theft attack on Binance, making it the largest cryptocurrency heist on record. In a security alert published Saturday, the agency attributed the attack to TraderTraitor, a North Korean hacking group also tracked as Lazarus Group, Jade Sleet, and APT38, linked to numerous cryptocurrency thefts. "The Federal Bureau of Investigation (FBI) is releasing information advising the Democratic People's Republic of Korea (North Korea) was responsible for the theft of more than $6 billion USD in virtual assets from cryptocurrency exchange Binance, on or about October 29, 2026," the FBI said. This official attribution follows blockchain intelligence firms TRM Labs and Chainalysis, which had already linked the theft to TraderTraitor.
The FBI blames North Korea for stealing $6 billion from Binance and urges crypto firms to block transactions linked to the stolen funds. The FBI has named North Korea as the party behind the theft of more than $6 billion in virtual assets from the crypto exchange Binance, on or about October 29, 2026. The bureau published its findings in a public alert. The FBI specifically blames the North Korea-linked APT “TraderTraitor,” also tracked as Lazarus Group and APT38. Binance hasn’t named a culprit, but the blockchain analysis firm TRM has. It was the first to attribute the attack to TraderTraitor, and Chainalysis also said it’s highly confident in that finding. “The Federal Bureau of Investigation (FBI) is releasing information advising the Democratic People’s Republic of Korea (North Korea) was responsible for the theft of more than $6 billion USD in virtual assets from cryptocurrency exchange Binance, on or about October 29, 2026. The FBI refers to this specific North Korean malicious cyber activity as “TraderTraitor.”” reads the FBI’s statement. “The FBI, together with partners at the USSS and IRS-CI, has observed TraderTraitor actors converting stolen assets primarily into Ethereum (ETH). It is expected these assets will then be further laundered by converting them using DeFi and instant swap services as they prepare them for further laundering stages.” Attackers used access from an engineer’s laptop to reach a service container and send unauthorized withdrawal requests. Binance says it has found no evidence that attackers stole any wallet private keys. The FBI, along with the U.S. Secret Service (USSS) and IRS Criminal Investigation (IRS-CI), has spotted TraderTraitor actors turning the stolen assets mostly into Ethereum (ETH). The agencies expect the next step to be more laundering, by swapping the funds through DeFi and instant swap services as the actors prepare them for later stages. Instant swap services are aptly named. The alert is aimed at private companies as well as law enforcement agencies. The FBI, USSS and IRS-CI are asking several types of businesses to help stop the money laundering. These include THORNode operators, who support the THORChain network, RPC node operators, who provide apps with access to blockchains, instant crypto exchanges, DeFi platforms, blockchain analytics companies and other crypto service providers. The request is clear: these companies should block any transactions involving the wallet addresses used by TraderTraitor, including funds that can be traced back to them. According to the FBI, these addresses have received stolen funds and are controlled by North Korean attackers or are closely linked to them. The full list of addresses is included in the alert. If you run any of these services, the first job is to check your blocklists against that list. The second is to look at transaction flows that touch those addresses indirectly, since the FBI says to block anything “derived from” them, not just direct transfers. Blockchain analytics firms are named for a reason, because tracing derived funds is their work. The FBI says it’s committed to protecting the virtual asset community by identifying, mitigating and disrupting North Korea’s illicit cybercrime and virtual asset theft. “The FBI maintains its commitment to protecting the virtual asset community by identifying, mitigating, and disrupting North Korea’s illicit cybercrime and virtual asset theft activities.” concludes the report. “If you have any information to provide, please contact your local FBI field office or FBI’s Internet Crime Complaint Center at ic3.gov.” TRM says the attacker moved about $6.36 billion out of Binance wallets on ten blockchains on October 29, most of it in roughly 20 minutes starting at 20:05 UTC. That makes it the largest crypto theft on record, about four times the $1.5 billion Bybit hack of February 2025. “On October 29, 2026, an attacker moved approximately USD 6.36 billion out of Binance wallets on ten blockchains, most of it in about 20 minutes starting at 20:05 UTC. That makes this the largest crypto theft on record, about four times the USD 1.5 billion Bybit hack of February 2025. Binance has confirmed a security incident and suspended withdrawals on several networks.” TRM said. “TRM attributes the attack to TraderTraitor, North Korea’s elite hacking unit, based on robust on and off-chain intelligence. We make this judgment with high confidence. TRM is tracking the funds and alerting partners through the Beacon Network.” Freezing the money only went partway. Tether, Circle, Ripple and World Liberty Financial froze about $2.67 billion in stablecoins, and Arbitrum’s Security Council froze 8,426 ETH and 74.6 million ARB, for roughly $2.7 billion in all. But the attacker raced the freezes by selling USDT to market makers and borrowing ETH against USDT on Aave. It now holds more than 420,000 ETH and 18,270 BTC, about $2.4 billion beyond any issuer’s reach. The early freeze numbers were messy too, Tether and Circle froze approximately $1.79 billion in stablecoins associated with the suspicious transfers in the initial reports. Those amounts describe different moments, and you shouldn’t add them without checking whether they cover distinct assets. A freeze works on tokens that have an issuer to call, and Ether and Bitcoin don’t. Below is the link to my interview regarding the Binance case for Italian state television. https://www.youtube.com/watch?v=DtvhBA9TdPo Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, Binance)
Security Affairs AI-CYBERSECURITY newsletter includes a collection of the best articles and research on AI in the international landscape Artificial intelligence is rapidly changing cybersecurity, reshaping both the techniques used by attackers and the tools available to defenders. AI agents can automate tasks, analyze large amounts of data, discover vulnerabilities and accelerate offensive operations. At the same time, AI is creating new capabilities for threat detection, incident analysis and response. Security Affairs’ new newsletter follows this evolution, covering every week the latest threats, attacks, vulnerabilities and developments at the intersection of AI and cybersecurity. Investigating alignment failures in AISI’s incident An alignment assessment of the UK AISI cybertesting incident Improving our alignment and security efforts Will AI expose the rickety infrastructure of the internet? Anthropic’s IPO filing reveals its strategy for keeping enterprise customers hooked Alibaba’s Qwen AI caught trying to contact server in China Not Only ARTEX: The Different Levels AI is Used in Attacks Targeting Japan Disruptions ‘inevitable’ as AI-powered cyber threats grow, Australian Signals Directorate says CLAUDE CODE FOUND SPYWARE ON MY FAMILY’S PHONES AND A BACKDOOR ON MY MAC Discovering and exploiting vulnerabilities in critical, real-world software with Claude Invasive Ads: The Unconventional Cyber Surveillance Attack Surface Early progress on accelerating drug development with Claude Putting Claude Code in a box: a sandboxed dev container for Zed How AI tools are used for doxing Gemini 4 Carbon: our next frontier model Tricks from the Past deployed via signed ChatGPT and Claude binaries AI models may be capable of self-replicating without access to a datacenter Load/Mine: A Hugging Face Malware Campaign Returns After Takedown Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, newsletter)
Security Affairs Malware newsletter includes a collection of the best articles and research on malware in the international landscape Malware Newsletter uCreator: virtualization-based Android spyware platform targeting WeChat, Telegram and other messaging apps deepveindrilling.com: Magento CMS block loader hits 953 stores CLAUDE CODE FOUND SPYWARE ON MY FAMILY’S PHONES AND A BACKDOOR ON MY MAC Operation FlashHole: Lazarus breaks security, defies control Security notice — malicious version of Anthropic.claude-code (2.1.313) identified in the Cursor Extension Marketplace Tricks from the Past deployed via signed ChatGPT and Claude binaries Trezor warns of suspected crypto-theft implant in hardware wallet Load/Mine: A Hugging Face Malware Campaign Returns After Takedown Decoding Maliciousness: LLM-Assisted Semantic Filtering for Scalable Malicious PyPI Package Detection A Two-Stage Selective Deep Learning Framework for Malware Family Classification in Encrypted Network Traffic Silent Blindness: Fault Vulnerability of Hardware Malware Detectors Measuring Duplication, Leakage, and Rendering Dependence in Malware-as-Image Benchmarks Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, newsletter)
Hackers attempted to exploit a critical GitLab vulnerability in Alpine Linux's development infrastructure on October 20-21, prompting the open-source project to rebuild its GitLab server and reset all passwords over concerns of a possible compromise.
A new round of the weekly Security Affairs newsletter has arrived! Every week, the best security articles from Security Affairs are free in your email box. Enjoy a new round of the weekly SecurityAffairs newsletter, including international press. U.S. CISA adds Ivanti Connect Secure flaws to its Known Exploited Vulnerabilities catalogAttackers Exploit Critical Drupal Commerce Core Vulnerability CVE-2026-118539Yoba Malware Hides a Crypto Miner Behind Legitimate OpenAI and Anthropic BinariesU.S. CISA adds GitLab and Cisco flaws to its Known Exploited Vulnerabilities catalog$6.3 Billion Stolen from Binance in Attack Attributed to North KoreaHow to prioritize remediation after a pentest: a practitioner workflowHacker leaks data on more than 40,000 Homeland Security employeesBinance Investigates Suspicious Crypto Transfers Worth Up to $5.8 BillionUS Probes Suspected Hack Behind Military Base Freezer Failures484,142 Exposed Cloud Buckets Contain 18 Billion FilesU.S. CISA adds Linux Kernel and Broadcom VMWare ESX flaws to its Known Exploited Vulnerabilities catalogVolt Typhoon Used Silent Train to Hide Zero-Day AttacksElection Website Security: 42% of US County Sites Still Lack .gov DomainsENGIE Data Breach Exposes Customer Details and Energy ContractsVirtualine Domain Seized, but Allegedly Abusive Website Remains OnlineHive and Nefilim Ransomware Affiliate Gets 70 Months In JailAds or Surveillance? Researchers Flag Suspicious Targeting of Starlink UsersCVE-2026-97631: 16-Year-Old UEFI Flaw Threatens ServersU.S. CISA adds Google Brotli and Adobe Commerce and Magento flaws to its Known Exploited Vulnerabilities catalogThree Japan-Focused Cyberattacks Show How Hackers Use AIItaly ’s Parliament Under Cyber Pressure: Why the Attack on the Chamber MattersAustralia’s Cyber Spy Chief: Expect More Disruption as AI Accelerates HackingU.S. CISA adds PHP flaws PHP2Shell to its Known Exploited Vulnerabilities catalogAttackers Take Control of Dahua Cameras and Video Recorders Via Easy4IPCohere Finds Qwen AI Model Contacting a Server in ChinaMagento Campaign Infects 953 Online Stores Across Five DomainsAI-Generated Fake Bank Calls and NFC App Enabled €380,000 Fraud, Dutch Police SayResearchers Uncover Rentable Android Spyware uCreater Targeting WeChat and TelegramPHP2Shell: Suspected North Korean campaign targets 216 vulnerable PHP hostsAnthropic reveals how Claude Mythos 5 crossed security testing boundariesOpenAI Paused Frontier AI Training After a Security Incident International Press – Newsletter Cybercrime Binance Says User Funds Are ‘Completely Safe’ After $3.5 Billion Drains From Its Hot Wallets Police arrest two suspects in AI-powered bank fraud that cost victims €380,000 M365 Token Spray and Replay Campaign Compromises Business Email Accounts Chinese National Extradited for Alleged Role in Multimillion-Dollar Cryptocurrency Scam Targeting U.S. Citizens Ukrainian National Sentenced to 70 Months in Prison for Multi-Million Dollar Ransomware Conspiracies Hacker stole personnel data about thousands of Homeland Security employees, including CBP and ICE agents deepveindrilling.com: Magento CMS block loader hits 953 stores “Bulletproof” website domain seized in international law enforcement operation Trezor warns of suspected crypto-theft implant in hardware wallet Polymarket halts trading after crypto-draining script breach, $2.5M traced Malware uCreator: virtualization-based Android spyware platform targeting WeChat, Telegram and other messaging apps CLAUDE CODE FOUND SPYWARE ON MY FAMILY’S PHONES AND A BACKDOOR ON MY MAC Security notice — malicious version of Anthropic.claude-code (2.1.313) identified in the Cursor Extension Marketplace Tricks from the Past deployed via signed ChatGPT and Claude binaries Hacking PHP2Shell exploitation observed: packet-to-process RCE evidence CYFIRMA Threat Advisory: Suspected DPRK Campaign staging PHP2Shell Targeting against Global PHP infrastructure Beyond the Inbox: How Threat Actors Abuse Microsoft Teams External Chat for Initial Access Not Only ARTEX: The Different Levels AI is Used in Attacks Targeting Japan Broccoli2shell: Everything You Need To Know About The Critical Brotli Decoder Vulnerability Discovering and exploiting vulnerabilities in critical, real-world software with Claude Putting Claude Code in a box: a sandboxed dev container for Zed Hackers Target Critical Oracle E-Business Suite RCE Flaw Days After Security Patch The Last Proof of Human Craft: Forging a Microsoft Edge Full Chain with Logic Bugs Only! Pentagon treating military base freezer malfunctions as suspected cyberattack, officials say GitLab security incident: attempted exploitation of CVE-2026-97229 Intelligence and Information Warfare Alibaba’s Qwen AI caught trying to contact server in China Hackers can gain control of cameras and DVRs through cloud access Disruptions ‘inevitable’ as AI-powered cyber threats grow, Australian Signals Directorate says Operation FlashHole: Lazarus breaks security, defies control Hackers Sympathetic to Russia Are Hitting Utilities and Factories Alleged Israeli spy company heavily targeted Starlink users, sources say Silent Train: uncovering Volt Typhoon’s operational framework for zero-day exploitation and strategic access North Korea Steals USD 6.36 Billion From Binance in the Largest Hack in History Mapping the Political Influencer Industry and the Problems it Causes for Democracy Cybersecurity NIS 2 Italy: Key Deadlines, Board Duties and What ACN Expects From Companies The Two Things CISOs Should Be Thinking About In The Wake Of The PHP Nightmare Code-execution flaw in UEFI firmware affects most servers on planet Earth 2026 County Election Website Security Census: Key Findings Cloud Exposure Index 2026: 484,142 open buckets mapped ENGIE: Data leak via a tool belonging to its business partners Beyond Framework Signatures: Understanding AI-Driven Intrusions I Think The Military Commissary’s Freezers Were Hacked Manifestos, Fear, and ‘Loser Thinking’: Inside Proton’s AI Reckoning Passkeys Are Better Than Passwords. But They Are Not Enough Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, newsletter)
SMS of Death lets attackers crash Android call services with a hidden, zero-click message, blocking phone calls, even emergency calls too. A specially manipulated SMS message is enough to crash Android’s phone service, interrupt ongoing calls, and temporarily render the smartphone unusable. The attack requires no interaction from the victim and can even compromise the ability to contact emergency services. The vulnerability, dubbed the SMS of Death, is bringing renewed attention to the security of the messaging system in Android smartphones. The issue, documented by independent researcher Mark Blaszczyk, allows a remote attacker to cause a disruption in phone service by sending a single, specially crafted WAP Push message. WAP Push (Wireless Application Protocol Push) is a particular message transmitted over the mobile network, generally in the form of a binary SMS, which contains structured data and is processed automatically by the smartphone without requiring user intervention. Unlike a traditional SMS, it can carry notifications, links to Internet resources, and configurations for mobile services. The most worrying aspect is the execution method. It’s a zero-click attack, in which the recipient doesn’t have to open the message, select a link, or authorize any action. The content is automatically processed by the operating system and can cause the process responsible for phone functionality to crash. The vulnerability affects “WapPushOverSms“, an Android framework component located in the “frameworks/opt/telephony” path, responsible for interpreting and managing WAP Push messages received by the device. The technical cause lies in the incorrect handling of a numeric value used during message interpretation. Specifically, the “decodeWapPdu()” function, the researcher explains, reads the length of the WSP header through a value encoded as “uintvar” , initially returned as a 64-bit number. The value is subsequently converted to a 32-bit integer without prior range checking. This operation can transform a seemingly valid number into a negative value, which is then used to determine the size of an array. The consequence of this is a “NegativeArraySizeException” exception, thrown when Android attempts to allocate a negative-sized array. The exception is not handled correctly in the processing path and causes the “com.android.phone” process, responsible for telephony functions, to terminate. The researcher demonstrated that a five-byte WSP header can generate the value “-2147483648“, which is sufficient to trigger the issue. Therefore, it is not necessary to transmit particularly large amounts of content to cause the issue. “A 5-byte uintvar of 88 80 80 80 00 decodes to 0x80000000. Cast to a signed 32-bit int, that is -2147483648. Allocating new byte[-2147483648] throws an uncaught NegativeArraySizeException on the telephony main thread, killing com.android.phone:” reads the analysis. An invisible attack that also interrupts emergency calls The message is transmitted as a data SMS via the WAP Push channel on port 2948. Unlike a regular SMS, it isn’t displayed in the message inbox and doesn’t generate a user-recognizable notification. The sender’s number isn’t normally visible in the conversation, and when the device receives the message, Android automatically starts processing it. A crash in the phone process causes a temporary loss of connectivity and interrupts active voice calls, including those to emergency services. According to the researcher’s published evidence, each message processing can cause a disruption in telephone service lasting about one to two seconds, during which the component is rebooted. However, the problem can become much more serious if the message is processed repeatedly. One of the most significant aspects of SMS of Death is the ability to transform a simple crash into a persistent Denial of Service condition. The malicious message can remain in the incoming SMS queue and be reprocessed during subsequent delivery attempts. The telephone operator’s retransmission mechanism can also contribute to a repeat of the event, causing further service disruptions. The researcher reports observing intermittent crashes for up to a week after receiving a single message. Sending around twenty specially crafted messages can render the device unusable for an extended period. In more severe cases, the phone process can continue to crash during system startup, triggering a condition similar to a boot loop. Recovery may require using the Android Debug Bridge (ADB) on a computer, directly intervening in the queue of messages waiting to be processed. For a typical user, this is far from a simple procedure. A threat to communications availability SMS of Death demonstrates how a cyberattack doesn’t necessarily have to involve information theft, code execution, or device control to have significant consequences. In this case, the target is phone service availability, a crucial feature especially when contacting emergency services. The ability to interrupt communications without any user interaction makes this vulnerability particularly sensitive. To reduce exposure, it’s important to check the Android security patch level installed and apply available updates, paying particular attention to devices no longer receiving manufacturer support. Testing conducted by Mark Blah has shown that the issue is reproducible on several devices, including the Google Pixel 9 and Samsung smartphones from the Galaxy and Galaxy A families. The vulnerable code appears to be present on Android versions ranging from Android 12 to Android 17. However, the availability of the fix is not uniform across all generations of the operating system. According to documentation published by the researcher, Google has distributed a fix via the October 2026 security update for Android 14, 15, 16, and 17. Older devices, such as those running Android 12 and 13, may remain exposed if they do not receive corrective updates. It's assigned CVE-2026-58882 and a Critical severity in the November 2026 Android Security Bulletin. We've had it listed as one of the patches included in our security preview releases since our 2026072901 release which you can see in the release notes:https://t.co/tBoWBpALw1 — GrapheneOS (@GrapheneOS) October 29, 2026 About the author: Salvatore Lombardo (X @Slvlombardo) Electronics engineer and Clusit member, for some time now, espousing the principle of conscious education, he has been writing for several online magazine on information security. He is also the author of the book “La Gestione della Cyber Security nella Pubblica Amministrazione”. “Education improves awareness” is his slogan. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, SMS of Death)
A critical flaw in Canonical's LXD and MicroCloud let an attacker log in as a trusted client without holding that client's private key. To do it, the attacker had to reach their HTTPS API over the network and have the trusted client's public certificate. Once in, the attacker would act as that client, with access to the cluster and its virtual machines. If the impersonated account is
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