Executive summary

In 2025, several threat actors exploited CVE-2025-53770 and CVE-2025-53771 (ToolShell vulnerabilities) in on-premises Microsoft SharePoint to achieve remote code execution through crafted HTTP requests. One of the threat actors deployed ransomware on the target’s systems after successful exploitation. Microsoft attributed this ransomware-related ToolShell activity to Storm-2603, distinguishing it from other actors whose observed activity was primarily focused on credential theft, espionage, and data exfiltration.

In December, our retrospective analysis identified activity consistent with Storm-2603’s TTPs. This activity involved the attacker using the IIS worker process as an in-memory bridge to the customer’s infrastructure, keeping the access and control layer entirely within w3wp.exe without writing anything to disk. Although subsequent post-exploitation activity generated disk artifacts, the bridge used to maintain access remained memory-resident.

Let’s look in more detail at this attack.

The attack chain, from reconnaissance to infrastructure compromise

Intrusion timeline: Strategic latency and exploitation

The intrusion analyzed in this report occurred in two stages, with a temporal gap between initial access and subsequent exploitation activity. The attack timeline was as follows:

• Dec 20 – Vulnerability fingerprinting: A single HTTP request targeted a SharePoint administrative endpoint associated with the ToolPane component to determine whether the host was vulnerable to CVE-2025-53770.
• Dec 21-23 – Tactical latency: A three-day period of silence followed the initial probe, separating the vulnerability fingerprinting from subsequent malicious activity and potentially reducing the likelihood of immediate detection.
• Dec 24 – High-intensity re-engagement: On Christmas Eve, when security staffing at many companies is reduced, the full RCE chain was executed through the IIS worker process (w3wp.exe) on the same endpoint as the initial probe.
• Dec 26 – Incident discovery: The three-day gap initially prevented the December 20 probe from being correlated with subsequent malicious activity, marking the event as low priority. Retrospective analysis established that the probe was the initial phase of the subsequent intrusion.

Intrusion timeline

Initial access validation phase

Vulnerability fingerprinting

A vulnerability fingerprinting request on December 20 marked the first observed interaction with the target. The request originated from infrastructure associated with ONYPHE, an internet-scale attack surface discovery and management platform for identifying exposed assets, vulnerabilities, and cyberthreats. Unlike widely used tools such as Shodan and Censys, which merely provide a list of internet-facing assets correlated with the open-source data about known vulnerabilities, ONYPHE actually checks if the target system is vulnerable.

Fingerprinting with ONYPHE

The request characteristics were consistent with ONYPHE’s vulnerability checks, which use active, non-intrusive probes to identify internet-exposed systems vulnerable to specific CVEs. For ToolShell, ONYPHE adopted the approach used by Niels Hofmans (hazcod). Namely, it attempts to inject a harmless marker into the target server to find out if it is vulnerable.

OFFICIAL ONYPHE CVE CHECKER (TOOLSHELL)

This phase served as a validation step against the SharePoint execution surface exposed over HTTP:

• The target processed structured requests through the ASP.NET pipeline without rejection.
• The request path reached application logic capable of handling serialized input.
• No defensive controls interrupted or altered the execution flow at the application boundary.

Approximately 12 minutes later, a second inbound connection originated from infrastructure associated with Pfcloud UG, a provider operating under a bulletproof hosting (BPH) model. Given the timing and the IP’s documented history of scanning and abusive activity, the connection may represent follow-on reconnaissance against the newly identified target. No exploitation was attempted at this stage.

A connection from an IP address belonging to Pfcloud UG BPH

Active exploitation: Santa took your data and left a backdoor

The main malicious activity occurred on December 24. The actor targeted the same SharePoint host with a crafted request from 45.91.94.204 (45.91.92.0/22, ASN 63473, HostHatch, LLC). This resulted in a webshell execution within the IIS worker process (w3wp.exe). The payload was not written to disk; rather, it was loaded directly into the ASP.NET runtime and used during subsequent request processing. The implant remained resident for the lifetime of the affected application domain. This runtime state was lost when the application pool was recycled or IIS was restarted.

Attack originated from an SPN node

The IP address 45.91.94.204 is a Safing Privacy Network (SPN) node. SPN routes each connection through a multilayer VPN path with multiple relay nodes that can obscure the originating IP.

SPN promotes itself as a network that offers strong privacy protection for internet traffic. Its infrastructure resembles Tor’s in many ways, but with some differences. It features the following capabilities:

• Speed: Near-VPN performance for high-speed data transfer.
• Layer 3 support: Supports multiple protocols like TCP, UDP, ICMP (unlike Tor, which only supports TCP).
• App-routing: Direct tunneling without a browser.
• Multi-identity: Simultaneous exit nodes to bypass IP rate limits.
• Fixed-size packets and padding make it harder to analyze its traffic.

It is less common than Tor, and frequently bypasses standard Tor blocklists.

Webshell architecture

The webshell the attackers injected into the w3wp.exe runtime is a multi-module in-memory framework, where execution is orchestrated through the main component (G.cs) and extended via multiple functional units across namespaces.

The implementation follows a modular C# project structure in which each capability is isolated into its own compilation unit and invoked dynamically at runtime.

Webshell components

“G.cs” pipeline control and C2

Pipeline control

The main webshell component is G.cs. It orchestrates the implant’s execution context, lifecycle, C2 and session management.

To ensure persistence within the runtime, G.cs performs the following actions:

• Registers a custom VirtualPathProvider using the non-public RegisterVirtualPassProviderInternal method from the ASP.NET HostingEnvironment class so that ASP.NET uses it for virtual-path processing. The provider is automatically added to the chain of registered virtual path providers.
  
• Delegates the provider’s .NET Remoting lifetime policy to the previous provider in the chain, preserving the existing lifetime behavior.
  

Additionally, to evade defenses and enable the VirtualPathProvider to be registered even if the application in whose AppDomain the webshell is running is precompiled, the module implements the trust control subversion (T1553) technique:

• Uses reflection to check if the application is precompiled.
• Extracts pointers for internal state fields for the BuildManager class, which manages application compilation, and modifies them at runtime.
  
• Uses reflection once again to force runtime injection.

G.cs uses the GetCacheKey() function as the interception point for the virtual path processing flow. It processes incoming requests to the SharePoint server and checks their headers to filter out requests from the attacker.

The specific User-Agent in the request headers triggers the webshell execution. If the request doesn’t match this condition, the implant remains dormant, making its traffic harder to recognize.

Command and control layer (in-memory transport)

The same core module (G.cs) implements the communication layer.

Instead of relying on external frameworks or standard beacons, it uses a custom binary protocol with XOR-based obfuscation to exchange data with the C2. These communications blend into normal application traffic. The module uses the GetCacheKey() hook to intercept standard HTTP requests. When a specific User-Agent is detected in the request headers, the module parses it, extracts the payload, and establishes a TCP connection to a target host inside the target’s network if needed.

Bidirectional TCP tunnel initialization

G.cs establishes a full-duplex proxy tunnel to support interactive control. It stores active target sockets in memory to pair concurrent HTTP read and write channels.

TCP connections from the C2 server to arbitrary hosts inside the target network are supported for pivoting and lateral movement. The traffic is proxied through the SharePoint host.

Target-to-attacker data channel

Attacker-to-target data channel

As can be seen in the screenshots, the attackers used the suo5 tool to implement traffic proxying. Its authors promote it as a stable forward-proxying tool for scenarios where outbound internet connections are prohibited.

To increase its performance, G.cs manipulates ThreadPool by raising its limits for concurrent operations and maximum timeout.

ThreadPool manipulation

“Prog.cs”: Execution entry point and task dispatcher

Prog.cs is the entry point that receives command-line arguments and passes them to uk/London.cs. The latter acts as the orchestrator, handling command parsing and privilege escalation (via named pipe impersonation). We’ll review it in more detail later. If the “suo5” command is provided, London.cs triggers G.cs to hook into the ASP.NET runtime as a memory shell. Once active, G.cs independently processes incoming web traffic inside the web server memory.

Host interaction modules

The following modules provide direct system interaction capabilities, each implemented as a separate unit. London.cs triggers these modules when it receives a corresponding command from Prog.cs.

“FileDownloader.cs”

The FileDownloader.cs module downloads arbitrary files from external URLs provided by the C2. It stores the downloaded files in the Program Data directory without any user interaction.

“LoggedOnUsers.cs”

This module enumerates logged-on users and identifies their session types (interactive, service, remote). The results are stored in memory. The attackers can retrieve them using the proxy and tunneling capabilities provided by G.cs.

“ProcessList.cs”

This module lists running processes with full paths to the corresponding executables on the disk. The results are stored in memory and the attackers can retrieve them via G.cs.

“NetworkInfo.cs”

This module extracts information about existing network connections, including protocols, local and remote IPs and ports, and connection state. It also enumerates network interfaces and headers. The results are stored in memory and can be retrieved by the attackers via G.cs.

“MSI.cs”

This module silently installs and executes MSI packages previously downloaded by FileDownloader.cs.

“User.cs”

This module creates administrative accounts using the native SAM RPC subsystem (SAMRPCNative namespace) rather than standard command-line tooling. This may indicate an attempt to evade detection and provide the attackers with privileged stealthy access to target systems that persists after the webshell has finished its work.

SAMRPCNative

The SAMRPCNative namespace implements a low-level C# P/Invoke wrapper for the native Windows samlib.dll library, comprising three types: SAMStructs.cs, SAMMethods.cs, and SAMEnums.cs.

“SAMEnums.cs”

Implements structured bitwise lists and constant flags mapping numeric codes to their security meaning in SAM, and translates system status return codes.

“SAMStructs.cs”

Defines native data structures required for P/Invoke memory alignment and for marshalling. It securely formats and aligns data structures.

“SAMMethods.cs”

Contains external API declarations (P/Invoke) imported from samlib.dll to perform actions such as connecting to the SAM server, creating users and opening domains.

This layer is crucial for the attackers because it enables account operations.

Privilege escalation

The uk namespace contains offensive primitives for privilege escalation. To elevate privileges to SYSTEM, it uses a local token impersonation technique via the Encrypting File System Remote Protocol (EFSRPC) interface adopted from the EfsPotato tool.

This functionality is implemented in the uk/London class. It creates a malicious named pipe while the EfsrTiny class (a tiny version of the EfsPotato tool) connects to the LSASS.exe RPC server. Then it’s London.RpcThread function calls the EfsRpcEncryptFileSrv function from EfsrTiny with a crafted \\localhost path. This tricks LSASS.exe into connecting back to the attacker’s named pipe. Finally, the London.Exp function uses ImpersonateNamedPipeClient to coerce and impersonate the LSASS.exe token and spawn a new process via CreateProcessAsUser with SYSTEM privileges. As the webshell resides in the IIS worker process, it has the necessary SeImpersonatePrivilege to perform this technique.

The uk namespace also implements a minimal MSRPC client layer by explicitly defining structures such as RPC_CLIENT_INTERFACE and MIDL_STUB_DESC, thereby spoofing an interface stub. This enables direct interaction with the Windows RPC runtime (Rpcrt4.dll) without relying on MIDL-generated stubs or higher-level RPC abstractions. This provides direct control over binding and marshaling that goes beyond the constraints of standard client APIs.

Post-exploitation activity

Shortly after the webshell injection, the infected worker process spawned msiexec.exe to install a remote MSI payload hosted in Supabase cloud storage. The payload was Velociraptor, an open-source digital forensics and incident response tool – namely version 0.73.4.0 vulnerable to CVE-2025-6264 – that the attackers abused for command execution on the target network.

In October 2025, Cisco Talos reported Velociraptor being leveraged by threat actors as a post-compromise execution framework and attributed this activity to Storm-2603. This tool enables arbitrary command execution, and can be used for various purposes. In our case, it was used for persistence, discovery, data collection and payload execution.

The threat actor used Velociraptor to deploy multiple independent tunneling mechanisms, including Zoho Assist-Unattended Support for unauthorized remote access, VS Code CLI tunneling, Cloudflare Tunnel, and a lightweight reverse proxy utility. By doing so, they attempted to maintain access even if some of their tools were detected and deleted. At this stage, they also no longer depended on the IIS worker process lifecycle and ToolShell vulnerability.

EDR-killing attempts

Once the threat actor had access to the target system, they were able to upload a vulnerable driver that is used by many attackers to terminate EDR solutions. We were able to detect the main driver at C:\programdata\rsndispot.sys (054a32d6033b1744dca7f49b2e466ea2). In addition, the adversary used a malicious DLL (2d9ebf9c042f596f435865b40edad928) in a sideloading attack. This DLL was identified as Warlock ransomware’s AV killer. It was possibly intended to kill security solutions using the vulnerable rsndispot.sys driver.

Ransomware deployment attempt

The attackers made attempts to launch ransomware on the compromised system. However, the Proactive Defense Module in Kaspersky Endpoint Security blocked the ransomware process, effectively preventing this activity. The malware DLL we obtained was identified as Warlock ransomware.

What defenders really need to look for

ToolPane deserialization and CLR artifacts

When the attackers tried to access ToolPane, both the dynamically compiled App_Web_toolpane.aspx assembly and the Microsoft.GeneratedCode helper assembly were present within the same application domain.

Both assemblies are used in legitimate scenarios. App_Web_* assemblies are generated during normal ASP.NET page compilation, while Microsoft.GeneratedCode is typically produced by the XmlSerializer during legitimate serialization workflows. However, a request to the ToolPane endpoint that results in immediate loading of Microsoft.GeneratedCode within the same execution window indicates that the runtime is processing serialized data supplied through the request.

Detecting deserialization exploitation

First, it is highly recommended to read this article (in Russian) on how deserialization exploit attempts in the CLR should be detected, with examples of YARA rules and monitoring recommendations. A summary of the main idea of the detection logic is provided below.

Deserialization vulnerabilities should be treated as a distinct class of exploitation events. In the CLR, deserialization is not merely the conversion of bytes into values – it is the reconstruction of an object graph from an incoming stream. Serialization and deserialization are performed by formatters. One of the most widely used formatters is BinaryFormatter. It uses the NRBF format, which defines the stream as a sequence of records describing classes, arrays, primitives, and references between objects. The shape of the resulting graph is determined by the data supplied to the deserializer. This means that the input stream defines not only the values but also the specific types, relationships, and lifecycle points that the runtime must materialize.

Microsoft states explicitly that BinaryFormatter is unsafe and cannot be made safe; beginning with .NET 9, the in-box implementation is effectively removed from ordinary use. However, Microsoft continues to use it in its products, including SharePoint, because it is powerful and easy to use. To fix BinaryFormatter-related issues, architectural changes are needed. Similar warnings also apply to related unsafe mechanisms, including LosFormatter (used in the vulnerable page).

The primary risk of unsafe deserialization is that the CLR may resolve unexpected types, instantiate objects outside normal constructor invariants, and invoke special lifecycle mechanisms such as ISerializable, IObjectReference, IDeserializationCallback, and methods marked [OnDeserialized]. In other words, the attacker-controlled data can force the runtime to construct a graph containing types with executable side effects.

A practical detection strategy should therefore focus on graph-construction artifacts. CLR loader events identify the relevant assemblies and application domains. Memory and metadata inspection then reveal the type names and generated serializer structures associated with the suspected gadget chain. In ToolShell-like scenarios, unsafe deserialization usually produces helper assemblies (evidence in loader events) with metadata containing fully qualified type names, gadget class names, and string representations of the constructed generic types (just scan via YARA for it). In our case, the artifacts to look for include Microsoft.GeneratedCode, App_Web_toolpane.aspx* (evidence of deserialization in loader events) as well as strings identifying ObjectDataProvider, ExpandedWrapper, and LosFormatter (evidence that the input stream was deserialized into dangerous objects). Note that these indicators are not significant in isolation; their value lies in the fact that they show the runtime has already received data requiring helper code generation to reconstruct a dangerous object graph.

Gadget objects that exploit vulnerabilities like those associated with ToolShell are conveniently represented in corpus-style tooling such as ysoserial.net/YSoNet, which aggregate known gadget chains for unsafe .NET deserialization. For defensive purposes, the value of these projects lies not in their offensive capability but in the catalog of types and structures that can be searched for in streams, memory, and helper assemblies. The presence of any of them in memory or in a helper assembly should be treated as a strong indicator of exploitation.

To simplify deserialization exploit detection, we created a tool named CLRDeserializationExploitFinder. It can detect ToolShell exploitation attempts, as well as other gadget chains covered by ysoserial.net.

Detecting a VPP-based webshell/runtime implant

A mechanism based on VirtualPathProvider should be treated as a separate class of runtime implant (no files involved). In ASP.NET, HostingEnvironment.RegisterVirtualPathProvider registers a new virtual-path provider (VPP) in the application’s compilation system. Once registered, the application can serve virtual paths during dynamic compilation as though they were ordinary on-disk pages. Dynamic compilation does not require the payload to exist as a file in the filesystem. It operates entirely within the trust boundary of the application itself. During the compilation process, temporary files may be generated, but they cannot be treated as reliable forensic artifacts. Because VPPs can also be used for legitimate purposes, the defensive model should begin with an inventory of the legitimate VirtualPathProvider implementations. In all other cases, the registration or use of a new VPP should be considered anomalous.

To see how the dynamic generation of virtual pages using ASP.NET, IIS, and the CLR works, and to learn how to detect such activity, use our project VirtualPathProvider Lab.

The most useful telemetry source for this technique is the Microsoft-Windows-IIS-Configuration ETW provider. To detect virtual paths, events 25 and 47 are of primary value. Event 25 records the mapping of ConfigPath to PhysicalPath, and event 47 records the mapping of the configuration path to a directory. In the case of a VPP-backed path, these events create an inconsistency: IIS and ASP.NET behave as if the path has a physical representation, while the corresponding file does not exist on disk. In the example provided by VirtualPathProvider Lab, this appears as a mapping of the virtual resource googlecheck.aspx to a physical path where no real file is present. This discrepancy between configuration resolution and filesystem reality is the primary indicator of virtual content.

The second indicator is more negative than positive. For disk-backed files, IIS normally creates a FileChangeNotificationMonitor chain, as reflected in events 17, 18, 19, and 21. For virtual paths served by a custom VirtualPathProvider, this chain is absent or does not match the normal profile of an ordinary file because there is no physical object or corresponding on-disk change lifecycle. Consequently, the presence of positive events 25 and 47 combined with the absence of the expected events 17, 18, 19, and 21 for the same path forms a reliable detection pattern: the path exists for IIS as a configuration-resolved resource, yet it is not backed by the filesystem and is not accompanied by normal file-change monitoring.

To sum up, the hunting target here is the discrepancy between how IIS/ASP.NET resolves a resource and what is actually present on the disk and in the change-monitoring chain. For known applications, such cases can be filtered through an allowlist of legitimate VirtualPathProvider implementations. Anything else should be treated as a potential runtime implant and examined using ETW, filesystem verification, and memory inspection of the w3wp.exe process.

Attribution

Microsoft tracks Storm-2603 as a standalone threat actor with no known links to other groups. While they are careful about assessing the actor’s motivation, they mention that it has previously deployed Warlock and LockBit ransomware following active exploitation of on-premises SharePoint vulnerabilities. In the analyzed incident, we observed similar SharePoint exploitation activities, as well as an EDR killer associated with Warlock ransomware and unsuccessful Warlock launch attempts.

Other documented intrusions have identified Storm-2603 as leveraging known flaws within perimeter mail gateways to achieve initial access, subsequently staging the Velociraptor framework with elevated privileges and establishing persistent encrypted tunnels via commercial remote management software to orchestrate secondary lateral movement. This aligns with the post-exploitation behavior we observed in our case. Together with the ToolShell exploitation scenario, this allows us to attribute this case to Storm-2603 with moderate confidence.

Conclusion

From the incident analyzed in this article, the main defensive lesson is that exploiting an internet-facing SharePoint server can enable fileless code execution directly inside the application trust boundary. Storm-2603 did not require a conventional dropper, persistent service, or exposed C2 endpoint; instead, it deployed a webshell inside w3wp.exe, from which tunneling, privilege escalation, credential access, and lateral movement could be orchestrated. Detection therefore needs to move beyond file-based webshell hunting toward runtime behavior, such as anomalous w3wp.exe activity, unexpected outbound connections, RPC coercion, token impersonation, and the introduction of remote-management or DFIR tooling after application compromise.

To correctly attribute malicious activity and develop appropriate response measures, defenders should reconstruct execution chains and temporal relationships across application, host, and identity layers. They should also preserve memory and IIS telemetry, and correlate low-signal reconnaissance with subsequent privileged activity rather than evaluating events in isolation. A confirmed SharePoint exploit with code execution should trigger host-level containment, persistence hunting, credential and exposed MachineKey rotation, and TTP-driven investigation rather than remediation limited to the initial exploit or known IOCs.

Kaspersky products detect the threats mentioned in this article as:

• Trojan.MSIL.Agent.gen
• Trojan.Win64.DLLhijack.cdp

Indicator of compromise

Executive Summary

On October 21, 2026, the Node.js project released security updates that fix CVE-2026-111497, an out-of-bounds write in the Brotli decoder bundled with the runtime. Vendors have labeled the vulnerability Node2Shell, Brotli2Shell, Broccoli2Shell, and HTTP2Shell, because the flaw sits in Google's Brotli library and reaches software well beyond Node.js. Any application that decompresses attacker-controlled data with a vulnerable Brotli library could be at risk. Google, as the CVE Numbering Authority, scored it 9.2 under CVSS v4.0, and Node.js rates it high.

CISA added CVE-2026-111497 to its Known Exploited Vulnerabilities (KEV) catalog on October 27, 2026, with a November 10, 2026 deadline for federal civilian agencies. The public telemetry we reviewed shows exploitation attempts against honeypot sensors, and researchers have demonstrated code execution in lab environments. As of October 29, 2026, we are not aware of a public report of a confirmed compromise of a production system.

This report details Node2Shell's mechanism, affected versions, and observed activity, and outlines how SentinelOne customers and partners can strengthen their defenses.

Key Highlights

• One flaw, many patches. Node.js, Google Brotli, Bun, .NET, Python, Envoy, Istio, and PHP for Windows each shipped their own fix. Updating one does not update the others.

• Attempts are confirmed. Production compromise is not. ELLIO and Previdian have each recorded an exploitation attempt against their honeypot sensors. Previdian notes that such observations do not show the target was vulnerable or that the attempt succeeded.

• A default Node.js server is not exposed by itself. Something must hand attacker-controlled Brotli data to the decoder. Node.js names direct node:zlib use. Express 5 request bodies and built-in fetch() responses reach the same decoder, and Imperva reproduced both in the lab.

• A failed exploit still costs you. The Node.js advisory lists a crash as an outcome, and Sysdig reports that crashes have been easy to reproduce. Repeated crashes are a denial of service against any service that decodes attacker-influenced Brotli.

• The fix was visible for nearly eight months before release. Google committed it on February 27, 2026, with no release and no advisory. End-of-life Node.js 20, 23, and 25 are not expected to receive a fix.

Organizations should take the following steps immediately:

• Upgrade Node.js to 22.23.4, 24.22.1, or 26.12.1, then restart processes and rebuild container images.

• Inventory every other runtime and library that embeds the Brotli decoder, and patch internet-facing services first.

• Where patching must wait, reject inbound requests that carry Content-Encoding: br at the proxy or load balancer.

What is Node2Shell

Node2Shell is a memory corruption flaw in the Brotli decoder, the code that expands Brotli-compressed data. A Brotli stream signals switches between block types. When a stream declares only one block type, a switch is invalid and the decoder should reject it. The vulnerable decoder instead reported the invalid switch as a request for more input. The caller supplied more bytes, and the decoder kept appending them to an eight-byte internal buffer and wrote past its end, according to the Node.js advisory.

Node.js lists the outcomes as a crash, disclosure of process memory, or remote code execution, and the CVE record describes the attacker as unauthenticated. Imperva demonstrated code execution on the official Node.js 24.22.0 arm64 image. Microsoft describes the .NET impact as information disclosure, denial of service, or elevation of privilege.

Node2Shell at a Glance

How Attacker-Controlled Brotli Reaches the Decoder

Disclosure Timeline

Exploitation in the Wild

Exploitation attempts have been recorded, but a successful attack on a production system has not been publicly reported as of October 29, 2026. CISA added CVE-2026-111497 to its KEV catalog on October 27, 2026, with a November 10, 2026 deadline for federal civilian agencies under BOD 26-04.

The public telemetry comes from honeypots. Two threat intelligence providers each recorded an attempt: ELLIO saw a request built for Node.js 24.22.0 on x86-64 on October 26, and Previdian saw one from a U.S.-based address on October 27. Previdian notes that such observations do not show the target was vulnerable or that the attempt succeeded.

Researchers have reproduced code execution only in lab testing. Imperva ran code on the official Node.js 24.22.0 arm64 image through fetch() and Express 5 request bodies, needed multiple attempts, and did not test every Node.js build. Public proof-of-concept code appeared within hours of the October 21 release, according to Sysdig, so organizations should expect further attempts, particularly against internet-facing Express 5 services.

Observed Indicators

Patch Status

Node.js has fixed every supported release line: 22.x, 24.x, and 26.x. It will not patch end-of-life lines, which the project says are always affected when a security release occurs. Organizations running Node.js 20, 23, or 25 have no patch to apply and must upgrade to a supported line.

Recommended Mitigations

Patch, Restart, and Rebuild

• Upgrade to a fixed Node.js release, update every Dockerfile that pins an older tag, and restart or redeploy every workload.
• Confirm patch status from the Node.js release version running in each workload, not a bundled library version string, which may not reflect a backported fix.

Find Every Copy of the Decoder

• Inventory Bun, .NET, Python Brotli, Envoy, Istio, PHP for Windows, and statically built executables, including staging, CI, build images, and developer machines.
• Where Node.js links the system Brotli library, as it does on Debian, check your distribution’s advisory for the Node.js package as well as brotli. Debian’s tracker lists both.
• PHP for Windows fixed the flaw with rebuilt packages that keep the same PHP version number, so php -v looks the same before and after. Check the -1 build suffix or the SHA-256 on the download page.
• Move off Node.js 20, 23, and 25 on a defined date.

Reduce Exposure Until Patching Finishes

• Reject requests with Content-Encoding: br at the WAF or proxy, or set the body-parser inflate option to false so compressed bodies return HTTP 415. The second option also rejects gzip and deflate bodies, so test client traffic first.
• Restrict server-side fetches to trusted destinations. Link previews, webhooks, and crawlers send attacker-influenced URLs into fetch(), and removing br from Accept-Encoding does not stop a hostile server from replying with Brotli.
• Enable a vendor WAF rule as a stopgap where one exists. It does not replace the upgrade.

Limit the Blast Radius

• Run Node.js services as a non-root user and limit outbound connections from Node.js hosts. Docker runs containers as root by default, and the official node image provides a node user (uid 1000) that you must select with USER or -u.

Watch for Failed Attempts

• Alert on repeated worker crashes after requests with Content-Encoding: br. Where core dumps are enabled, restrict who can read them and shorten retention, because a core file can hold memory contents from other users’ sessions.

How SentinelOne Protects You

Node2Shell exploits the Brotli decoder inside the Node.js process. Patching Node.js and every other copy of the Brotli decoder remains the primary control. SentinelOne provides detection and visibility into post-exploitation behavior while that work is underway.

The Singularity agent builds a storyline of process activity on a host, including the Node.js services targeted by Node2Shell. These processes have a predictable life: they answer requests, call APIs, and read files. They do not normally start /bin/sh, run reconnaissance commands, download executables into /tmp, or open connections to arbitrary hosts. Behavioral detection looks for that deviation from normal behavior. 

Detection in Singularity AI SIEM

Platform detection content looks at what happens on the host after exploitation, not at the request body. 

Correlation rules tie these together. A request to the service, or an outbound fetch to an untrusted host, followed by a process spawn or file drop from the same Node.js process is the shape of a successful chain. 

Visibility at the Edge

Sysdig reports that the crash is easy to reproduce, which makes repeated worker crashes and restarts a useful early signal. Web server, WAF, and CDN logs may show requests with Content-Encoding: br followed by bursts of HTTP 5xx responses and unexplained worker restarts. Singularity AI SIEM can ingest those sources alongside endpoint data if you forward them. 

Hunt with Purple AI While You Patch

Patching is the only complete fix. While that work runs, analysts can ask Purple AI questions such as:

• Show me shell processes whose parent or prior image was node, nodejs, or bun since October 21.
• Show me new executable files written by the Node.js service account since October 21.
• Show me outbound connections from the Node.js service account to destinations we have never seen.
• Show me hosts with a spike in 500 responses followed by a Node.js process restart.

The last question depends on web server logs being ingested. 

Confirm Agent Saturation on Node.js Hosts

Confirm the agent is deployed, healthy, and in protect mode on every server and container host that runs Node.js, Bun, or another service that decompresses Brotli. Start with internet-facing Express 5 services and services that fetch external URLs.

Disclaimer

Information contained herein is current as of October 29, 2026, and is subject to change as patch releases and threat activity evolve. Recipients should not rely solely on this report for security decisions and should consult with their security teams and SentinelOne account representatives for current guidance.

StyleSmuggler exploitation of CVE-2026-75650 drops the PSK Agent RAT and a PHP webshell on Magento stores. See how to detect and respond.

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• NIK C2 Framework Integrates OpenAI API to Analyze Stolen Data in Live Attacks

The post PSK Agent RAT Hits Magento Stores Through StyleSmuggler Exploitation appeared first on Daily CyberSecurity.

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:

Network indicators:

An exposed workspace shows AhsayCBS webshell access turned into SOCKS proxy tunnels with Chisel and GOST across 141 hosts, per the logs.

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The post Exposed Workspace Shows AhsayCBS Webshells Turned Into SOCKS Proxy Tunnels appeared first on Daily CyberSecurity.

CISA adds Linux kernel vulnerability CVE-2026-46300, chained with PHP2Shell for root, and VMware ESX flaw CVE-2026-41709 to KEV. Patch now.

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Hackers exploit YOOtheme Pro vulnerability CVE-2026-102785 (CVSS 10) for remote code execution, dropping webshells. Update to 5.0.53 now.

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U.S. Cybersecurity and Infrastructure Security Agency (CISA) adds Google Brotli and Adobe Commerce and Magento flaws to its Known Exploited Vulnerabilities catalog.

The U.S. Cybersecurity and Infrastructure Security Agency (CISA) added Google Brotli and Adobe Commerce and Magento vulnerabilities to its Known Exploited Vulnerabilities (KEV) catalog:

Below are the flaws added to the catalog:

• CVE-2026-111497 (CVSS score: 9.2) – An unchecked return value vulnerability in Google Brotli that could allow a remote, unauthenticated attacker to corrupt decoder memory, expose sensitive information, cause a denial-of-service, or potentially execute arbitrary code by sending a specially crafted Brotli-compressed stream.
• CVE-2026-48356 (CVSS score: 9.3) – An unrestricted file upload vulnerability in Adobe Commerce and Magento that could allow an attacker to execute arbitrary code with the privileges of the current user, potentially gaining elevated access or taking control of the victim’s account or session.

Brotli is a data-compression algorithm developed by Google.It reduces the size of web content, such as HTML, CSS, JavaScript, and other text-based resources. Security researchers have observed exploitation attempts targeting CVE-2026-111497 (aka Node2Shell), a critical flaw in Google’s Brotli library. Previdian recorded one attempt by October 27, while ELLIO researchers recently detected traffic carrying a public exploit that could execute code on vulnerable systems. The activity may indicate attempts to exploit the flaw on a larger scale, but its scope remains unclear. An attacker can trigger the vulnerability to cause crashes, expose sensitive memory or remote code execution when applications decompress attacker-controlled Brotli data. Because Brotli is widely used, multiple projects and vendors have released security updates, including Node.js, PHP, Bun, and Microsoft .NET. Cybersecurity firm Wiz warned that 91% of cloud environments contain resources vulnerable to the flaw, while 82% have publicly exposed Node.js instances.

The second flaw added to the catalog is an unrestricted file upload vulnerability tracked as CVE-2026-48356, dubbed PolyShell. Recently WordPress security firm Sansec warned of attacks exploiting CVE-2026-48356 to infect at least 953 of online stores. The attackers reportedly used a Magento CMS block to insert a malicious loader capable of retrieving and executing a payload hosted under their control.

So far, researchers have not recovered the subsequent payload, leaving the campaign’s ultimate objectives unclear. The activity is believed to have been underway since at least July 2025 and remains ongoing.

According to Binding Operational Directive (BOD) 22-01: Reducing the Significant Risk of Known Exploited Vulnerabilities, FCEB agencies have to address the identified vulnerabilities by the due date to protect their networks against attacks exploiting the flaws in the catalog.

Experts also recommend that private organizations review the Catalog and address the vulnerabilities in their infrastructure.

CISA orders federal agencies to fix the flaws by October 29, 2026.

Follow me on Twitter: @securityaffairs and Facebook and Mastodon

Pierluigi Paganini

(SecurityAffairs – hacking, CISA)

CISA adds PHP2Shell flaws CVE-2026-109618 and CVE-2026-109619 to the CISA KEV catalog after confirmed RCE and suspected DPRK-linked scanning.

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U.S. Cybersecurity and Infrastructure Security Agency (CISA) adds PHP flaws PHP2Shell to its Known Exploited Vulnerabilities catalog.

The U.S. Cybersecurity and Infrastructure Security Agency (CISA) added the PHP PHP2Shell vulnerabilities to its Known Exploited Vulnerabilities (KEV) catalog:

• CVE-2026-109618 – PHP Memory Corruption Vulnerability 
• CVE-2026-109619 – PHP Stale Internal Request-State Vulnerability 

On October 21, PHP released emergency patches for two critical vulnerabilities, CVE-2026-109618 and CVE-2026-109619 (CVSS score of 9.2). The flaws affect all PHP versions that handle web requests.

The vulnerable code dates back to PHP 5.4, released in 2012, and resides in the PHP engine itself rather than in individual applications. Attackers can exploit the flaws to execute code remotely by sending specially crafted HTTP requests, without authentication.

Exploitation attempts increased rapidly after the vulnerabilities became public. Cloudflare researchers reported tens of thousands of attempts within the first 45 minutes, hundreds of thousands in just two hours, and millions by October 22.

Cyfirma reported that a group of North Korea-linked group has identified 216 hosts that may still be vulnerable to PHP2Shell. The operators communicate through a private I2P channel that Cyfirma has monitored since October 22. On October 24, they reported that 216 hosts appeared to be unpatched or only partly protected, earlier than Cyfirma expected.

They are checking the systems from 21 compromised machines. At the time Cyfirma captured the conversation, the operators had already checked about one-third of the targets.

“Two handles active since March 2026, coordinating in a closed I2P channel; staging infrastructure shared in-channel.” states the cybersecurity firm. “Target set and scan nodes. “On 24 October the operators reported 216 unpatched or partially mitigated hosts and named 21 scan nodes; the same addresses were seen performing web-shell tooling and upload-progress session-poisoning attempts.”

The scanning is slow on purpose. Volume stays low, the machines rotate, and any node that shows filtering or rate limiting gets pulled and never reused. That’s why a rate-based alert will probably stay quiet.

According to Binding Operational Directive (BOD) 22-01: Reducing the Significant Risk of Known Exploited Vulnerabilities, FCEB agencies have to address the identified vulnerabilities by the due date to protect their networks against attacks exploiting the flaws in the catalog.

Experts also recommend that private organizations review the Catalog and address the vulnerabilities in their infrastructure.

CISA orders federal agencies to fix the flaws by October 29, 2026.

Follow me on Twitter: @securityaffairs and Facebook and Mastodon

Pierluigi Paganini

(SecurityAffairs – hacking, CISA)

Sansec tracks a Magento skimmer campaign that infected 953 stores, using five domains, bot filtering and malicious code hidden in CMS blocks.

A criminal group has infected 953 online stores since July 2025, mostly Magento shops, according to Sansec researchers. The attackers add a small piece of code to store pages. When someone visits a shop, their browser contacts the attackers’ server to get further instructions. The server checks whether the visitor is a real shopper or a security scanner. If it detects a scanner, it hides the malicious activity.

“deepveindrilling.com is the newest web address of a criminal group that breaks into online stores. The group adds a small piece of code to the store’s pages.” Sansec’s analysis states. “The group has done this to 953 stores since July 2025, mostly Magento shops. It moves to a new address every few months.”

The group’s latest domain is deepveindrilling.com. Sansec found it in the CMS blocks of 83 stores, almost all running Magento. CMS blocks are content sections that Magento adds to store pages. The domain was registered on October 6, 2026, and the first stores began loading it the following evening.

This is the fifth domain linked to a campaign that Sansec has tracked since July 2025 and reported in March as the PolyShell mass attack. Most stores using the latest domain had already been targeted. Of the 83 stores, 60 had previously loaded one of the group’s malicious scripts, and 34 had been doing so since October 2025.

The domain sits behind Cloudflare, and its front page returns a 404. It only answers script requests from visitors of infected stores. The loader code lives in the cms_block table, and Sansec’s scanner, eComscan, found it there on 148 server installs over the campaign, never in core_config_data or theme files.

eComscan first found the new loader at 21:54 UTC on October 7. By the end of October 8, Sansec’s crawler had found it on 74 stores, as two scripts. The first reads a value called fpCode from the visitor’s browser storage, and when the value is set, the script puts the stored code in an invisible div and clicks it. The browser then runs the code as an event handler, without using eval().

Nothing in the block writes fpCode, so the script from deepveindrilling.com has to set it. A visitor who received the payload once runs it again on every later page view, even when the domain is offline or blocked. The March version deleted its stored payload after one run.

On October 23 the operator rewrote the blocks again, and the loader appeared on 9 more stores. All 71 stores Sansec re-crawled by October 24 run the new version. It replaces the plain script tag with an inline loader that sends a browser fingerprint to https://deepveindrilling.com/?s=49b7ca5f&fp=.

The fingerprint flags automated browsers through navigator.webdriver, Selenium globals and ChromeDriver’s cdc_ properties. It also adds the GPU name from WebGL, CPU cores, memory, screen size, time zone, languages and client hints. The server picks its response from all of that.

The filter works. When urlscan.io loaded an infected store with an automated Chrome browser, it got HTTP 200, and Sansec found no added content in the page. Sansec hasn’t captured the final payload yet.

Older payloads give a hint of what real visitors get. Sansec Watch, the firm’s monitoring service for content security policies, shows what the earlier stage served. On December 12, 2025, a customer logged in to an infected store got a script from manulfie.click that loaded https://crypto-insider.top/ssl/ and, five minutes later, https://leqdger.click/ssl/.

On December 15 and 16, neswnsssn.click inserted the same leqdger.click script on two more stores. leqdger.click is a misspelling of the crypto wallet brand Ledger. It was registered on September 12, 2025 and has since expired.

Before October, the second script was the “Script (async)” integration of Keitaro, a commercial ad tracker that doubles as a traffic distribution system, a tool that sorts visitors. It sends the referrer, page title and query string to a base64 encoded URL such as https://additionalver.com/Mb2z8J?&se_referrer=, where the path is the Keitaro campaign alias. The system then decides per visitor whether to return a payload, a redirect or nothing.

“When Sansec requested the additionalver.com script as a Windows desktop visitor on October 24, it returned QWE=123;, a statement that does nothing, and set a cookie that marks the visitor for 24 hours. deepveindrilling.com drops Keitaro and moves the bot filtering into the loader.” continues the report.

Here are the five loader domains over 15 months:

Two other domains were found on a small number of stores: hdfdsi84j3h.top, registered three minutes after lanhd6549tdhse.top, and c85grt98sd.com.

Two older domains, neswnsssn.click and manulfie.click, expired and were registered again in May 2026. Both now use the nameservers of abovedomains.com, a domain parking service. Between August and October 2026, Sansec still found the manulfie.click loader on nine stores and the neswnsssn.click loader on six. When visitors open these stores, their browsers send information about the previous page and the current page title to the new domain owner. The browsers also run any script returned by that domain.

Sansec Watch received reports of visitors’ browsers connecting to all four earlier domains. One domain, additionalver.com, was linked to around 52,000 reports.

The October activity did not rely on a new vulnerability. Of the 83 affected stores, 60 had already been infected. Earlier attacks appear to have exploited file upload flaws in Magento. In March, the group injected its malicious code into 471 stores in just one hour by uploading webshells through PolyShell (CVE-2026-48356).

Some infected Magento stores had files uploaded through PolyShell or SessionReaper (CVE-2025-54236). In a separate group of 3,024 stores affected by another skimmer, the infection rate was 32%.

One Magento 2.4.7-p1 store contained PolyShell webshells disguised as PNG images, alongside malicious code in a CMS block. The code switched from additionalver.com to deepveindrilling.com on October 7. The store had not installed Adobe’s PolyShell fix, APSB26-73.

However, installing all patches does not guarantee a store is safe. Another store running Magento 2.4.7-p10 had no missing core patches but was infected on October 23. Earlier scans had already found a rogue admin account and a GSocket backdoor.

Sansec recommends checking CMS content for malicious code, including base64-encoded domains. Administrators should search both the cms_block and cms_page tables for suspicious patterns such as fpCode and se_referrer. They should also inspect uploaded files for webshells, check for unknown admin accounts, and look for suspicious processes. Removing the malicious scripts is not enough: store owners must also identify and fix the vulnerability or backdoor that allowed the attackers in.

After cleanup, change the database, admin and API integration passwords. Install APSB26-73 for PolyShell and APSB25-88 for SessionReaper. Sansec Shield blocks both attacks while you schedule the upgrade.

Sansec lists the indicators of compromise at the end of its report.

“If your store loads any domain from the indicators of compromise, the attacker can write to your database. Deleting the script does not remove that access. See How to check your store.” concludes the report.

Follow me on Twitter: @securityaffairs and Facebook and Mastodon

Pierluigi Paganini

(SecurityAffairs – hacking, Magento)

Our weekly CVE report for Oct 19-25, 2026 tracks 4,222 new CVEs and 12 exploited vulnerabilities, including PHP2Shell flaws not yet in CISA KEV.

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• This Weekly CVE Report Finds CISA KEV Trailing Daily Cybersecurity by Up to 8 Days
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• Weekly CVE Report Tracks 2,652 New Flaws and Seven Exploited Vulnerabilities (Sept. 28 - Oct. 4, 2026)

The post Weekly CVE Report Flags PHP2Shell and FortiSIEM Attacks Still Missing From CISA KEV appeared first on Daily CyberSecurity.

The HTTP2Shell vulnerability expands Node2Shell risks across HTTP stacks, enabling remote code execution via flawed Brotli decompression engines.

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• Electrum Wallet Vulnerability Exploits RBF Fee Bumps
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The post HTTP2Shell Vulnerability Expands Node2Shell Risk appeared first on Daily CyberSecurity.

Cyfirma links PHP2Shell scans of 216 vulnerable hosts to a suspected North Korean campaign using AI to rank public exploits.

On October 21, PHP released emergency patches for two critical vulnerabilities, CVE-2026-109618 and CVE-2026-109619, both rated 9.2 on the CVSS scale. The flaws affect all PHP versions that handle web requests. The vulnerable code dates back to PHP 5.4, released in 2012, and resides in the PHP engine itself rather than in individual applications. Attackers can exploit the flaws to execute code remotely by sending specially crafted HTTP requests, without authentication.

Exploitation attempts increased rapidly after the vulnerabilities became public. Cloudflare researchers reported tens of thousands of attempts within the first 45 minutes, hundreds of thousands in just two hours, and millions by October 22.

Cyfirma reported that a group of North Korea-linked group has identified 216 hosts that may still be vulnerable to PHP2Shell. The operators communicate through a private I2P channel that Cyfirma has monitored since October 22. On October 24, they reported that 216 hosts appeared to be unpatched or only partly protected, earlier than Cyfirma expected.

They are checking the systems from 21 compromised machines. At the time Cyfirma captured the conversation, the operators had already checked about one-third of the targets.

“Two handles active since March 2026, coordinating in a closed I2P channel; staging infrastructure shared in-channel.” states the cybersecurity firm. “Target set and scan nodes. “On 24 October the operators reported 216 unpatched or partially mitigated hosts and named 21 scan nodes; the same addresses were seen performing web-shell tooling and upload-progress session-poisoning attempts.”

The scanning is slow on purpose. Volume stays low, the machines rotate, and any node that shows filtering or rate limiting gets pulled and never reused. That’s why a rate-based alert will probably stay quiet.

Who’s behind it? Cyfirma assesses with moderate confidence a link to the Lazarus family of North Korean groups, based on language, operating style and old habits with PHP web shells and hijacked web servers. It has no shared malware or infrastructure to point to yet, and it admits that a Korean-speaking criminal crew without state backing would fit the same facts. Its confidence in the campaign itself is high.

Government agencies and banks come first. Next in line are logistics, ports, airports and aviation, manufacturing, and online transaction and trading platforms. The countries named are the US, Japan, South Korea, Thailand, Indonesia, Australia, Taiwan, India, Singapore, the UAE, Saudi Arabia and the EU.

Cyfirma has not named any victims or seen the full list of targeted systems. The operators have not announced when they plan to launch attacks, and the researchers have not confirmed that this group has compromised systems on a large scale. Cyfirma gives the campaign an imminence score of 78 out of 100. The operators plan to check all 216 hosts, select the most promising targets, and decide which publicly available exploits to adapt.

The group does not appear to be developing new exploits. Instead, its members use a local AI model to rank public exploit repositories, then test the results manually in a lab. Cyfirma believes this approach could help attackers turn newly disclosed vulnerabilities into working attacks more quickly. However, researchers warn that some proof-of-concept exploits shared online are fake and may contain malware, so downloading and running them can be dangerous.

Technical details leaked on October 20, ahead of coordinated disclosure, and the PHP project shipped fixes on October 21. Apache mod_php with default settings and prefork is the confirmed worst case for 109618, with full unauthenticated code execution. For 109619, PHP-FPM is validated through the upload-progress path.

One correction matters for your temporary controls. Early coverage tied the session upload-progress field to 109618, but PHP’s advisories put it under 109619 only. Disabling upload progress does nothing for 109618, so the two bugs need different stopgaps.

Triggering the bug is easy, but reliable code execution isn’t. The attacker has to land a follow-up request on the same worker and shape heap data into the freed space, otherwise the worker crashes or answers normally. VulnCheck reports working code execution on x86-64 with ASLR enabled against PHP 8.4.10 to 8.4.26, while ARM64 only leaks memory so far.

Short of code execution, attackers have already shown worker crashes, arbitrary file reads through OPcache and a leak of about 67 KB of process memory. The fixed releases are PHP 8.5.12, 8.4.27, 8.3.36 and 8.2.35. PHP 8.1, 8.0 and 7.4 get only minimal backports, and vendors such as cPanel EasyApache, CloudLinux, TuxCare and Plesk carry their own builds, so confirm coverage with yours.

Anything from PHP 5.4 to 7.3 has no fix, so migrate or isolate it. Restart every worker after patching, because a reload that keeps old workers alive leaves the stale state in place. Isolate anything internet-facing that you can’t patch within hours, starting with Apache mod_php.

If you suspect a host is compromised, capture memory and logs before you patch, since a restart destroys the CVE-2026-109619 state. Then rebuild the host from a trusted image and rotate every secret the PHP worker could reach. Hunt back to October 17, the earliest known exploitation, and use long time windows.

The strongest single signal is a PHP or web server process spawning something it never normally spawns. Application logs will usually show nothing, because the bug fires before application code runs. Look for odd multipart POSTs to endpoints that never take uploads, bursts of worker crashes and memory errors, new PHP files in web roots, and new cron jobs, local users or SSH keys.

Cyfirma includes Sigma, Splunk and YARA content, though it says the YARA set hasn’t been tested on real application code yet. It also lists 21 scan-node addresses, 17 source addresses from October 23 and 15 staging addresses, all defanged. Some scan nodes are described as compromised and may belong to victims, so hunt for prior contact before assuming who owns them.

If you can’t patch yet, the advisory suggests limits such as a low post_max_size and disabling session upload progress for 109619. Cyfirma is clear that none of it replaces patching. The operators’ own selection rule is the most useful detail in the whole advisory, because they dropped every host that showed a fixed version or sat behind heavy filtering. That makes a current version string a cheap filter against you, and for once being boring pays off.

“Hosts still bannering older PHP are the ones the operators kept.” Cyfirma states.

Follow me on Twitter: @securityaffairs and Facebook and Mastodon

Pierluigi Paganini

(SecurityAffairs – hacking, PHP2Shell)

Pierluigi Paganini answers questions about the PHP2Shell vulnerability, early exploitation, AI-assisted attacks, and the risks to businesses, servers, and personal data.

1. Your latest report reveals that hackers began exploiting the PHP2Shell vulnerability four days before it was publicly disclosed. First, can you explain what PHP is, how widely it is used across the internet, and why this particular security flaw is so serious?

PHP is a programming language widely used to build websites and web applications. It powers a significant share of the web. PHP2Shell is serious because it can allow attackers to execute commands on a vulnerable server remotely. Depending on the configuration and the server’s privileges, this could give threat actors access to sensitive data, applications and other systems.

2. According to AWS researchers, attacks were already underway before the vulnerability became public. How did researchers uncover this early exploitation, and what does it tell us about the ability of hackers to identify security weaknesses before developers can fix them?

AWS researchers detected exploitation attempts against their honeypot network four days before public disclosure and the release of patches. This shows that attackers can discover or weaponize vulnerabilities before defenders have time to respond. AI-assisted analysis may accelerate this process, helping attackers turn technical information into working exploits in hours rather than days. However, detected attempts do not automatically mean successful compromises.

3. The vulnerability could potentially allow attackers to execute malicious commands on affected servers. What does that mean in practical terms, and what risks does it pose to websites, businesses and the personal information of their users?

Remote command execution means an attacker may be able to make a server run commands without authorization. Depending on the attacker’s access, they could steal data, install malware, change website content, or use the compromised server to attack other systems. Businesses could face service disruption, financial losses and reputational damage. Users may also be exposed if their personal information is stored on an affected system. The actual impact depends on the server’s configuration and security controls.

4. The latest findings raise concerns about the gap between discovering a vulnerability and protecting systems against it. How quickly can attackers exploit these weaknesses, and are businesses adequately prepared to respond when critical security flaws emerge?

The window can be extremely short. In some cases, attackers can develop or adapt an exploit within hours, leaving organizations little time to react. Many businesses still rely on patching schedules that are too slow for internet-facing systems. Organizations need continuous monitoring, rapid patching, tested incident response plans and clear visibility into exposed assets. They should also investigate suspicious activity dating back to before a vulnerability was publicly disclosed, because attacks may already be underway.

5. With PHP powering a significant portion of the internet, could we see further attacks exploiting this vulnerability? What should website operators do immediately to protect their systems, and should ordinary internet users be concerned about their data?

Further attacks are possible, particularly against systems that remain unpatched or exposed. Website operators should apply the relevant security updates, check whether their PHP applications are vulnerable, and review logs for suspicious activity dating back to October 17. They should also restrict access and investigate signs of compromise. Ordinary users do not need to panic, but they should keep their accounts protected with unique passwords and multi-factor authentication where available. Whether personal data is at risk depends on which services they use and whether those systems were compromised.

AI-assisted tools are increasingly being used to analyze code and identify vulnerabilities. How much of a role could artificial intelligence play in helping attackers discover and exploit flaws like PHP2Shell, and can defenders use the same technology to their advantage?

AI can help analyze code, identify suspicious patterns and speed up the process of turning a vulnerability into a working exploit. This lowers some of the technical barriers for attackers and may shorten the time between discovering a flaw and exploiting it. However, AI is not the only factor, and we should not assume that every attack is AI-driven. Defenders can use the same capabilities to review code, prioritize vulnerabilities, analyze logs and detect suspicious behavior. The real advantage comes from combining AI with reliable data, skilled security teams and effective response processes.

This case shows that attackers may exploit a vulnerability before a patch is available. Does this mean traditional vulnerability management is no longer enough, and how should organizations rethink their security strategy?

Traditional vulnerability management remains essential, but patching alone is not enough when attackers may act before a fix becomes available. Organizations need to know which internet-facing systems they operate, understand their exposure and have additional controls in place. These include limiting access to vulnerable services, monitoring suspicious activity, applying temporary mitigations and preparing incident response procedures. They should also investigate whether systems were targeted before the vulnerability was disclosed. The goal is to reduce the opportunity for exploitation and detect attacks quickly, even when a patch is not yet available.

Below is another interview I gave to TRT World discussing critical PHP vulnerabilities, the risks they pose to web servers, and the growing number of exploitation attempts. I explain why rapid patching and effective security measures are essential to minimize potential damage.

Follow me on Twitter: @securityaffairs and Facebook and Mastodon

Pierluigi Paganini

(SecurityAffairs – hacking, newsletter)

Palo Alto confirms PAN-OS PHP2Shell flaws CVE-2026-109618 and CVE-2026-109619 affect the firewall management interface. Restrict access and patch.

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The post Palo Alto Networks Confirms PHP2Shell Flaws Affect PAN-OS and Panorama appeared first on Daily CyberSecurity.

A decade-old flaw in the language behind much of the web, found by AI and serious enough to take Wikipedia offline. Driving licenses sold for AED 10,000 to people who never sat for a test. A police sweep across 21 African countries that ended in more than 1,400 arrests. 

This weekly roundup covers stories where the weak point was not always a firewall, but the trust built into code libraries, license agreements, and the frameworks organizations use to measure their own security. 

Here are the stories that shaped the week. 

The Cyber Express Weekly Roundup 

PHP2Shell Flaws Knock Wikipedia and Government Sites Offline 

Two critical vulnerabilities in PHP, the scripting language that powers a large share of the web, have triggered urgent patching warnings from agencies around the world. Dubbed PHP2Shell and tracked as CVE-2026-109618 and CVE-2026-109619, the flaws have already been linked to outages at Wikipedia and a number of government websites. Read more… 

INTERPOL’s Operation Serengeti III Nets 1,462 Arrests Across Africa 

An INTERPOL-coordinated crackdown spanning 21 countries has ended in 1,462 arrests. Operation Serengeti III ran from June to August 2026 and targeted fraud, ransomware, business email compromise and online scams. Investigators disrupted 10,909 malicious networks and recovered more than USD 105 million. The operation also identified nearly 66,000 victims, linked to combined losses of more than USD 385 million. Read more… 

ASD Essentials Replaces the Essential Eight 

The Australian Signals Directorate has launched ASD Essentials, a broader successor to the Essential Eight, the security baseline Australian organizations have long been measured against. Where the Essential Eight centered on a fixed set of mitigation strategies, the new framework extends its scope to enterprise IT, operational technology and cloud environments. Read more… 

Dubai Police Bust AED 10,000 Fake Driving License Scheme 

Dubai Police have arrested an eight-member gang that sold driving licenses for AED 10,000 each. Customers received licenses issued in a neighboring country without ever travelling there, taking a lesson or sitting a test. The gang then helped buyers convert those licenses into UAE licenses by exploiting legitimate license exchange agreements between the two countries. Read more… 

Fintechs Urged to Treat Software Supply Chain Security as Enterprise Risk 

Fintech firms vet their commercial vendors closely, but the open-source libraries, container images and third-party components inside their systems often arrive with far less scrutiny. That mismatch leaves a blind spot in how financial technology companies manage risk. Vijendra Katiyar of CleanStart points to examples such as a widely used Redis image that carried 185 known CVEs, and the Shai-Hulud worm, which spread through malicious npm packages. Read more… 

Conclusion 

Taken together, this week’s stories show how much of today’s risk comes from what organizations already trust: decade-old code, inherited software components, official exchange agreements and security checklists that measure effort rather than results. Follow The Cyber Express for next week’s weekly cybersecurity roundup and ongoing coverage of these stories. 

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