EXPLORE DETECTIONS
Linux Suspicious React or Next.js Child Process
This analytic detects Linux processes such as sh, bash, and common Linux LOLBINs being spawned by React or Next.js application servers. In the context of CVE-2025-55182 / React2Shell / CVE-2025-66478 for Next.js, successful exploitation can lead to arbitrary JavaScript execution on the server, which in turn is commonly used to invoke Node's child_process APIs (for example child_process.execSync) to run OS-level commands. Public proof-of-concept payloads and observed in-the-wild exploit traffic show patterns where the vulnerable React Server Components handler triggers process.mainModule.require('child_process').execSync() to execute binaries such as ping, curl, or arbitrary shells on the underlying host. This detection focuses on suspicious child processes where a Next/React server process spawns an uncommon process. Such activity might be a strong indicator of exploitation of the aforementioned vulnerability.
Linux Suspicious Redis Activity
The following analytic detects Redis processes spawning a system shell command. This can indicate exploitation activity of a redis server to gain code execution.
Linux Suspicious Staging of Alternate System Files
The following analytic detects the creation of sensitive system files such as nsswitch.conf, passwd, shadow, sudoers, and NSS shared libraries outside of their canonical /etc or /var/lib/docker paths. This technique is associated with CVE-2025-32463 (chwoot), where an attacker stages a fake root directory containing spoofed system configuration files and NSS libraries to manipulate how privileged processes such as sudo resolve name services, enabling local privilege escalation by hijacking library loading without modifying the real /etc directory.
Linux Suspicious XDG Autostart
The following analytic detects the creation of a .desktop file within XDG autostart directories, including the system-wide /etc/xdg/autostart and user-local ~/.config/autostart paths. Adversaries abuse XDG autostart entries to achieve persistence on Linux desktop environments — any .desktop file placed in these directories is automatically executed when a user logs into a graphical session. This technique is used by post-exploitation frameworks such as PANIX to survive reboots without requiring root on user-local paths, or to achieve system-wide persistence when writing to /etc/xdg/autostart.
Linux System Network Discovery
The following analytic identifies potential enumeration of local network configuration on Linux systems. It detects this activity by monitoring processes such as "arp," "ifconfig," "ip," "netstat," "firewall-cmd," "ufw," "iptables," "ss," and "route" within a 30-minute window. This behavior is significant as it often indicates reconnaissance efforts by adversaries to gather network information for subsequent attacks. If confirmed malicious, this activity could enable attackers to map the network, identify vulnerabilities, and plan further exploitation or lateral movement within the environment.
Linux System Reboot Via System Request Key
The following analytic detects the execution of the SysReq hack to reboot a Linux system host. It leverages Endpoint Detection and Response (EDR) data to identify processes executing the command to pipe 'b' to /proc/sysrq-trigger. This activity is significant as it is an uncommon method to reboot a system and was observed in the Awfulshred malware wiper. If confirmed malicious, this technique could indicate the presence of suspicious processes and potential system compromise, leading to unauthorized reboots and disruption of services.
Linux Telnet Authentication Bypass
Detects an authentication bypass in telnet tracked as CVE-2026-24061. An attacker can supply a specifically crafted USER environment variable (-f root) that is passed to /usr/bin/login. Because this input isn't sanitized an attacker can force the system to skip authentication and login directly as root.
Linux UDEV Rule Created
The following analytic detects the creation of files within udev rules directories, including /etc/udev/rules.d and /usr/lib/udev/rules.d. Adversaries abuse udev rules to achieve persistent code execution by embedding RUN+= directives that trigger arbitrary commands whenever a matching device event occurs, such as a USB device being connected or a network interface coming online. Because udev rules execute in the context of the udev daemon with elevated privileges, this technique can provide both persistence and privilege escalation. It is used by post-exploitation frameworks such as PANIX and is effective on headless servers where device events still fire despite no interactive user session.
Linux Unix Shell Enable All SysRq Functions
The following analytic detects the execution of a command to enable all SysRq functions on a Linux system, a technique associated with the AwfulShred malware. It leverages Endpoint Detection and Response (EDR) data to identify processes executing the command to pipe bitmask '1' to /proc/sys/kernel/sysrq. This activity is significant as it can indicate an attempt to manipulate kernel system requests, which is uncommon and potentially malicious. If confirmed, this could allow an attacker to reboot the system or perform other critical actions, leading to system instability or further compromise.
Linux Usermod Root UID Set
The following analytic detects the use of usermod to set a user's UID to 0. This functionally sets the user as a root user with full permissions. This approach can be used to bypass regular privilege escalation mechanisms, giving the attacker full control over the system while appearing as a regular user in most monitoring tools.
Linux Visudo Utility Execution
The following analytic detects the execution of the 'visudo' utility to modify the /etc/sudoers file on a Linux system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. This activity is significant because unauthorized changes to the /etc/sudoers file can grant elevated privileges to users, potentially allowing adversaries to execute commands as root. If confirmed malicious, this could lead to full system compromise, privilege escalation, and persistent unauthorized access, severely impacting the security posture of the affected host.
Living Off The Land Detection
The following correlation identifies multiple risk events associated with the "Living Off The Land" analytic story, indicating potentially suspicious behavior. It leverages the Risk data model to aggregate and correlate events tagged under this story, focusing on systems with a high count of distinct sources. This activity is significant as it often involves the use of legitimate tools for malicious purposes, making detection challenging. If confirmed malicious, this behavior could allow attackers to execute code, escalate privileges, or persist within the environment using trusted system utilities.
LLM Model File Creation
Detects the creation of Large Language Model (LLM) files on Windows endpoints by monitoring file creation events for specific model file formats and extensions commonly used by local AI frameworks. This detection identifies potential shadow AI deployments, unauthorized model downloads, and rogue LLM infrastructure by detecting file creation patterns associated with quantized models (.gguf, .ggml), safetensors model format files, and Ollama Modelfiles. These file types are characteristic of local inference frameworks such as Ollama, llama.cpp, GPT4All, LM Studio, and similar tools that enable running LLMs locally without cloud dependencies. Organizations can use this detection to identify potential data exfiltration risks, policy violations related to unapproved AI usage, and security blind spots created by decentralized AI deployments that bypass enterprise governance and monitoring.
Loading Of Dynwrapx Module
The following analytic detects the loading of the dynwrapx.dll module, which is associated with the DynamicWrapperX ActiveX component. This detection leverages Sysmon EventCode 7 to identify processes that load or register dynwrapx.dll. This activity is significant because DynamicWrapperX can be used to call Windows API functions in scripts, making it a potential tool for malicious actions. If confirmed malicious, this could allow an attacker to execute arbitrary code, escalate privileges, or maintain persistence on the host. Immediate investigation of parallel processes and registry modifications is recommended.
Local Account Discovery With Wmic
The following analytic detects the execution of `wmic.exe` with command-line arguments used to query local user accounts, specifically the `useraccount` argument. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity is significant as it indicates potential reconnaissance efforts by adversaries to enumerate local users, which is a common step in situational awareness and Active Directory discovery. If confirmed malicious, this behavior could lead to further targeted attacks, privilege escalation, or lateral movement within the network.
Local LLM Framework DNS Query
Detects DNS queries related to local LLM models on endpoints by monitoring Sysmon DNS query events (Event ID 22) for known LLM model domains and services. Local LLM frameworks like Ollama, LM Studio, and GPT4All make DNS calls to repositories such as huggingface.co and ollama.ai for model downloads, updates, and telemetry. These queries can reveal unauthorized AI tool usage or data exfiltration risks on corporate networks.
Log4Shell CVE-2021-44228 Exploitation
The following analytic identifies potential exploitation of Log4Shell CVE-2021-44228 by correlating multiple MITRE ATT&CK tactics detected in risk events. It leverages Splunk's risk data model to calculate the distinct count of MITRE ATT&CK tactics from Log4Shell-related detections. This activity is significant because it indicates a high probability of exploitation if two or more distinct tactics are observed. If confirmed malicious, this activity could lead to initial payload delivery, callback to a malicious server, and post-exploitation activities, potentially resulting in unauthorized access, lateral movement, and further compromise of the affected systems.
Log4Shell JNDI Payload Injection Attempt
The following analytic identifies attempts to inject Log4Shell JNDI payloads via web calls. It leverages the Web datamodel and uses regex to detect patterns like `${jndi:ldap://` in raw web event data, including HTTP headers. This activity is significant because it targets vulnerabilities in Java web applications using Log4j, such as Apache Struts and Solr. If confirmed malicious, this could allow attackers to execute arbitrary code, potentially leading to full system compromise. Immediate investigation is required to determine if the attempt was successful and to mitigate any potential exploitation.
Log4Shell JNDI Payload Injection with Outbound Connection
The following analytic detects Log4Shell JNDI payload injections via outbound connections. It identifies suspicious LDAP lookup functions in web logs, such as `${jndi:ldap://PAYLOAD_INJECTED}`, and correlates them with network traffic to known malicious IP addresses. This detection leverages the Web and Network_Traffic data models in Splunk. Monitoring this activity is crucial as it targets vulnerabilities in Java web applications using log4j, potentially leading to remote code execution. If confirmed malicious, attackers could gain unauthorized access, execute arbitrary code, and compromise sensitive data within the affected environment.
Logon Script Event Trigger Execution
The following analytic detects the modification of the UserInitMprLogonScript registry entry, which is often used by attackers to establish persistence and gain privilege escalation upon system boot. It leverages data from the Endpoint.Registry data model, focusing on changes to the specified registry path. This activity is significant because it is a common technique used by APT groups and malware to ensure their payloads execute automatically when the system starts. If confirmed malicious, this could allow attackers to maintain persistent access and potentially escalate their privileges on the compromised host.
LOLBAS With Network Traffic
The following analytic identifies the use of Living Off the Land Binaries and Scripts (LOLBAS) with network traffic. It leverages data from the Network Traffic data model to detect when native Windows binaries, often abused by adversaries, initiate network connections. This activity is significant as LOLBAS are frequently used to download malicious payloads, enabling lateral movement, command-and-control, or data exfiltration. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, escalate privileges, or maintain persistence within the environment, posing a severe threat to organizational security.
M365 Copilot Agentic Jailbreak Attack
Detects agentic AI jailbreak attempts that try to establish persistent control over M365 Copilot through rule injection, universal triggers, response automation, system overrides, and persona establishment techniques. The detection analyzes the PromptText field for keywords like "from now on," "always respond," "ignore previous," "new rule," "override," and role-playing commands (e.g., "act as," "you are now") that attempt to inject persistent instructions. The search computes risk by counting distinct jailbreak indicators per user session, flagging coordinated manipulation attempts.
M365 Copilot Application Usage Pattern Anomalies
Detects M365 Copilot users exhibiting suspicious application usage patterns including multi-location access, abnormally high activity volumes, or access to multiple Copilot applications that may indicate account compromise or automated abuse. The detection aggregates M365 Copilot Graph API events per user, calculating metrics like distinct cities/countries accessed, unique IP addresses, number of different Copilot apps used, and average events per day over the observation period. Users are flagged when they access Copilot from multiple cities (cities_count > 1), generate excessive daily activity (events_per_day > 100), or use more than two different Copilot applications (app_count > 2), which are anomalous patterns suggesting credential compromise or bot-driven abuse.
M365 Copilot Failed Authentication Patterns
Detects M365 Copilot users with failed authentication attempts, MFA failures, or multi-location access patterns indicating potential credential attacks or account compromise. The detection aggregates M365 Copilot Graph API authentication events per user, calculating metrics like distinct cities/countries accessed, unique IP addresses and browsers, failed login attempts (status containing "fail" or "error"), and MFA failures (error code 50074). Users are flagged when they access Copilot from multiple cities (cities_count > 1), experience any authentication failures (failed_attempts > 0), or encounter MFA errors (mfa_failures > 0), which are indicators of credential stuffing, brute force attacks, or compromised accounts attempting to bypass multi-factor authentication.