Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
7,129 detections
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Detects Ollama server errors and failures during model loading operations that may indicate malicious model injection, path traversal attempts, or exploitation of model loading mechanisms to achieve remote code execution. Adversaries may attempt to load specially crafted malicious models or exploit vulnerabilities in the model loading process to execute arbitrary code on the server. This detection monitors error messages and failure patterns that could signal attempts to abuse model loading functionality for malicious purposes.
Detects indicators of compromise associated with malicious Firefox wallet-clone extensions (Rabby/OKX clones) by checking for known file hashes, extension file names in Firefox profile directories, and network connections to known C2 domains and workers.dev subdomains.
Detects indicators of compromise associated with malicious Firefox wallet-clone extensions (Rabby/OKX clones) by checking for known file hashes, extension file names in Firefox profile directories, and network connections to known C2 domains and workers.dev subdomains.
Performs an IOC sweep for a known XMRig cryptojacking campaign exploiting a REDCap vulnerability (CVE-2026-90817), detecting matching network connections, file hashes, and process activities across endpoints.
Detects potential remote shell behavior where a process named libgcrypt establishes an outbound connection on TCP port 9999 and spawns a shell (such as sh, bash, dash, zsh, or ash) within a short time window.
Detects an exploitation attempt against PoeLLM or LiteLLM MCP test/connection endpoint using a python3 stdio payload that fetches data from port 81.
Detects the PoeLLM ELF payload (masquerading as libgcrypt) via Italian-language poem/agent log strings and embedded LiteLLM MCP exploit strings
Detects the PoeLLM ELF payload (masquerading as libgcrypt) via Italian-language poem/agent log strings and embedded LiteLLM MCP exploit strings
Detects DNS lookups, TLS connections, and network traffic associated with the PoeLLM cryptominer communicating with Kryptex mining pools, as well as Stratum mining protocol login attempts.
Detects a sequential pattern where a browser process (Chrome, Edge, or Safari) accesses an AI chat platform's conversation API, followed within 5 minutes by a network connection to a known unauthorized exfiltration destination (api.pbapi.xyz). This pattern suggests the potential use of a browser-based tool or extension (e.g., Poper Blocker or similar) to intercept and exfiltrate AI chat history.
Detects DNS lookups and outbound network connections to known command and control (C2) and staging domains associated with the STAC4924 campaign. The rule performs strict matching on domain names to ensure that subdomains are identified while avoiding false positives from partial string matches within URLs.
This rule monitors NetScaler Syslog events for file creation or modification actions targeting web-accessible directories (/netscaler/, /var/vpn/, /var/netscaler/logon/) involving common web shell scripting extensions (.php, .pl, .sh, .py). It is designed to detect post-exploitation activity such as the manual deployment of backdoors following a vulnerability compromise.
Detects the first-ever observed network connection from a device to WhatsApp Web or Telegram Web within a 30-day lookback period. This behavior is used to identify potentially unauthorized companion-device linking to a user's messenger account, which could indicate credential theft or unauthorized access.
The following analytic detects the creation of new user accounts on Linux systems using commands like "useradd" or "adduser." It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as adversaries often create new user accounts to establish persistence on compromised hosts. If confirmed malicious, this could allow attackers to maintain access, escalate privileges, and further compromise the system, posing a severe security risk.
The following analytic detects the execution of the "At" application in Linux, which can be used by attackers to create persistence entries on a compromised host. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and parent process names associated with "at" or "atd". This activity is significant because the "At" application can be exploited to maintain unauthorized access or deliver additional malicious payloads. If confirmed malicious, this behavior could lead to data theft, ransomware attacks, or other severe consequences. Immediate investigation is required to determine the legitimacy of the execution and mitigate potential risks.
The following analytic detects the creation of new user accounts on Linux systems using commands like "useradd" or "adduser." It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as adversaries often create new user accounts to establish persistence on compromised hosts. If confirmed malicious, this could allow attackers to maintain access, escalate privileges, and further compromise the system, posing a severe security risk.
The following analytic detects the use of the Linux 'xclip' command to copy data from the clipboard. It leverages Linux Auditd telemetry, focusing on process names and command-line arguments related to clipboard operations. This activity is significant because adversaries can exploit clipboard data to capture sensitive information such as passwords or IP addresses. If confirmed malicious, this technique could lead to unauthorized data exfiltration, compromising sensitive information and potentially aiding further attacks within the environment.
The following analytic detects the use of the 'dd' command to overwrite files on a Linux system. It leverages data from Linux Auditd telemetry, focusing on process execution logs that include command-line details. This activity is significant because adversaries often use the 'dd' command to destroy or irreversibly overwrite files, disrupting system availability and services. If confirmed malicious, this behavior could lead to data destruction, making recovery difficult and potentially causing significant operational disruptions.
The following analytic detects suspicious file and directory discovery activities, which may indicate an attacker's effort to locate sensitive documents and files on a compromised system. This behavior often precedes data exfiltration, as adversaries seek to identify valuable or confidential information for theft. By identifying unusual or unauthorized attempts to browse or enumerate files and directories, this analytic helps security teams detect potential reconnaissance or preparatory actions by an attacker, enabling timely intervention to prevent data breaches or unauthorized access.
The following analytic detects the insertion of a Linux kernel module using the insmod utility. It leverages data from Linux Auditd, focusing on process execution logs that include process names and command-line details. This activity is significant as it may indicate the installation of a rootkit or malicious kernel module, potentially allowing an attacker to gain elevated privileges and bypass security detections. If confirmed malicious, this could lead to unauthorized code execution, persistent access, and severe compromise of the affected system.
The following analytic detects attempts to stop a service on Linux systems. It leverages data from Linux Auditd. This activity is significant as adversaries often stop or terminate security or critical services to disable defenses or disrupt operations, as seen in malware like Industroyer2. If confirmed malicious, this could lead to the disabling of security mechanisms, allowing attackers to persist, escalate privileges, or deploy destructive payloads, severely impacting system integrity and availability.
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