Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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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.
Detects potential abuse of the Linux Magic SysRq (System Request) key by adversaries with root or sufficient privileges to manipulate or destabilize a system.
Writing to /proc/sysrq-trigger can crash the system, kill processes, or bypass standard logging.
Monitoring SysRq abuse helps detect stealthy post-exploitation activity.
Correlate with related EXECVE or PROCTITLE events to identify the process or user responsible for the access or modification.
Writing to /proc/sysrq-trigger can crash the system, kill processes, or bypass standard logging.
Monitoring SysRq abuse helps detect stealthy post-exploitation activity.
Correlate with related EXECVE or PROCTITLE events to identify the process or user responsible for the access or modification.
Detects when an AI agent or assistant process attempts to access sensitive local configuration or credential files (e.g., .aws/credentials, .env, id_rsa, service account keys, .netrc) that typically fall outside the scope of its intended function. This behavior is indicative of unauthorized access, potentially due to prompt injection or malicious exploitation of the agent's file system permissions.
Detects instances where a process that is not a recognized web browser (chrome, msedge, firefox, brave, opera, or explorer) accesses multiple unique browser cookie files. This is a common pattern for credential harvesting malware attempting to steal browser sessions.
Detects unauthorized process activity that reads, copies, archives, or transmits the NetScaler ns.conf configuration file. This file contains highly sensitive information, including AAA/LDAP bind credentials and certificates. Adversaries may stage or exfiltrate this file following the exploitation of vulnerabilities (such as CVE-2026-88771 or CVE-2026-88772) on NetScaler appliances to facilitate further network compromise.
Detects runtime processes associated with AI agents or scripting environments (e.g., python, node, deno) accessing sensitive credential storage paths and files, such as SSH keys, cloud provider configuration directories, and local environment variables, which may indicate credential exfiltration.
Detects the use of standard Linux command-line utilities (e.g., cat, curl, vi) to read or reference sensitive cloud credential files or Kubernetes service account tokens. This behavior is a common indicator of credential theft or discovery activities following initial access to a Linux host.
Detects the use of standard Linux command-line utilities (e.g., cat, curl, vi) to read or reference sensitive cloud credential files or Kubernetes service account tokens. This behavior is a common indicator of credential theft or discovery activities following initial access to a Linux host.
Detects unauthorized access to or generation of Citrix NetScaler core dump files, trace outputs, or memory diagnostic artifacts. These artifacts are high-value targets for attackers, such as those exploiting CVE-2026-88772, who attempt to extract session tokens, credentials, and sensitive data residing in memory via these diagnostic mechanisms.
Detects unauthorized access or modification attempts to common web browser credential storage files (such as 'Login Data' or 'key4.db') by processes other than standard, trusted web browsers (e.g., Chrome, Edge, Firefox). This behavior is indicative of credential harvesting, where an adversary attempts to steal saved login information from browser data stores.
Detects potential insider threats or departing employees by correlating abnormal bulk data downloads from cloud repositories (SharePoint/OneDrive/Box/etc.) with subsequent sensitive data access and exfiltration signals. It uses baseline behavioral profiling, requires a threshold of sensitive file interactions, and mandates high-confidence exfiltration indicators (removable media usage, suspicious external domains, or multiple combined destination events) to reduce noise. Archive/staging activity is used as an optional confidence booster.
Detects unauthorized processes accessing sensitive credential storage files (e.g., 'Login Data', 'Cookies', 'Local State') from known Chromium-based browser profile directories. This activity is indicative of credential harvesting by infostealer malware attempting to exfiltrate browser data before performing decryption.
Detects unauthorized access, reading, or downloading of sensitive NetScaler system files, including configuration files (ns.conf), licensing files, and cryptographic material (private keys/certificates). This activity often indicates post-exploitation reconnaissance, staging, or data theft attempts following initial access.
The following analytic identifies a possible non-common browser process accessing its browser user data profile.
This tactic/technique has been observed in various Trojan Stealers, such as SnakeKeylogger, which attempt to gather sensitive browser information and credentials as part of their exfiltration strategy.
Detecting this anomaly can serve as a valuable pivot for identifying processes that access lists of browser user data profiles unexpectedly.
This detection uses a lookup file `browser_app_list` that maintains a list of well known browser applications and the browser paths that are allowed to access the browser user data profiles.
This tactic/technique has been observed in various Trojan Stealers, such as SnakeKeylogger, which attempt to gather sensitive browser information and credentials as part of their exfiltration strategy.
Detecting this anomaly can serve as a valuable pivot for identifying processes that access lists of browser user data profiles unexpectedly.
This detection uses a lookup file `browser_app_list` that maintains a list of well known browser applications and the browser paths that are allowed to access the browser user data profiles.
This Analytic detects the execution of a process attempting to access the hosts file.
The hosts file is a critical file for network configuration and DNS resolution.
If an attacker gains access to it, they can redirect traffic to malicious websites, serve fake content or block legitimate security websites.
The hosts file is a critical file for network configuration and DNS resolution.
If an attacker gains access to it, they can redirect traffic to malicious websites, serve fake content or block legitimate security websites.
Detects the use of legitimate data transfer utilities (s5cmd, rclone) configured to exfiltrate sensitive document types (.doc, .pdf, .sql, etc.) to S3-compatible cloud storage. The rule correlates this activity with recent access to files containing sensitive credentials or configuration data, a pattern observed in double-extortion campaigns.
This rule detects potential browser-based credential and session theft by monitoring for a process that accesses multiple sensitive web browser artifacts (cookies, local state, session storage) across several user profiles in a short timeframe. It correlates this activity with the subsequent creation of a compressed archive in a temporary directory followed by an outbound network connection, indicating a multi-stage exfiltration workflow.
Detects unauthorized processes attempting to read sensitive session files or databases associated with messaging applications like Telegram, Signal, and Discord. Accessing these files (such as 'tdata', 'db.sqlite', or 'leveldb') allows for full account takeover by extracting session tokens, enabling attackers to bypass authentication and 2FA.
Detects unauthorized access to DPAPI master key files located in the Windows user profile (%APPDATA%\Microsoft\Protect\). This activity is commonly associated with infostealers attempting to decrypt browser-stored credentials (cookies, passwords) by manually accessing the master key files after exfiltrating encrypted data stores.
Detects unauthorized processes attempting to read or create sensitive browser credential files ('Login Data', 'Web Data', 'Cookies', 'logins.json', 'key4.db'). This activity is commonly associated with information-stealing malware (such as Lumma, StealC, Vidar, RedLine, and Amadey) that targets browser databases to extract stored secrets, often bypassing standard browser process access.
This rule detects a multi-stage attack chain where a device shows signs of credential theft (accessing browser credential stores or cookies) followed closely by a successful cloud authentication from that same user using a device identifier not previously observed in the last 30 days.
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