Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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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.
Detects non-CLI and non-DevOps utility processes accessing sensitive cloud credential and configuration files, including AWS SSO caches, AWS credentials, kubeconfig, and gcloud configuration files. This behavior is indicative of credential theft or discovery by infostealers attempting to gain unauthorized access to cloud environments.
Detects a suspicious sequence where a process loads the .NET CLR (clr.dll or mscoree.dll) and shortly thereafter accesses specific low-prevalence file extensions (.raw or .pak) within high-risk directories (AppData/Local/Temp, ProgramData, or Users/Public). This behavior is characteristic of shellcode loaders (such as the WAV-shellcode loader) that decrypt and execute RAT payloads from obfuscated containers in memory. The rule intentionally excludes common desktop applications to minimize false positives.
This rule detects attempts to steal the Active Directory database (ntds.dit) and associated security files by creating volume shadow copies using native Windows utilities like ntdsutil, vssadmin, or wmic. It also monitors for direct file system access or creation attempts related to these sensitive database files within shadow copy paths.
Detects the use of cloud synchronization utilities such as rclone (including renamed binaries) or Mega command-line tools to interact with cloud storage services. The rule correlates this execution with preceding mass file enumeration or access activity on the same host, which is a common pattern in ransomware double-extortion scenarios.
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 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 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 attempting to read sensitive wallet extension storage files or desktop application wallet data folders, a behavior characteristic of the Psychedelic Stealer malware designed to harvest cryptocurrency credentials prior to exfiltration.
Detects evidence of potential adversarial interference with Windows Defender updates and MRT.exe (Malicious Software Removal Tool). The rule correlates high volumes of temporary file creation in user directories with unauthorized access to MRT.exe by third-party processes and repeated Windows Defender update failures, indicating a potential attempt to disrupt endpoint security operations.
Detects an suspicious progression of activities originating from identified AI assistant/agent processes. The rule monitors for a chain of behaviors occurring within a 15-minute window: execution of an AI agent/tool, followed by host discovery (network/system enumeration), access to sensitive local credential files, archival of data, and outbound network communication to common file-sharing/exfiltration platforms.
Detects Node.js or ProfileQuickHost processes performing file operations (read, write, rename) on sensitive browser directories, such as 'Local Extension Settings' or wallet-related folders. This pattern is commonly observed in credential-stealing malware attempting to extract browser-stored secrets or cryptocurrency wallet data.
Detects various techniques used to escape from Linux container isolation, including accessing container daemon control sockets (Docker/containerd), abusing namespace operations (nsenter/setns), mounting sensitive host filesystem paths (e.g., /proc/1/root), performing chroot escapes, and abusing privileged capabilities or socket-based execution.
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.
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.
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 access to sensitive browser credential and cookie files by processes that are not recognized web browsers or their designated update components. This behavior is highly indicative of credential theft activity where an adversary is attempting to extract stored credentials or session cookies from local browser storage.
Detects unauthorized access by a non-browser process to Chromium-based 'Cookies' SQLite database files, which are used by browsers like Chrome, Edge, and Brave. This behavior is a strong indicator of credential theft or session hijacking, as attackers target these files to extract session cookies and bypass MFA for cloud services.
Detects unauthorized access attempts to Chromium-based browser credential storage files, specifically 'Login Data' (SQLite database) and 'Local State' (master key storage), by processes other than standard browsers or known security products. This activity is a common indicator of credential harvesting by information stealers such as LummaC2, Vidar, and RedLine.
Detects a multi-stage activity chain indicative of infostealer malware, including reading the browser 'Local State' file (containing the master key), accessing a browser process (likely for memory dumping or token extraction), and initiating an immediate outbound network connection to an external destination.
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