Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects instances where a process other than the authorized password manager application (e.g., 1Password, Bitwarden, KeePass) attempts to create, modify, rename, or read sensitive database files associated with these password managers. This behavior may indicate an attempt to exfiltrate or compromise stored credentials.
This rule detects potentially unauthorized or suspicious developer activity, specifically focusing on the execution of custom scripts like 'temp_auto_push.bat', automated task execution via VS Code 'tasks.json' files, and git commands that bypass security checks using '--no-verify'. These activities may indicate repository tampering, modification of source code integrity, or attempts to automate actions that circumvent established development workflows.
This rule detects potential reconnaissance activity on macOS by monitoring for the execution of 'system_profiler' to gather system application data or the use of 'grep' to search for process information (ps). Such activity is often associated with initial discovery phases where an adversary gathers information about the target system.
Detects the loader-pool.db PNG-format file used to smuggle encrypted payload modules inside IDAT chunks, reassembled after a sleep-based evasion delay
Detects Node.js processes spawning child Node.js processes where the command line references known IPFS gateways, the 'sync.js' script name, or specific Content Identifiers (CIDs) associated with Miasma malware or related decentralized distribution activity.
Detects the creation of specific sensitive file names (AGENTS.md, MEMORY.md, IDENTITY.md, USER.md, SOUL.md) within 30 minutes of a process execution containing email-related keywords (email, mail, inbox). This pattern is often indicative of an adversary staging sensitive information for exfiltration after gaining access to or interacting with an email client or communication tool.
Detects Node.js processes executing with command-line arguments containing potential GitHub personal access tokens (PAT) or secrets while simultaneously initiating a network connection to the GitHub API. This behavior is indicative of potential credential abuse or automated exfiltration of sensitive tokens.
Detects the use of the 'dscl' utility on macOS with the '-authonly' flag. This command is often used by adversaries or for unauthorized scripts to verify credentials against the local directory service without requiring a full login session, which is a common indicator of credential testing or brute-force attempts.
Detects outbound network connections to identified infrastructure associated with 'Pink' (CL-CRI-1147) passkey-enrollment vishing campaigns. These operators use phone-based social engineering to direct victims to phishing domains that relay credentials and MFA tokens in real-time to register attacker-controlled passkeys. The rule monitors for specific DNS query patterns containing passkey-related keywords and known malicious IP addresses used by the threat group.
Detects potential residential proxy usage based on network connections to known proxy-related ports and proxy infrastructure indicators in CrowdStrike Falcon logs.
Detects file access or read operations performed against known password manager database files (1Password, Bitwarden). This activity may indicate an attempt to acquire credentials from local storage.
Heuristic detection of high-entropy, obfuscated script content consistent with AI-generated (WormGPT-derived) malware formatted specifically to evade AV/EDR detection tooling
This rule identifies AnyDesk remote access software activity by correlating process execution with network connection events between hosts. It tracks the AnyDesk application attempting to establish a network connection within a 5-minute window, effectively monitoring potential interactive remote access sessions across the network.
This rule monitors for suspicious Git command-line activity that may indicate an attempt to rewrite commit history or force push changes while bypassing verification checks. It detects specific patterns including the execution of 'temp_auto_push.bat', force amending of commits with '--no-verify', and forced pushing with '--no-verify'. These actions are commonly associated with code tampering or the rapid deployment of unauthorized changes to a repository.
Detects the creation or modification of a file at a specific Launch Agent path commonly used for macOS persistence. Attackers often place property list (.plist) files in /Library/LaunchAgents/ to achieve persistence across system reboots or user logons.
Detects the use of shell interpreters (sh, bash, zsh) to download content from GitHub using curl (specifically using the -s silent flag) and subsequent base64 decoding of potentially obfuscated payloads using the base64 command with the -D flag.
MAL AMOS yara x
YARA
This YARA rule detects the Amos Stealer malware on macOS by matching a specific Mach-O dylib hash and entitlement hash, which are consistent markers for samples of this information stealer.
Detects outbound POST requests using the grpc-web+proto content type where the X-Request-Id header contains a base64-encoded JSON blob. This pattern is associated with exfiltrating sensitive data, such as mnemonic phrases or private keys, to a malicious endpoint masquerading as an Injective testnet gRPC-web node.
This rule detects potential ransomware activity by monitoring for high-frequency file rename events (more than 50 in a single dataset aggregation) or file renames resulting in the specific extension '.God8Damn'. Such patterns are highly characteristic of ransomware encrypting local or network files.
Detects a process creating files named 'README.TXT' or 'README.txt' across three or more distinct directories. This behavior is a common indicator of ransomware activity, where an adversary drops ransom notes in multiple folders to maximize visibility after encrypting files.
This rule detects the execution of Java JAR files from suspicious or unconventional directories (such as Temp, AppData, Downloads, or Public folders). It filters out known build tools and CI/CD agents to focus on potentially malicious or unauthorized execution of JAR files that might indicate payload staging or execution in high-risk locations.
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