Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
7,141 detections
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Detects successful authentication events from accounts identified as service, managed, or robot accounts (e.g., svc_, sa_, msol, robot) originating from external IP addresses or exhibiting an unusually high number of distinct source IP addresses. This behavior is indicative of potential credential theft, token replay attacks, or unauthorized usage of service account identities.
This rule detects indicators of Adversary-in-the-Middle (AiTM) phishing infrastructure, specifically targeting Evilginx2 and similar proxy frameworks. It identifies unauthorized execution of Evilginx2 binaries, the presence of specific session and configuration files (.evilginx/, phishlets, session databases), unauthorized modifications to Nginx configurations, and the dropping of suspicious HTML lure files into web document roots by scripting interpreters.
Detects Node.js executing the malicious setup.cjs script or easy-day-js with the --no-warnings flag, associated with the Mastra npm supply chain compromise.
Detects instances where common web server processes initiate command interpreters or system utilities. This behavior is a common indicator of web shell execution, where an adversary uses a web application vulnerability to gain remote code execution on the underlying server.
Detects various techniques used by an attacker to escape a container environment or gain unauthorized access to host-level resources. This includes the execution of namespace manipulation tools (nsenter, unshare, chroot), unauthorized access to the docker.sock Unix socket, mounting of host filesystems from within a container, and direct attempts to access the host's /proc/1 namespace files.
This rule detects instances where AutoGen Studio or related AI agents initiate potentially suspicious child processes such as command interpreters (cmd, powershell, bash) or known Living-off-the-Land Binaries (LOLBins) while simultaneously establishing outbound WebSocket connections to Model Context Protocol (MCP) endpoints. This behavior is indicative of an attacker attempting to leverage AI agent automation capabilities for code execution or remote system interaction.
This rule detects potential command and control (C2) beaconing activity by identifying devices communicating with known suspicious remote IP addresses at regular, repeating intervals. It calculates the time difference between consecutive connections to specific suspicious IPs and identifies devices that establish at least three connections within one-hour bins, adhering to a 1 to 15-minute heartbeat interval.
This rule detects multiple outbound network connections to a specific set of known malicious C2 IP addresses on specific ports within a one-hour window. This behavior is indicative of a multi-payload delivery pattern associated with malware families such as Remcos, Agent Tesla, and RedLine.
Detects an unknown/network-active parent process spawning shells used by ArachneC2 interactive shell or exec functionality.
This rule detects file creation or modification events associated with the Miasma campaign. It identifies specific payload files (e.g., .claude/setup.mjs, .vscode/tasks.json), the use of specific marker strings in file metadata or origin URLs, and obfuscated task configurations in VS Code directories that leverage bun or node to execute base64-encoded or character-encoded commands.
This rule detects potentially unauthorized installation of extensions in Visual Studio Code (VSIX) or plugins in JetBrains IDEs (IntelliJ, PyCharm, WebStorm). It flags installations that do not originate from verified official marketplaces, which may indicate the manual installation of malicious or supply-chain compromised development environment extensions.
Detects the spawning of shell processes (sh, bash, dash, zsh, ksh) as children of ffmpeg, ffprobe, jellyfin-ffmpeg, or emby-ffmpeg. This behavior is indicative of a successful AVBuffer.free pointer hijack vulnerability (CVE-2026-8461, also known as PixelSmash) being exploited to achieve arbitrary command execution. The rule specifically looks for these parent processes spawning shell children, or if the child process command line contains common shell paths or network-related commands, and filters for specific user accounts often associated with media servers or generic system accounts.
Detects the execution of Python interpreters spawned by code editors or runtime environments (e.g., VS Code, Cursor, Node.js) with specific command-line arguments involving 'init'. This behavior may indicate an adversary attempting to leverage development tools to execute custom scripts, potentially for automated deployment, persistence, or malicious payload initialization.
Detects high-volume failed authentication attempts targeting a specific user account from a source IP address, indicative of 'MFA fatigue' or 'push bombing' attacks. This rule monitors endpoint-visible logon failure events as captured by EDR telemetry to identify potential attempts to circumvent multi-factor authentication by spamming the user with requests.
Detects instances where 'node.exe' or 'node' initiates the deletion of 'setup.cjs' from the 'node_modules/easy-day-js' directory. This includes both direct file system deletion (fs.rmSync) and shell-based deletion commands (e.g., 'del /f', 'rm -f') executed by 'node.exe'. This activity is indicative of potential malicious behavior, possibly related to supply chain attacks or post-exploitation cleanup.
Detects unauthorized processes attempting to access or perform operations on sensitive files such as browser credentials, SSH keys, crypto wallets, and password manager databases. The rule specifically looks for non-standard or unsigned processes interacting with a high volume of these files in a short time frame, which is indicative of credential harvesting by infostealer malware like Lumma, Redline, or Vidar.
Detects outbound network connections from workstations or servers to common message broker ports (MQTT 1883/8883, AMQP 5672). These protocols are sometimes abused for command-and-control (C2) communication, as they allow for pub/sub messaging patterns that can blend into legitimate network traffic.
Detects malicious node.exe processes engaging in system, user, process, or network discovery behaviors. This behavior is indicative of malicious npm packages, such as those seen in the Polymarket Trap, Miasma, or MYRA campaigns, which perform reconnaissance before attempting credential exfiltration.
Detects indicators of the MYRA rootkit, specifically the creation of a hidden shared library in /usr/local/lib/, persistence via a shell script in /etc/profile.d/, and the execution of processes utilizing the LD_PRELOAD environment variable to load the malicious library.
Detects outbound network connections from internal hosts to domains associated with the Polymarket Trap / Miasma infostealer campaign, including dedicated servers (stitch-production.org, log-taker.store) and vercel.app serverless endpoints used for credential exfiltration.
Detects persistence mechanisms associated with the MYRA Linux RAT, including the use of LD_PRELOAD to load a malicious shared library from a hidden path, persistent shell profile modification via /etc/profile.d/.sh.local, and process masquerading as systemd-userdbd. This rule is linked to the Polymarket Trap campaign involving malicious npm packages.
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