Detections

Explore public detection logic contributed by the community across SIEM and rule languages.

20,483 detections

Detects rapid, multi-category system and network enumeration performed by AI-assisted development tools (e.g., Cursor, Claude, ChatGPT). The rule triggers when an AI agent process performs a comprehensive scan of the host environment, specifically covering machine/OS identity, network configuration, process listings, installed software, and access to sensitive credential files within a 10-minute window. It requires at least 5 distinct discovery categories and 6 total events to reduce noise from routine debugging. Covers T1082, T1016, T1057, T1518
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Arnold Chan@slaz
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Detections.ai Community
1 month ago
203
Detects potential multimodal prompt injection attacks where an AI agent process ingests a file (PDF, image, etc.) from a browser or mail client and subsequently performs suspicious downstream activity, such as spawning a shell with execution primitives or making network connections to rare, non-reputable external domains. Covers T1204, T1059, T1105
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Arnold Chan@slaz
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Detections.ai Community
1 month ago
003
Detects AI coding assistants and LLM agent frameworks spawning shell processes or interpreters to execute potentially malicious commands. The rule monitors for suspicious activity often associated with download cradles, credential access, reconnaissance, or persistence, while excluding standard interactive terminal usage.
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Arnold Chan@slaz
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Hunters
1 month ago
104
This rule detects activity associated with the exploitation of PaperCut vulnerabilities. It looks for connections to known malicious IP addresses, the presence of specific credential harvesting tools, reconnaissance commands (whoami, tasklist) executed by the PaperCut application (pc-app.exe), suspicious PowerShell downloads of AnyDesk, and unauthorized access to PaperCut configuration files or log files containing known exploit strings.
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Arnold Chan@slaz
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Detections.ai Community
1 month ago
004
This rule detects a suspicious sequence of events where a process related to Electron (a framework often used for cross-platform desktop applications) executes a hidden, obfuscated PowerShell command, followed by a subsequent PowerShell command to modify Microsoft Defender settings by adding an exclusion path for the 'AppData' directory within 10 minutes of the first process.
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Arnold Chan@slaz
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Detections.ai Community
1 month ago
006
Detects parent process ID (PPID) spoofing associated with RMMCRAT and other HVNC malware, where an injected process (e.g., smartscreen.exe) spawns child processes while spoofing explorer.exe as the parent. The rule monitors for a mismatch between the reported parent (explorer.exe) and the actual creator (smartscreen.exe) and focuses on burst activity of common shell and browser processes.
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Arnold Chan@slaz
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Detections.ai Community
1 month ago
104
This rule detects potentially malicious process execution chains where VBScript or unknown/suspicious binaries (such as ProfileQuickHost.exe) are used to launch Node.js processes, specifically targeting JavaScript files or VBScript agents. This pattern is often associated with the execution of remote access trojans (RATs) or custom malware loaders.
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Kaung Khant Ko@kaungkhantko
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Detections.ai Community
1 month ago
108
Detects attempts to modify the Redis configuration (dir or dbfilename) followed by a SAVE command, which can be leveraged to write arbitrary data to critical system files such as crontabs for persistence.
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Georgios Maragos@Gmarak
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Detections.ai Community
2 months ago
14116
Detects an exploitation chain originating from a Chrome browser process, characterized by the execution of an anomalous child process followed by the creation of an executable file named 'chrome_cleanup.exe' within a short time window. This sequence is indicative of multi-stage exploitation, involving remote code execution via browser vulnerability and subsequent privilege escalation.
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Ankit Mehta@Secvyn
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Hunters
1 month ago
006
Detects potential arbitrary file download using a Microsoft Office application
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SigmaHQ Detections@sigmaHQ
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SigmaHQ
1 month ago
003
This rule detects malicious activity by monitoring for specific known indicators, including hashes of malicious files (ProcessRollup2), network connections to known C2 infrastructure (NetworkConnectIP4), and URL clicks (UrlClick) associated with malicious domains or paths. It acts as a multi-stage indicator correlation rule to identify execution or communication with known threats.
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Arnold Chan@slaz
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Detections.ai Community
1 month ago
206
Detects a potential ClickFix social engineering attack pattern where a user manually launches a script interpreter (e.g., PowerShell, cmd, mshta) from Windows Explorer followed within 30 minutes by the execution of 'rnpkeys.exe', a known indicator in installer side-load chains.
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Ankit Mehta@Secvyn
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SlimKQL
1 month ago
202
Detects the Sauron Loader execution chain, which involves a specific process (rnpkeys.exe, rnp.dll, or tdwp.dll) performing a network request (beacon), followed by the creation of a new file (executable, script, or library), and the subsequent execution of that same file. This pattern is characteristic of operator-issued download-and-run tasking.
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Ankit Mehta@Secvyn
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Detection & Hunting Community
1 month ago
202
Detects the execution chain of the Sauron Loader malware. The rule identifies the side-loading of 'rnp.dll' by 'rnpkeys.exe' originating from the 'C:\ProgramData\keyroll' directory, followed by the loading of 'tdwp.dll' which facilitates in-memory decryption and execution. Persistence is confirmed by the creation or update of a scheduled task named 'keyroll'.
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Ankit Mehta@Secvyn
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Detections.ai Community
1 month ago
102
Detects the creation or modification of a scheduled task utilizing 'tdwp.dll' and containing the 'keyroll' argument in the command line. This pattern is indicative of a specific persistent mechanism often associated with unauthorized activity or potential malware.
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Ankit Mehta@Secvyn
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Detections.ai Community
1 month ago
102
This rule detects potential supply chain compromise by monitoring common Linux package managers (pip, npm, apt, gem) for installation commands targeting non-standard, unauthorized registries, and for the subsequent execution of shells or network utilities by these package manager processes.
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Ankit Mehta@Secvyn
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Detections.ai Community
1 month ago
000
Detects instances where a suspicious PowerShell, mshta, or pwsh command, typically involving download or obfuscation patterns, is launched as a child process of Windows Explorer within two minutes of a modification to the Explorer RunMRU registry key. This behavior is indicative of an adversary attempting to achieve execution, often following user interaction or persistence triggers.
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Ibrahim Saud@tektrix
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Detections.ai Community
1 month ago
000
Detects unauthorized access to sensitive Chromium-based browser files such as 'Login Data' (password database) or 'Local State' (encryption keys) by processes other than standard web browsers (Chrome, Edge, Brave, Firefox). The rule also elevates risk if the access is followed by the execution of known data-handling tools like sqlite, python, or powershell, which are frequently used by information stealers to parse and exfiltrate browser-stored credentials.
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Ibrahim Saud@tektrix
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Detections.ai Community
1 month ago
000
Detects attempts to enumerate or access Windows Credential Manager and vault files, often used by adversaries to extract cached credentials. The rule monitors for execution of vaultcmd.exe, invocation of keymgr.dll via rundll32.exe, and unauthorized access to credential storage paths in AppData by non-system processes.
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Ibrahim Saud@tektrix
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Detections.ai Community
1 month ago
000
Detects the execution of a Control Panel (.cpl) item using control.exe or rundll32.exe. The rule specifically looks for CPL files that were recently written to suspicious directories (Temp, AppData, Downloads) and correlates this execution with subsequent suspicious behaviors, such as spawning Python processes, outbound network connections, or registry modifications consistent with key storage or payload persistence.
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Ankit Mehta@Secvyn
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Detections.ai Community
1 month ago
000
Detects a multi-stage loader execution chain, potentially named GHAPPIER, by identifying sequential stages of execution. These stages include initial marker file checks, shell-based command dispatching via curl/wget, renaming and execution of secondary payloads, Node.js-based runtime staging, and the eventual creation of implant artifacts in common paths such as VSCode extensions or hidden directories.
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Ibrahim Saud@tektrix
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Detections.ai Community
1 month ago
108
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