Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
40,063 detections
Filters
Last updated
All Time
Detection languages
15,361
13,596
2,797
1,803
1,725
Contributors
7,678
5,999
5,315
4,504
4,191
Categories
18,113
9,760
3,854
3,760
3,714
Platforms
40,063
7,331
6,542
3,907
3,666
Products / Services
10,442
9,677
6,722
1,888
1,879
MITRE Techniques
13,738
13,119
8,068
5,949
4,415
CVEs
49
45
30
30
29
IDS Classtypes
214
56
43
23
19
IDS Protocols
183
172
20
16
8
Detects the presence of ZIP archive files masquerading as 'Evernote.zip' that contain 'notepad++.exe', which is a pattern frequently observed in VBScript-based downloaders utilizing binary sideloading to execute malicious payloads.
Detects the use of WScript or CScript to query the Win32_OperatingSystem WMI class for the InstallDate property. This behavior is commonly used by malware to identify the system installation time, which can serve as a simple heuristic for sandbox evasion or environmental keying.
Detects anomalous, mass-access behavior targeting plaintext credential files, certificate stores, private keys, or password-manager databases located on general-purpose network shares. Such activity is often indicative of an adversary conducting discovery or collection of credentials for lateral movement and privilege escalation.
Detects the use of package installation commands (pip, npm, npx) where the package name resembles popular AI/ML libraries (tensorflow, torch, openai, langchain) but is not an exact match for known legitimate versions. This is a common indicator of a typosquatting supply chain attack, where attackers attempt to trick users into installing malicious packages with similar names to trusted software.
Detects the installation of known AI-powered coding assistant extensions (e.g., Copilot, Tabnine, Cursor) on a device, followed by significant source-code repository cloning or archival activity by Git within two hours. This pattern is potentially indicative of intellectual property theft or sensitive source code exfiltration facilitated by a newly introduced AI tool.
Detects command-line execution of scripting tools (e.g., PowerShell, Python, WScript) that simultaneously reference AI service domains (OpenAI, Anthropic), perform network request functions, reference API keys, and utilize encoding or decoding operations. This pattern is indicative of unauthorized use of local scripts to interact with AI APIs, potentially for data exfiltration or automated processing of sensitive information.
Detects anomalous, mass-access behavior targeting plaintext credential files, certificate stores, private keys, or password-manager databases located on general-purpose network shares. Such activity is often indicative of an adversary conducting discovery or collection of credentials for lateral movement and privilege escalation.
This rule correlates email attachment delivery events with subsequent process creation events on the recipient's endpoint. It identifies scenarios where a known document reader or mail client (Outlook, Word, Excel, PowerPoint, Adobe Acrobat/Reader) spawns a suspicious child process (PowerShell, CMD, WScript, CScript, MSHTA, Rundll32, or Regsvr32) shortly after a file is delivered via email to the same user. This detects the immediate execution phase of a phishing attack.
Detects the installation of known AI-powered coding assistant extensions (e.g., Copilot, Tabnine, Cursor) on a device, followed by significant source-code repository cloning or archival activity by Git within two hours. This pattern is potentially indicative of intellectual property theft or sensitive source code exfiltration facilitated by a newly introduced AI tool.
Detects the deletion of .zip files within the Public directory by VBScript or JScript interpreters (wscript.exe or cscript.exe). This pattern is often indicative of a malicious dropper or downloader that extracts a payload from a staged archive and subsequently removes the evidence to hinder analysis and forensics.
Detects a network activity pattern from a single host characterized by a high volume of connections to a wide range of remote hosts and ports, combined with an exceptionally low coefficient of variation in inter-request timing. This pattern is indicative of an automated, scriptable agent or AI-driven reconnaissance tool rather than a human-operated scanner, likely performing automated asset discovery or network enumeration.
Detects a multi-stage malware execution pattern consistent with VelvetCake activity. This includes staging text-based files in C:\Users\Public, downloading PowerShell scripts to temporary directories, executing the script via PowerShell, and subsequently deleting both the staged text files (except for specific allowed filenames) and the payload script.
Detects the execution of Windows Script Host (wscript.exe/cscript.exe) loading WMI-related modules (wbemprox.dll, wbemcomn.dll, etc.). This pattern is often indicative of scripts attempting to query WMI for system information, environment configuration, or locale data, which is a common reconnaissance technique used by malware to identify the victim environment.
Detects the installation or execution of virtual camera or synthetic media software followed shortly by the launch of common video conferencing applications on the same endpoint. This behavior may indicate an attempt to inject synthetic video feeds into meetings for impersonation or business email compromise (BEC) attacks.
Detects a single source IP address rapidly attempting connections to multiple distinct devices and ports within a short time window. This behavior is indicative of an automated agent or scanner performing reconnaissance and potentially attempting to chain exploitation across multiple targets.
Detects PowerShell ScriptBlock events (4104) that exhibit characteristics of AI/LLM-generated code, specifically the presence of highly descriptive inline commentary combined with encoded commands or remote network download cradles. This pattern is often used by automated tools to generate syntactically randomized obfuscated scripts to evade detection.
Detects video and audio conferencing clients (e.g., Zoom, Microsoft Teams, Webex) loading either unsigned DLLs or DLLs commonly associated with virtual camera and audio injection tools (e.g., OBS VirtualCam, Snap Camera). This behavior is characteristic of deepfake injection techniques used to manipulate video conference participants.
Detects instances where a Python-based machine learning framework (identifiable by command-line arguments related to model loading like pickle or PyTorch loading functions) spawns a suspicious child process, such as a shell or network utility. This behavior indicates a potential malicious machine learning model, such as a booby-trapped pickle file, executing arbitrary code upon deserialization.
Detects the creation of browser extension files (manifest.json, .crx, .xpi) within the default user profile directories for Google Chrome, Microsoft Edge, and Mozilla Firefox. This behavior is indicative of potential unauthorized browser extension installation, which can be used for persistence or data exfiltration.
Detects web reconnaissance activity characterized by the use of User-Agent strings associated with automated AI agent frameworks and headless browser libraries (e.g., LangChain, AutoGPT, Playwright, Selenium, python-requests). These tools are frequently utilized in autonomous exploitation scenarios to scan multiple URI paths on a web server in a short timeframe.
This rule detects interactive or user-triggered execution of PowerShell carrying indicators of payload downloading, inline evaluation, or Base64 encoding. Because explorer.exe is the parent process, it typically signifies a payload delivered via email/web that a user opened directly such as an .lnk shortcut.
Page 204 of 1908