Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
4,230 detections
Filters
Last updated
All Time
Detection languages
2,175
1,018
354
260
212
Contributors
1,013
818
688
574
212
Categories
20,456
11,839
5,877
5,156
4,865
4,230
Platforms
2,933
889
585
248
198
Products / Services
2,239
1,453
841
416
344
MITRE Techniques
1,164
1,087
870
798
439
CVEs
22
20
20
18
17
IDS Classtypes
927
408
222
146
104
IDS Protocols
1,914
105
70
47
6
Detects anomalous network traffic associated with Vidar Stealer 2.0, where an HTTP GET request for a .jpg file is directed to a bare IP address, and the server's response header fails to indicate an image content type despite the requested file extension.
Detects outbound HTTP POST requests destined for 'aware-cr1.com' targeting the URI '/api/beacon', which is indicative of command-and-control (C2) communication associated with the 2CLoader malware.
Detects outbound HTTP POST requests destined for 'aware-cr1.com' targeting the URI '/api/beacon', which is indicative of command-and-control (C2) communication associated with the 2CLoader malware.
Detects outbound HTTP POST requests destined for 'aware-cr1.com' targeting the URI '/api/beacon', which is indicative of command-and-control (C2) communication associated with the 2CLoader malware.
Detects network activity associated with a malicious NPM package (typosquatting) beaconing to a known C2 server (69.48.229.140) and performing command and control operations, including command polling and data exfiltration, specifically from a Node.js environment.
Detects network activity (DNS queries, TLS SNI, and HTTP Host headers) associated with known RedFlick command-and-control (C2) infrastructure domains: etia.ca, groy.cc, and secure-dns-hub.com.
This rule detects HTTP POST requests originating from legacy and outdated User-Agent strings (specifically older versions of Internet Explorer). Such User-Agent strings are frequently utilized by automated tools, scripts, or malware beaconing to command-and-control (C2) servers, as they are often used to evade security filters or simulate legacy traffic. The rule filters out common content types like text/html or image files to focus on potentially malicious API interactions.
Detects the creation of ransom or extortion note files within known web server directories immediately following suspicious HTTP activity targeting an internet-facing host. This pattern aligns with known ShinyHunters extortion tactics where applications are exploited to deploy extortion notes.
Detects user authentication events where the MFA redirect URL points to an unrecognized or potentially malicious domain. This behavior is indicative of an Adversary-in-the-Middle (AiTM) attack, such as the TrustSink credential capture technique, where the user is diverted from the legitimate identity provider to an attacker-controlled OIDC endpoint to capture credentials or session tokens.
Detects incoming HTTP requests to a FortiSandbox device that contain suspicious command injection patterns. The rule monitors both the URI and the request body for shell metacharacters such as ';', '|', '`', '$(', or '&&', followed by common binary execution commands (e.g., cat, wget, curl, bash, rm). This behavior is indicative of an exploit attempt targeting CVE-2026-39808.
Detects HTTP GET requests to network appliance management interfaces containing query parameters commonly used to trigger remote command or shell execution (e.g., cmd, exec, shell, command), which is characteristic of web shell activity.
Detects HTTP GET requests to the URI '/dpixel', which is a known default beacon URI pattern associated with the Brute Ratel C4 command and control framework's 'Badger' implant.
Detects an exploitation attempt targeting SharePoint through the EditingPageParser by injecting a Register directive. This attempt involves bypassing SafeControls through specifically crafted HTTP POST requests to ToolPane.aspx, utilizing the GetPartPreviewAndPropertiesFromMarkup function to execute malicious tag patterns.
Detects a sequence of potentially malicious activity targeting LLM-related API endpoints. The rule identifies a client IP performing rapid, distinct endpoint discovery, followed by a POST request to sensitive paths, and concluding with a spike in 5xx server-side errors, indicating potential exploitation attempts or fuzzing against Large Language Model interfaces.
Detects web servers serving significantly different content sizes to AI crawlers (e.g., ChatGPT-User, ClaudeBot) compared to human web browsers for the same URL, which is a technique used to serve benign content to automated scrapers while delivering malicious or deceptive content to human users.
Detects web servers serving significantly different content sizes to AI crawlers (e.g., ChatGPT-User, ClaudeBot) compared to human web browsers for the same URL, which is a technique used to serve benign content to automated scrapers while delivering malicious or deceptive content to human users.
Detects web servers serving significantly different content sizes to AI crawlers (e.g., ChatGPT-User, ClaudeBot) compared to human web browsers for the same URL, which is a technique used to serve benign content to automated scrapers while delivering malicious or deceptive content to human users.
Detects HTTP POST requests directed at Grav CMS endpoints where the __unique_form_id__ parameter contains directory traversal sequences (e.g., ../). This behavior is indicative of attempted exploitation of CVE-2026-42608, aimed at bypassing file system restrictions to write files to unauthorized locations outside the application's expected form data directory.
This rule detects malicious HTTP POST requests attempting a path traversal attack against Grav CMS, specifically targeting the __unique_form_id__ parameter. The rule looks for directory traversal sequences ('..') following the parameter, which is characteristic of attempts to read sensitive files outside the intended web directory.
Detects two-stage phishing activity involving an initial visit to a Vercel-hosted lure page followed by the exfiltration or relay of fingerprinting and sandbox-evasion results to api.telegram.org.
Detects anomalous high-volume API requests to machine learning model inference endpoints (e.g., predict, classify, score). This behavior is characteristic of adversarial example crafting, where an attacker sends rapid, near-duplicate requests to estimate gradients or probe confidence scores to manipulate model output.
Page 22 of 202