Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
228 detections
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Detects high-rate scanning and automated service probing patterns consistent with network reconnaissance. This includes tracking TCP SYN floods to external segments, excessive HTTP/login requests, and automated fingerprinting probes (e.g., favicon or admin panel enumeration). The rule accounts for common uptime monitoring services to minimize false positives.
Detects the initialization of containerized LLM-based gateway services (e.g., litellm, ollama) followed closely in time by high-volume network traffic containing suspicious web injection payloads (e.g., SQLi, XSS, SSRF). The rule correlates process execution events for container launches with network activity originating from the same device to identify potential unauthorized or unsupervised automated exploitation.
Detects high-volume bursts of HTTP GET requests against specific paths associated with a Vite development server (/@vite/client, /@fs/, /src/) on port 5173. This pattern is indicative of automated reconnaissance or vulnerability scanning targeting an exposed development environment.
Detects high-volume bursts of HTTP GET requests against specific paths associated with a Vite development server (/@vite/client, /@fs/, /src/) on port 5173. This pattern is indicative of automated reconnaissance or vulnerability scanning targeting an exposed development environment.
Detects high-volume bursts of HTTP GET requests against specific paths associated with a Vite development server (/@vite/client, /@fs/, /src/) on port 5173. This pattern is indicative of automated reconnaissance or vulnerability scanning targeting an exposed development environment.
This rule monitors network traffic from internal devices and security logs for communication with a curated list of known malicious or suspicious scanner IP addresses, indicating potential reconnaissance or probing activity by external actors.
This rule monitors network traffic from internal devices and security logs for communication with a curated list of known malicious or suspicious scanner IP addresses, indicating potential reconnaissance or probing activity by external actors.
Detects high-frequency, automated web scraping behavior targeting product catalog pages, indicative of content harvesting for site replication. The rule monitors IIS logs for non-browser user agents (e.g., Python, curl, Scrapy) performing rapid, multi-page requests within a short duration.
Detects high-frequency, automated web scraping behavior targeting product catalog pages, indicative of content harvesting for site replication. The rule monitors IIS logs for non-browser user agents (e.g., Python, curl, Scrapy) performing rapid, multi-page requests within a short duration.
Detects high-velocity, automated web activity originating from a single source IP that simultaneously performs widespread endpoint enumeration, tests sensitive authentication flows, and attempts common injection attack payloads. This behavior is indicative of an automated scanner, crawler, or malicious botnet agent performing rapid discovery and exploitation attempts.
Detects automated reconnaissance and enumeration of sensitive web application paths (admin, config, environment files, etc.) from a single source IP. The rule identifies high-frequency request patterns that target specific non-public surface areas while excluding known search engine crawlers and monitoring bots.
This rule monitors for active reconnaissance or scanning behavior by detecting a single source IP interacting with a high number of distinct ports/hosts within a short time frame (NetworkScan) or accessing a high number of distinct URI paths/404 errors (WebContentScan).
This rule monitors web server logs (IIS, W3C, and Azure Application Gateway) for incoming traffic that matches known malicious or automated vulnerability scanners, as well as requests for common system fingerprinting paths (such as /server-status or /.git/config). The rule aggregates these hits by source IP address to identify potential active reconnaissance or vulnerability scanning attempts.
Detects automated directory fuzzing attempts against Azure Storage blobs targeting sensitive files like .env, .pem, or configuration files. The rule identifies suspicious activity by aggregating requests from Anonymous or SAS-authenticated sources that exhibit high request volumes and access to multiple distinct sensitive paths, while excluding known legitimate bulk-transfer and synchronization tools.
Detects automated directory fuzzing attempts against Azure Storage blobs targeting sensitive files like .env, .pem, or configuration files. The rule identifies suspicious activity by aggregating requests from Anonymous or SAS-authenticated sources that exhibit high request volumes and access to multiple distinct sensitive paths, while excluding known legitimate bulk-transfer and synchronization tools.
Detects automated directory fuzzing attempts against Azure Storage blobs targeting sensitive files like .env, .pem, or configuration files. The rule identifies suspicious activity by aggregating requests from Anonymous or SAS-authenticated sources that exhibit high request volumes and access to multiple distinct sensitive paths, while excluding known legitimate bulk-transfer and synchronization tools.
Detects automated reconnaissance and enumeration of sensitive web application paths (admin, config, environment files, etc.) from a single source IP. The rule identifies high-frequency request patterns that target specific non-public surface areas while excluding known search engine crawlers and monitoring bots.
Detects automated reconnaissance and enumeration of sensitive web application paths (admin, config, environment files, etc.) from a single source IP. The rule identifies high-frequency request patterns that target specific non-public surface areas while excluding known search engine crawlers and monitoring bots.
Detects high-rate scanning and automated service probing patterns consistent with network reconnaissance. This includes tracking TCP SYN floods to external segments, excessive HTTP/login requests, and automated fingerprinting probes (e.g., favicon or admin panel enumeration). The rule accounts for common uptime monitoring services to minimize false positives.
Detects high-rate scanning and automated service probing patterns consistent with network reconnaissance. This includes tracking TCP SYN floods to external segments, excessive HTTP/login requests, and automated fingerprinting probes (e.g., favicon or admin panel enumeration). The rule accounts for common uptime monitoring services to minimize false positives.
Detects the initialization of containerized LLM-based gateway services (e.g., litellm, ollama) followed closely in time by high-volume network traffic containing suspicious web injection payloads (e.g., SQLi, XSS, SSRF). The rule correlates process execution events for container launches with network activity originating from the same device to identify potential unauthorized or unsupervised automated exploitation.
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