Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
205 detections
Filters
Last updated
All Time
Detection languages
117
32
20
18
9
Contributors
72
45
32
9
8
Categories
105
95
39
34
28
Platforms
108
63
28
21
16
Products / Services
75
29
29
27
27
MITRE Techniques
18,108
15,613
12,573
8,384
6,203
CVEs
2
2
1
1
1
IDS Classtypes
24
3
3
2
IDS Protocols
23
5
1
1
1
Detects a suspicious pattern of activity where a single source IP performs network reconnaissance followed by rapid-fire failed authentication attempts against multiple distinct hosts within a short time window. This behavior is indicative of automated, agentic AI-driven offensive tooling rather than manual activity.
Detects anomalous, high-frequency interaction with AI model endpoints characterized by a high volume of requests with a near 1:1 ratio of distinct inputs. This pattern is indicative of automated knowledge-distillation or model extraction attacks against AI services.
This rule detects reconnaissance activity targeting Azure App Service instances, specifically looking for attempts to access web-shell-like files, WordPress administration paths, PHP-CGI endpoints, or LangFlow code validation paths. It identifies potential Storm-3168 actor infrastructure through known IOCs or through patterns of high-volume, path-diverse HTTP requests that deviate from typical noise, helping to distinguish targeted probing from common internet background scanning.
Detects high-frequency connection attempts to SMB (445) or RDP (3389) ports originating from a single source, which is characteristic of the SoftPerfect Network Scanner (NetScan) tool performing reconnaissance or network discovery sweeps.
Detects a sequence of events where a host connects to 'speedtest.net' followed by a burst of outbound SYN packets to TCP port 37777. This pattern may indicate an adversary using legitimate web traffic to calibrate their scanning rate or bypass network controls before performing a targeted scan.
Detects high-frequency, unauthenticated Redis PING/AUTH attempts indicative of a reconnaissance scan against the Redis service. This activity typically represents an adversary performing mass fingerprinting or attempting to identify unsecured Redis instances.
Detects login attempts to internet-exposed FortiGate SSL VPN or firewall management interfaces using suspicious, generic, or potentially leaked administrative account patterns such as fgt-*, FortiManager-*, or tech-support-*. These accounts typically suggest attempts to exploit default, hardcoded, or publicly shared credentials on network infrastructure.
Detects the execution of the legacy 'finger.exe' network utility with suspicious command-line arguments. The finger protocol is an outdated service that provides information about users on a system; adversaries may abuse this utility to perform network reconnaissance, identify user accounts, or gather system-level information.
Detects high-frequency TCP connection attempts to port 37777, commonly associated with Dahua surveillance devices. The rule uses a threshold of 200 connection attempts within 60 seconds from a single source, which is characteristic of mass-scanning or brute-force activity.
This rule monitors for two distinct stages of potential account recovery abuse. First, it detects the execution of suspected Python scripts on an endpoint that contain command-line arguments related to password recovery or authentication codes. Second, it monitors network proxy/web logs for a high volume of API requests to the 'easy4ipcloud.com' domain associated with password reset or device probe endpoints, which may indicate automated credential stuffing or unauthorized device account recovery attempts.
Detects high-volume DNS queries for subdomains of 'twilio.com' originating from a single source IP within a short 5-minute window, indicative of subdomain enumeration or reconnaissance.
This rule monitors for the execution of common penetration testing tools (nuclei, ffuf, sqlmap) and detects network activity towards OpenRouter.ai, a platform that provides access to various Large Language Models (LLMs), often used in automation or script-assisted reconnaissance and exploitation. It also flags instances where container runtimes are used to execute these same tools.
Detects anomalous, high-frequency, multi-channel probing of AI agent input surfaces. The rule identifies external actors sending instruction-injection style keywords across different communication channels (e.g., email, Teams, calendar) within a short window, which may indicate an attempt to identify and manipulate an organization's autonomous agents. Covers T1595.002
Detects anomalous, high-frequency, multi-channel probing of AI agent input surfaces. The rule identifies external actors sending instruction-injection style keywords across different communication channels (e.g., email, Teams, calendar) within a short window, which may indicate an attempt to identify and manipulate an organization's autonomous agents. Covers T1595.002
Detects anomalous, high-frequency, multi-channel probing of AI agent input surfaces. The rule identifies external actors sending instruction-injection style keywords across different communication channels (e.g., email, Teams, calendar) within a short window, which may indicate an attempt to identify and manipulate an organization's autonomous agents. Covers T1595.002
This rule detects potential automated reconnaissance activity characteristic of the 'Strix' tool against web applications. It uses two detection mechanisms: tracking high-frequency requests that match typical scanning path patterns (excluding OPTIONS requests) and monitoring for a surge in 404 or 400 error responses, which often indicates an automated vulnerability scanner attempting to probe non-existent resources.
Detects usage of the 'finger' utility via command line within scripts or batch files, or network connections originating from the 'finger' process on port 79 to non-internal destinations. This activity is commonly associated with internal network reconnaissance to gather user information or system details.
This rule detects potential scanning activity by identifying high-frequency network connection attempts from a source to multiple distinct internal targets within a short 2-minute time window, specifically targeting suspicious ports (9191, 9192) or a known malicious IP address (45.142.193.132).
Detects high-volume HTTP GET requests containing the 'SetupCompleted' URI pattern directed at PaperCut servers, which is indicative of automated exploitation attempts targeting authentication bypass vulnerabilities.
Detects network traffic consistent with automated penetration testing frameworks such as Nuclei, ffuf, or sqlmap. This rule monitors HTTP user-agent strings and triggers an alert if high-frequency scanning activity from a single source is observed within a 60-second window, which is indicative of automated reconnaissance or vulnerability scanning.
Detects repeated HTTP POST requests containing 'ScreenConnect' in the URI, originating from external networks. This pattern is indicative of automated scanning activity attempting to exploit potential authentication bypass or session hijacking vulnerabilities (such as CVE-2026-84869) in ConnectWise ScreenConnect instances.
Page 4 of 10