Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
40,045 detections
Filters
Last updated
All Time
Detection languages
15,346
13,598
2,797
1,803
1,721
Contributors
7,678
5,999
5,315
4,504
4,191
Categories
18,106
9,754
3,848
3,756
3,712
Platforms
40,045
7,313
6,542
3,903
3,660
Products / Services
10,431
9,678
6,722
1,887
1,878
MITRE Techniques
13,736
13,114
8,062
5,947
4,414
CVEs
49
45
30
30
29
IDS Classtypes
214
56
43
23
19
IDS Protocols
183
172
20
16
8
This rule identifies potential command and control (C2) activity by correlating DNS over HTTPS (DoH) requests initiated by non-browser or unsigned processes with subsequent network connections to a known malicious C2 domain (gw.proxyvector.cc) within a 15-minute window.
Detects network connection attempts or established connections to a specific remote IP (103.141.13.26) on UDP port 3479. This pattern is often associated with command and control infrastructure or unauthorized data communication.
Detects evidence of PowerShell AMSI (Antimalware Scan Interface) bypass attempts, including memory patching of amsiContext, usage of known malicious RC4 keys, or reflection-based tampering with AmsiUtils. These techniques are commonly used by attackers to disable AMSI scanning capabilities and execute malicious scripts undetected.
Detects the loading of the ProRAM implant DLL (somkernl.dll) when initiated by known sideloading carriers such as 360speedld.exe or SoftupNotify.exe, which are signed utilities frequently abused for this purpose.
This rule detects unauthorized or suspicious access to sensitive configuration and credential files (e.g., .aws, .azure, .ssh) by specific DLP (Data Loss Prevention) or automation scripts, and identifies subsequent attempts to expose environment variables or sensitive tokens within process command lines.
This rule detects unauthorized or suspicious access to sensitive configuration and credential files (e.g., .aws, .azure, .ssh) by specific DLP (Data Loss Prevention) or automation scripts, and identifies subsequent attempts to expose environment variables or sensitive tokens within process command lines.
Detects Terraform processes (init or apply) executed in conjunction with suspicious post-deployment indicators, such as references to '.terraform' folders, execution of automation scripts like 'dlp.sh' or 'dlp-docker.sh', or access to sensitive telemetry data external to typical infrastructure management.
Detects network activity from infrastructure provisioner tools (Terraform, Coder) or shell processes attempting to connect to suspicious domains (e.g., coder-infra.com) that resemble legitimate infrastructure domains. This activity is indicative of credential theft, specifically targeting OIDC tokens, SSH keys, or authentication tokens during automated provisioning processes.
Detects the execution of Terraform commands (init, apply, plan) that interact with the 'registry.coder.com' domain, or direct network connections to the associated infrastructure. This may indicate the use of unauthorized or compromised Infrastructure-as-Code (IaC) modules or malicious supply chain activity involving Terraform configurations.
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 text/report artifacts (markdown, JSON, plain text) produced by an autonomous AI-driven vulnerability research pipeline that decompiles binaries, traces cross-references, hypothesizes memory-safety flaws, and generates/debugs proof-of-concept exploits
Detects anomalous activity patterns consistent with automated firmware reverse engineering pipelines, specifically correlating the high-frequency execution of firmware analysis tools (e.g., binwalk, Ghidra) with the creation of vulnerability research knowledge base artifacts. This behavioral heuristic aims to identify potential AI-driven or automated zero-day discovery workflows by tracking tool usage density and output characteristics.
This rule detects potential automated web scraping activity by identifying high-volume, repetitive network requests directed towards domains identified as government (.gov) or military (.mil). It correlates these network patterns with the execution of common browser automation frameworks (e.g., Puppeteer, Playwright) or headless browsers, indicating a likely coordinated scraping operation or bot activity.
Detects anomalous access to specific memory sections associated with Desktop Window Manager (dwm.exe) followed by a crash of the same process within a 30-minute window. This behavioral pattern is often indicative of exploitation attempts targeting DWM memory management to achieve code execution or privilege escalation.
This rule detects potential command and control (C2) activity associated with the CurlRAT malware. It monitors for both the execution of 'curl' or 'curl.exe' processes with command lines containing known C2 domains, and network traffic originating from internal devices directed toward those same domains.
AD CS ESC8 Certificate Request via NTLM
Cortex XDR
Detects Active Directory Certificate Services (AD CS) certificate requests that utilize NTLM authentication rather than Kerberos. This behavior is indicative of potential NTLM relay attacks (ESC8), where an attacker forces a machine account to authenticate to the AD CS web enrollment endpoint via NTLM to obtain a certificate on behalf of that machine.
Triggers on any Sysmon "FileExecutableDetected" event, which triggers every time a PE that is monitored by the config is created.
Adversaries may use Valid Accounts to interact with remote systems using Windows Remote Management (WinRM). The adversary may then perform actions as the logged-on user.
Detects Node.js or Electron processes executing a command to list installed applications while originating from or residing within suspicious, writable directories such as Temp, AppData, or Public folders, which is a common pattern for reconnaissance by malicious software or droppers.
Detects Node.js or Electron processes executing a command to list installed applications while originating from or residing within suspicious, writable directories such as Temp, AppData, or Public folders, which is a common pattern for reconnaissance by malicious software or droppers.
Page 460 of 1907