Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
4,308 detections
Filters
Last updated
All Time
Detection languages
2,033
733
398
298
243
Contributors
1,048
740
503
372
239
Categories
20,443
11,831
5,875
5,151
4,864
4,308
Platforms
2,570
792
615
382
319
Products / Services
767
739
659
512
348
MITRE Techniques
2,347
917
847
814
635
CVEs
9
6
6
6
5
IDS Classtypes
81
66
44
14
14
IDS Protocols
136
79
11
7
4
Detects Kerberoasting attempts by identifying suspicious Ticket Granting Service (TGS) requests (Event ID 4769) for the 'krbtgt' service or other services where the ticket encryption type is RC4 (0x17) or older/weaker encryption types (0x18), especially when these requests are not preceded by a corresponding Ticket Granting Ticket (TGT) request (Event ID 4768).
Detects Kerberos service tickets for the 'krbtgt' account that utilize RC4 encryption (0x17) or possess a suspiciously long lifetime (exceeding 10 hours). These conditions are common indicators of Golden Ticket attacks or potential credential manipulation, as legitimate TGTs should not typically exhibit these properties.
Detects the execution of known LLMNR, NBT-NS, and SMB relaying tools such as Responder, Inveigh, and MultiRelay, or the use of their specific command-line arguments, which are commonly used by adversaries for adversary-in-the-middle attacks to capture or relay authentication material.
Detects the execution of known LLMNR, NBT-NS, and SMB relaying tools such as Responder, Inveigh, and MultiRelay, or the use of their specific command-line arguments, which are commonly used by adversaries for adversary-in-the-middle attacks to capture or relay authentication material.
This rule detects potential token theft or MFA bypass by correlating successful MFA-satisfied interactive sign-ins with subsequent non-interactive sign-ins (token refreshes) occurring within a 60-minute window from different IP addresses for the same user. This pattern is indicative of an attacker stealing a session token (e.g., via session cookie theft or AiTM phishing) and using it to authenticate from a different location.
This rule detects the addition of new certificates or client secrets to Azure App Registrations or Service Principals by a user who is not recorded as an owner of that application in the last 90 days. This behavior is a common persistence and privilege escalation technique used by attackers to maintain access to cloud environments.
This rule detects various methods used to extract credentials protected by the Windows Data Protection API (DPAPI). It covers multiple vectors, including Mimikatz DPAPI module usage, unauthorized loading of the dpapi.dll library, suspicious access to browser or WiFi credential stores, and cross-process memory access to the LSASS process to extract DPAPI master keys.
Detects unauthorized processes attempting to access sensitive browser files such as 'Login Data', 'Cookies', and 'Web Data' files typically located in Chrome or Firefox user profiles. This behavior is indicative of credential theft or data exfiltration, as these files contain saved usernames, passwords, and session cookies.
This rule detects potential keylogging activity by identifying either low-level Windows keyboard hooks initiated by suspicious processes or the creation of suspicious log files in user-writable directories by unsigned processes. It filters out common, trusted browser processes and signed Microsoft binaries to reduce noise.
This rule monitors for a combination of suspicious behaviors including C2 network activity to known malicious IPs, potential credential theft targeting browser data files, suspicious library loading (such as keyloggers or potential sideloading) from temp or appdata directories, and beaconing activity using non-standard or suspicious processes. The rule calculates a risk score based on the number and type of indicators observed on a single host.
Detects rapid or aggregate access to multiple sensitive credential or configuration files across various applications (e.g., AWS, Docker, Kubernetes, Crypto wallets, AI tools) within a short timeframe. This behavior is indicative of credential harvesting or post-compromise reconnaissance to steal secrets and keys.
Detects command-line activity searching for, accessing, or displaying sensitive credential and configuration files on Linux systems, such as AWS credentials, Kubernetes configurations, Docker settings, or application API keys. This behavior is indicative of credential theft or discovery attempts.
This rule detects potentially malicious OAuth activity in Azure AD where a user performs a device code authentication flow, followed by a refresh token request from a different, anomalous IP address or geographical location. This behavior is indicative of an attacker who has successfully phished a device code from a user and is now attempting to maintain persistent access to the account via a stolen refresh token.
Detects unauthorized access to browser credential databases, cloud configuration files, and developer secret stores by processes spawned from Node.js or npm. This behavior is indicative of IronWorm and similar infostealers exploiting malicious npm package preinstall hooks to harvest sensitive developer credentials.
Device Code Phishing Attempt
Cortex XDR
Detects attempts to abuse the device code flow targeting Microsoft Office and Authentication Broker applications from unmanaged, non-compliant devices with unbound tokens. This activity is indicative of a phishing attack where an adversary is trying to steal application access tokens.
Detects the node.exe process (npm lifecycle) accessing sensitive web browser credential databases (e.g., Login Data, Cookies, Web Data, and Firefox profiles). This activity is indicative of infostealer malware, such as Miasma or MYRA, which often leverages malicious npm packages to drop and execute SQLite-based credential exfiltration logic directly from disk.
Detects Node.js processes spawning from common command-line shells (npm, cmd, bash) and accessing sensitive credential directories, such as '.claude' or Docker 'config.json'. This behavior is indicative of credential theft or unauthorized access attempts often associated with malicious npm packages or supply chain attacks.
Detects unauthorized access to sensitive credential files (Docker, AWS, npm, PyPI, GCloud) by Node.js processes, specifically when those processes are triggered by common npm lifecycle scripts like preinstall, postinstall, or peer dependency scripts. This is a common pattern observed in supply chain attacks involving malicious npm packages.
Detects instances where node.js or npm processes access multiple sensitive configuration or credential files (such as .aws/credentials, .npmrc, .pypirc, .gitconfig, or docker configs) and subsequently perform DNS queries against known malicious or suspicious C2 domains.
Detects when a web browser (Chrome, Edge, Firefox, Brave, Opera) initiates a process that is not another instance of itself. This could indicate a drive-by download, execution of malicious code, or other suspicious activity originating from a browser.
This rule detects multiple successful RDP logons (LogonType 10) within a one-hour window by accounts containing '$' (often machine accounts) or 'admin' (privileged accounts). This behavior can be indicative of an adversary attempting to gain remote access and potentially deploy ransomware or other malicious payloads, especially if multiple source IPs are observed.
Page 81 of 206