Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects Kerberos service ticket requests (Event ID 4769) that utilize weak RC4 encryption (0x17). This behavior is characteristic of Kerberoasting, where attackers request service tickets for service accounts in order to perform offline brute-force attacks to recover the service account password. The rule filters out machine account requests and focuses on standard user or service account requests.
This rule detects potential Kerberoasting activity by monitoring for an unusually high volume of Kerberos Service Ticket (TGS) requests (event ID 4769) for non-machine service accounts, specifically focusing on tickets encrypted with RC4 (etype 23). A high count of ticket requests or distinct Service Principal Names (SPNs) requested by a single client in a short period is characteristic of an adversary attempting to harvest hashes for offline cracking.
Detects access to sensitive Active Directory objects, such as the Domain Admins group or other privileged accounts, as identified by specific GUIDs in Windows Event ID 4662. This event is generated when an object in Active Directory is accessed, and monitoring these specific GUIDs can indicate an adversary attempting to perform reconnaissance or modify sensitive directory objects.
Detects instances where processes typically associated with browser-related tasks (smartscreen.exe, ctfmon.exe) load ntdll.dll while another process on the same device is running with browser-specific command-line arguments (e.g., --user-data-dir, --profile-directory). This pattern is often indicative of process injection or credential harvesting attempts where an adversary attempts to interact with or scrape data from active browser sessions.
Detects instances where processes typically associated with browser-related tasks (smartscreen.exe, ctfmon.exe) load ntdll.dll while another process on the same device is running with browser-specific command-line arguments (e.g., --user-data-dir, --profile-directory). This pattern is often indicative of process injection or credential harvesting attempts where an adversary attempts to interact with or scrape data from active browser sessions.
Detects instances where processes typically associated with browser-related tasks (smartscreen.exe, ctfmon.exe) load ntdll.dll while another process on the same device is running with browser-specific command-line arguments (e.g., --user-data-dir, --profile-directory). This pattern is often indicative of process injection or credential harvesting attempts where an adversary attempts to interact with or scrape data from active browser sessions.
Detects network activity indicating interaction with known Adversary-in-the-Middle (AiTM) phishing infrastructure. This includes direct outbound connections to specific suspicious IP addresses and the identification of proxied Microsoft 365 OAuth authorization flows redirected to non-Microsoft hosts.
This rule detects potential Quasar RAT post-infection activity by correlating the identification of keystroke logging API usage (specifically SetWindowsHookEx, GetAsyncKeyState, or GetKeyboardState) followed by an outbound network connection from the same process identifier within a ten-minute window, indicative of remote control functionality.
Detects instances where processes typically associated with browser-related tasks (smartscreen.exe, ctfmon.exe) load ntdll.dll while another process on the same device is running with browser-specific command-line arguments (e.g., --user-data-dir, --profile-directory). This pattern is often indicative of process injection or credential harvesting attempts where an adversary attempts to interact with or scrape data from active browser sessions.
This rule detects illicit OAuth consent grant events where an application is granted high-risk delegated permissions. This behavior is indicative of consent phishing or 'ConsentFix' tradecraft, where attackers trick users into granting permissions to a malicious application, thereby enabling unauthorized access to mail and file data.
This rule detects illicit OAuth consent grant events where an application is granted high-risk delegated permissions. This behavior is indicative of consent phishing or 'ConsentFix' tradecraft, where attackers trick users into granting permissions to a malicious application, thereby enabling unauthorized access to mail and file data.
Detects the execution of 'filemanager.exe' and its subsequent access to sensitive browser credential stores (e.g., 'Login Data', 'cookies.sqlite'), occurring within a short window following a PowerShell-based ClickFix-style attack chain. The rule correlates initial suspicious script execution (often involving hidden windows, obfuscated commands, or web requests) with follow-on credential harvesting behavior on the same host.
This rule detects potential unauthorized access to Azure Key Vault secrets by correlating AI agent process execution on an endpoint with subsequent Key Vault API activity by the same user account. It tracks common AI coding/assistant tools running locally on a device and triggers an alert if the same identity accesses cloud-plane Key Vault operations within 5 minutes. Additionally, it identifies anomalous bulk secret enumeration patterns in Key Vault audit logs to detect potential automated exfiltration.
This rule detects potential unauthorized access to Azure Key Vault secrets by correlating AI agent process execution on an endpoint with subsequent Key Vault API activity by the same user account. It tracks common AI coding/assistant tools running locally on a device and triggers an alert if the same identity accesses cloud-plane Key Vault operations within 5 minutes. Additionally, it identifies anomalous bulk secret enumeration patterns in Key Vault audit logs to detect potential automated exfiltration.
This rule detects potential unauthorized access to Azure Key Vault secrets by correlating AI agent process execution on an endpoint with subsequent Key Vault API activity by the same user account. It tracks common AI coding/assistant tools running locally on a device and triggers an alert if the same identity accesses cloud-plane Key Vault operations within 5 minutes. Additionally, it identifies anomalous bulk secret enumeration patterns in Key Vault audit logs to detect potential automated exfiltration.
This rule detects potential unauthorized access to Azure Key Vault secrets by correlating AI agent process execution on an endpoint with subsequent Key Vault API activity by the same user account. It tracks common AI coding/assistant tools running locally on a device and triggers an alert if the same identity accesses cloud-plane Key Vault operations within 5 minutes. Additionally, it identifies anomalous bulk secret enumeration patterns in Key Vault audit logs to detect potential automated exfiltration.
Detects AI coding assistants and LLM agent frameworks attempting to access, read, or export sensitive credential files, registry hives, or application secrets. The rule monitors both file system operations on known secret locations and process execution patterns indicative of credential dumping or API token retrieval by these tools.
Detects AI coding assistants and LLM agent frameworks attempting to access, read, or export sensitive credential files, registry hives, or application secrets. The rule monitors both file system operations on known secret locations and process execution patterns indicative of credential dumping or API token retrieval by these tools.
Detects unauthorized processes reading Chromium or Firefox cookie and login stores that specifically reference Claude.ai or Anthropic domains. This rule is designed to identify infostealer malware attempting to hijack active Claude sessions by analyzing process file access and command-line arguments, filtered against known-legitimate security, sync, and development tools. The detection logic mandates corroborating evidence of suspicious execution paths or subsequent outbound network activity.
Detects unauthorized processes attempting to read or copy sensitive browser data files (e.g., Cookies, Login Data, Local Storage) often targeted by info-stealing malware such as Vidar, LummaC2, or RedLine. The rule filters out legitimate browser-related processes and adds security context by requiring evidence of unsigned code, execution from suspicious locations (Temp/Downloads), or associated outbound network activity to reduce false positives.
Detects SectopRAT (Arechclient2) .NET RAT binaries deployed via tampered libcef.dll / JetBrains helper DLL sideloading that harvest browser credentials, cookies, credit card data, and files. Tightened to require combined sideload+family+target indicators plus .NET CLR import evidence, avoiding standalone generic strings.
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