Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects Microsoft 365 OAuth access tokens issued via device_code grant — the mechanism abused by the EvilTokens phishing kit — followed within 1 hour by mailbox forwarding rule creation for the same account, indicating persistent BEC access post-compromise.
Detects an unusually high frequency of storage blob and container listing operations within a short time window. This behavior often indicates an attempt to enumerate the contents of Azure Blob Storage, which may be a precursor to data discovery or exfiltration.
Detects instances where a Node.js process accesses, creates, or modifies sensitive files such as SSH private keys, AWS credentials, or application configuration files (.npmrc, .pypirc, .docker/config.json). This behavior is often indicative of credential theft or unauthorized configuration access by a compromised Node.js application.
Detects node.exe accessing Chrome browser extension storage directories associated with popular cryptocurrency wallets. This activity is indicative of the Miasma/MYRA infostealer (peer-math.js) attempting to harvest encrypted wallet vaults from developer workstations, as seen in the Polymarket Trap supply chain attack.
This rule detects when processes other than standard web browsers (Chrome, Edge, Firefox, Brave, Opera) attempt to read sensitive browser data files such as 'Login Data', 'Cookies', or 'Web Data'. These files contain stored credentials and session information, and unauthorized access is a common technique used by credential-stealing malware to exfiltrate sensitive user information.
Detects the creation of large archive files (.zip, .7z, .rar, .tar) exceeding 100MB within sensitive or high-risk directory paths such as Desktop, Temp, or AppData, or on non-system drives. This behavior is often associated with the staging of sensitive data for exfiltration.
This rule detects when a Node.js or NPM process accesses multiple sensitive browser credential files (Login Data or Cookies) within a short window, while simultaneously reading shell history files (bash_history, zsh_history, or fish_history). This behavior is characteristic of an automated credential harvester or infostealer script executing within a Node.js environment.
Detects the execution of Python interpreters or scripts involving keylogging-related libraries (pynput, pyxhook, pywin32, keyboard) or direct access to Linux input devices (/dev/input/event*), correlated with the creation of temporary script files, which is a common pattern for Python-based keyloggers.
Detects Node.js processes accessing paths associated with cryptocurrency wallets and browser extension storage (MetaMask, Phantom, Exodus, Electrum). This behavior is characteristic of infostealer malware attempting to exfiltrate seed phrases, keys, or wallet configuration data.
This rule detects multiple unauthorized access attempts to files identified as machine learning models, weights, or checkpoints. It specifically looks for Security Event ID 4656 (Auditing of object access) where the object name contains keywords like 'model', 'weights', or 'checkpoint', and the access mask indicates unauthorized access attempts (0x0001 for ReadData or ListDirectory, 0x0002 for WriteData or AddFile). The rule then summarizes these attempts by user, computer, and hour, triggering an alert if 5 or more attempts are made within an hour.
This rule detects potentially malicious web requests attempting to access sensitive local system files, environment variables, or application source code within a cloud-hosted application environment. The detection logic focuses on URI patterns indicative of Local File Inclusion (LFI) attempts targeting proc filesystem nodes, credential configuration files, or application source bundles.
Detects the execution of the NUKESPED backdoor component 'zlrenu8.exe', which is associated with Kimsuky threat actor activity. This component is known to function as a keystroke logger using SetWindowsHookEx to capture user input.
Detects the use of the 'net.exe' or 'net1.exe' command-line utilities to mount a local SMB share using loopback addresses (127.0.0.1 or localhost) with the '/tcpport' argument. This pattern is indicative of attempts to exploit CVE-2026-24294 to bypass NTLM authentication protections through local reflection or relay attacks.
Detects NTLM-authenticated network logons (Logon Type 3) originating from loopback IP addresses (127.0.0.1, ::1, or 0.0.0.0). This behavior is often associated with NTLM relay attacks or credential reflection bypass techniques where an adversary forces a local service to authenticate to itself to gain unauthorized access.
Detects the execution of known NTLM relay and credential harvesting tools such as Responder, Inveigh, and PetitPotam, or suspicious command-line patterns involving the use of 'net use' or common Windows binaries (e.g., powershell.exe, rundll32.exe) to access or force SMB authentication with remote IP addresses.
This rule detects the execution of common Python-based network relay and exploitation tools such as Impacket's ntlmrelayx, smbserver.py, and PetitPotam. These tools are frequently utilized by adversaries to conduct NTLM relay attacks, perform remote service execution, or facilitate credential dumping.
This rule detects the use of 'net.exe' or 'net1.exe' to configure a non-standard SMB port, which is a technique often used for NTLM reflection and relay attacks. By specifying an arbitrary port for SMB traffic, an attacker may attempt to bypass standard network filtering or establish a listener to intercept authentication requests.
This rule detects potential unauthorized access to Azure Storage Blobs following the retrieval of storage account access keys. It correlates 'ListKeys' API events with subsequent blob access activities from either unrecognized IP addresses or via Shared Access Signature (SAS) tokens, indicating potential credential misuse.
Detects anomalous SMB NTLM authentication traffic indicative of relay attacks, where an attacker intercepts an authentication request and relays it to another SMB service.
Detects the creation or modification of Power Automate Flows that utilize non-Microsoft domains for HTTP or webhook connectors. Adversaries may abuse Power Automate to automate exfiltration or command-and-control tasks by connecting workflows to external malicious endpoints.
Detects high-frequency file creation, modification, or renaming activity within the user's Desktop directory. This pattern is often associated with ransomware activity, automated data exfiltration tools staging files, or malicious droppers unpacking payloads.
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