Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects the execution of known NTLM Certificate Enrollment Service (CES) relay tools alongside subsequent outbound network connections to Active Directory Certificate Services (AD CS) CES enrollment endpoints. This behavioral pattern indicates an adversary attempting to relay NTLM authentication to an AD CS server to request a certificate, typically for privilege escalation or persistence.
Detects the execution of known Python scripts (CES_Enroll.py, ntlm_ces_relay.py, ces_negotiate_ntlm.py) often associated with NTLM relay attacks or forced authentication techniques. These scripts leverage Python to interact with authentication protocols or relay requests.
Detects anomalous file access patterns by Semantic Kernel agent processes attempting to read sensitive host files such as SSH keys, configuration files, and credential stores. This activity is indicative of malicious use of agent file-transfer capabilities, potentially exploiting path traversal vulnerabilities or prompt injection to exfiltrate sensitive data from the host environment into the agent sandbox.
Detects anomalous file access patterns by Semantic Kernel agent processes attempting to read sensitive host files such as SSH keys, configuration files, and credential stores. This activity is indicative of malicious use of agent file-transfer capabilities, potentially exploiting path traversal vulnerabilities or prompt injection to exfiltrate sensitive data from the host environment into the agent sandbox.
Detects instances where package management tools (pip, npm, docker) spawn child processes that immediately attempt to read sensitive files (SSH keys, cloud/container credentials) or perform outbound network connections. This behavior is indicative of a trojanized supply-chain package executing malicious post-install logic to exfiltrate secrets or establish initial communication with a C2 server.
Detects anomalous activities within CI/CD runner environments, specifically targeting unauthorized processes attempting to access OIDC identity tokens or executing package publication commands (e.g., npm, twine) outside of standard runner execution workflows. This technique is consistent with supply chain compromise efforts to exfiltrate identity tokens and publish malicious artifacts.
Detects anomalous child processes spawned by a Langflow server process that subsequently search for sensitive information, such as cloud credentials or database connection strings, within the command line arguments. This behavioral pattern is indicative of remote code execution exploitation followed by credential harvesting, potentially targeting vulnerabilities like CVE-2026-33017.
Detects Kerberos TGS (Ticket Granting Service) requests where the ticket encryption type is RC4 (0x17). This behavior, commonly referred to as Kerberoasting, involves requesting service tickets for accounts with service principal names (SPNs) set, often to perform offline brute-force attacks against the service account's password hash. The rule excludes common service account patterns and machine accounts to reduce noise.
Detects the use of the native Windows reg.exe utility to save the Security Account Manager (SAM) registry hive to a file. This technique is commonly used by adversaries to perform offline credential extraction or to dump local account password hashes.
Detects a specific command and control (C2) communication pattern characteristic of RatHat malware, involving the retrieval of HTML overlay templates followed by the submission of captured form or credential data from the same device within a one-hour window.
Detects a specific sequence of commands indicative of the RatHat malware on Android, involving waking the device followed by automated unlock gestures or PIN input and password retrieval commands.
Detects unauthorized file creation within the /root/.hermes/loot/ directory and specific path combinations associated with the CARBONATO botnet's Hermes Agent. This activity is indicative of credential harvesting, specifically the exfiltration of AI API keys for various LLM providers.
Detects the creation of a local administrative account with a hidden naming convention (ending in $) accompanied by a cluster of registry modifications designed to obscure the account from the logon screen, enable WDigest plaintext credential caching, relax RDP authentication requirements (disable NLA, enable RDP), and disable UAC remote restrictions. This activity is highly indicative of post-exploitation persistence and credential harvesting configurations following initial access via SQL server exploitation.
Detects Node.js or Python processes exhibiting behavior characteristic of information-stealing malware (such as the WaterPlum suite: BeaverTail, InvisibleFerret, OtterCookie, StoatWaffle). The rule monitors for these processes accessing sensitive files related to browser credentials, cryptocurrency wallets, scanned identification documents, or performing collection activities like keylogging and screen capturing.
Detects unauthorized access to web browser credential stores (e.g., Login Data, Cookies) by non-browser processes, such as scripting engines (PowerShell, WScript) or common LOLBins (MSBuild, InstallUtil). This behavior is indicative of credential theft or data exfiltration attempts by malicious software, including stealers like PureLog.
Detects access to sensitive cryptocurrency wallet files (e.g., wallet.dat, keystore) by processes typically associated with LOLBins (Living-off-the-land Binaries) like PowerShell, MSBuild, or non-executable utilities. This activity is indicative of credential theft or data staging for exfiltration of cryptocurrency assets.
Detects anomalous, mass-access behavior targeting plaintext credential files, certificate stores, private keys, or password-manager databases located on general-purpose network shares. Such activity is often indicative of an adversary conducting discovery or collection of credentials for lateral movement and privilege escalation.
Detects potential credential reuse attacks such as Pass-the-Hash, Pass-the-Ticket, or Overpass-the-Hash. The rule identifies a 'NewCredentials' logon (e.g., runas /netonly) followed by a network or remote interactive logon to a target system (such as a domain controller) by the same user, without an interactive logon session occurring between the events, which is indicative of using stolen credentials to move laterally.
Detects potential credential reuse attacks such as Pass-the-Hash, Pass-the-Ticket, or Overpass-the-Hash. The rule identifies a 'NewCredentials' logon (e.g., runas /netonly) followed by a network or remote interactive logon to a target system (such as a domain controller) by the same user, without an interactive logon session occurring between the events, which is indicative of using stolen credentials to move laterally.
Detects credential-spraying activity against WordPress sites where an actor performs a sequence of POST requests targeting both xmlrpc.php (typically the 'wp.getUsersBlogs' method) and wp-login.php. The rule specifically identifies attackers that attempt to evade detection or increase success rates by falling back to wp-login.php after initially using XML-RPC within a short duration.
Detects the creation of a Volume Shadow Copy followed by an attempt to access or copy sensitive files, specifically the NTDS.dit database or the SYSTEM registry hive, from that shadow copy. This behavior is indicative of an attempt to perform offline credential theft.
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