Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
3,613 detections
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Detects usage of the Rclone utility (or renamed binaries) to transfer data to various cloud storage backends (S3, Mega, pCloud, SFTP, WebDAV). The detection correlates this activity with the execution of common archival tools (7-Zip, WinRAR, TAR) within one hour prior to the data transfer, which is a common behavior of adversaries preparing data for exfiltration.
Detects high-risk GitHub events indicating data or asset exposure, such as creating public repositories with specific naming conventions, changing private repositories to public, or creating new gists, which could indicate exfiltration of sensitive assets or information.
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 a multi-stage phishing attack targeting cryptocurrency users, specifically Ledger wallet holders. The detection identifies an initial request to a Vercel-hosted domain for a BIP-39 wordlist (a common recovery phrase standard) followed by a POST request to a similar Vercel-hosted endpoint, suggesting the exfiltration of a secret recovery phrase.
Detects anomalous, bulk file access activity targeting browser credential databases, cryptocurrency wallet data, and application-specific configuration stores, characteristic of the SectopRAT 'DeployBrowserKey' command.
Detects potential NTLM relay attacks targeting Active Directory Certificate Services (AD CS) web enrollment endpoints, including subsequent certificate requests for sensitive templates. This pattern identifies attackers attempting to relay NTLM authentication to AD CS and potentially requesting certificates for high-privilege templates (e.g., DomainController, SubCA, Machine, or User) to facilitate privilege escalation.
Detects instances where common scripting or development language runtimes (Go, Node.js, WScript, CScript) access sensitive configuration or credential files, such as AWS credentials, Kubernetes configs, SSH keys, or environment files. This activity is often indicative of credential harvesting or unauthorized access to sensitive secrets by potentially malicious scripts or processes.
Detects unauthorized processes attempting to access sensitive browser profile files, such as login credentials, cookies, and session state files. This behavior is indicative of credential harvesting or session theft by malicious software.
This rule detects potential Browser-in-the-Browser (BitB) phishing attacks by identifying suspicious browser pop-up behavior (using window.open flags, hidden address/toolbars) on non-identity-provider domains, followed shortly by credential submission patterns on the same device. This pattern mimics legitimate OAuth or SSO windows to harvest user credentials.
Detects a multi-stage exfiltration sequence consisting of: (1) bulk file archival/compression using common utilities, (2) suspicious OAuth grant or service principal authorization, and (3) abnormal network egress volume to common ports. This pattern suggests an adversary is preparing data, establishing or leveraging an identity/application for cloud access, and subsequently exfiltrating data.
Detects unauthorized file deletion activity performed directly by a Node.js process acting as a ChainScript RAT. The RAT uses a bundled node.exe process, operating within specific masquerading directories in the user profile, to execute file system operations such as recursive deletion without spawning child processes.
Detects adversarial prompts designed to induce an Large Language Model (LLM) to disclose sensitive information, such as PII, secrets, or internal training data. This rule monitors the input prompt for extraction-based patterns and evaluates the model's response for indicators of a sensitive data leak (DLP hit).
This rule monitors for indicators of compromise (IOCs) associated with the TrustSink rogue MFA provider, specifically tracking the domain 'trident-sip-filter.ngrok-free.dev' and its associated OIDC discovery path. It aggregates signals from sign-in logs, audit logs, DNS queries, and network proxy traffic to identify attempts to interact with this malicious infrastructure.
Detects HTTP beaconing activity associated with the MacSync stealer malware on macOS platforms. The rule monitors for specific URI patterns ('curl', 'dynamic', 'gate') combined with macOS-specific User-Agent strings and potential credential exfiltration headers, indicating established communication with a Command and Control (C2) server.
Detects unauthorized processes accessing sensitive credential storage files (e.g., 'Login Data', 'Cookies', 'Local State') from known Chromium-based browser profile directories. This activity is indicative of credential harvesting by infostealer malware attempting to exfiltrate browser data before performing decryption.
Detects potential insider threats or departing employees by correlating abnormal bulk data downloads from cloud repositories (SharePoint/OneDrive/Box/etc.) with subsequent sensitive data access and exfiltration signals. It uses baseline behavioral profiling, requires a threshold of sensitive file interactions, and mandates high-confidence exfiltration indicators (removable media usage, suspicious external domains, or multiple combined destination events) to reduce noise. Archive/staging activity is used as an optional confidence booster.
This rule correlates multiple telemetry sources to detect a sophisticated cloud identity compromise chain. It identifies suspicious MFA-satisfied logins (potentially via Adversary-in-the-Middle) followed by the granting of high-risk OAuth application consents, the creation of persistent mailbox rules for hiding or forwarding communications, and finally mass data access or exfiltration from the user's account.
Detects potential insider threats or departing employees by correlating abnormal bulk data downloads from cloud repositories (SharePoint/OneDrive/Box/etc.) with subsequent sensitive data access and exfiltration signals. It uses baseline behavioral profiling, requires a threshold of sensitive file interactions, and mandates high-confidence exfiltration indicators (removable media usage, suspicious external domains, or multiple combined destination events) to reduce noise. Archive/staging activity is used as an optional confidence booster.
Detects the AgtaBackup RAT keylogger process, which masks itself as a legitimate Windows service ('Window Security Health Services.exe'), running with SYSTEM privileges and lacking a valid digital signature.
This rule detects the creation or modification of mailbox rules (forwarding, redirection, or deletion) that occurs within two hours of a sign-in event flagged as high-risk, specifically those indicative of AiTM or token-replay attacks. It further filters for rules involving sensitive keywords, suggesting an attempt to intercept or hide specific categories of emails post-compromise.
Detects attempts to bypass Vite development server file access restrictions by appending query parameters such as ?raw, ?import&raw, or ?import&url&inline to request paths for sensitive files like .env, cloud credentials, and terraform state files.
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