Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule monitors for high-frequency download or access events of files associated with AI development, such as model weights (.pt, .ckpt, .safetensors, .h5, .onnx, .pkl, .pb), checkpoints, and research datasets from cloud storage. It triggers when an account accesses or downloads 20 or more unique AI-related files within a 60-minute window, which is indicative of unauthorized data exfiltration of intellectual property.
Detects instances where end users grant high-risk delegated permissions (e.g., Mail.Read, Files.ReadWrite.All) to applications via OAuth consent flows outside of standard administrator approval processes. This behavior is indicative of device-code phishing or consent phishing campaigns designed to gain unauthorized access to user data.
Detects a sequence of suspicious identity activities indicative of a post-account-takeover pivot. The rule identifies a user profile engaging in corporate VPN SAML SSO probing, triggering a MFA enrollment interrupt in Azure, and subsequently accessing multiple Microsoft 365 applications (Azure Portal, OfficeHome, SharePoint) within a 30-minute window.
This rule detects a multi-stage Business Email Compromise (BEC) attack involving executive impersonation via look-alike domains, followed by the creation of suspicious inbox rules (forwarding/deletion) by the victim, and concurrent external communication (Teams/Zoom meeting invites) from a newly created account. It correlates these activities to identify sophisticated payment fraud campaigns.
This rule detects potential 'Quishing' (QR code phishing) attempts by identifying email-based phishing detection or malicious URL indicators followed by a user clicking the URL and subsequently authenticating to a corporate system from a mobile device (Android or iOS) within a 30-minute window. This behavior often suggests an adversary successfully harvesting credentials via a QR code link and then using them to gain unauthorized access.
Detects instances where a user successfully authenticates to a cloud service (Azure AD) from an unrecognized ASN, device, or country without performing MFA, followed shortly by high-risk actions such as mailbox rule creation, mass file downloads, or OAuth application registration. This pattern is indicative of session cookie theft or OAuth token replay used to bypass MFA.
Detects anomalous authentication behavior consistent with Adversary-in-the-Middle (AiTM) phishing attacks, specifically where an MFA-authenticated session is accessed from multiple, disjoint geographic locations or IP addresses within a short timeframe, indicating the replay of stolen session tokens.
This rule monitors for suspicious network traffic to SMTP services and specific external email addresses, as well as process command lines containing these indicators. Additionally, it tracks email events involving specific sender, recipient, and subject indicators which may be indicative of unauthorized communication or data exfiltration attempts.
Detects a sequence of suspicious identity activities indicative of a post-account-takeover pivot. The rule identifies a user profile engaging in corporate VPN SAML SSO probing, triggering a MFA enrollment interrupt in Azure, and subsequently accessing multiple Microsoft 365 applications (Azure Portal, OfficeHome, SharePoint) within a 30-minute window.
This rule detects email activity involving DocuSign-themed messages that contain attachments and also coincide with URL click events that redirect users to Microsoft-related login portals (e.g., login.microsoftonline.com or teams.microsoft.com) containing 'redirect' or 'redirect_uri' parameters. This behavior is indicative of a phishing attempt designed to capture credentials or perform OAuth consent phishing by mimicking legitimate document-signing workflows.
This rule detects potential Business Email Compromise (BEC) attempts where external senders impersonate corporate executives (e.g., CEO, CFO) by spoofing display names or using lookalike/cousin domains. It triggers when these impersonations are combined with urgent, financial-themed language in the email subject line (e.g., wire transfers, payroll changes, or gift card requests).
Detects coordinated credential stuffing campaigns originating from shared IP infrastructure. The rule identifies patterns where multiple distinct accounts are targeted with failed sign-in attempts, characterized by rapidly rotating user-agent/device fingerprints while remaining under standard per-account and per-IP throttling limits. It focuses on the combination of high unique account counts, rotating user-agents, and sustained activity over a specific time window.
This rule monitors incoming email events for potential phishing attempts by identifying messages from unknown sender domains (not seen in the last 90 days) that contain known AI-generated response phrases in the subject line or personalized content matching the recipient's display name. This helps detect spearphishing lures that attempt to build trust through impersonation or leverage LLM-generated phrasing common in automated phishing campaigns.
Detects evidence of indirect prompt injection (IPI) attempts within email and document content processed by enterprise AI assistants. The rule flags instruction-hijack phrases, hidden obfuscation characters (e.g., zero-width, white-on-white text), and encoded blocks specifically targeting AI model behavior.
This rule identifies email patterns that involve either the presence of email attachments (specifically .eml or .msg files) or common email forwarding/reply indicators (e.g., 'FW:', 'FWD:', 'RE:'). By aggregating emails based on the sender and subject, it provides a summary of communication volume, which can be used to identify anomalous email activity or potential phishing staging/thread hijacking.
This rule detects email activity containing keywords related to SSO or passkey updates combined with urgent terminology and support request language. This pattern is commonly indicative of phishing attempts aimed at stealing credentials or tricking users into providing sensitive information.
This rule detects potential quishing (QR code phishing) attempts by identifying emails with one or more image attachments and minimal URL content, followed by a risky user authentication event within a 3-hour window. This behavior is indicative of an attacker attempting to harvest credentials via a malicious QR code that redirects the user to a phishing site.
This rule detects potential quishing (QR code phishing) attempts by identifying emails with one or more image attachments and minimal URL content, followed by a risky user authentication event within a 3-hour window. This behavior is indicative of an attacker attempting to harvest credentials via a malicious QR code that redirects the user to a phishing site.
Detects a suspicious sequence of events indicative of social engineering. An administrator or help-desk account resets a user's password, followed within 15 minutes by the target user performing a successful MFA authentication from a device or IP address not associated with that user in the preceding 30 days, suggesting an attacker hijacked the session or coerced the user after the password reset.
Hunts network, HTTP, and email-URL telemetry for known ARToken infrastructure (operator backend IPs and phishing/panel domains). Bounded to a 30-day lookback and tiers matches by confidence: domain hits are high-confidence (attacker-registered infrastructure), while IP hits are medium-confidence since the IPs sit on shared DigitalOcean hosting that can be reassigned to unrelated tenants once ARToken's infrastructure is taken down. Empty/unparsed indicator fields are excluded to avoid spurious matches in the HTTP event parsing branch. No known file hashes are associated with this intel.
Aggregates password/MFA reset actions by the performing helpdesk identity, alerting on abnormal reset volume or resets outside a normal ticket pattern — a proxy for a support agent being repeatedly socially engineered or a fraudulent-ticket abuse pattern.
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