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Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects potential AI prompt injection attacks delivered via email. It identifies users clicking external links directed at AI platforms (like OpenAI, Claude, or Perplexity) that contain suspicious URL parameters or instructions designed to manipulate or override the AI's standard behavior. It correlates the web request with a subsequent process execution on the host that includes suspicious keywords in the command line, suggesting an attempt to automate or exploit the AI assistant through local system interaction.
This rule detects potential AI prompt injection attacks delivered via email. It identifies users clicking external links directed at AI platforms (like OpenAI, Claude, or Perplexity) that contain suspicious URL parameters or instructions designed to manipulate or override the AI's standard behavior. It correlates the web request with a subsequent process execution on the host that includes suspicious keywords in the command line, suggesting an attempt to automate or exploit the AI assistant through local system interaction.
This rule detects potential AI prompt injection attacks delivered via email. It identifies users clicking external links directed at AI platforms (like OpenAI, Claude, or Perplexity) that contain suspicious URL parameters or instructions designed to manipulate or override the AI's standard behavior. It correlates the web request with a subsequent process execution on the host that includes suspicious keywords in the command line, suggesting an attempt to automate or exploit the AI assistant through local system interaction.
This rule detects anomalous, high-volume activity in Jira, such as mass ticket viewing, searching, exporting, or attachment downloading, which is often characteristic of unauthorized data exfiltration or reconnaissance. The detection triggers when an account exceeds thresholds of 200 issue-related actions or 8 attachment downloads within a 7-day period.
This rule detects activity involving known malicious indicators of compromise (IOCs), including specific IP addresses, domains, and file hashes. It monitors network connections, file system activity, process execution, and email attachments, filtering out common security tools to reduce noise.
This rule detects activity involving known malicious indicators of compromise (IOCs), including specific IP addresses, domains, and file hashes. It monitors network connections, file system activity, process execution, and email attachments, filtering out common security tools to reduce noise.
This rule detects activity involving known malicious indicators of compromise (IOCs), including specific IP addresses, domains, and file hashes. It monitors network connections, file system activity, process execution, and email attachments, filtering out common security tools to reduce noise.
Detects phishing messages that deliberately mix writing systems/scripts/homoglyphs in the Subject line as ONE corroborating signal within a broader, multi-signal phishing pattern. Hardened against false positives: brand/product tokens (e.g. Microsoft, PayPal) are stripped before the Latin-script check so a lone brand mention in non-Latin correspondence doesn't count as mixed-script; the suspicious-URL signal requires a high-confidence credential/urgency term (not generic business vocabulary); sender risk requires the domain to be new to both the recipient and the organization; and at least three independent signal categories (not two) must corroborate the language-confusion signal before the rule fires.
Detects phishing messages that deliberately mix writing systems/scripts/homoglyphs in the Subject line as ONE corroborating signal within a broader, multi-signal phishing pattern. Hardened against false positives: brand/product tokens (e.g. Microsoft, PayPal) are stripped before the Latin-script check so a lone brand mention in non-Latin correspondence doesn't count as mixed-script; the suspicious-URL signal requires a high-confidence credential/urgency term (not generic business vocabulary); sender risk requires the domain to be new to both the recipient and the organization; and at least three independent signal categories (not two) must corroborate the language-confusion signal before the rule fires.
Detects phishing messages that deliberately mix writing systems/scripts/homoglyphs in the Subject line as ONE corroborating signal within a broader, multi-signal phishing pattern. Hardened against false positives: brand/product tokens (e.g. Microsoft, PayPal) are stripped before the Latin-script check so a lone brand mention in non-Latin correspondence doesn't count as mixed-script; the suspicious-URL signal requires a high-confidence credential/urgency term (not generic business vocabulary); sender risk requires the domain to be new to both the recipient and the organization; and at least three independent signal categories (not two) must corroborate the language-confusion signal before the rule fires.
Detects malicious AI assistant links delivered via spearphishing emails that contain pre-populated prompt injection query parameters. The rule correlates the clicking of a high-signal URL (containing parameters like prompt, system, or lengthy search queries with malicious keywords) originating from an email with a subsequent active session to the same AI service within 5 minutes, confirming potential weaponized AI assistant session manipulation. Covers T1566.002, T1204.001
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 detects when a user or service account accesses or downloads a high volume of files from SharePoint or OneDrive that match sensitive keywords such as 'password', 'secret', 'credential', or specific file extensions like '.pem', '.pfx', and '.env'. This behavior is indicative of an attempt to harvest credentials, API keys, or configuration secrets from cloud storage repositories.
Detects high-volume file download, access, or preview activity originating from known anonymous proxy services. This behavior is indicative of potential data exfiltration or unauthorized mass data collection from cloud storage applications.
This rule detects anomalous, high-volume activity from a single user account interacting with known public generative AI platforms (e.g., ChatGPT, Copilot, Claude). It uses a threshold-based approach on CloudAppEvents logs to identify potential reconnaissance or bulk information gathering activities which could signify unauthorized use or automated data scraping via AI interfaces.
Detects instances where a single user account exhibits an anomalously high frequency of requests to public generative-AI platforms within a one-hour window, potentially indicating automated reconnaissance or data staging activities using AI tools.
Detects high-volume file download, access, or preview activity originating from known anonymous proxy services. This behavior is indicative of potential data exfiltration or unauthorized mass data collection from cloud storage applications.
Detects instances where a user grants high-privilege permissions (Mail.Read, Files.ReadWrite.All, offline_access) to an OAuth application within 24 hours of a risky sign-in event. This pattern is commonly observed in AiTM (Adversary-in-the-Middle) or consent-phishing attacks designed to establish persistent access to cloud resources.
Detects instances where an administrative user performs a password or MFA method reset on a target account, followed within 24 hours by a sign-in event from that same target account flagged with a high or medium risk level. This sequence is characteristic of Adversary-in-the-Middle (AiTM) attacks, where an attacker utilizes stolen session tokens and subsequently coerces helpdesk or administrative intervention to reset MFA requirements to maintain unauthorized access.
This rule detects potential account compromise by identifying scenarios where a user's security information (MFA methods) is modified, deleted, or updated, followed shortly after (within 24 hours) by a successful sign-in using a less secure authentication method (SMS, Voice, OATH, or Mobile App notification) instead of a stronger method like FIDO2 or Passkey.
Detects web-proxy or webserver log activity associated with domains registered within the last 30 days that mimic branding or structure of major identity providers (Microsoft, Okta, Google, Duo). This behavior is characteristic of adversary-in-the-middle (AiTM) phishing infrastructure staging.
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