Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects the exposure of Kuwaiti Civil ID numbers in various log sources, including process command lines, additional device event fields, and email subjects/sender addresses. A Kuwaiti Civil ID is identified by a 12-digit number starting with 1, 2, or 3.
This rule detects the exposure of Kuwaiti Civil ID numbers in various log sources, including process command lines, additional device event fields, and email subjects/sender addresses. A Kuwaiti Civil ID is identified by a 12-digit number starting with 1, 2, or 3.
Detects emails where the subject line contains a 13-16 digit number, potentially indicating sensitive numerical patterns like a Credit Card. The rule looks for contiguous digits or common dashed/spaced credit card formats and filters out common automated email keywords to reduce false positives.
Detects emails where the subject line contains a 13-16 digit number, potentially indicating sensitive numerical patterns like a Credit Card. The rule looks for contiguous digits or common dashed/spaced credit card formats and filters out common automated email keywords to reduce false positives.
This rule detects emails that contain keywords related to 'civil id' in English or Arabic and subsequently extract potential Civil ID numbers from the email body. It then validates the extracted numbers based on birth month and day, and categorizes the risk level based on sender's domain and attachment count. This could indicate data exfiltration or sensitive information exposure.
This rule detects inbound emails from 'azure-noreply@microsoft.com' that are delivered to the inbox, are the first contact from this sender, and contain specific keywords in the subject line such as 'severity', 'activated', 'deactivated', 'alert', 'invoice', 'payment', 'order', or 'fund'. This could indicate phishing attempts or social engineering using a spoofed Microsoft sender to deliver urgent or financially-themed messages.
This rule detects potential social engineering attempts in external direct messages (DMs) by identifying messages that impersonate IT helpdesk or support personnel, or discuss 'working from home' scenarios. It specifically looks for keywords like 'helpdesk', 'help desk', 'IT support', and 'working from home' in the recipient details or sender display name of external messages within the last 5 days. The goal is to surface lures that aim to trick users into revealing sensitive information or performing actions under false pretenses.
Detects inbound emails originating from 'azure-noreply@microsoft.com' with subjects that typically indicate financial transactions (e.g., 'Invoice Paid', 'Payment Reference') or system alerts (e.g., 'MemorySpike', 'DiskFull'). This pattern could be indicative of phishing attempts leveraging a seemingly legitimate sender to trick recipients.
This rule detects inbound emails with an SVG attachment that is disguised as an HTML file and contains a long, base64-like string in its filename. This pattern is often used in phishing campaigns to bypass traditional email filters and deliver malicious content, potentially leading to credential harvesting or malware delivery. The rule specifically looks for files with a '.svg' extension but a 'html' file type, combined with a filename containing a string of at least 20 alphanumeric characters, plus or minus, equal signs, which is characteristic of base64 encoding.
Attackers take advantage of the emojis because they help the content stand out and gets more attention than plain text. It not apply just to Email Subject if not that it also applies to Attachment Files which based on my experience, legitime file not use to have icons in their file names.For example:
📄Invoice.pdf
🔐Reset_Password.html
📦Delivery_Document.zip
This can help find:
- Suspicious files dropped on emails
- Files downloaded from phishing emails
- User-downloaded /opening scam files
- Payloads with social engineering names
📄Invoice.pdf
🔐Reset_Password.html
📦Delivery_Document.zip
This can help find:
- Suspicious files dropped on emails
- Files downloaded from phishing emails
- User-downloaded /opening scam files
- Payloads with social engineering names
This rule identifies accounts receiving the most email and highest threat exposure by analyzing inbound email events. It counts total emails, delivered emails, junked emails, blocked emails, and specifically highlights emails with detected threats (Phish, Malware, Spam) that were successfully delivered to the user's inbox. The output is sorted to prioritize accounts with the highest number of delivered threats.
Detects incoming emails from Atlassian Jira Cloud subdomains where the subject line matches known spam patterns used in a campaign abusing the service's reputation.
Detects the creation or modification of Exchange inbox rules that move messages to potentially risky folders (e.g., Deleted Items, Junk Email, RSS Feeds, Archive, Clutter) or are configured to delete messages. This behavior can be indicative of an attacker attempting to hide malicious emails, exfiltrate data, or disrupt email flow.
https://www.kaseya.com/blog/dkim-replay-attacks-apple-paypal-invoice-abuse/
This KQL check for email links that were clicked/ email attachment that were opened before recall
Detects emails containing threat-actor lure links to trojanized ScreenConnect MSI files, specifically targeting 'cld.pt' downloads with 'COMPROBANTE*.msi' patterns. The rule also looks for 'COMPROBANTE' keywords in email subjects as a fallback.
This rule tracks and deduplicates alert emails from a Managed Security Service Provider (MSSP). It identifies emails from a specific sender, filters by subject keywords (e.g., severity levels), and groups them into sessions to prevent duplicate alerts. It also extracts case numbers from portal URLs found within the emails.
This simple query helps detect whether a user interacted with the URL within the email message or not.
Where emabeded URL contain googlecontent
KQL Query from file: Detecting Threat Actor Storm-2657
Detects high-fidelity execution of executables spawned from Office applications or browsers after engaging with recruitment-themed lures (e.g., LinkedIn, careers). Focuses on EXE files created and executed within 24 hours in user directories (Downloads, Temp, AppData), reflecting Subtle Snail’s social engineering and document-based initial access methods.
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