Detections

Explore public detection logic contributed by the community across SIEM and rule languages.

3,750 detections

Detects incoming emails to finance or accounts payable aliases that contain urgent payment-related keywords and suspicious patterns, such as subject lines including dollar amounts or emails where the Reply-To address domain differs from the Sender domain. This activity is highly indicative of BEC fraud attempts targeting payment redirection.
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Arnold Chan@slaz
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Detections.ai Community
2 months ago
004
Detects successful post-delivery email remediation actions (such as ZAP, manual deletion, or moving to junk) performed on emails associated with the Operation DoppelBrand campaign, identified by specific sender domains (service-nowinc.com, domainlify.net) or suspicious invoice-fraud subject lines.
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Arnold Chan@slaz
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Detections.ai Community
2 months ago
004
Detects inbound emails from external domains containing keywords related to financial transactions or urgency in the subject line (specifically matching forwarded or reply formats) accompanied by PDF or HTML attachments. This heuristic approach attempts to identify potential business email compromise (BEC) or phishing lures mimicking invoices or approval requests in environments where raw email body content is unavailable.
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Arnold Chan@slaz
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Detections.ai Community
2 months ago
004
This rule detects scenarios where a device registration event occurs within 15 minutes of a device-code authentication sign-in for the same user. This pattern is indicative of potential account manipulation where an adversary registers their own device to an existing account to maintain persistent access or bypass multi-factor authentication (MFA).
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Arnold Chan@slaz
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Detection & Hunting Community
1 month ago
001
This rule detects potential OAuth token theft resulting from device code phishing (e.g., N0va phishkit). It monitors for a successful OAuth device-code authentication followed within 5 minutes by a secondary successful sign-in for the same user from a different IP address, different browser client, or different user agent, which is a common indicator of an adversary-in-the-middle (AiTM) session hijacking attempt.
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Arnold Chan@slaz
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Midnight Slayer
1 month ago
001
Detects anomalous non-interactive sign-ins that bypass multi-factor authentication (MFA) requirements from a geographical location not previously seen in the user's historical sign-in baseline. This pattern is indicative of attackers using stolen OAuth access or refresh tokens to maintain persistence and bypass standard authentication controls.
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Arnold Chan@slaz
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Detection & Hunting Community
1 month ago
001
This rule detects potential credential or token theft attempts from the N0va phishing kit. It monitors for emails lacking attachments that contain specific URL patterns associated with verification or session initialization, coupled with social engineering tactics such as spoofing legitimate service display names or subjects (e.g., Microsoft Teams, DocuSign, etc.) while originating from non-legitimate domains.
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Arnold Chan@slaz
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Midnight Slayer
1 month ago
001
This rule detects potential credential or token theft attempts from the N0va phishing kit. It monitors for emails lacking attachments that contain specific URL patterns associated with verification or session initialization, coupled with social engineering tactics such as spoofing legitimate service display names or subjects (e.g., Microsoft Teams, DocuSign, etc.) while originating from non-legitimate domains.
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Arnold Chan@slaz
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Detection & Hunting Community
1 month ago
001
This rule detects phishing emails where the sender's display name impersonates well-known brands (e.g., Microsoft, DocuSign, Zoom) while using a non-official domain. It further correlates these suspicious senders with embedded URLs that either match the targeted brands' domains or contain patterns commonly associated with device code phishing or API verification workflows.
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Arnold Chan@slaz
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Hunters
1 month ago
001
This rule detects phishing emails where the sender's display name impersonates well-known brands (e.g., Microsoft, DocuSign, Zoom) while using a non-official domain. It further correlates these suspicious senders with embedded URLs that either match the targeted brands' domains or contain patterns commonly associated with device code phishing or API verification workflows.
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Arnold Chan@slaz
Defender - KQL
1 month ago
001
Detects anomalous access to sensitive Microsoft Graph API endpoints (messages, mailFolders, contacts) following a device-code authentication. The rule specifically flags activity from non-standard or generic client applications, which is a pattern often associated with post-compromise proxying or token abuse, such as the 'ARToken' technique.
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Ibrahim Saud@tektrix
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Detections.ai Community
1 month ago
000
This rule detects high-volume, burst-pattern email distribution associated with a 'New VM Received' subject line, originating from non-authorized or non-corporate domains. This behavior is indicative of potential mass phishing or spam campaigns mimicking automated voicemail notifications.
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Arnold Chan@slaz
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Detection & Hunting Community
1 month ago
000
This rule detects high-volume, burst-pattern email distribution associated with a 'New VM Received' subject line, originating from non-authorized or non-corporate domains. This behavior is indicative of potential mass phishing or spam campaigns mimicking automated voicemail notifications.
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Arnold Chan@slaz
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Midnight Slayer
1 month ago
000
This rule detects high-volume, burst-pattern email distribution associated with a 'New VM Received' subject line, originating from non-authorized or non-corporate domains. This behavior is indicative of potential mass phishing or spam campaigns mimicking automated voicemail notifications.
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Arnold Chan@slaz
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Detections.ai Community
1 month ago
000
This rule monitors incoming emails by cross-referencing sender addresses against a hardcoded list of known malicious or suspicious email accounts. It associates these emails with any potential attachments to provide context for further investigation.
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Ankit Mehta@Secvyn
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SlimKQL
2 months ago
208
Detects anomalous administrative activity within Microsoft Exchange by identifying accounts performing multiple suspicious operations within a short timeframe. This rule flags activities like modifying transport rules, virtual directories, or management role assignments, which are often indicative of malicious persistence or configuration tampering, while excluding known authorized service accounts.
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Arnold Chan@slaz
Defender - KQL
1 month ago
001
Detects inbound traffic, sign-ins, and email activity originating from the Anthropic IP range 160.79.104.0/21.
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Arnold Chan@slaz
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Detections.ai Community
2 months ago
004
This rule correlates email sender activity with network, cloud, and identity logs by matching traffic against a specific IP range (160.79.104.0/21) identified as malicious or suspicious. The rule flags instances where this range appears as an email sender IP or as a remote/source IP address across various infrastructure indices, potentially indicating adversary communication or exfiltration.
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Arnold Chan@slaz
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Detections.ai Community
2 months ago
004
This rule detects various event types (email, identity authentication, network connections, and cloud audit logs) originating from or interacting with the specific IP address range 160.79.104.0/22 to 160.79.111.0/22, which is associated with known malicious activity.
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Arnold Chan@slaz
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Detections.ai Community
2 months ago
104
Detects high-volume credential guessing attacks against applications, characterized by a large number of distinct source IP addresses and geographic locations. This pattern is indicative of attackers using residential proxy pools to distribute their attempts, effectively bypassing traditional rate limiting or per-IP WAF thresholds while targeting authentication or error oracles. The rule specifically filters for relevant failure codes and enforces constraints on IP/user diversity to reduce noise from single-point-of-failure events or legitimate infrastructure.
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Arnold Chan@slaz
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Hunters
2 months ago
002
Detects high-volume credential guessing attacks against applications, characterized by a large number of distinct source IP addresses and geographic locations. This pattern is indicative of attackers using residential proxy pools to distribute their attempts, effectively bypassing traditional rate limiting or per-IP WAF thresholds while targeting authentication or error oracles. The rule specifically filters for relevant failure codes and enforces constraints on IP/user diversity to reduce noise from single-point-of-failure events or legitimate infrastructure.
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Arnold Chan@slaz
Defender - KQL
2 months ago
002
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