Detections

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

3,750 detections

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.
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Arnold Chan@slaz
Defender - KQL
1 month ago
001
Detects potential vishing activity associated with the Sauron Loader threat. The rule identifies a pattern of a mailbox receiving a high volume of inbound emails (spam or subscription bombs) within a short window, followed by the user launching remote assistance tools like Quick Assist or AnyDesk on the same endpoint, indicating an attacker-guided remote session.
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Ibrahim Saud@tektrix
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Detections.ai Community
1 month ago
103
This rule detects potential phishing or HTML smuggling attempts by identifying a burst of anomalously large SVG file attachments delivered via email to multiple recipients, followed by a correlation with web browser process network activity from the same recipients shortly after the email delivery.
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Ibrahim Saud@tektrix
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Detections.ai Community
1 month ago
004
Detects potential callback phishing (Telephone-Oriented Attack Delivery) by correlating phishing-themed emails containing phone numbers with no hyperlinks to the subsequent execution or installation of common remote monitoring and management (RMM) software on the same host within a six-hour window. The rule further refines this by ensuring the RMM tool was not previously installed on the user's system.
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Ibrahim Saud@tektrix
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Detections.ai Community
1 month ago
004
Detects a suspicious sequence of events where a user's registered phone number or recovery contact method is updated, followed shortly by an MFA factor reset or new factor enrollment. This pattern is a common indicator of a SIM-swap attack, where an attacker intercepts SMS-based MFA codes after hijacking the victim's phone number.
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Ibrahim Saud@tektrix
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Detections.ai Community
1 month ago
504
Detects the Sauron Loader vishing chain: a mailbox hit by a high-volume, high-distinct-sender email bombing burst (raised from 20 to 50 emails/hour and now requiring 15+ distinct senders, to exclude single-sender retry loops or broken automation), followed within 2 hours by the same user launching Quick Assist or AnyDesk — consistent with an attacker posing as IT support after the flood. Also fixes a schema bug where EmailEvents was queried with TimeGenerated instead of Timestamp, and a post-join column reference bug.
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Arnold Chan@slaz
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Detections.ai Community
1 month ago
004
Detects the execution of known remote access and support tools (Quick Assist, Microsoft Remote Assistance, or AnyDesk) on a host that has recently received a high volume of emails (over 50). This behavior often aligns with social engineering campaigns where a user is instructed to download or launch a remote support tool under false pretenses.
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Ibrahim Saud@tektrix
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Detections.ai Community
1 month ago
003
Detects anomalous access to SharePoint or OneDrive by identities identified as bots, agents, or service principals. The rule specifically monitors for multiple file accesses where the file names suggest the presence of secrets, passwords, or API keys, which may indicate a compromised or malicious AI agent attempting to harvest credentials.
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Ibrahim Saud@tektrix
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Detections.ai Community
1 month ago
505
Detects anomalous, high-volume file access or preview activity in OneDrive or SharePoint from non-browser user agents. This pattern is indicative of potential data exfiltration where adversaries use scripted clients or APIs to mass-read or preview files, bypassing traditional download-based alerts.
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Ankit Mehta@Secvyn
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SlimKQL
1 month ago
003
Detects anomalous, high-volume file access or preview activity in OneDrive or SharePoint from non-browser user agents. This pattern is indicative of potential data exfiltration where adversaries use scripted clients or APIs to mass-read or preview files, bypassing traditional download-based alerts.
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Ankit Mehta@Secvyn
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Detections.ai Community
1 month ago
203
Detects a suspicious pattern where a user authenticates via device code and, within a short timeframe (two hours), registers a new device to their account. This behavior is indicative of the 'ARToken' persistence pattern, where an adversary uses a stolen session to bind a rogue device to the victim's account, potentially to mint a Primary Refresh Token (PRT) for persistent access.
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Ibrahim Saud@tektrix
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Detections.ai Community
1 month ago
107
Detects phishing emails that impersonate invoice-related correspondence, containing SharePoint links and a .url shortcut file attachment, a common technique for credential harvesting or malware delivery.
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Ibrahim Saud@tektrix
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Detections.ai Community
1 month ago
107
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.
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Arnold Chan@slaz
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Hunters
1 month ago
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.
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Arnold Chan@slaz
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Hunters
1 month ago
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.
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Arnold Chan@slaz
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Midnight Slayer
1 month ago
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.
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Arnold Chan@slaz
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Detection & Hunting Community
1 month ago
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.
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Arnold Chan@slaz
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Detections.ai Community
1 month ago
101
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.
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Arnold Chan@slaz
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Hunters
1 month ago
001
This rule detects scenarios where a user completes a device-code authentication flow, followed immediately (within 10 minutes) by the registration of an unmanaged, non-compliant device to their account. This behavior is indicative of potential token theft (e.g., EvilTokens) where an adversary uses a stolen token to register their own device as a persistent, unmanaged entity, bypassing standard corporate device enrollment controls.
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Arnold Chan@slaz
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Detections.ai Community
1 month ago
207
Detects the 'auto-exfil' pattern associated with the TeamFiltration tool. The rule identifies a sequence of SharePoint Online file access immediately followed by a Microsoft Graph token request from the SharePoint Online Web Client Extensibility app. To reduce noise, it mandates a very short time delta between events (<= 60 seconds) combined with either the use of an IP address/ASN not previously seen for the account in the last 14 days, or a preceding failed sign-in attempt for the account.
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Arnold Chan@slaz
Defender - KQL
1 month ago
002
Detects a suspected account compromise sequence where an account first authenticates to a Microsoft Teams application from infrastructure associated with TeamFiltration (e.g., specific AWS ranges), followed by a rapid (within 2 minutes) authentication to a VPN or SAML service provider from a different IP address, indicative of an account pivot.
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Arnold Chan@slaz
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Hunters
1 month ago
002
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