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Arnold Chan

@slaz
Trusted contributorCompletionist
2 followers2,099 downloads4,337 copies51 likes25,903 views

7,555 detections

Detects non-interactive sign-in events involving OIDC or external authentication tokens shortly after a known Coder registry compromise. The rule flags high-risk or at-risk single-factor authentication sessions, which may indicate the replay of stolen tokens harvested during the compromise window.
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Arnold Chan@slaz
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Hunters
2 months ago
000
Detects anomalous authentication patterns indicative of session token replay attacks. The rule identifies instances where a user successfully performs an MFA-authenticated, interactive sign-in, followed shortly (within 5-60 minutes) by a single-factor sign-in for the same user. The suspicious activity is characterized by a shift to a different IP/ASN, an unmanaged device (non-trusted), and a change in browser or OS identifiers, all while the user's geolocation (city/country) remains consistent. This behavioral pattern is highly consistent with the use of residential proxy infrastructure to replay stolen session tokens, effectively bypassing MFA.
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Arnold Chan@slaz
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Hunters
2 months ago
000
Detects the loading of Linux kernel modules using insmod or modprobe from non-standard, writable directories such as /tmp, /var/tmp, /dev/shm, /home, /root, or /opt. This behavior is highly indicative of an adversary attempting to install a malicious kernel module or rootkit to gain persistence or privilege escalation, while bypassing standard package management paths.
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Arnold Chan@slaz
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Hunters
2 months ago
000
Detects a suspicious sequence of interactive sign-ins to Microsoft management and profile-related applications from unmanaged devices occurring within a tight 15-minute window. This behavior is indicative of post-compromise activity, potentially associated with activity clusters such as UNC6671, where an attacker interacts with account settings shortly after compromising a session.
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Arnold Chan@slaz
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Hunters
2 months ago
000
This rule monitors network connections, DNS resolutions, and email URLs to detect traffic communicating with a known adversary-in-the-middle (AiTM) phishing infrastructure panel. By correlating security logs, DNS events, and email URL data against this specific indicator of compromise, it identifies potential interaction with phishing sites designed to capture credentials or session tokens.
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Arnold Chan@slaz
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Hunters
2 months ago
000
Detects inbound emails containing keywords associated with known ransomware extortion tactics (e.g., mention of specific ransomware names, data leaks, and payment demands). The rule filters for inbound traffic to identify external threats while excluding potential internal-generated traffic.
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Arnold Chan@slaz
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Hunters
2 months ago
000
Detects anomalous email access patterns across multiple mailboxes using specific application identifiers or access contexts often associated with automated collection tools. This rule flags when an application accesses more than 20 items across distinct mailboxes in a 15-minute window, which is indicative of potential unauthorized bulk email collection or data exfiltration.
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Arnold Chan@slaz
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Hunters
2 months ago
000
Detects anomalous access to Microsoft 365 services (OneDrive, SharePoint, Mail) where the user activity is characterized by a high volume of operations (more than 30) across a diverse set of objects (more than 15) within a one-hour window. The detection focuses on activity originating from specific common automation/scripting User-Agents (Microsoft Graph, Python httpx/requests) that deviate from typical, focused service account behavior.
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Arnold Chan@slaz
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Hunters
2 months ago
000
Detects successful Azure AD sign-ins where the user agent identifies as a Python requests library version (2.33.1 or 2.34.2) that has not been observed for that specific user account in the preceding 14 days. This behavior is indicative of anomalous automated or scripted access.
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Arnold Chan@slaz
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Hunters
2 months ago
000
This rule detects suspicious, high-volume file access or download operations in SharePoint and OneDrive. It filters for activities performed by specific user agents commonly associated with automated scripting tools (e.g., python-requests, Microsoft.Graph.Client), which indicates potential automated exfiltration or unauthorized bulk data collection.
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Arnold Chan@slaz
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Hunters
2 months ago
000
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