Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects potential phishing activity by correlating emails containing links to a specific suspicious domain and path with network activity (DNS queries or direct network connections) to the same domain. It targets both the initial email delivery and subsequent user interaction (e.g., clicking the link or downloading files).
Detects the creation or update of an Azure Sentinel analytics rule. This can indicate administrative activity related to security monitoring configuration. Monitoring these changes is crucial for detecting potential tampering with detection capabilities or the introduction of malicious rules.
Detects Server-Side Request Forgery (SSRF) attempts using a double-slash URL manipulation technique. Adversaries append an absolute local file path (e.g., /etc/passwd or /proc/self/environ) after a GitHub blob URL to bypass URL validation logic commonly found in web proxies, API gateways, and serverless environments like GCP Cloud Run.
Detects high-volume data access or exfiltration from Azure/Office 365 cloud environments via common scripting/automation user-agents. The rule monitors API requests to users, mail, drive, or files endpoints by non-standard clients (e.g., python, curl, Go) from previously unseen locations, calculating a risk score based on the number of endpoints accessed and the total data volume.
This rule detects the execution of python.exe or python3.exe using the '-m init' command line flag while attempting to establish a network connection over specific, often non-standard, ports (4444, 1337, 9001, 9002, 5555, 6666, 7777, 8888). This pattern is commonly associated with reverse shells or C2 agent check-ins initiated via Python scripts.
This rule detects a multi-stage phishing campaign involving Gmail-hosted emails containing 'short.gy' URLs. It monitors for three distinct signals: (1) Outbound network connections to 'chongdaotang.net', (2) User clicks on 'short.gy' links arriving from Gmail addresses, and (3) Inbound or BCC-delivered emails from Gmail senders containing 'short.gy' links. This combination is highly indicative of a phishing operation attempting to redirect users to a malicious infrastructure.
This rule monitors for security alerts related to Azure AI services, specifically detecting credential theft attempts and LLM jailbreak attempts that have been blocked or detected by Azure's built-in content filtering mechanisms.
Detects HTTP requests to Gitea instances containing the X-WEBAUTH-USER header, which facilitates user impersonation when Gitea is misconfigured with 'REVERSE_PROXY_TRUSTED_PROXIES = *' in a Docker environment. This vulnerability (CVE-2026-20896) allows an unauthenticated attacker to impersonate any user, including administrators.
Detects network communication to the Telegram Bot API (api.telegram.org) initiated by processes other than standard web browsers. This behavior is indicative of malicious activity such as automated data exfiltration, C2 communication, or victim status reporting, as observed in ransomware campaigns like BQTLock.
Detects Azure Privileged Identity Management (PIM) role activations that occur outside of standard business hours (before 07:00 or after 19:00 UTC, or on weekends) or involve high-privilege roles (e.g., Global Administrator, Privileged Role Administrator, Security Administrator).
This rule detects potentially malicious use of libcurl or curl.exe involving mTLS-related flags (e.g., --cert, --key, --cacert) in non-browser processes. It specifically flags instances where such processes perform repeated HTTPS connections to remote hosts, which may indicate exploitation of CVE-2026-8932 related to mTLS connection reuse and authentication bypass.
This rule detects potential command and control (C2) beaconing activity by identifying devices communicating with known suspicious remote IP addresses at regular, repeating intervals. It calculates the time difference between consecutive connections to specific suspicious IPs and identifies devices that establish at least three connections within one-hour bins, adhering to a 1 to 15-minute heartbeat interval.
This rule monitors for security alerts related to Azure AI services, specifically detecting credential theft attempts and LLM jailbreak attempts that have been blocked or detected by Azure's built-in content filtering mechanisms.
Detects a sequence of failed authentication attempts potentially indicative of an offline JWT secret brute-force attack (using tools like jwt_tool), followed by a successful authentication from the same source. This pattern suggests the adversary successfully recovered the secret, forged a valid token, and subsequently used it to gain unauthorized access.
Detects network requests to Microsoft Graph API that appear to be modifying authentication methods, specifically targeting phone-based multi-factor authentication settings. This may indicate an adversary attempting to register their own MFA device to a compromised account to maintain persistence or bypass MFA.
This rule detects potentially malicious Wi-Fi network enumeration or profile creation using 'netsh' or 'nmcli', as well as the loading of 'wlanapi.dll' by non-standard, unauthorized processes. Such activities can indicate an attempt by an adversary to discover, connect to, or exfiltrate data via nearby wireless networks.
Detects artifacts and activity related to the exploitation of CVE-2026-55200 in libssh2. The rule monitors for the creation or execution of identified libpwn exploit harness files and scripts, as well as suspicious network activity involving TCP port 2222, which is the default port for the PoC rogue SSH server. It also correlates the appearance of these exploit files with subsequent outbound SSH connections.
Detects unauthorized or suspicious processes reading sensitive credential files (AWS, .env, etc.) and subsequently initiating a successful network connection to an external destination within a 5-minute window. This behavior is a common indicator of credential exfiltration.
This rule identifies network activity or DNS queries associated with the known malicious domain 'perplexity-ai.online'. It monitors both endpoint network event logs and DNS logs to alert on any attempts to establish communication with or resolve this specific domain.
Detects high-volume data access or exfiltration from Azure/Office 365 cloud environments via common scripting/automation user-agents. The rule monitors API requests to users, mail, drive, or files endpoints by non-standard clients (e.g., python, curl, Go) from previously unseen locations, calculating a risk score based on the number of endpoints accessed and the total data volume.
Detects AzureHound or roadtx bulk enumeration of Microsoft Graph API endpoints using tokens obtained via CVE-2025-0309 CA policy bypass, characterized by high-frequency GET requests to /v1.0/users, /v1.0/groups, /v1.0/devices, /v1.0/servicePrincipals from Edge client IDs (f44b1140-bc5e-48c6-8dc0-5cf5a53c0e34 or 22098786-6e16-43cc-a27d-191a01a1e3b5) with password-only amr claim and no MFA.
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