Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects network traffic to or from IP addresses known to be associated with the active exploitation of the SharePoint vulnerability CVE-2025-53770.
This rule detects HTTP POST requests to specific SharePoint application paths, including ToolPane.aspx and SignOut.aspx. These paths have historically been associated with SharePoint vulnerabilities, particularly unauthenticated remote code execution (RCE) exploits. The rule aims to identify attempts to interact with these sensitive endpoints, which could indicate an attacker attempting to exploit known vulnerabilities or perform unauthorized actions.
This rule detects potential proxy bypass attempts by identifying network connections on common web ports (80, 443) that are allowed by the network firewall but do not appear in the proxy logs. It correlates network traffic from the Network_Traffic data model with proxy traffic from the Web data model. The rule specifically filters for external-bound traffic by excluding private and multicast IP addresses.
This package searches for evidence of Windows Shortcut (.lnk) files executing code to download a .wsf file from a remote WebDAV share hosted on a trycloudflare[.]com subdomain. This package searches in both web logs and endpoint process logs.
An ongoing malware campaign tracked as SERPENTINE#CLOUD has been identified as leveraging the Cloudflare Tunnel infrastructure and Python-based loaders to deliver memory-injected payloads through a chain of shortcut files and obfuscated scripts.
For initial access, the threat actors are luring users to execute malicious .lnk files (shortcut files) disguised as documents to silently fetch and execute remote code.
These payloads use cmd.exe to silently retrieve additional stages over WebDAV (via Cloudflare Tunnel subdomains) and invoke WSF or batch-based droppers.
trycloudflare[.]com allows a threat actor to expose a tunnel to the internet using legitimate infrastructure that will likely blend into benign traffic. trycloudflare dynamically generates subdomains formed of random words and so it's not enough to just look for such connections since they are also used by developers in testing. Therefore this search looks for further procedures attributed to the campaign.
An ongoing malware campaign tracked as SERPENTINE#CLOUD has been identified as leveraging the Cloudflare Tunnel infrastructure and Python-based loaders to deliver memory-injected payloads through a chain of shortcut files and obfuscated scripts.
For initial access, the threat actors are luring users to execute malicious .lnk files (shortcut files) disguised as documents to silently fetch and execute remote code.
These payloads use cmd.exe to silently retrieve additional stages over WebDAV (via Cloudflare Tunnel subdomains) and invoke WSF or batch-based droppers.
trycloudflare[.]com allows a threat actor to expose a tunnel to the internet using legitimate infrastructure that will likely blend into benign traffic. trycloudflare dynamically generates subdomains formed of random words and so it's not enough to just look for such connections since they are also used by developers in testing. Therefore this search looks for further procedures attributed to the campaign.
This analytic detects repeated blocked connection attempts from the same initiator to the same responder within a short time window. It leverages Cisco Secure Firewall Threat Defense logs and identifies connections where the action is set to Block, and the number of occurrences reaches or exceeds a threshold of ten within a one-minute span. This pattern may indicate a misconfigured application, unauthorized access attempts, or early stages of a brute-force or scanning operation. If confirmed malicious, this behavior may represent an attacker probing the network, attempting lateral movement, or testing firewall rules for weaknesses.
This analytic detects outbound connections from Cisco Secure Firewall Threat Defense to commonly abused file sharing and pastebin-style hosting domains. It specifically looks for allowed connections where the URL matches a predefined list of known data hosting or temporary storage services. These platforms are frequently used by adversaries for malware delivery, data exfiltration, command and control (C2) beacons, or staging of encoded payloads. Identifying such connections can indicate potential abuse of legitimate infrastructure as part of an attacker's kill chain, suggesting tool staging, credential dumping, or outbound data leaks over HTTP(S).
This analytic detects outbound network connections initiated by command-line tools such as `curl` or `wget` as observed in Cisco Secure Firewall Threat Defense logs. It specifically looks for allowed connections where the `EVE_Process` or `ClientApplication` fields indicate the use of these utilities. While these tools have legitimate uses, adversaries frequently abuse them to download malicious payloads, retrieve additional tools, or establish staging infrastructure on compromised systems. Detection of this activity may indicate the download phase of an attack chain or the retrieval of command-and-control utilities.
KQL Query from file: Hunting Exposed JDWP
KQL Query
KQL Query from file: Fortigate Belsen Leak KQL Check
KQL Query
KQL Query from file: Sentinel Timeroasting KQL detection
KQL Query from file: Discord Invite Hijacking Detection
'This rule identifies a web request with a user agent header known to belong PowerShell. <br>You can add custom Powershell indicating User-Agent headers using a watchlist, for more information refer to the [UnusualUserAgents Watchlist](https://aka.ms/ASimUnusualUserAgentsWatchlist).<br><br>
This analytic rule uses [ASIM](https://aka.ms/AboutASIM) and supports any built-in or custom source that supports the ASIM WebSession schema (ASIM WebSession Schema)'
This analytic rule uses [ASIM](https://aka.ms/AboutASIM) and supports any built-in or custom source that supports the ASIM WebSession schema (ASIM WebSession Schema)'
The following analytic identifies anomalous network traffic volumes between Kubernetes workloads or between a workload and external sources. It leverages Network Performance Monitoring metrics collected via an OTEL collector and pulled from Splunk Observability Cloud. The detection compares recent network metrics (tcp.bytes, tcp.new_sockets, tcp.packets, udp.bytes, udp.packets) over the last hour with the average over the past 30 days to identify significant deviations. This activity is significant as unexpected spikes may indicate unauthorized data transfers or lateral movement. If confirmed malicious, it could lead to data exfiltration or compromise of additional services, potentially resulting in data breaches.
The analytic identifies anomalous inbound network traffic volumes from processes within containerized workloads. It leverages Network Performance Monitoring metrics collected via an OTEL collector and pulled from Splunk Observability Cloud. The detection compares recent metrics (tcp.bytes, tcp.new_sockets, tcp.packets, udp.bytes, udp.packets) over the last hour with the average over the past 30 days. This activity is significant as it may indicate unauthorized data reception, potential breaches, vulnerability exploitation, or malware propagation. If confirmed malicious, it could lead to command and control installation, data integrity damage, container escape, and further environment compromise.
This analytic detects new UDP communication between a source and destination workload pair within a Kubernetes cluster. It uses Network Performance Monitoring (NPM) metrics from an OTEL collector via Splunk Observability Cloud. The detection compares network activity over the last hour with the past 30 days to identify new inter-workload communication. Such changes in network behavior can indicate potential security threats or anomalies, including unauthorized connections that may lead to data breaches, privilege escalation, lateral movement, or disruption of critical services.
This analytic identifies newly observed TCP communication between source and destination workloads within a Kubernetes cluster. It leverages Network Performance Monitoring metrics to compare recent network activity with historical data (past 30 days) to detect new inter-workload communications. Such new connections can indicate changes in application behavior or potential security threats, including unauthorized access, lateral movement, privilege escalation, data exfiltration, or disruption of critical services.
'The purpose of this content is to identify successful phishing links accessed by users. Once a user clicks on a phishing link, we observe successful network activity originating from non-Microsoft network devices. These devices may include Palo Alto Networks, Fortinet, Check Point, and Zscaler devices.'
Remote Monitoring and Management (RMM) programs are IT to manage remote endpoints. Attackers have begun to abuse these programs to persist or provide C2 channels.
https://github.com/jischell-msft/RemoteManagementMonitoringTools
https://github.com/jischell-msft/RemoteManagementMonitoringTools
'This creates an incident in the event a client generates excessive amounts of DNS queries for non-existent domains.
This analytic rule uses [ASIM](https://aka.ms/AboutASIM) and supports any built-in or custom source that supports the ASIM DNS schema'
This analytic rule uses [ASIM](https://aka.ms/AboutASIM) and supports any built-in or custom source that supports the ASIM DNS schema'
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