Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule monitors for outbound TCP and HTTP traffic to a known malicious infrastructure hosting a fake or compromised 'Corp MDM' (Mobile Device Management) admin panel, which serves as a Command and Control (C2) channel for spyware.
Detects high-volume database or CRM read activity performed by a user, followed by significant outbound network traffic to known public cloud storage, file-sharing, or messaging services. This pattern is consistent with data staging and exfiltration behaviors often attributed to the ShinyHunters threat actor group.
This rule identifies DNS queries or web requests directed toward a known list of domain names associated with the 'ShipmentsFree' family of shipping-rebate scams. These scams are typically designed to lure consumers into unknowingly signing up for recurring, undisclosed subscription services.
This rule identifies DNS queries or web requests directed toward a known list of domain names associated with the 'ShipmentsFree' family of shipping-rebate scams. These scams are typically designed to lure consumers into unknowingly signing up for recurring, undisclosed subscription services.
Detects a sequence of Tox P2P protocol packets consisting of a Cookie Request, Cookie Response, and Crypto Handshake within a single network flow. This pattern is indicative of the Tox P2P protocol, which is sometimes abused by malware (e.g., AvisLoader) for command-and-control (C2) communications to evade traditional network inspection.
Detects Node.js processes exhibiting network activity patterns consistent with 'EtherHiding' style C2 infrastructure, characterized by multiple distinct connections to external WebSocket hosts on non-standard TCP ports within a short timeframe, potentially following interaction with a blockchain/JSON-RPC endpoint.
Detects anomalous, chained execution patterns initiated by AI agents integrated with Model Context Protocol (MCP) servers. The rule identifies a multi-stage sequence involving the invocation of browser automation tools followed by shell execution and subsequent outbound network activity, potentially indicating an AI-driven attack chaining filesystem, shell, or cloud-API access in an unauthorized manner.
This rule detects network connections from internal devices to a list of known malicious domains and IP addresses. These indicators are commonly associated with command and control (C2) infrastructure or malicious activity, and alerting on these connections can help identify compromised systems within the environment.
Detects outbound TCP traffic originating from the internal network destined for a known malicious IP associated with Meterpreter Java payload retrieval and post-exploitation Command and Control (C2) activity, potentially related to PaperCut vulnerability exploitation.
The following analytic identifies the `finger.exe` utility being spawned with a command line containing an `@` character, indicating a remote host/server was specified.
The Finger protocol utility is largely obsolete in modern environments and has been abused by adversaries as a living-off-the-land binary (LOLBIN) to retrieve encoded payloads or establish covert command-and-control communication with an attacker-controlled finger server.
This behavior is uncommon in legitimate enterprise usage and warrants investigation.
The Finger protocol utility is largely obsolete in modern environments and has been abused by adversaries as a living-off-the-land binary (LOLBIN) to retrieve encoded payloads or establish covert command-and-control communication with an attacker-controlled finger server.
This behavior is uncommon in legitimate enterprise usage and warrants investigation.
This rule monitors network, file, and process telemetry for matches against a predefined list of malicious IP addresses, domain names, and file hashes. It flags suspicious network connections to known C2 infrastructure and the presence or execution of known malicious files based on SHA256 and SHA1 indicators.
The EntraIdSignInEvents table exposes the JA4 fingerprint in the GatewayJA4 field. But looking at the complete fingerprint alone does not tell us much. The interesting part starts when we break it down. With a simple KQL query, we can separate the TLS version, SNI, number of cipher suites, extensions and ALPN, together with the cipher and extension hashes. Then we can see how many sign-ins, users, IPs and countries are behind each JA4. The idea is simple: understand what is normal first, then look for what is different.
This rule identifies potential malicious activity by detecting the presence of known malicious file hashes (SHA256 and MD5) on endpoints and monitoring for DNS queries or network connections to a known malicious staging domain (yapw.life). The detection correlates file execution, file activity, and network communication to identify stages of an attack such as malware installation or C2 communication.
This rule monitors for known malicious file hashes (MD5) and network traffic (domains and specific URLs) associated with known threats. It aggregates events from DeviceFileEvents, DeviceProcessEvents, and DeviceNetworkEvents to identify potential interactions with threat infrastructure.
Detects outbound network connections to domains, URLs, and IP:Port combinations identified as malicious in the ThreatFox OSINT feed. This rule covers various commodity malware C2 and payload delivery infrastructure.
Detects lateral movement (SMB, RDP, WinRM) performed by a host within 5 minutes of communicating with specific commercial LLM APIs. This rule targets behaviors indicative of automated lateral movement driven by an LLM-based C2, excluding common administrative tools, standard business hours, and frequently used processes.
This rule monitors endpoint telemetry for occurrences of known malicious file hashes, C2 IP addresses, and C2 domains. It aggregates file creation/modification events, process execution, and network connections to detect activity associated with known malicious entities.
This rule monitors endpoint telemetry for occurrences of known malicious file hashes, C2 IP addresses, and C2 domains. It aggregates file creation/modification events, process execution, and network connections to detect activity associated with known malicious entities.
This rule monitors endpoint telemetry for occurrences of known malicious file hashes, C2 IP addresses, and C2 domains. It aggregates file creation/modification events, process execution, and network connections to detect activity associated with known malicious entities.
Detects outbound network connections from devices to specific suspicious domains identified in the detection logic. This rule monitors for connections to 'ns2.asiainfo.it.com' and 'www.wordcheck.info', which may be indicative of malware communication, command and control, or malicious web activity.
This rule monitors for known malicious file hashes, IP addresses, C2 domains, and specific URL markers associated with identified threat activity. It consolidates multiple detection vectors including file activity, process execution, network connections, and DNS queries to identify compromised devices communicating with attacker-controlled infrastructure.
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