Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects DNS queries, HTTP host headers, and TLS SNI traffic associated with known credential-phishing domains used by the threat actor TA419. The rule monitors for communication to a specific list of domains identified as malicious or used in phishing campaigns.
This rule detects various malicious indicators including known file hashes, IP addresses, domains, and specific URLs associated with threat activity. It consolidates hits from process, file, and network telemetry to alert on potential compromise or communication with identified command-and-control (C2) infrastructure.
This rule monitors for network and user interaction with known phishing domains that impersonate Microsoft, Outlook, or Teams services. It aggregates data from DNS queries, device network connections, inbound email URL links, and web URL click events to identify potential phishing attempts using a community-sourced watchlist from phishunt.io.
Detects DNS queries and TLS SNI traffic associated with known lookalike domains used by the threat actor TA419 for organization impersonation, which are commonly utilized in spearphishing campaigns.
Detects DNS queries, HTTP host headers, and TLS SNI traffic associated with known credential-phishing domains used by the threat actor TA419. The rule monitors for communication to a specific list of domains identified as malicious or used in phishing campaigns.
This rule performs a retrospective hunt for indicators of compromise (IOCs) associated with the ClosedQuorum malware. It identifies suspicious activity by matching against known file hashes, specific filenames, and network traffic directed towards services (such as DeepSeek, OpenRouter, Mistral, and Discord) which the malware uses for C2 or data exfiltration. The detection logic aggregates results from file system, process, and network telemetry.
Detects DNS lookups and outbound network connections to known command and control (C2) and staging domains associated with the STAC4924 campaign. The rule performs strict matching on domain names to ensure that subdomains are identified while avoiding false positives from partial string matches within URLs.
This rule detects various malicious indicators including known file hashes, IP addresses, domains, and specific URLs associated with threat activity. It consolidates hits from process, file, and network telemetry to alert on potential compromise or communication with identified command-and-control (C2) infrastructure.
Detects indicators associated with the ChatGPT Custom GPT ClickFix campaign across process, network connection, DNS, and HTTP telemetry: known malicious file hashes, C2 IP addresses, the chattypetty.com domain, and known payload-hosting URLs.
Detects DNS queries and TLS SNI traffic associated with known lookalike domains used by the threat actor TA419 for organization impersonation, which are commonly utilized in spearphishing campaigns.
Detects process/file hashes associated with the ChatGPT Custom GPT ClickFix
campaign via Windows process_creation telemetry. Split from the combined IOC
hunt: Sigma supports only one logsource per rule, so IP/domain/URL matching live
in separate network_connection and dns_query rules.
campaign via Windows process_creation telemetry. Split from the combined IOC
hunt: Sigma supports only one logsource per rule, so IP/domain/URL matching live
in separate network_connection and dns_query rules.
This rule detects various malicious indicators including known file hashes, IP addresses, domains, and specific URLs associated with threat activity. It consolidates hits from process, file, and network telemetry to alert on potential compromise or communication with identified command-and-control (C2) infrastructure.
The following analytic detects DNS queries initiated by the Windows AI Platform to domains associated with Hugging Face, OpenAI, and other popular providers of machine learning models and services. Monitoring these DNS requests is important because it can reveal when systems are accessing external AI platforms, which may indicate the use of third-party AI resources or the transfer of sensitive data outside the organization’s environment. Detecting such activity enables organizations to enforce data governance policies, prevent unapproved use of external AI services, and maintain visibility into potential data exfiltration risks. Proactive monitoring provides better control over AI model usage and helps safeguard organizational data flows.
The following analytic identifies DNS queries from internal hosts to dynamic domain providers. It leverages DNS query logs from the `Network_Resolution` data model and cross-references them with a lookup file containing known dynamic DNS providers. This activity is significant because attackers often use dynamic DNS services to host malicious payloads or command-and-control servers, making it crucial for security teams to monitor. If confirmed malicious, this activity could allow attackers to bypass firewall blocks, evade detection, and maintain persistent access to the network.
Detects process/file hashes associated with the ChatGPT Custom GPT ClickFix
campaign via Windows process_creation telemetry. Split from the combined IOC
hunt: Sigma supports only one logsource per rule, so IP/domain/URL matching live
in separate network_connection and dns_query rules.
campaign via Windows process_creation telemetry. Split from the combined IOC
hunt: Sigma supports only one logsource per rule, so IP/domain/URL matching live
in separate network_connection and dns_query rules.
Detects DNS lookups and outbound network connections to known command and control (C2) and staging domains associated with the STAC4924 campaign. The rule performs strict matching on domain names to ensure that subdomains are identified while avoiding false positives from partial string matches within URLs.
Detects network connections or DNS resolutions to known anonymous file-sharing, cloud storage, and messaging-bot API endpoints (e.g., transfer.sh, Telegram Bot API, Discord CDN) commonly abused by adversaries for data staging and exfiltration. This rule specifically targets non-browser processes that attempt to communicate with these endpoints, helping identify potentially malicious exfiltration activities.
This rule performs a multi-source correlation (network, DNS, file, and process telemetry) to detect artifacts associated with the Coruna campaign. It looks for known malicious file hashes (SHA1), C2 IP addresses, and specific domain/URL patterns in process command lines and network connections.
This rule performs a multi-source correlation (network, DNS, file, and process telemetry) to detect artifacts associated with the Coruna campaign. It looks for known malicious file hashes (SHA1), C2 IP addresses, and specific domain/URL patterns in process command lines and network connections.
Detects indicators associated with the ChatGPT Custom GPT ClickFix campaign across process, network connection, DNS, and HTTP telemetry: known malicious file hashes, C2 IP addresses, the chattypetty.com domain, and known payload-hosting URLs.
This rule detects potential DNS tunneling or beaconing activity by monitoring DNS queries for patterns commonly associated with command and control traffic. It flags anomalous DNS behavior including excessively long query strings, usage of TXT or NULL record types, frequent NXDOMAIN responses, or high volumes of such queries from a single host, which are indicative of data exfiltration or C2 communication via the DNS protocol.
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