Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects the first-ever observed network connection from a device to WhatsApp Web or Telegram Web within a 30-day lookback period. This behavior is used to identify potentially unauthorized companion-device linking to a user's messenger account, which could indicate credential theft or unauthorized access.
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.
Detects anomalous process execution and loopback network activity associated with the Model Context Protocol (MCP) 'exec_in_session' method. This pattern indicates an adversary potentially abusing the AI coding assistant's MCP server capabilities to execute arbitrary commands as a C2 channel.
Detects anomalous process execution and loopback network activity associated with the Model Context Protocol (MCP) 'exec_in_session' method. This pattern indicates an adversary potentially abusing the AI coding assistant's MCP server capabilities to execute arbitrary commands as a C2 channel.
Detects architecture-specific Mach-O 64-bit AMOS Stealer binaries dropped by the Macfinger ClickFix first-stage shell script, requiring multiple distinctive C2 endpoint strings alongside Mach-O magic and size constraints to reduce false positives
IOC hunt across DNS, network, and file-hash telemetry for the Ledger Google Ads phishing campaign. Vercel redirect domains are matched by exact hostname (DNS query name / parsed URL host) rather than substring, eliminating false positives from unrelated strings that merely contain a look-alike domain fragment. GCS bucket and Google Sites path IOCs remain substring-matched since the bucket IDs and page slugs are already highly specific.
Detects a sequential pattern where a browser process (Chrome, Edge, or Safari) accesses an AI chat platform's conversation API, followed within 5 minutes by a network connection to a known unauthorized exfiltration destination (api.pbapi.xyz). This pattern suggests the potential use of a browser-based tool or extension (e.g., Poper Blocker or similar) to intercept and exfiltrate AI chat history.
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 connection attempts by known AI agent or development-related runtime processes (python, node, etc.) to external endpoints that are not contained within a defined allowlist of LLM and infrastructure providers. This is intended to identify potential data exfiltration or unauthorized command and control communication originating from LLM-powered applications.
Detects a specific multi-stage exfiltration pattern characterized by initial reconnaissance/fingerprinting probes against sensitive service endpoints (/health, /docs, /openapi.json) followed by unauthorized access to artifact storage endpoints (/files or /file?name=) from the same device against the same host within a short timeframe.
Detects DNS resolution attempts for newly registered or suspicious top-level domains (e.g., .xyz, .top, .live) that mimic corporate Identity Provider (IdP) or Single Sign-On (SSO) login portals. This behavior is characteristic of Adversary-in-the-Middle (AiTM) phishing kits like EvilProxy or Tycoon2FA, intended to capture user session tokens. Note: This rule monitors for initial domain contact and should be correlated with subsequent authentication logs to identify potential session hijacking.
Detects git checkout of a bare 40-hex commit SHA or the literal FETCH_HEAD ref, executed by a recognized AI coding-agent process. Narrowed to the Plugin4Shell exploitation fingerprint (checkout of a pinned-SHA-shaped or FETCH_HEAD ref) rather than ordinary branch/tag checkouts, which these agents perform constantly during normal plugin installs.
This rule detects a high frequency of SMB (port 445) connection attempts from a single internal source host to a large number of distinct internal destination hosts within a short timespan. This activity is indicative of internal network scanning, host discovery, or automated lateral movement attempts using SMB.
This rule monitors network traffic, DNS queries, and user web clicks to detect interactions with known malicious domains associated with credential harvesting and phishing campaigns, specifically those impersonating security or SSO portals.
Detects the installation or execution of known MALFEX malicious packages via npm or Node.js, which are used as entry points for polyglot or disguised payload delivery.
This analytic detects suspicious python package installations where the package name resembles popular Python libraries but may be typosquatted or slightly altered.
Typosquatting is a common technique used by attackers to trick users into installing malicious packages that mimic legitimate ones.
This detection leverages Cisco NVM flow telemetry and checks for pip or poetry package managers with the "install" or "add" flags, making outbound connections to package repository such as `pypi.org` with known or suspected typo package names.
Typosquatting is a common technique used by attackers to trick users into installing malicious packages that mimic legitimate ones.
This detection leverages Cisco NVM flow telemetry and checks for pip or poetry package managers with the "install" or "add" flags, making outbound connections to package repository such as `pypi.org` with known or suspected typo package names.
The following analytic identifies DNS queries to domains associated with the 3CX supply chain attack. It leverages the Network_Resolution datamodel to detect these suspicious domain indicators. This activity is significant because it can indicate a potential compromise stemming from the 3CX supply chain attack, which is known for distributing malicious software through trusted updates. If confirmed malicious, this activity could allow attackers to establish a foothold in the network, exfiltrate sensitive data, or further propagate malware, leading to extensive damage and data breaches.
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 attempts to redirect Python package resolution away from the default PyPI index. This is achieved by monitoring pip CLI arguments (--index-url, --extra-index-url), environment variables (PIP_INDEX_URL, PIP_EXTRA_INDEX_URL, PIP_CONFIG_FILE), or the creation/modification of pip configuration files (pip.conf, pip.ini). Such activity is often associated with software supply chain attacks where an adversary attempts to force the installation of malicious packages from a controlled repository.
This rule detects network beaconing behavior consistent with Cobalt Strike's default malleable C2 profiles. It monitors HTTP GET and POST requests for specific URI patterns commonly used by default Cobalt Strike configurations and triggers when multiple such requests are observed from a single host over a 15-minute window.
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