Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects network connections from internal devices to a specific remote IP address (134.122.155.135) over port 443. This is characteristic of communication with a known or suspicious Command and Control (C2) server.
Detects instances where a command-line utility or scripting interpreter references a 'SKILL.md' configuration file, typically associated with agent-skill payloads, followed shortly by network-based file download activity. This behavior is indicative of a secondary payload retrieval triggered by an adversary-controlled or poisoned configuration file.
Detects the presence of package.json or node_modules artifacts associated with a known typosquatting campaign targeting the 'sorted-btree' npm package. The rule identifies suspicious package names or dependencies by matching against a list of known malicious packages used in the campaign, or by detecting the co-occurrence of the malicious 'indexed-btree' package alongside the legitimate 'sorted-btree' package.
Detects network activity associated with the AMOS (Atomic macOS Stealer) malware, specifically identifying C2 communication patterns such as heartbeat beacons and agent tasking requests occurring over non-standard port 8133.
Detects the use of download utilities (curl or wget) from terminal shells with command-line arguments containing 'force=1' and architecture-specific identifiers (e.g., arm64, x86_64). This pattern matches the execution chain commonly associated with ClickFix-based AMOS malware distribution, where users are tricked into pasting commands to download and execute architecture-specific payloads.
Detects host and browser fingerprinting reconnaissance behavior associated with the Macfinger/ClickFix infection chain. The rule identifies multiple disparate indicators (ClickFix tracker domain, ipinfo.io geolocation lookups, and known AMOS C2 IP contact) occurring on the same device within a 15-minute window, effectively reducing noise from isolated or benign network lookups.
Detects host and browser fingerprinting reconnaissance behavior associated with the Macfinger/ClickFix infection chain. The rule identifies multiple disparate indicators (ClickFix tracker domain, ipinfo.io geolocation lookups, and known AMOS C2 IP contact) occurring on the same device within a 15-minute window, effectively reducing noise from isolated or benign network lookups.
Detects host and browser fingerprinting reconnaissance behavior associated with the Macfinger/ClickFix infection chain. The rule identifies multiple disparate indicators (ClickFix tracker domain, ipinfo.io geolocation lookups, and known AMOS C2 IP contact) occurring on the same device within a 15-minute window, effectively reducing noise from isolated or benign network lookups.
Detects host and browser fingerprinting reconnaissance behavior associated with the Macfinger/ClickFix infection chain. The rule identifies multiple disparate indicators (ClickFix tracker domain, ipinfo.io geolocation lookups, and known AMOS C2 IP contact) occurring on the same device within a 15-minute window, effectively reducing noise from isolated or benign network lookups.
Detects potential 'ClickFix' activity where a browser or file explorer process initiates a shell (e.g., cmd, powershell, osascript) with command arguments indicative of remote script execution, obfuscated commands, or web-based payload downloading. This pattern matches known social engineering tactics that entice users to copy-paste malicious code into a system shell.
Detects a multi-stage attack chain where an AI-agent's configuration file (e.g., mcp.json) is modified, followed immediately by the execution of a tool process (node/python) spawned by the agent, the subsequent spawning of a shell process, and finally an outbound network connection to a rare, non-standard domain. This sequence is indicative of AI agent tool poisoning, where malicious tool definitions are introduced to execute arbitrary commands and exfiltrate data. Coverts T1195.002, T1565.001, T1059
Detects a multi-stage, automated attack chain performed by AI-based development agents (e.g., Claude, Copilot, Cursor). The rule correlates process discovery, credential access, file collection (staging), and exfiltration over web services within a tight 15-minute window, identifying potential autonomous exploitation and data exfiltration. Covered T1046, T1552.001, T1078, T1005, T1567
Detects extension code implementing Caesar-shift-over-base64 decoding of a remote proxy server list fetched from dead-drop hosts, feeding a PAC-based proxy configuration
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 network navigation to Google Sites pages that match patterns associated with known Ledger-impersonating phishing campaigns. These sites are often reached through redirect chains involving Google Ads, cloud storage, and rotating domains.
Detects network navigation to Google Sites pages that match patterns associated with known Ledger-impersonating phishing campaigns. These sites are often reached through redirect chains involving Google Ads, cloud storage, and rotating domains.
Detects network navigation to Google Sites pages that match patterns associated with known Ledger-impersonating phishing campaigns. These sites are often reached through redirect chains involving Google Ads, cloud storage, and rotating domains.
Detects instances where a Claude process accesses a 'SKILL.md' file, followed closely by a suspicious command execution on the same device. The rule specifically targets command-line activity that involves encoding, download-and-execute patterns, staging in sensitive directories, or communication with suspicious domains/IPs, which is indicative of a supply-chain or poisoned agent-skill file attack.
Detects instances where the Claude Desktop application spawns a terminal or shell process that immediately executes commands associated with downloading remote payloads or accessing sensitive cryptocurrency wallet artifacts. This behavior is indicative of potential AI prompt injection where an AI assistant is coerced into executing malicious commands without user authorization.
This rule detects potential backdoor deployment or persistence mechanisms in AI coding assistants like Claude Code and Cursor. It identifies when specific configuration or hook files (e.g., settings.json, hooks.js) are modified, followed within a two-hour window by the execution of suspicious child processes (e.g., powershell.exe, curl.exe, python) initiated by the coding assistant's CLI tool.
Detects the creation of potential hook or startup scripts within hidden AI coding assistant directories (.claude, .vscode, .cursor) followed by the execution of a process from those same directories. This pattern is consistent with supply chain compromises where trojanized extensions or MCP servers are leveraged for code execution.
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