Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
252 detections
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Detects 'docker login' commands targeting an Azure Container Registry (.azurecr.io) that explicitly include username and password flags in the command line string. This behavior may indicate credential usage for registry authentication.
This rule detects file creation, renaming, or modification events targeting files associated with cryptocurrency wallets, such as wallet.dat or files containing the string 'keystore'. Accessing these files is a common technique used by malware or malicious actors to steal cryptocurrency credentials and private keys.
Detects successful execution of the 'List Credentials' action on an Azure Container Registry. This action returns the administrative credentials (username and password) for the registry, which may indicate unauthorized credential access or preparation for exfiltration or lateral movement.
Detects unauthorized access to sensitive credential files, such as credentials.json or .env, through HTTP GET requests, or the creation/staging of a credentials file at /tmp/creds.json, which may indicate attempts to exfiltrate or steal credentials from the local system.
Detects unauthorized execution of AWS CLI commands within a shell spawned by Amazon Q or AWS language server processes. This activity typically involves the 'aws sts get-caller-identity' command being piped to 'curl' for remote exfiltration of cloud credentials, a behavior associated with the exploitation of CVE-2026-12957 in MCP auto-execution.
Detects Node.js processes attempting to access sensitive files or environment variables associated with Claude AI (e.g., Anthropic API keys, .claude configuration files), which may indicate credential harvesting activity by malicious code.
Detects suspicious file creation activities by system authentication processes (e.g., sshd, su, sudo, PAM) in known staging directories (/tmp, /var/tmp, /dev/shm) or SSH configuration paths. The rule looks for hidden files or files with specific extensions (.log, .dat, .bak) often associated with malicious backdoored components used to intercept and store credentials in plaintext.
Detects Node.js processes accessing sensitive configuration and credential files such as AWS credentials, .npmrc tokens, SSH keys, and Docker configurations. This behavior is consistent with infostealer activity, including malicious supply chain attacks designed to harvest developer secrets from local filesystems.
Detects suspicious child processes spawned by AI-powered IDE assistants (e.g., Amazon Q, Claude Code, Cursor) that perform AWS credential inspection combined with network exfiltration commands. This activity is indicative of exploitation of IDE extension vulnerabilities (e.g., CVE-2026-12957) where malicious configuration files force the IDE to spawn shell processes to steal and transmit cloud environment credentials.
This query detects multiple TTPs associated with the Storm-2561 campaign that uses SEO poisoning to distribute fake VPN clients for credential theft. It looks for known file hashes, a specific code-signing certificate, malicious DLL side-loading and C2 communication patterns.
Detects Node.js, NPM, or NPX processes attempting to read sensitive shell history files (e.g., .bash_history, .zsh_history). This behavior is characteristic of malicious activity such as infostealers attempting to harvest credentials or reconnaissance data from a compromised developer environment, notably observed in supply chain campaigns like the Polymarket Trap.
This query detects multiple TTPs associated with the Storm-2561 campaign that uses SEO poisoning to distribute fake VPN clients for credential theft. It looks for known file hashes, a specific code-signing certificate, malicious DLL side-loading and C2 communication patterns.
Detects potential exploitation of Firefox SmartWindow (CVE-2026-20253) involving token exfiltration via specific URL patterns, unauthorized configuration preference tampering (e.g., smartwindow.enabled, smartwindow.apiKey), or modifications to browser configuration files (prefs.js, user.js) by the Firefox process.
Detects anomalous or unauthorized processes accessing sensitive AI configuration files, SSH configurations, version control files, and shell history within a short time window. The rule flags instances where non-standard or unexpected applications exhibit broad or high-frequency access to these files, which are common targets for credential harvesting or exfiltration by adversaries.
Detects suspicious subprocesses spawned by IDE or extension host processes (like VS Code or Amazon Q) that execute AWS CLI commands to enumerate identity or immediately exfiltrate data via curl/wget, indicating potential credential abuse by malicious plugins or MCP servers.
Detects a Node.js process, spawned as a child of an IDE (e.g., VS Code, Cursor), that accesses local AWS configuration or credential files followed by an outbound network connection to a non-AWS destination. This behavior is indicative of potential cloud credential exfiltration via malicious Model Context Protocol (MCP) server execution.
Cloud extortion group exploiting exposed credentials and storage
Fraud framework powering scam domains for crypto and gambling schemes
macOS malware used by JINX-0164 against cryptocurrency developers
Detects Node.js processes accessing sensitive AWS credential or configuration files. This behavior is indicative of unauthorized information gathering, often associated with infostealer malware payloads delivered via malicious npm packages.
This rule detects the invocation of local AI/LLM CLI utilities (e.g., ollama, llama-cpp) by potentially malicious or automated parent processes. It specifically flags scenarios where non-interactive or scripting-based processes (such as WScript, PowerShell, or Python) attempt to use these AI tools, particularly when the command line includes keywords related to sensitive data discovery, such as 'password', 'token', or 'private key'. This behavior is characteristic of infostealer activity (e.g., QUIETVAULT) attempting to use AI tools for automated reconnaissance and credential extraction.
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