Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
707 detections
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This rule detects potential unauthorized access and environment variable disclosure attempts against the Flowise API, specifically targeting the MCP (Model Context Protocol) endpoints. It monitors for path traversal-like patterns using case variants (e.g., /api/v1/env), unauthorized access to configuration endpoints, and the exposure of sensitive data like API keys, database URLs, and passwords in API responses or request headers.
Detects MCP (Model Context Protocol) server processes or developer tools spawned by IDE extensions performing environment variable enumeration to access cloud credentials (e.g., AWS, Azure, GCP, GitHub tokens) followed by anomalous outbound network connections, indicating potential exfiltration of sensitive developer environment secrets.
Detects BQTLock ransomware activity where the BQT.exe process accesses browser credential stores (Chrome, Firefox, Opera) and writes the extracted data to a temporary file at C:\Windows\Temp\bqt_passwords.txt.
This rule detects potential exfiltration attempts from a GCP Cloud Run container by monitoring for outbound network connections triggered by processes reading sensitive local files. The files being monitored include environment variable configurations (/proc/self/environ) and application source code, which may contain credentials or service account tokens.
Detects Python interpreters launched by development tools (like VS Code or Node) that execute commands containing obfuscated code, base64-encoded strings, or strings indicative of credential harvesting and clipboard interaction, which may indicate malicious activity or post-exploitation behavior.
Detects unauthorized access to sensitive developer artifacts, including Git credentials, GitHub CLI configurations, GitHub Desktop logs, and VS Code global storage files by suspicious processes (Node.js, Python, or VS Code executables) commonly leveraged in the 'Contagious Interview' campaign associated with the Lazarus Group for credential theft.
This rule detects node.js processes, typically initiated via npm or during package installation scripts, accessing sensitive Claude Code configuration or credential files. This behavior is indicative of a malicious supply chain attack where a compromised npm package attempts to exfiltrate developer authentication secrets.
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 rapid access (three or more distinct files within a 60-second window) to sensitive files typically containing credentials or configuration data (e.g., SSH keys, AWS credentials, environment files, container configurations). This pattern is often associated with credential dumping or reconnaissance activity by an adversary attempting to harvest secrets.
This rule monitors for Node.js processes accessing sensitive configuration files, environment files, or credential stores (such as AWS credentials). It also triggers when command line arguments or file contents contain patterns indicative of infostealer activity, including specific indicators related to the Polymarket campaign or credentials collection functions like Git or Docker credential harvesting.
This rule monitors SIEM-ingested HTTP or API gateway traffic for JSON Web Tokens (JWT) where the base64url-encoded payload claims contain sensitive identifiers such as 'password' or 'role'. Detection of these fields in unencrypted JWT claims indicates poor development practices leading to excessive data exposure or potential credential leakage within web session management.
This rule detects potentially malicious command-line execution patterns aimed at harvesting sensitive credentials. It monitors for the access or searching of configuration files (e.g., AWS, .env, or Github tokens) and the dumping of environment variables that likely contain API keys or secrets, specifically targeting services like AWS, Anthropic, and GitHub.
This rule detects potential credential harvesting activities by monitoring for suspicious environment variable enumeration or unauthorized access to sensitive credential and configuration files (such as AWS credentials and .env files) by common command-line utilities. It is designed to identify post-compromise reconnaissance where an adversary attempts to extract cloud provider or AI API keys.
Detects unauthorized or suspicious processes reading sensitive credential files (AWS, .env, etc.) and subsequently initiating a successful network connection to an external destination within a 5-minute window. This behavior is a common indicator of credential exfiltration.
Detects path traversal exploitation attempts targeting Dell Wyse Management Suite (WMS) processes or Java components, specifically looking for attempts to access sensitive system files such as SAM, NTDS.dit, or registry hive files via directory traversal sequences.
This rule detects potential developer workstation compromise by monitoring for suspicious child process creation (shells/interpreters) from common development tools (Code.exe, node.exe, python.exe) followed by subsequent authentication to cloud environments (Azure, AWS, or GCP) within a 60-minute window. This behavior is indicative of a developer's machine being compromised to harvest session tokens or credentials to access cloud resources.
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 attempts by a Chrome web browser process to create, modify, or rename files associated with sensitive user credentials or application data, such as AWS credentials, browser logins, or environment configuration files. This behavior is indicative of credential theft or staging of sensitive information.
Detects suspicious patterns of LLM API usage by monitoring outbound traffic to major LLM providers (OpenAI, Anthropic, Google, DeepSeek, Mistral, Cohere). The rule identifies potential API key theft ('LLMjacking') by flagging high request volumes from single keys, usage of the same key across multiple distinct source IP addresses, and direct outbound traffic bypassing established proxy/gateway configurations.
This rule detects potential unauthorized access to Azure Storage Blobs following the retrieval of storage account access keys. It correlates 'ListKeys' API events with subsequent blob access activities from either unrecognized IP addresses or via Shared Access Signature (SAS) tokens, indicating potential credential misuse.
This rule monitors for processes attempting to enumerate sensitive environment variables related to cloud services (AWS) and AI platforms (Anthropic), or access specific configuration and credential files (e.g., .aws/credentials, .config/anthropic). It specifically correlates these behaviors with potentially suspicious parent processes like bash, node, or python interpreters, which may indicate an AI agent or a reverse shell performing reconnaissance or credential exfiltration.
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