Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects rapid post-exploitation activity where a developer's workstation, upon executing VS Code (Code.exe), initiates outbound network connections to major cloud service or CI/CD provider APIs (GitHub, AWS, Azure, GCP) within 60 minutes. This behavior is indicative of the Lazarus Group 'Contagious Interview' campaign, where stolen credentials from developer environments are used to pivot into cloud infrastructure or CI/CD pipelines.
This rule monitors process creation events for 'git' or 'npm' command-line activities that are highly indicative of potential software supply chain attacks. It triggers when known malicious package names or suspicious patterns are detected within command-line arguments, or when a workflow involves a git clone followed by subsequent npm installation, which is a common vector for deploying malicious dependencies.
Detects instances where Python or Node.js processes access common cloud provider credential files (e.g., AWS, GCP, Azure). This behavior is often associated with infostealer campaigns, such as the 'Contagious Interview' campaign, where adversaries attempt to harvest stored cloud credentials for further compromise.
Detects instances where scripting or development processes (e.g., node.exe, python.exe, code.exe) access sensitive SSH private key files or PEM files, followed by an immediate outbound network connection to a non-private IP address within 60 seconds. This activity is indicative of credential exfiltration or unauthorized use of stolen keys.
This rule monitors DNS query events for domains known to be associated with phishing campaigns, specifically those spoofing Microsoft or other common service brands. These domains are frequently used in credential harvesting attacks to lure users to malicious landing pages.
Detects network activity and command line execution associated with known malicious browser extensions, specifically targeting AdBlock for Youtube-related extensions and associated command-and-control infrastructure.
Detects Python or Node.js processes executing command-line interpreters (e.g., cmd.exe, bash) while simultaneously performing network connections. This behavior is indicative of a post-exploitation activity where an attacker leverages language-specific package paths to execute shell commands and establish command-and-control communication.
Detects unauthorized processes reading AI assistant configuration files (e.g., Claude, Cursor, Gemini, VS Code) followed by an outbound network connection from a common scripting or runtime environment (node, python, bun) within 60 seconds, potentially indicating credential or configuration exfiltration.
This rule monitors for suspicious child processes (such as bash, curl, or aws cli) spawned by the Amazon Q IDE language server. These patterns are indicative of potential abuse of IDE plugins or language servers to execute system commands, exfiltrate credentials, or interact with cloud APIs, potentially utilizing custom MCP (Model Context Protocol) configurations.
This rule detects potentially suspicious process executions originating from the Amazon Q language server or related processes. It monitors for commands involving MCP configuration files, AWS STS identity verification, and generic HTTP POST requests, which may indicate unauthorized activity or credential/configuration harvesting.
Detects unauthorized or suspicious AWS CLI operations and sensitive IAM API calls initiated from developer tooling processes like VS Code (including the Amazon Q extension host). This behavior is often associated with the exploitation of MCP (Model Context Protocol) auto-execution vulnerabilities, where attackers attempt to use compromised developer environments to escalate privileges or exfiltrate cloud credentials.
Detects Node.js being used to establish reverse shell connections
Detects unexpected subprocesses (bash, sh, npx) spawned by the Amazon Q language server process. This behavior is indicative of a potential exploit targeting the Model Context Protocol (MCP) auto-execution vulnerability (tracked as CVE-2026-12957), where a malicious .amazonq/mcp.json workspace configuration is used to gain unauthorized code execution.
Detects Node.js executing the malicious 'setup.cjs' script with the '--no-warnings' flag. This execution sequence is associated with the 'easy-day-js' typosquat postinstall hook used in the Mastra npm supply chain compromise.
Detects the obfuscated 'setup.cjs' dropper associated with the easy-day-js npm supply chain attack. The rule identifies unique obfuscated variable names, XOR-encoded marker bytes, and specific campaign-related strings, including postinstall commands, package names, second-stage payload filenames, self-deletion patterns, and TLS bypass indicators.
Detects high-volume file modification or renaming operations targeted at specific file extensions (dcm, tf, parquet) commonly associated with sensitive data (DICOM imaging, Terraform states, database parquet files) or operations occurring within Docker volume directories. The detection uses an aggregation logic to identify when a process performs more than 20 such file operations on an endpoint, which may indicate bulk data exfiltration, encryption, or tampering.
Detects Python processes (potentially associated with the ChocoPoC RAT, slogsec, or logcrypt modules) performing file read or access operations against common browser credential store files such as Login Data, Cookies, and Web Data on Windows and macOS systems.
This rule detects attempts by common scripting or utility processes (like osascript, curl, zsh, bash, sh, python) to access sensitive macOS files, specifically targeting browser data (Chrome, Brave, Firefox) and keychain files. This activity could indicate an attacker attempting to exfiltrate credentials, session tokens, or other sensitive user data.
Detects outbound network connections from devices to known FortiBleed command and control (C2) servers and sniffer infrastructure IP addresses. This rule helps identify potential compromises involving the FortiBleed vulnerability exploitation.
This rule detects potential command and control (C2) beaconing activity by identifying devices communicating with known suspicious remote IP addresses at regular, repeating intervals. It calculates the time difference between consecutive connections to specific suspicious IPs and identifies devices that establish at least three connections within one-hour bins, adhering to a 1 to 15-minute heartbeat interval.
Detects anomalous authentication patterns where a single source IP address exhibits multiple failed login attempts followed by at least one successful login within a 10-minute time window, indicative of credential stuffing or password spraying.
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