Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
7,141 detections
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Detects a network-connected Linux process exhibiting beaconing behavior (high-frequency external connections) that is subsequently correlated with unauthorized file access to sensitive Linux configuration files (/etc/passwd, /etc/shadow, or authorized_keys). This rule filters out common system processes and standard management binaries, targeting suspicious command-and-control activity combined with credential theft.
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 the execution of the VSCode npm infostealer campaign, specifically identifying the usage of a fake eslint loader named 'vscode-eioesocxetd.js'. This malware performs sandbox and environment checks for CI/CD, cloud, and containerized environments (AWS, Azure, GCP, GitHub Codespaces, etc.) to evade analysis before proceeding to deploy infostealers or backdoors.
Detects post-exploitation of CVE-2026-22778 (vLLM libopenjp2 JPEG2000 cdef heap overflow). The overwritten AVBuffer free() pointer is set to system(), so a successful exploit spawns /bin/sh -c as a direct child of the vLLM/python inference process. Matches a shell or download/exec utility launched with a vLLM/python/uvicorn parent, excluding benign python worker children.
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.
Detects execution of AWS STS commands to identify the caller's identity in conjunction with suspicious outbound POST requests. This pattern is commonly used by adversaries to verify compromised cloud credentials or enumerate permissions within an AWS environment.
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 clearing of bash command history to remove forensic evidence
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 attempts to disable or flush system firewall rules on Linux platforms using common tools like iptables, ufw, firewalld, and nftables. Adversaries often perform these actions to bypass network controls and facilitate unauthorized communication.
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 reconnaissance activities targeting binaries or modules containing libssh2 library references or specific PHP symbol offsets (e.g., zend_execute_internal, zif_system). This rule monitors for common administrative and debugging tools like ldd, strings, readelf, and nm being used to scan files for sensitive symbols or version information, as well as specific PHP enumeration patterns and validation script executions, which are commonly associated with the triage process for CVE-2026-55200.
Detects the creation of specific AI assistant configuration files (e.g., Cursor, Claude, Gemini, VS Code) by alternative runtimes. The Miasma malware targets modern AI coding assistants to inject hidden backdoors that trigger automatically when projects are opened.
This rule detects the use of 'sshpass' in conjunction with specific command-line arguments that indicate automated SSH logins. It specifically looks for the presence of '/sshpass/' and '/StrictHostKeyChecking=no/' in the command line, combined with common administrative account names like 'psoft', 'oracle', or 'linuxadm'. This activity is often associated with lateral movement techniques, as observed in attacks like those by ShinyHunters targeting Oracle/PeopleSoft environments.
Monitors for the use of 'sshpass' to automate SSH logins using specific accounts commonly associated with Oracle/PeopleSoft administration (psoft, oracle, linuxadm). This technique was observed in ShinyHunters attacks.
Detects the execution of known exploit-related binaries or command lines, including references to specific exploit source files (e.g., 'pedit_cow_demo', 'packet_edit_meme'), CVE identifiers, or specific traffic control (tc) configurations potentially used in exploitation activities.
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