Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects a classic malicious model attack chain where a Python process reads a .pkl, .pt, or .bin model file, and immediately spawns an anomalous system shell or execution utility. This indicates potential Remote Code Execution (RCE) via deserialization vulnerabilities in machine learning models.
Correlates the execution of the initial dropper script (setup.cjs) to the hardcoded C2 IPs, indicating potential compromise by the Mastra NPM malware.
Correlates a non-browser headless runtime (Node/Bun) querying the GitHub API for the specific commit 'OhNoWhatsGoingOnWithGitHub'. This could indicate a supply chain attack where malicious code within a Node.js or Bun.js application attempts to communicate with a GitHub dead drop for command and control or data exfiltration.
This rule identifies potential ransomware activity by detecting a process performing a high volume of file rename or modification operations within a short time window. It specifically targets files with known ransomware extensions or those using randomized 6-8 character alphanumeric extensions, while excluding common administrative, backup, and temporary file operations performed by trusted software.
This rule identifies the execution of MeshCentral for remote command execution, specifically looking for command-line arguments containing 'meshctrl.js' and 'RunCommand'. This activity has been observed in attacks such as those by ShinyHunters to stage payloads on compromised Oracle PeopleSoft servers.
Detects the creation of common ransomware note file names with suspicious extensions.
Detects the explicit execution of the 'gh-token-monitor.sh' script, which is indicative of active credential harvesting on the endpoint. This script is designed to monitor and potentially exfiltrate GitHub tokens.
Detects the use of the 'watch' command in conjunction with 'cat' to continuously poll the GitHub CLI config file (.config/gh/hosts.yml). This activity could indicate an attacker attempting to capture new GitHub tokens as soon as a developer logs in.
Detects outbound network connections to 'filev2.getsession.org', a known Miasma payload hosting and exfiltration domain. This domain has been associated with Miasma malware for command and control and data exfiltration activities.
Detects Node.js initiating network connections over port 2222. This activity is associated with the Miasma botnet, which uses port 2222 as a fallback C2/SSH channel when standard ports are blocked.
Detects command-line parameters indicating RPC calls to Ethereum/Polygon for C2 resolution, specifically looking for 'eth_call' and known blockchain service providers like 'drpc.org', 'tenderly', 'flashbots', or 'blastapi'. This activity is indicative of the EtherHiding malware.
This rule detects the execution of 'system_profiler' with command-line arguments 'SPMemoryDataType' or 'SPHardwareDataType'. This behavior is indicative of malware, specifically AMOS Stealer, attempting to enumerate system hardware and memory details to detect virtualized environments (e.g., QEMU, VMware) as an anti-analysis technique.
Description: Detects the Stage 2 C2 tasking loop from the malicious Next.js developer-targeting campaign.Stage 2 spawns a child Node.js process and executes attacker-supplied JavaScript via STDIN ('-') or eval ('-e'), sourced from a parent Node process that previously beaconed to Vercel staging infrastructure. Trojanized assets involved include jquery.min.js and env-setup.js loaded from
known malicious repo structures.
known malicious repo structures.
This rule detects network connections from devices to a list of known malicious domains. These domains are associated with various threats including phishing, C2 beacons, data exfiltration, ransomware, cryptominers, and credential harvesting. The rule aggregates connections by remote URL and time, flagging any domain with at least one connection.
Detects Python enumerating browser extensions and desktop wallet paths associated with cryptocurrency key material. This rule specifically looks for Python processes executing with command-line arguments containing keywords related to cryptocurrency wallets (e.g., 'wallet', 'keystore', 'metamask').
Detects Python recursively searching repositories for .env files, package credentials, API keys, access tokens, or connection strings. This activity is associated with the InvisibleFerret malware.
Detects Python creating an archive containing browser, wallet, developer, cloud, and authentication data from multiple collection roots. This activity is associated with the InvisibleFerret malware.
Detects a small Go-based scanner, identified by its renamed executable 'go.exe', when launched with command-line arguments indicative of broad network scanning activities such as CIDR range specifications (/16, /24), service discovery, or exploit module usage. This behavior is often associated with initial access or reconnaissance phases by adversaries.
Detects the execution of base64 decoded strings directly outputted from scripting processes, which is a common technique for obfuscation and payload delivery.
This rule detects processes generating network connections to multiple known malicious ports (4444, 7211, 8000, 8001, 8002, 10000) within a 30-minute window. This behavior is indicative of command and control (C2) beaconing, where a compromised system periodically communicates with a C2 server. The rule is designed to identify a pattern of C2 activity that might be used across different threat campaigns, leveraging ports commonly associated with various C2 frameworks or custom C2 implementations.
Detects curl commands transmitting harvested GitHub tokens (ghp_ prefix) in the request body, representing the final exfiltration stage of gh-token-monitor.
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