The LLM-Based Attack Chain Triage by Host rule is designed to analyze multiple endpoint security alerts coming from the same host. It leverages a machine learning model (LLM) to evaluate various indicators such as command lines, parent processes, file operations, DNS queries, registry changes, and the progression of MITRE ATT&CK tactics to identify coherent attack patterns. By generating verdicts (True Positive, False Positive, or Suspicious) along with a confidence score and a summary explanation, it allows security analysts to prioritize their investigations effectively. The rule filters out benign activities and focuses on corroborated malicious behaviors, assisting in accurate threat detection. Analysts are equipped with detailed investigative guidance and potential false positive considerations to improve the analysis accuracy, particularly emphasizing high-confidence alerts requiring immediate attention. Additionally, the setup ensures that necessary LLM integrations are in place for optimal functionality, allowing for sophisticated threat detection across diverse environments.