{"description":"Trending threats, MITRE ATT\u0026CK coverage, and detection metadata. Fed continuously.","favicon":"https://feed.craftedsignal.io/favicon-32x32.png","feed_url":"https://feed.craftedsignal.io/tags/attack-chain/feed.json","home_page_url":"https://feed.craftedsignal.io/","icon":"https://feed.craftedsignal.io/apple-touch-icon.png","items":[{"_cs_actors":[],"_cs_cpes":[],"_cs_cves":[],"_cs_exploited":false,"_cs_has_poc":false,"_cs_poc_references":[],"_cs_products":[],"_cs_severities":["critical"],"_cs_tags":["threat-detection","llm","attack-chain"],"_cs_type":"advisory","_cs_vendors":[],"content_html":"\u003cp\u003eThis Elastic Security rule, released in February 2026 and updated in April 2026, is designed to correlate multiple endpoint security alerts from the same host. It utilizes an Elastic Managed LLM (gp-llm-v2), available in version 9.3.0+, to analyze command lines, parent processes, file operations, DNS queries, registry modifications, module loads, and MITRE ATT\u0026amp;CK tactics progression. The LLM provides a verdict (TP/FP/SUSPICIOUS) with a confidence score and a summary explanation. The goal is to help security analysts prioritize hosts exhibiting corroborated malicious behavior while filtering out benign activity, ultimately improving triage efficiency and response effectiveness. The rule requires a minimum Elastic Stack version of 9.3.0.\u003c/p\u003e\n\u003ch2 id=\"attack-chain\"\u003eAttack Chain\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eMultiple endpoint security alerts are generated on a single host due to suspicious activities.\u003c/li\u003e\n\u003cli\u003eThe rule aggregates these alerts based on the host ID and name, collecting relevant data such as process command lines, parent processes, file paths, DNS queries, and registry modifications.\u003c/li\u003e\n\u003cli\u003eThe aggregated data is formatted into a summary string, which serves as context for the LLM.\u003c/li\u003e\n\u003cli\u003eThe LLM analyzes the summary, considering factors like suspicious domains, encoded payloads, download-and-execute patterns, and persistence mechanisms.\u003c/li\u003e\n\u003cli\u003eThe LLM provides a verdict (TP/FP/SUSPICIOUS), a confidence score, and a short summary explaining its reasoning.\u003c/li\u003e\n\u003cli\u003eThe rule filters the results, focusing on findings where the LLM verdict is either \u0026quot;tp\u0026quot; (true positive) or \u0026quot;suspicious\u0026quot; and the confidence score is above 0.7.\u003c/li\u003e\n\u003cli\u003eThe LLM summary is mapped to ECS fields like \u003ccode\u003emessage\u003c/code\u003e and \u003ccode\u003eevent.reason\u003c/code\u003e for timeline visibility.\u003c/li\u003e\n\u003cli\u003eHigh-confidence TP verdicts trigger immediate host isolation to contain potential compromise, followed by IOC extraction and remediation steps.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2 id=\"impact\"\u003eImpact\u003c/h2\u003e\n\u003cp\u003eSuccessful exploitation can lead to delayed incident response and allow attackers to persist in the environment. The LLM-based approach aims to reduce false positives, allowing analysts to focus on genuine threats. Failing to detect and respond to correlated malicious activity can lead to data breaches, system compromise, and operational disruption. The rule helps analysts prioritize investigation of hosts with multiple, related alerts that suggest an ongoing attack campaign.\u003c/p\u003e\n\u003ch2 id=\"recommendation\"\u003eRecommendation\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eEnsure your Elastic Stack is upgraded to version 9.3.0 or later to support the ES|QL COMPLETION command.\u003c/li\u003e\n\u003cli\u003eDeploy the \u0026quot;LLM-Based Attack Chain Triage by Host\u0026quot; rule in your Elastic environment and tune the confidence threshold based on your environment's noise level.\u003c/li\u003e\n\u003cli\u003eReview the investigation guide linked in the rule metadata to understand how to triage and analyze the LLM's verdicts.\u003c/li\u003e\n\u003cli\u003eConfigure an LLM connector to utilize the rule with a different LLM provider, as detailed in the \u003ca href=\"https://www.elastic.co/docs/explore-analyze/ai-features/llm-guides/llm-connectors\"\u003eLLM connector documentation\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eEnable endpoint security alerts with sufficient context to ensure the LLM has enough data for accurate analysis (process command lines, parent processes, file paths, DNS queries, registry modifications, module loads).\u003c/li\u003e\n\u003c/ul\u003e\n","date_modified":"2024-01-03T10:00:00Z","date_published":"2024-01-03T10:00:00Z","id":"https://feed.craftedsignal.io/briefs/2024-01-llm-attack-chain-triage/","summary":"This rule correlates multiple endpoint security alerts from the same host and leverages an LLM to analyze various endpoint activities, determining if they form a coherent attack chain and prioritizing hosts with corroborated malicious behavior.","title":"LLM-Based Attack Chain Triage by Host","url":"https://feed.craftedsignal.io/briefs/2024-01-llm-attack-chain-triage/"},{"_cs_actors":[],"_cs_cpes":[],"_cs_cves":[{"id":"CVE-2023-46604"}],"_cs_exploited":false,"_cs_has_poc":false,"_cs_poc_references":[],"_cs_products":[],"_cs_severities":["medium"],"_cs_tags":["llm","attack-chain","triage"],"_cs_type":"advisory","_cs_vendors":[],"content_html":"\u003cp\u003eThis detection strategy focuses on identifying potential attack chains targeting Large Language Models (LLMs) by correlating multiple alerts originating from a single host. While the provided source material lacks specific details on threat actors or campaigns, the underlying logic aims to proactively identify potentially malicious sequences of events. This is particularly important as LLMs become increasingly integrated into various systems, making them attractive targets for adversaries. Defenders need to focus on attack sequences and identifying unusual combinations of events that may indicate malicious activities, particularly where those activities impact the models themselves.\u003c/p\u003e\n\u003ch2 id=\"attack-chain\"\u003eAttack Chain\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003e\u003cstrong\u003eInitial Access:\u003c/strong\u003e (Hypothetical based on potential attack vectors) An attacker gains initial access to a system with access to LLMs through methods such as exploiting a vulnerability in a web application (e.g., CVE-2023-46604 if it provides remote code execution).\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003ePrivilege Escalation:\u003c/strong\u003e The attacker attempts to escalate privileges using techniques like exploiting kernel vulnerabilities (e.g., using a publicly available exploit for a known privilege escalation vulnerability).\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eDiscovery:\u003c/strong\u003e The attacker performs reconnaissance activities to identify LLM-related resources, such as configuration files, API endpoints, or data stores. This may involve using tools like \u003ccode\u003efind\u003c/code\u003e or \u003ccode\u003egrep\u003c/code\u003e to search for specific file names or patterns.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCredential Access:\u003c/strong\u003e The attacker attempts to obtain credentials that can be used to access LLMs, such as API keys or database credentials. They may use tools like Mimikatz (if on Windows) or attempt to read configuration files containing credentials.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLateral Movement:\u003c/strong\u003e The attacker uses compromised credentials or exploits vulnerabilities to move laterally to other systems within the network that have access to LLMs. This might involve using SSH or RDP to connect to other systems.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eData Exfiltration/Model Poisoning:\u003c/strong\u003e The attacker attempts to exfiltrate sensitive data from the LLM or poison the model by injecting malicious data or code. Data exfiltration might involve using tools like \u003ccode\u003escp\u003c/code\u003e or \u003ccode\u003ersync\u003c/code\u003e to copy data to an external server. Model poisoning might involve manipulating training data or model parameters.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eImpact:\u003c/strong\u003e The attacker achieves their objective, such as stealing sensitive information, disrupting services, or compromising the integrity of the LLM.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2 id=\"impact\"\u003eImpact\u003c/h2\u003e\n\u003cp\u003eSuccessful exploitation of LLMs can lead to significant consequences, including data breaches, service disruptions, and reputational damage. Compromised LLMs can be used to generate disinformation, manipulate financial markets, or even control critical infrastructure systems. The lack of concrete numbers in the source material prevents quantifying victim counts or sectors targeted, but the potential for widespread impact underscores the importance of proactive detection measures.\u003c/p\u003e\n","date_modified":"2024-01-02T12:00:00Z","date_published":"2024-01-02T12:00:00Z","id":"https://feed.craftedsignal.io/briefs/2024-01-02-llm-attack-chain-triage/","summary":"This detection logic correlates multiple alerts on a single host to triage potential attack chains targeting Large Language Models (LLMs).","title":"LLM Attack Chain Triage by Host - Multiple Alert Correlation","url":"https://feed.craftedsignal.io/briefs/2024-01-02-llm-attack-chain-triage/"}],"language":"en","title":"CraftedSignal Threat Feed - Attack-Chain","version":"https://jsonfeed.org/version/1.1"}