Arnold Chan
@slazTrusted contributorCompletionist
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7,555 detections
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Detects instances where AI coding assistants or tools (e.g., Claude, Copilot, Gemini) invoke git.exe to clone or fetch repositories from non-standard (non-GitHub) hosts, followed by a checkout operation within a short timeframe. This behavior may indicate an attacker using automated tools to stage or exfiltrate sensitive code repositories to unauthorized infrastructure.
Detects a multi-stage attack pattern against publicly exposed AI service endpoints. The detection correlates: (1) Enumeration of sensitive AI/MCP-related API endpoints (minimum 3 distinct hits) in a 10-minute window; (2) Subsequent POST requests to AI service endpoints within 15 minutes of the enumeration; and (3) A spike of 5 or more server errors (HTTP 5xx) within 10 minutes following the POST activity. This pattern suggests automated reconnaissance followed by attempted exploitation of exposed AI model interfaces or orchestration services. Covers T1595.002, T1190
Detects potential multimodal prompt injection attacks where an AI agent process ingests a file (PDF, image, etc.) from a browser or mail client and subsequently performs suspicious downstream activity, such as spawning a shell with execution primitives or making network connections to rare, non-reputable external domains. Covers T1204, T1059, T1105
This rule detects potentially malicious child processes spawned by Visual Studio Code (Code.exe) when using VS Code tasks (tasks.json). It identifies suspicious CLI arguments often associated with downloading, executing, or obfuscating scripts (e.g., PowerShell, curl, python, mshta) that are not part of standard development workflows like npm, yarn, or git, which are explicitly filtered out as noise.
This rule detects internal users submitting potentially sensitive data to public generative AI applications (e.g., ChatGPT, Claude, Gemini) where Microsoft Purview DLP or a comparable inspection service has triggered a policy match. It correlates cloud application outbound signals with content inspection logs to identify high-risk exfiltration events while excluding sanctioned enterprise-licensed AI instances.
This rule detects internal users submitting potentially sensitive data to public generative AI applications (e.g., ChatGPT, Claude, Gemini) where Microsoft Purview DLP or a comparable inspection service has triggered a policy match. It correlates cloud application outbound signals with content inspection logs to identify high-risk exfiltration events while excluding sanctioned enterprise-licensed AI instances.
Detects reconnaissance behavior (System Information, Account, and Network Discovery) performed by processes associated with AI and LLM coding assistants. The rule monitors for a chain of suspicious activities, such as executing discovery commands or excessive network scanning within a 10-minute window, identifying potential misuse of developer-oriented agent tools for post-exploitation reconnaissance.
Detects reconnaissance behavior (System Information, Account, and Network Discovery) performed by processes associated with AI and LLM coding assistants. The rule monitors for a chain of suspicious activities, such as executing discovery commands or excessive network scanning within a 10-minute window, identifying potential misuse of developer-oriented agent tools for post-exploitation reconnaissance.
Detects network connections to known SideCopy/ReverseRAT command-and-control infrastructure, including a static C2 IP address and two C2/hosting domains (matched exactly and as proper subdomains to avoid substring false positives).
This rule monitors for indicators of compromise (IOCs) associated with the threat actor SideCopy and their associated malware, such as ReverseRAT. It hunts for specific malicious SHA256 file/process hashes, connections to known malicious C2 infrastructure (IPs and domains), requests to known malicious URLs, and user interaction with these URLs via email clicks.
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