Arnold Chan
@slazTrusted contributorCompletionist
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7,544 detections
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Detects high-rate scanning and automated service probing patterns consistent with network reconnaissance. This includes tracking TCP SYN floods to external segments, excessive HTTP/login requests, and automated fingerprinting probes (e.g., favicon or admin panel enumeration). The rule accounts for common uptime monitoring services to minimize false positives.
Detects text/report artifacts (markdown, JSON, plain text) produced by an autonomous AI-driven vulnerability research pipeline that decompiles binaries, traces cross-references, hypothesizes memory-safety flaws, and generates/debugs proof-of-concept exploits
Detects anomalous activity patterns consistent with automated firmware reverse engineering pipelines, specifically correlating the high-frequency execution of firmware analysis tools (e.g., binwalk, Ghidra) with the creation of vulnerability research knowledge base artifacts. This behavioral heuristic aims to identify potential AI-driven or automated zero-day discovery workflows by tracking tool usage density and output characteristics.
This rule detects potential automated web scraping activity by identifying high-volume, repetitive network requests directed towards domains identified as government (.gov) or military (.mil). It correlates these network patterns with the execution of common browser automation frameworks (e.g., Puppeteer, Playwright) or headless browsers, indicating a likely coordinated scraping operation or bot activity.
Detects the initialization of containerized LLM-based gateway services (e.g., litellm, ollama) followed closely in time by high-volume network traffic containing suspicious web injection payloads (e.g., SQLi, XSS, SSRF). The rule correlates process execution events for container launches with network activity originating from the same device to identify potential unauthorized or unsupervised automated exploitation.
Detects the initialization of containerized LLM-based gateway services (e.g., litellm, ollama) followed closely in time by high-volume network traffic containing suspicious web injection payloads (e.g., SQLi, XSS, SSRF). The rule correlates process execution events for container launches with network activity originating from the same device to identify potential unauthorized or unsupervised automated exploitation.
Detects high-velocity, automated web activity originating from a single source IP that simultaneously performs widespread endpoint enumeration, tests sensitive authentication flows, and attempts common injection attack payloads. This behavior is indicative of an automated scanner, crawler, or malicious botnet agent performing rapid discovery and exploitation attempts.
Detects high-velocity, automated web activity originating from a single source IP that simultaneously performs widespread endpoint enumeration, tests sensitive authentication flows, and attempts common injection attack payloads. This behavior is indicative of an automated scanner, crawler, or malicious botnet agent performing rapid discovery and exploitation attempts.
Detects high-velocity, automated web activity originating from a single source IP that simultaneously performs widespread endpoint enumeration, tests sensitive authentication flows, and attempts common injection attack payloads. This behavior is indicative of an automated scanner, crawler, or malicious botnet agent performing rapid discovery and exploitation attempts.
This rule detects potential command and control (C2) activity associated with the CurlRAT malware. It monitors for both the execution of 'curl' or 'curl.exe' processes with command lines containing known C2 domains, and network traffic originating from internal devices directed toward those same domains.
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