Arnold Chan
@slazTrusted contributorCompletionist
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7,555 detections
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Detects high-volume POST requests containing HEIF, HEIC, or AVIF image content types, indicative of automated fuzzing or exploitation attempts against an image-processing endpoint targeting CVE-2026-32882.
This rule detects potential exploitation attempts targeting vulnerabilities within the libheif image processing library. It monitors for image processing binaries (e.g., convert, magick) executing with command-line arguments related to HEIF/HEIC files, followed by multiple application crash events (SIGSEGV, coredumps) associated with libheif on the same device within a short time window.
Detects potential local privilege escalation via Windows ALPC (Advanced Local Procedure Call) heap-based buffer overflow by identifying a non-SYSTEM Chrome process tree spawning child processes with SYSTEM privileges. The rule specifically monitors for suspicious child binaries or paths characteristic of exploit payloads while excluding legitimate Chrome update and crash handler processes.
Detects rapid, machine-speed, sequential attack-path adaptation by suspected AI agent binaries. The rule monitors for a correlation of four stages occurring within a tight 20-minute window: 1) Failed service connections across three or more distinct protocols (SMB, SSH, HTTP, RDP, WinRM), 2) Credential access attempts (Logon events or execution of known credential-dumping tools), and 3) A lateral movement pivot to a new, previously un-targeted internal destination. The rule specifically keys on the InitiatingProcessId and InitiatingProcessCreationTime to ensure the sequence is attributed to a single process instance, distinguishing it from human manual penetration testing activity. Covers T1190, T1210, T1078
This rule detects command-line activity where a remote resource is downloaded via 'curl', decoded using 'base64', and then piped directly into a shell interpreter (sh, bash, or zsh). This pattern is commonly used in fileless execution or stage-one payload delivery by attackers to execute malicious scripts directly from memory.
This rule detects command-line activity where a remote resource is downloaded via 'curl', decoded using 'base64', and then piped directly into a shell interpreter (sh, bash, or zsh). This pattern is commonly used in fileless execution or stage-one payload delivery by attackers to execute malicious scripts directly from memory.
Detects an anomalous burst of git clone, fetch, or download events performed by a single GitHub user account against a large number of private repositories within a short timeframe (15-minute window). This pattern often indicates unauthorized bulk collection or exfiltration of sensitive source code by an actor using a valid account.
Detects GitHub OAuth authorization, repository cloning, or ZIP downloads from source IP addresses that have not been observed for the specific actor within the past 30 days. This rule helps identify potential account compromise or token theft where an attacker attempts to access repository data from a new location, while specifically excluding bot/service accounts known for rotating CI infrastructure IPs.
Hunts network, HTTP, and email-URL telemetry for known ARToken infrastructure (operator backend IPs and phishing/panel domains). Bounded to a 30-day lookback and tiers matches by confidence: domain hits are high-confidence (attacker-registered infrastructure), while IP hits are medium-confidence since the IPs sit on shared DigitalOcean hosting that can be reassigned to unrelated tenants once ARToken's infrastructure is taken down. Empty/unparsed indicator fields are excluded to avoid spurious matches in the HTTP event parsing branch. No known file hashes are associated with this intel.
Detects file, process, and network activity associated with the TraderTraitor/KelpDAO malware campaign based on known hashes, filenames, paths, and C2 infrastructure.
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