Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects AI coding assistants and LLM agent frameworks spawning shell processes or interpreters to execute potentially malicious commands. The rule monitors for suspicious activity often associated with download cradles, credential access, reconnaissance, or persistence, while excluding standard interactive terminal usage.
Detects the use of common command-line tools (curl, wget, python, etc.) to interact with known cloud provider metadata service IP addresses or URL paths, often performed by shell processes. This behavior is frequently associated with initial reconnaissance or attempts to steal identity tokens from cloud-based virtual machines.
This rule detects potential unauthorized access to sensitive information by correlating the execution of command-line tools (sed, bash, sh) with secret-related flags or file references, followed shortly by execution of cloud CLI commands that enumerate or retrieve secrets. This behavior is indicative of an attacker attempting to parse or extract sensitive data stored in local configuration files or memory, and then fetching specific secret values from a cloud secret manager.
Detects incoming emails processed by Rspamd that exhibit suspicious authentication header signatures. The rule identifies emails lacking DKIM, ARC, or user agent information while simultaneously showing a zero receive count, potentially indicating spoofed, unauthenticated, or malformed phishing attempts.
This rule monitors network traffic, DNS queries, and user web clicks to detect interactions with known malicious domains associated with credential harvesting and phishing campaigns, specifically those impersonating security or SSO portals.
Detects usage of the Android Debug Bridge (ADB) 'pair' command initiated by processes other than known legitimate Android development tools (e.g., adb.exe, Android Studio). This may indicate an attacker attempting to wirelessly pair a malicious device or gain unauthorized access to an Android device over the network.
Detects the initialization of known-malicious Terraform providers (dockerd) followed within one hour by the execution of a 'go run' process from the .terraform directory. This pattern is indicative of a supply chain compromise where an adversary leverages a typosquatted or malicious Terraform provider to facilitate the execution of a secondary payload.
Detects the initialization of known-malicious Terraform providers (dockerd) followed within one hour by the execution of a 'go run' process from the .terraform directory. This pattern is indicative of a supply chain compromise where an adversary leverages a typosquatted or malicious Terraform provider to facilitate the execution of a secondary payload.
Detects network beaconing activity associated with the TuxBot v3 IoT botnet. The rule identifies specific User-Agent strings (TuxBot/3, BusyBox wget, Wget/1.x), requests for .elf files, and connections to suspicious top-level domains frequently used for botnet command-and-control (C2) infrastructure.
Detects endpoint DNS resolution for the 'mysignins.microsoft.com' domain. This domain is frequently used in Adversary-in-the-Middle (AiTM) phishing attacks to facilitate user interaction with legitimate Microsoft portals while the attacker intercepts session tokens.
Detects a suspicious runtime pattern in GitHub Actions where a runner environment installs the Bun runtime and immediately executes an action's index.js file. This pattern is characteristic of supply chain attacks where a compromised GitHub Action uses mutable-tag pinning to smuggle and execute an obfuscated JavaScript payload before legitimate action logic.
This rule detects potentially unauthorized or suspicious repository interactions by monitoring 'git' commands executed by common AI-assisted coding tools or command-line developer utilities. It flags occurrences where a checkout operation occurs without accompanying standard git history or status checks, which may indicate automated exfiltration or lateral movement attempt within a developer environment.
This rule detects potentially suspicious Git checkout operations initiated by AI coding assistant processes (such as Copilot, Claude, or Gemini). It looks for cases where these assistants execute a 'git checkout' command containing a specific commit hash (40 characters), followed by a secondary event where the git output indicates an 'ambiguous refname' error. This pattern may suggest an attempt by an AI tool to manipulate repository state or checkout arbitrary objects, potentially related to unauthorized code injection or malicious repository interaction.
Detects bulk file creation of XML files associated with Dahua credential exports, likely generated by a Python script, which is indicative of credential harvesting or staging for exfiltration.
Detects the execution of DahuaConsole processes that are spawned by or associated with the scannerdahua utility. This pattern is often associated with discovery or exploitation activities targeting Dahua devices.
This rule detects suspicious activity related to specific hardcoded indicators 'p2pwn', 'p2password', and references to 'cve-2024-39943'. It scans authentication logs (Identity) and EDR logs (process command lines) for these terms, flagging potential unauthorized access, credential use, or exploitation attempts associated with this specific threat activity.
This rule detects the creation or pushing of a local Git branch where the branch name matches the pattern of a 40-character SHA-1 or 64-character SHA-256 hash. This behavior is indicative of the Plugin4Shell exploitation technique, where attackers create branches named after specific commit SHAs to bypass security checks on self-hosted Git services like Bitbucket by pinning them to unintended commits.
Detects a suspected malicious activity pattern known as the Veltrix Capital fake job-lure. The rule monitors for a sequence where a user clones a job-task repository (containing 'veltrix-capital' in the command) followed by the installation of a suspicious dependency (using names like 'graph*' or 'big*' with npm or pip) within a 30-minute window on the same device.
Detects execution of the Go runtime invoking a local module ('go run .') initiated by infrastructure-as-code tools like Terraform or standard command-line interpreters. This pattern matches the behavior of the Graphalgo RAT, which uses a hash-gated mechanism within malicious modules to trigger a second-stage payload.
Detects the installation of known malicious npm or PyPI packages followed within 30 minutes by the execution of a package manager (node or python) performing network activity. This pattern matches the behavior of downloader stages in the 'Graphalgo' campaign, which uses coding-task lures to trick users into installing malicious packages.
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.
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