Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects HTML files that employ FlipBook branding as a lure for password-gated smuggling. The detection identifies client-side JavaScript logic that uses PBKDF2 and AES-GCM to decrypt a hidden redirect URL after user interaction with a password prompt.
Detects instances where the Redis server process or the Redis AOF check utility writes directly to common SSH authorized_keys file paths. This behavior is indicative of an attacker exploiting a Redis misconfiguration or vulnerability (e.g., Redis RCE) to establish persistence by injecting an unauthorized SSH key.
Detects AI coding assistants and LLM agent frameworks attempting to access, read, or export sensitive credential files, registry hives, or application secrets. The rule monitors both file system operations on known secret locations and process execution patterns indicative of credential dumping or API token retrieval by these tools.
Detects anomalous behavioral patterns where an AI agent process (e.g., Claude Code, Cursor, Aider) performs multi-category discovery of its own runtime environment, plugins, and host configuration. The rule identifies agents that trigger processes spanning at least three distinct discovery categories (Network, File/Plugin, Software, or System) within a short timeframe, indicating potential reconnaissance of agent-authorized capabilities and tools rather than standard host exploration. Covers T1082, T1518, T1083, T1016
Detects rapid, multi-category system and network enumeration performed by AI-assisted development tools (e.g., Cursor, Claude, ChatGPT). The rule triggers when an AI agent process performs a comprehensive scan of the host environment, specifically covering machine/OS identity, network configuration, process listings, installed software, and access to sensitive credential files within a 10-minute window. It requires at least 5 distinct discovery categories and 6 total events to reduce noise from routine debugging. Covers T1082, T1016, T1057, T1518
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 attempts to modify the Redis configuration (dir or dbfilename) followed by a SAVE command, which can be leveraged to write arbitrary data to critical system files such as crontabs for persistence.
Detects high-frequency outbound network connections to known large language model (LLM) API providers (OpenAI, xAI, Anthropic) originating from a single endpoint within a short timeframe. This activity is indicative of potential API key theft, where an attacker uses a compromised legitimate key to exhaust credit quotas or exfiltrate data via unauthorized API calls, bypassing the intended application.
Detects high-frequency outbound network connections to known large language model (LLM) API providers (OpenAI, xAI, Anthropic) originating from a single endpoint within a short timeframe. This activity is indicative of potential API key theft, where an attacker uses a compromised legitimate key to exhaust credit quotas or exfiltrate data via unauthorized API calls, bypassing the intended application.
Detects unauthorized changes to network appliance configurations, specifically the installation of new software packages using opkg or the modification of network address translation (NAT) rules via iptables or nftables. These actions can be used to maintain persistent access or establish covert command and control infrastructure.
This rule detects potential supply chain compromise by monitoring common Linux package managers (pip, npm, apt, gem) for installation commands targeting non-standard, unauthorized registries, and for the subsequent execution of shells or network utilities by these package manager processes.
Detects the creation of a 'manifest.json' file within directory paths containing 'extensions' by a process other than standard web browsers. This behavior is indicative of unauthorized manual installation or modification of browser extensions, which may be used for persistence or to facilitate credential/session theft.
Detects instances where a process attempts to delete its own executable file image shortly after being launched. This behavior is often associated with malware attempting to minimize its forensic footprint, such as self-deleting installers or RAT (Remote Access Trojan) components that move to a different location or execute from memory.
Detects a multi-stage loader execution chain, potentially named GHAPPIER, by identifying sequential stages of execution. These stages include initial marker file checks, shell-based command dispatching via curl/wget, renaming and execution of secondary payloads, Node.js-based runtime staging, and the eventual creation of implant artifacts in common paths such as VSCode extensions or hidden directories.
This rule detects unauthorized access, creation, or modification of Git credential files (.git-credentials, .netrc) by processes other than standard Git credential management utilities. It also monitors command-line activity that references these credential stores to identify potential attempts to exfiltrate or manipulate stored credentials.
This rule detects artifacts and behaviors associated with the WaterPlum/Contagious Interview (also known as DeceptiveDevelopment) malware suite, including BeaverTail, InvisibleFerret, and related payloads. The rule specifically targets the presence of embedded family-name strings, as well as the execution of suspicious npm package installations triggered by VS Code task automation (tasks.json) or trust prompts, common in fake technical interview attack scenarios.
This rule monitors for the installation of specific known-malicious or suspicious NPM packages that are commonly used in supply chain attacks. It identifies the execution of npm (or node) commands to install or add packages explicitly listed as malicious by security research, which may indicate a supply chain compromise or an attempt to introduce unauthorized code into the development environment.
Detects the execution of cloud CLI tools (aws, az, gcloud) using Unix shells (bash, sh) to perform sensitive operations such as listing secrets, keys, or metadata, which may indicate credential discovery or exfiltration.
Detects anomalous file access patterns by Semantic Kernel agent processes attempting to read sensitive host files such as SSH keys, configuration files, and credential stores. This activity is indicative of malicious use of agent file-transfer capabilities, potentially exploiting path traversal vulnerabilities or prompt injection to exfiltrate sensitive data from the host environment into the agent sandbox.
Detects anomalous process execution originating from a Python-based sandbox (such as Semantic Kernel SessionsPythonPlugin in Azure Container Apps) that attempts to break out of the container isolation. The rule monitors for command-line arguments involving interaction with the Docker socket, sensitive host namespaces, or container privilege escalation techniques, indicative of exploitation attempts related to CVE-2026-25592.
Detects common runtime processes (Node.js, npx, Python) often used for Model Context Protocol (MCP) servers accessing sensitive local credential files such as AWS credentials, SSH private keys, or environment files. This behavior is indicative of potential credential harvesting by unauthorized MCP servers in developer environments.
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