Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
7,138 detections
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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 a multi-stage attack chain where an AI-agent's configuration file (e.g., mcp.json) is modified, followed immediately by the execution of a tool process (node/python) spawned by the agent, the subsequent spawning of a shell process, and finally an outbound network connection to a rare, non-standard domain. This sequence is indicative of AI agent tool poisoning, where malicious tool definitions are introduced to execute arbitrary commands and exfiltrate data. Coverts T1195.002, T1565.001, T1059
Detects a multi-stage, automated attack chain performed by AI-based development agents (e.g., Claude, Copilot, Cursor). The rule correlates process discovery, credential access, file collection (staging), and exfiltration over web services within a tight 15-minute window, identifying potential autonomous exploitation and data exfiltration. Covered T1046, T1552.001, T1078, T1005, T1567
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 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 potentially malicious command execution patterns in Linux environments, specifically targeting Node.js processes performing in-memory evaluation of code (often associated with fileless loaders) or the downloading of files with the .enc extension using common utilities like curl or wget, which may indicate C2 payload retrieval.
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 the execution of processes named 'nohup' with arguments suggesting renderer masquerading ('--type=renderer') containing suspicious terms like 'SystemUpdate' or 'iSync', originating from Cursor IDE helper processes or the terminal shell. This behavior is indicative of a backdoor or malicious process attempting to hide in plain sight by masquerading as legitimate IDE rendering components.
Detects the execution of 'setup.js' by the node.js runtime when initiated by 'npm install' or 'postinstall' hooks. This behavior is a common indicator of a malicious supply chain attack where a compromised or malicious npm package executes unauthorized code during the package installation or setup phase.
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 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 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 the presence of specific Go build path metadata associated with the sckit Go implant, including modules and internal package structures, within an executable or file.
Detects malicious BASH_ENV injection within GitHub Actions CI pipelines originating from the compromised sckit_poetry_build.py build backend. The injection, which targets GITHUB_ENV to introduce a malicious _pypi_bridge.sh script, is used to intercept PyPI publishing tokens during build processes, characteristic of the MemTensor/sckit supply chain attack.
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