Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects a sequence of potentially malicious activity involving the bulk reading or downloading of sensitive AI-related artifacts (model files, datasets), followed by the staging of these files into a compressed archive, and finally an outbound network transfer to an external IP address. This behavior pattern is indicative of data exfiltration of intellectual property.
This rule detects the execution of the Cloudflare 'cloudflared' utility from non-standard directory locations. Cloudflared is often abused by threat actors to establish reverse tunnels (e.g., TryCloudflare) to gain unauthorized, persistent, and encrypted external access to a compromised host, effectively bypassing standard perimeter network security controls.
Detects host-side indicators of potential container escapes and unauthorized access, including mounting sensitive container runtime sockets, executing containers with privileged flags, host namespace sharing, and the use of nsenter to break out of a container context.
Detects command-line execution patterns that exhibit hallmark traits of LLM-generated code, such as overly verbose and explanatory comments, appearing alongside common obfuscation or decoding primitives (e.g., base64, iex, eval). This combination suggests the use of AI to assist in creating decoy logic or burying malicious payloads within legitimate-appearing scripts to evade detection.
Detects a chained sequence of suspicious activity initiated by agentic AI development tools (e.g., Claude Code, Aider, AutoGen). The rule identifies the execution of these tools followed by local reconnaissance commands, lateral movement attempts, and outbound network connections originating from the same host, indicating potential automated exploitation or credential abuse.
Detects network communication to a suspicious staging domain (pdf.gusercontent.com) that mimics legitimate services. This traffic is associated with the 'onInstalled' event handler of a malicious browser extension, often used to signal a successful infection or initiate secondary payload delivery.
Detects instances where the Claude Desktop application spawns a terminal or shell process that immediately executes commands associated with downloading remote payloads or accessing sensitive cryptocurrency wallet artifacts. This behavior is indicative of potential AI prompt injection where an AI assistant is coerced into executing malicious commands without user authorization.
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 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 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 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 artifacts associated with the JWR phishing framework client engine, specifically focusing on the presence of self-referential .toString().search() anti-debugging checks, decoy variable naming conventions, and WebSocket communication components (JWRCID/JWRCVV, ws-worker.js) used for session establishment.
This rule detects multiple suspicious file write operations on a Linux host, specifically targeting directories often used for persistence such as /etc/apt/apt.conf.d, /etc/profile.d, and /etc/cron.d. The rule flags hosts where at least two of these specific persistence techniques are utilized within a 24-hour window, which is indicative of an automated, redundant persistence strategy often employed by cryptocurrency miners like XMRig.
This rule detects suspicious activity associated with Redis server processes attempting to write to SSH 'authorized_keys' files or executing commands (such as BGREWRITEAOF or directory reconfiguration) commonly used by attackers to inject malicious SSH public keys into a Redis installation for persistent remote access.
Detects malicious bash scripts identified as part of the BYOB C2 framework infrastructure. These scripts are designed to download and install second-stage payloads such as Meterpreter or XMRig-style cryptominers. The rule identifies these scripts by their specific naming conventions, embedded C2 infrastructure indicators, and persistence mechanisms including crontab, systemd, and rc.local.
Detects the creation or modification of a file within the /etc/cron.d/ directory, followed shortly by a Bash process invocation using /dev/tcp redirection. This pattern is commonly used by attackers to achieve persistence via cron and establish reverse shells, bypassing potential limitations of default system shells that do not support /dev/tcp syntax.
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.
This rule detects scenarios where an AI agent executes shell commands or system-level tools immediately after receiving a prompt injection attack from an untrusted source. It correlates agent audit logs—specifically identifying tool calls flagged for prompt injection—with actual endpoint process creation events. It flags process execution as 'High' severity if the command line matches common malicious patterns like encoded commands, IEX, or attempts to access credential material (e.g., lsass, mimikatz).
This rule detects known malicious activity including network connections to a specific C2 IP address (69.48.229.140), downloading files from specific C2 URLs, the presence of known malicious file hashes (SHA256), and communications involving specific actor-associated email addresses.
This rule detects a node.exe process initiating a network connection to a specific remote IP (69.48.229.140) on port 8080, followed by the termination of that same process within 45 seconds of the connection. This behavior is indicative of a short-lived beaconing process or an ephemeral network task associated with command-and-control activity.
This rule detects a node.exe process initiating a network connection to a specific remote IP (69.48.229.140) on port 8080, followed by the termination of that same process within 45 seconds of the connection. This behavior is indicative of a short-lived beaconing process or an ephemeral network task associated with command-and-control activity.
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