Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Matches archived SHA-256 hashes of known malicious CRX builds from the 'VPN for X' proxy farm extension family
Detects the presence of a specific, hardcoded Ed25519 public key associated with malicious cross-platform payloads (npm, Go modules, Terraform providers) that use Slack for C2 communication.
Detects the execution of the Graphalgo RAT second-stage payload. The rule identifies the spawning of a detached 'go run .' process, which performs an ephemeral key exchange using a hardcoded public key before C2 establishment. It also correlates this activity with parent processes common in software build environments (npm, terraform, go) to differentiate malicious staging from developer activity.
Detects an account takeover chain where a device loads a malicious JavaScript component ('load-addon.js') in a browser session, followed by repeated attempts to bypass bot detection mechanisms (targeting Google's 'errors/robot.png') as the payload attempts to force authentication challenges or password resets.
Detects network communication to specific Gusercontent endpoints consistent with malicious browser extensions exfiltrating captured session tokens or cookies. The rule monitors for POST requests containing parameters like leadId, email, and data, often used to transmit stolen authentication material.
Detects execution of package management commands (pip, npm, conda) that attempt to pull packages from registries other than the approved internal repositories. This behavior is indicative of potential AI supply-chain compromise, including malicious packages or namesquatting targeted at machine-learning or LLM-related development workflows.
Detects instances where machine learning runtime processes (e.g., Python, torch-model-loader) load serialized model files (e.g., .pkl, .joblib, .pth) and subsequently spawn suspicious child processes like command shells, scripting engines, or download utilities. This behavior is indicative of arbitrary code execution via unsafe deserialization of a malicious model artifact.
Detects high-volume outbound transfers of sensitive machine learning artifacts, such as model files, training datasets, and model registries, to unauthorized external destinations like personal email or unmanaged cloud storage. This behavior is indicative of potential intellectual property theft.
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.
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 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 instances where a process not associated with standard web browsers accesses, copies, or modifies sensitive browser files such as cookies, local storage, or session tokens. This behavior is highly indicative of information-stealer malware (e.g., Vidar, Lumma, RedLine) attempting to exfiltrate session data for account hijacking.
Detects network communication to specific Gusercontent endpoints consistent with malicious browser extensions exfiltrating captured session tokens or cookies. The rule monitors for POST requests containing parameters like leadId, email, and data, often used to transmit stolen authentication material.
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 the on-disk presence of a malicious Firefox browser extension, 'pdf-para-texto@extensao.local', used for credential or data harvesting. The rule monitors for the creation of its manifest file or XPI package in specific directories, indicating the staging or installation of the malicious extension.
Detects the presence of the i-Realm companion tool associated with the AnonyMousKIT PhaaS platform, which is designed to bypass iOS Activation Lock features.
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
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