Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
707 detections
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Detects anomalous activity from IDEs or AI coding assistant extension host processes (e.g., VS Code, Cursor, JetBrains). The rule alerts when these processes spawn children that access sensitive local credential files, perform network connections to non-standard/unexpected domains, or modify critical CI/CD build script configuration files, which is indicative of a supply chain compromise within the developer environment.
Detects activity associated with the CARBONATO botnet utilizing the GH0ST RAT agent to locate, stage, and exfiltrate sensitive AI API keys. The detection monitors for file write operations within specific hidden directories ('/root/.hermes/loot/') and the use of malicious command-line patterns or process context associated with Hermes Agent or GH0ST malware to search for configuration files (e.g., .env) containing sensitive API credentials.
Detects activity associated with the CARBONATO botnet utilizing the GH0ST RAT agent to locate, stage, and exfiltrate sensitive AI API keys. The detection monitors for file write operations within specific hidden directories ('/root/.hermes/loot/') and the use of malicious command-line patterns or process context associated with Hermes Agent or GH0ST malware to search for configuration files (e.g., .env) containing sensitive API credentials.
Detects activity associated with the CARBONATO botnet utilizing the GH0ST RAT agent to locate, stage, and exfiltrate sensitive AI API keys. The detection monitors for file write operations within specific hidden directories ('/root/.hermes/loot/') and the use of malicious command-line patterns or process context associated with Hermes Agent or GH0ST malware to search for configuration files (e.g., .env) containing sensitive API credentials.
Detects activity associated with the CARBONATO botnet utilizing the GH0ST RAT agent to locate, stage, and exfiltrate sensitive AI API keys. The detection monitors for file write operations within specific hidden directories ('/root/.hermes/loot/') and the use of malicious command-line patterns or process context associated with Hermes Agent or GH0ST malware to search for configuration files (e.g., .env) containing sensitive API credentials.
Detects activity associated with the CARBONATO botnet utilizing the GH0ST RAT agent to locate, stage, and exfiltrate sensitive AI API keys. The detection monitors for file write operations within specific hidden directories ('/root/.hermes/loot/') and the use of malicious command-line patterns or process context associated with Hermes Agent or GH0ST malware to search for configuration files (e.g., .env) containing sensitive API credentials.
Detects anomalous outbound network connections initiated by security and developer tooling (Trivy, Checkmarx, LiteLLM, Telnyx). This behavior is consistent with supply-chain attacks, such as those attributed to the UNC6780/TeamPCP threat group, where compromised tooling is leveraged to exfiltrate sensitive cloud credentials or API keys to external, non-vendor infrastructure.
Detects anomalous process activity that reads sensitive developer secrets (such as Anthropic, OpenAI, Gemini, or Hugging Face API keys) from common configuration or environment files, followed by an outbound network connection to a non-private IP address. This behavior is consistent with credential harvesting and exfiltration tactics used in supply chain attacks to hijack AI services.
Detects instances of common interpreter processes, specifically node.exe or python variants, accessing known cryptocurrency wallet files or mnemonic storage files on a Windows system. This behavior is highly indicative of malicious activity where an attacker uses lightweight script-based tools to perform credential harvesting or theft of cryptocurrency assets.
Detects the use of legitimate data transfer utilities (s5cmd, rclone) configured to exfiltrate sensitive document types (.doc, .pdf, .sql, etc.) to S3-compatible cloud storage. The rule correlates this activity with recent access to files containing sensitive credentials or configuration data, a pattern observed in double-extortion campaigns.
Detects potential path traversal or unauthorized access attempts by Semantic Kernel Agents (or related processes like Python/DotNet) to sensitive host files. The rule monitors for file read, creation, or modification activities targeting sensitive credentials, configuration files (e.g., .ssh, .aws, .kube), and system files, which could indicate a sandbox escape or malicious data collection.
Detects anomalous, mass-access behavior targeting plaintext credential files, certificate stores, private keys, or password-manager databases located on general-purpose network shares. Such activity is often indicative of an adversary conducting discovery or collection of credentials for lateral movement and privilege escalation.
Detects suspicious processes accessing cryptocurrency wallet data files or browser extension settings, characteristic of infostealer malware such as AMOS or GhostCode. The rule excludes common browser processes and legitimate wallet applications to identify unauthorized attempts to harvest wallet credentials, private keys, or seed phrases.
Detects anomalous activity in a CI/CD environment where a runner process accesses sensitive credential files (such as PyPI tokens or GitHub secrets) followed closely by a network connection to public package registry domains, potentially indicating a supply-chain compromise where stolen credentials are used to publish malicious packages.
Detects the transmission of sensitive credential-like patterns (such as API keys, private keys, and connection strings) to known external AI coding assistant and IDE plugin endpoints. This rule monitors cloud application events originating from developer workstations or CI/CD runners where such sensitive information may be inadvertently or maliciously sent to third-party AI services.
Detects unauthorized access to critical Chromium-based browser credential databases (such as Login Data, Cookies, and History) by processes other than recognized browser executables. This behavior is indicative of credential-harvesting activity, specifically matching techniques employed by infostealers like RevStealer.
This rule detects processes accessing sensitive credential, configuration, or environment files typically stored in locations like .aws, .ssh, .kube, or application user directories. It alerts when a non-standard or unexpected process touches multiple sensitive folders, or when specific suspicious process names or known malicious tools (such as 'NeedleStealer') are observed interacting with these files. This is indicative of potential credential harvesting or reconnaissance activities.
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.
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 file operations (open, read, write, rename) involving common sensitive credential files (e.g., .kdbx password databases, .ovpn and .pcf configuration files, .pem, and .ssh directory contents) by processes not authorized to interact with these files.
Detects automated searching (via grep, findstr, or select-string) for sensitive strings (e.g., API keys, private keys, wallet data) across multiple occurrences on a single host, or the creation of suspicious sensitive files (e.g., .env, credentials, wallet.dat) on devices where such automated sweeping activity has been observed. This pattern indicates an adversary staging data for exfiltration.
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