Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
707 detections
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Detects anomalous, high-volume automated API querying or bulk data exports within SaaS environments that target keywords associated with sensitive information, such as passwords, tokens, or configuration secrets. This pattern is characteristic of automated reconnaissance and unauthorized data harvesting, often observed in campaigns like those attributed to UNC6395.
This rule detects repeated access to the RubyGems legacy API key endpoint within a short timeframe from a single source. This behavior is indicative of automated credential harvesting, where an adversary attempts to brute force or cycle through various API keys to gain unauthorized access to RubyGems accounts.
This rule monitors for two related scenarios targeting the RubyGems legacy API-key endpoint. First, it identifies suspicious responses indicating an API key might have been served from a CDN cache, potentially leading to cross-user credential leaks. Second, it flags a high volume of requests to the same endpoint using gzip compression, which is a known technique associated with edge-cache poisoning attacks.
This rule detects unauthorized processes attempting to read sensitive configuration files (secrets.json, config.yaml) associated with AI coding assistants like Cline and Continue. By filtering out known, legitimate IDE and development processes, the rule highlights potential file-grabber activity indicative of an infostealer attempting to exfiltrate plaintext LLM API keys.
Detects process execution patterns where a process, such as an AI agent runtime, accesses sensitive local credential stores, configuration files (.aws, .kube, .ssh, .env), or executes commands to dump environment variables containing sensitive keywords like API_KEY or SECRET. This behavior is indicative of credential harvesting by an agent compromised or misconfigured to exfiltrate host-level secrets.
Detects instances where an AI agent's output contains sensitive credential patterns such as AWS access keys, JWTs, private keys, or Bearer tokens. This rule monitors user communication, generic events, and file creation events to identify potential security compromises or unintended data leakage from the AI model.
Detects anomalous AWS IAM and Secrets Manager API activity indicative of credential abuse following suspected exposure. The rule identifies suspicious patterns such as unauthorized enumeration of access keys, secret retrieval, and creation of new keys by non-service principals, reflecting activity associated with ShinyHunters-style operations where compromised credentials are abused.
Detects an suspicious progression of activities originating from identified AI assistant/agent processes. The rule monitors for a chain of behaviors occurring within a 15-minute window: execution of an AI agent/tool, followed by host discovery (network/system enumeration), access to sensitive local credential files, archival of data, and outbound network communication to common file-sharing/exfiltration platforms.
Detects Node.js or NPM process executions that reference Twilio-specific credential variables (ACCOUNT_SID, AUTH_TOKEN) within the command line, likely indicating an attempt to harvest these secrets from a developer environment.
Detects the Sckit supply chain implant reading sensitive credential files from a user's home directory. The rule monitors processes spawned by Node.js or Python interpreters (the primary loaders for the Sckit worm) attempting to access high-value files like SSH keys, .npmrc, .pypirc, and various token storage files, indicating potential credential harvesting behavior.
Detects the creation of files containing AI provider API keys within the /root/.hermes/loot/ directory, consistent with credential harvesting behavior observed by the CARBONATO GH0ST agent.
Detects unauthorized, unsigned processes accessing sensitive browser files (Cookies, Login Data, Local State) across common browsers (Chrome, Edge, Brave, Firefox) followed by an outbound network connection. This behavior is indicative of credential-harvesting infostealer malware.
Detects the deployment of the CARBONATO botnet's Hermes Agent within containerized environments. The rule identifies specific artifacts such as the 'SOUL.md' persona configuration, 'netd-svc' container references, and indicators of AI-focused credential theft (e.g., searching for OpenAI or Anthropic API keys) orchestrated by the GH0ST AI agent.
Detects anomalous access to SharePoint or OneDrive by identities identified as bots, agents, or service principals. The rule specifically monitors for multiple file accesses where the file names suggest the presence of secrets, passwords, or API keys, which may indicate a compromised or malicious AI agent attempting to harvest credentials.
Detects anomalous GitHub audit log activity indicative of a potential supply-chain compromise. The rule identifies workflows or automated entities (Bot/App) that access sensitive Action secrets followed by the publishing of packages to public registries like PyPI or npm. This activity can represent the precursor steps of token-theft-driven malicious package injection.
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 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 potential infostealer activity where an unsigned or non-system process accesses the Windows DPAPI master key store followed by access to common browser credential and cookie storage files within a 10-minute window. This behavior is indicative of malware attempting to decrypt and exfiltrate saved browser passwords and cookies.
Detects attempts to enumerate or access Windows Credential Manager and vault files, often used by adversaries to extract cached credentials. The rule monitors for execution of vaultcmd.exe, invocation of keymgr.dll via rundll32.exe, and unauthorized access to credential storage paths in AppData by non-system processes.
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.
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