Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects anomalous activity where an AI coding assistant process reads sensitive local credential files (such as .env, .aws/credentials, or SSH keys) followed by an outbound network connection to a destination not associated with known package registries or AI provider APIs. This pattern is indicative of potential prompt-injection attacks targeting autonomous AI coding tools to exfiltrate developer secrets.
This rule detects potential supply chain compromise by monitoring for the installation of pip or npm packages with names similar to popular AI/LLM frameworks (e.g., langchain, llamaindex, mastra, litellm). It then correlates these installations with subsequent suspicious activity, including potential beaconing to external IPs, execution of known crypto-miners, or access to sensitive local files (e.g., AWS credentials, SSH keys).
Detects the installation of AI agent-related packages (npm, pip, etc.) followed shortly by unauthorized access to sensitive files or credential stores on the same host. This pattern is indicative of a malicious or 'poisoned' AI agent tool performing credential theft or unauthorized exfiltration of sensitive system data.
Detects the granting of highly privileged OAuth or API scopes to AI-related applications (such as Copilot plugins, browser-automation agents, or AI connectors) immediately followed by high-volume data access activities (file reads or email transfers) performed by the same user account. This behavior is indicative of an attacker weaponizing a compromised identity to grant an external AI agent broad access to sensitive data for exfiltration or automated reconnaissance.
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 the granting of highly privileged OAuth or API scopes to AI-related applications (such as Copilot plugins, browser-automation agents, or AI connectors) immediately followed by high-volume data access activities (file reads or email transfers) performed by the same user account. This behavior is indicative of an attacker weaponizing a compromised identity to grant an external AI agent broad access to sensitive data for exfiltration or automated reconnaissance.
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 anomalous large-volume file download or export activity from cloud repositories (SharePoint, OneDrive, or Salesforce) occurring shortly after a help-desk-mediated credential or MFA reset for the same user. This behavior is indicative of account takeover, specifically vishing-to-exfiltration patterns used by threat actors.
This rule detects the use of the macOS 'security' utility to unlock the login keychain and modify its access-control list, a technique observed in PamStealer malware to facilitate subsequent unauthorized access to the keychain database.
This rule detects the use of the macOS 'security' utility to unlock the login keychain and modify its access-control list, a technique observed in PamStealer malware to facilitate subsequent unauthorized access to the keychain database.
This rule detects the use of the macOS 'security' utility to unlock the login keychain and modify its access-control list, a technique observed in PamStealer malware to facilitate subsequent unauthorized access to the keychain database.
This rule detects the use of the macOS 'security' utility to unlock the login keychain and modify its access-control list, a technique observed in PamStealer malware to facilitate subsequent unauthorized access to the keychain database.
Detects instances where Microsoft 365 Copilot surfaces content labeled with sensitivity markings to a user who only has access to that content via inherited or broad sharing permissions (e.g., Company link, Anonymous link, or inherited permissions), rather than explicit access. This identifies potential AI-retrieval exploitation patterns where Copilot inadvertently exposes sensitive information due to oversharing configurations.
This rule detects the creation, modification, or renaming of files that contain sensitive keywords (e.g., 'password', 'seed', 'wallet', 'recovery') by processes other than standard system or productivity applications. This activity is indicative of credential or sensitive data harvesting.
Detects the loading of the 'WbElevation.dll' module associated with SectopRAT, followed by suspicious access to browser, email, or cryptocurrency wallet credential stores within a 10-minute window on the same device.
This rule detects potential SectopRAT loader activity by identifying non-.NET-native processes accessing 'pool.db' in the ProgramData directory followed by the immediate loading of .NET CLR libraries (clr.dll or mscoreei.dll). This pattern is indicative of reflective loading of a decrypted .NET payload into memory within a target process.
This rule detects potential SectopRAT loader activity by identifying non-.NET-native processes accessing 'pool.db' in the ProgramData directory followed by the immediate loading of .NET CLR libraries (clr.dll or mscoreei.dll). This pattern is indicative of reflective loading of a decrypted .NET payload into memory within a target process.
This rule detects potential SectopRAT loader activity by identifying non-.NET-native processes accessing 'pool.db' in the ProgramData directory followed by the immediate loading of .NET CLR libraries (clr.dll or mscoreei.dll). This pattern is indicative of reflective loading of a decrypted .NET payload into memory within a target process.
Detects potential activity related to the SectopRAT loader by monitoring the execution sequence of 'ReportDump.exe'. The rule identifies the side-loading of a specific DLL ('sdkcra.dll') followed by the process reading a known configuration or database file ('pool.db') within a short time window, which is indicative of the loader's secondary decryption phase.
Detects potential activity related to the SectopRAT loader by monitoring the execution sequence of 'ReportDump.exe'. The rule identifies the side-loading of a specific DLL ('sdkcra.dll') followed by the process reading a known configuration or database file ('pool.db') within a short time window, which is indicative of the loader's secondary decryption phase.
Detects the granting of highly privileged OAuth or API scopes to AI-related applications (such as Copilot plugins, browser-automation agents, or AI connectors) immediately followed by high-volume data access activities (file reads or email transfers) performed by the same user account. This behavior is indicative of an attacker weaponizing a compromised identity to grant an external AI agent broad access to sensitive data for exfiltration or automated reconnaissance.
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