Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects unauthorized execution of a Bun runtime executing an 'index.js' payload within a GitHub Actions workflow context, specifically targeting known compromised action paths (e.g., issues-helper, maintain-one-comment). The detection requires correlation with either sensitive environment/secret access or anomalous outbound network activity originating from the same process, indicating potential credential theft or C2 communication by a malicious CI/CD pipeline component.
Detects unauthorized execution of a Bun runtime executing an 'index.js' payload within a GitHub Actions workflow context, specifically targeting known compromised action paths (e.g., issues-helper, maintain-one-comment). The detection requires correlation with either sensitive environment/secret access or anomalous outbound network activity originating from the same process, indicating potential credential theft or C2 communication by a malicious CI/CD pipeline component.
Detects unauthorized execution of a Bun runtime executing an 'index.js' payload within a GitHub Actions workflow context, specifically targeting known compromised action paths (e.g., issues-helper, maintain-one-comment). The detection requires correlation with either sensitive environment/secret access or anomalous outbound network activity originating from the same process, indicating potential credential theft or C2 communication by a malicious CI/CD pipeline component.
Detects unauthorized execution of a Bun runtime executing an 'index.js' payload within a GitHub Actions workflow context, specifically targeting known compromised action paths (e.g., issues-helper, maintain-one-comment). The detection requires correlation with either sensitive environment/secret access or anomalous outbound network activity originating from the same process, indicating potential credential theft or C2 communication by a malicious CI/CD pipeline component.
Detects high-volume, automated enumeration of cloud resources (SharePoint, OneDrive, or Exchange) via Graph API or search queries immediately following a user sign-in event. This behavior is indicative of session hijacking (token replay) followed by unauthorized scripted discovery, as opposed to legitimate user browsing activity.
This rule detects potential credential theft by monitoring for file access (creation, modification, or renaming) to common web browser sensitive files (Login Data or Cookies) followed by an outbound network connection from the same process within a 10-minute window. This behavior often indicates an adversary attempting to exfiltrate browser-stored credentials or session cookies.
Detects unauthorized processes attempting to modify, create, or rename sensitive browser files (Login Data or Cookies) belonging to Google Chrome or Microsoft Edge. The rule filters out known browser processes and looks for suspicious initiating processes characterized by unsigned code or execution from common staging areas like Temp folders.
This rule detects a multi-stage phishing attack, often referred to as 'quishing'. It identifies incoming emails containing image (JPG, PNG) or PDF attachments that embed previously unseen URLs, correlated with a subsequent sign-in event from a mobile device by the same recipient, which is a common indicator of Adversary-in-the-Middle (AiTM) credential harvesting.
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 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 a suspicious sequence of events where a user engagement (voice/video call) involving themes of urgency or helpdesk interaction is closely followed by a high-risk administrative action (e.g., password reset, MFA device enrollment, or financial transfer) by the same user, where the communication session context differs from standard activity, indicative of a deepfake-enabled vishing attack.
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 a suspicious sequence where an account undergoes a help-desk-initiated password or credential reset, followed shortly (within 30 minutes) by the registration of a new multi-factor authentication (MFA) device for that same account. This pattern is characteristic of social engineering attacks, such as those used by UNC3944/Scattered Spider, where an adversary impersonates a user to gain initial access and subsequently establishes persistence by adding an MFA device they control.
Detects suspicious account management activities, such as password resets or MFA re-enrollment, performed by an operator on a high-privileged or administrator account outside of typical business hours (06:00-20:00). This behavior is often indicative of help-desk impersonation or credential takeover attempts targeting Identity Provider (IdP) administrative roles.
Detects the execution of an unsigned, non-Apple binary initiated via xpcproxy that subsequently loads 'libpam.2.dylib' within a two-minute window. This behavior is indicative of a PamStealer-style attack, where a malicious or spoofed process attempts to harvest user credentials by leveraging system PAM libraries for validation.
Detects the execution of an unsigned, non-Apple binary initiated via xpcproxy that subsequently loads 'libpam.2.dylib' within a two-minute window. This behavior is indicative of a PamStealer-style attack, where a malicious or spoofed process attempts to harvest user credentials by leveraging system PAM libraries for validation.
Detects the execution of an unsigned, non-Apple binary initiated via xpcproxy that subsequently loads 'libpam.2.dylib' within a two-minute window. This behavior is indicative of a PamStealer-style attack, where a malicious or spoofed process attempts to harvest user credentials by leveraging system PAM libraries for validation.
Detects the execution of an unsigned, non-Apple binary initiated via xpcproxy that subsequently loads 'libpam.2.dylib' within a two-minute window. This behavior is indicative of a PamStealer-style attack, where a malicious or spoofed process attempts to harvest user credentials by leveraging system PAM libraries for validation.
Detects the execution of an unsigned, non-Apple binary initiated via xpcproxy that subsequently loads 'libpam.2.dylib' within a two-minute window. This behavior is indicative of a PamStealer-style attack, where a malicious or spoofed process attempts to harvest user credentials by leveraging system PAM libraries for validation.
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.
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