Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects potential poisoning of machine learning training data by identifying unauthorized file modifications to datasets, DVC repositories, or model registries that occur outside of authorized ETL or CI/CD windows.
This rule detects potential AI/ML supply chain compromise by monitoring for package installations (via pip, conda, poetry, etc.) from non-standard repositories, direct URLs, or version control systems within data science and CI/CD pipelines (e.g., Jupyter, Airflow, Jenkins). It further correlates this activity with subsequent suspicious outbound network connections from the host, which is indicative of slopsquatting or malicious dependency execution.
Detects anomalous, exploratory query patterns by an API key or service account against an AI model inference API. High distinct resource access volume in a short timeframe, potentially combined with off-hours activity, indicates reconnaissance phases such as output-ontology or model-family enumeration, which may precede adversarial-example crafting or verification attempts.
Detects instances where a user account consents to an OAuth application requesting high-privilege or sensitive scopes such as Mail.Read, Files.ReadWrite.All, or offline_access. This behavior is a common indicator of consent phishing attacks, where adversaries trick users into granting persistent access to their cloud resources via malicious OAuth applications.
This rule detects a multi-stage Business Email Compromise (BEC) attack involving executive impersonation via look-alike domains, followed by the creation of suspicious inbox rules (forwarding/deletion) by the victim, and concurrent external communication (Teams/Zoom meeting invites) from a newly created account. It correlates these activities to identify sophisticated payment fraud campaigns.
This rule detects a multi-stage Business Email Compromise (BEC) attack involving executive impersonation via look-alike domains, followed by the creation of suspicious inbox rules (forwarding/deletion) by the victim, and concurrent external communication (Teams/Zoom meeting invites) from a newly created account. It correlates these activities to identify sophisticated payment fraud campaigns.
This rule detects potential social engineering attacks where an external or federated user impersonates an IT or helpdesk entity within Microsoft Teams (via chats, messages, or calls) followed shortly by an MFA registration or password reset event for the targeted user. This sequence is indicative of a vishing or phishing attack aimed at account takeover.
Detects the granting of high-privilege permissions (e.g., mail.read, files.readwrite) to a newly registered OAuth application (less than 24 hours old), followed by high-volume API activity from that same application within one hour. This behavior is indicative of an illicit OAuth consent grant attack, where an adversary lures a user into granting permissions to a malicious application to maintain persistent access to cloud resources or perform data exfiltration.
Detects anomalous AI agent behavior characterized by a significant spike in resource or tool invocations within a 15-minute window, potentially indicating indirect prompt injection or unauthorized agent hijacking where an agent is coerced into excessive or unauthorized actions.
Detects malicious prompt injection attempts within an LLM conversation session. The rule monitors for ingested content containing common instruction-override keywords (e.g., 'ignore previous instructions', 'system prompt override'), followed immediately by the agent process executing unauthorized actions such as process launch, network connections, or file creation within the same session/user context.
Detects suspicious HTTP requests directed at SharePoint applications containing a malformed Register directive. The rule identifies attempts to leverage the 'publishingribbon' Tagprefix combined with unauthorized namespaces (e.g., System.Xaml, System.Windows.Data) to bypass the EditingPageParser.InitializeRegisterTable safelist check, a common technique for SharePoint remote code execution.
This rule detects scenarios where an AI agent executes shell commands or system-level tools immediately after receiving a prompt injection attack from an untrusted source. It correlates agent audit logs—specifically identifying tool calls flagged for prompt injection—with actual endpoint process creation events. It flags process execution as 'High' severity if the command line matches common malicious patterns like encoded commands, IEX, or attempts to access credential material (e.g., lsass, mimikatz).
This rule detects network activity and cloud API events indicative of an adversary utilizing the VAPI.ai platform to orchestrate automated voice-phishing (vishing) campaigns. It correlates outbound network connections to VAPI.ai endpoints with specific webhook callback patterns used to track call status for victim records.
Detects Microsoft Graph API requests targeting user information endpoints (/v1.0/users or /v1.0/me) using specific search filters associated with payroll or human resources and identified by the Axios HTTP client User-Agent string. This pattern is consistent with reconnaissance activities.
Detects specific, targeted search operations within M365 environments for keywords related to payroll, HR, and banking information. This behavior is indicative of post-compromise reconnaissance activity, often following AiTM (Adversary-in-the-Middle) session hijacking where an attacker attempts to locate financial documents and payroll correspondence.
Detects a specific concealment behavior attributed to the 'com.corp.mdm' Android implant. The rule monitors for a sequence of events where the application disables its own launcher (main activity) to hide from the app drawer, immediately followed by the initiation of a foreground service that masquerades as a system notification ('Android System' title with 'System service running.' text).
Monitors SaaS application integration service accounts (e.g., Salesforce connected apps) by establishing baselines for API call volume and accessed object types. The rule alerts on anomalous spikes in usage or deviations in the scope of accessed data objects, which could indicate the abuse of compromised OAuth tokens following a vendor supply-chain compromise.
Detects high-volume, anomalous data read operations performed by the Gainsight integration service account within Salesforce, which may indicate unauthorized use of the integration token for data exfiltration.
Detects anomalous, high-volume data access patterns within Salesforce, specifically monitoring for bulk API or SOQL query requests by a single user or application. This behavior is indicative of mass data exfiltration from a CRM instance, a tactic observed in extortion-themed campaigns such as those associated with the ShinyHunters group.
Detects unauthorized access to CRM platforms (Salesforce) using Salesloft or Drift OAuth tokens, where the source IP address does not match the known legitimate service provider IP ranges. This behavior is indicative of potential token theft or misuse, particularly relevant to supply-chain campaign activities.
Detects unauthenticated guest users abusing the Salesforce Experience Cloud Aura endpoint (/s/sfsites/aura) to perform high-volume metadata and schema enumeration. This activity typically involves tools like AuraInspector to harvest CRM data by repeatedly invoking Aura actions such as describeSObjects, getSObjectMetadata, ObjectInfoController, or RecordUiController.
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