Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
461 detections
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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 when the Google Cloud Config Connector (KCC) service account is granted highly privileged roles (roles/owner or roles/resourcemanager.organizationAdmin) at the Organization scope. This pattern is indicative of a potential 'ConfigConfusion' confused-deputy attack, where a compromised Kubernetes namespace with IAMPolicyMember creation rights could be used to escalate privileges to Organization Owner.
Detects OAuth token requests associated with the 'PDF Identity Verifier' application or specific client IDs, often used by malicious browser extensions or phishing applications to exfiltrate sensitive Google account data, specifically Gmail and user profile information.
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.
Detects potential SaaS supply-chain attacks where a third-party application or connector triggers an OAuth permission scope escalation or publishes a new version, followed by the associated service account initiating outbound network connections to rare or newly-registered domains within a 24-hour window.
Detects a suspicious sequence of events: an anomalous login (new device, new location, or high-risk sign-in) followed by the assumption of a highly privileged role (via AssumeRole, PIM, or role elevation) and subsequent sensitive administrative actions (e.g., creating access keys, modifying IAM policies, or disabling security auditing). This pattern is indicative of cloud identity compromise and privilege escalation, commonly seen in attacks involving groups like Scattered Spider.
Detects anomalous large-scale data export operations in Google BigQuery (via jobs.insert or jobs.query) performed by identities (service accounts, API keys) not previously observed in the environment. This activity is indicative of credential abuse where stolen third-party analytics or monitoring service tokens are utilized for illicit data exfiltration, a behavior consistent with tactics employed by threat actors such as ShinyHunters.
Detects clicks on sponsored search engine ads that redirect users to Google Cloud Storage (GCS) buckets with names or paths designed to impersonate the 'Ledger' brand. These artifacts are indicative of a malvertising campaign using typosquatted storage paths to deliver malicious payloads or credential harvesting pages.
Detects instances where a Google Cloud Config Connector (KCC) service account performs a SetIamPolicy operation that grants high-privilege roles (roles/owner or roles/resourcemanager.organizationAdmin) at the organization or folder scope. This activity often indicates an out-of-band configuration change, a compromised CI/CD pipeline, or an unauthorized escalation, as legitimate KCC changes should be tracked via GitOps records.
Detects unauthorized or suspicious use of Google Cloud Config Connector (KCC) service accounts to apply high-privilege IAM roles ('roles/owner' or 'roles/resourcemanager.organizationAdmin') at the Organization or Folder scope. This rule monitors for 'SetIamPolicy' API calls where the principal is identified as a KCC-related service account, flagging potential exploitation of KCC's authority to grant excessive permissions.
This rule monitors cloud application events for high-frequency, automated API calls directed at AI/ML inference endpoints. A high volume of queries (over 1000) accessing a diverse range of objects (over 50) within a short window (60 minutes) by a single identity or IP address is flagged. Such behavior is characteristic of model extraction or distillation attacks, where an adversary probes an AI model to replicate its capabilities or internal parameters.
Detects outbound HTTP/HTTPS POST requests where the URI, query string, or path contains evidence of sensitive credential material, such as AWS access keys, private key blocks, or common environment variable patterns, indicative of potential credential harvesting and exfiltration to an external destination.
This rule detects potential cost harvesting or unauthorized resource abuse by monitoring API endpoints (typically AI model gateways) for high-volume or resource-intensive queries associated with a specific identity. It correlates these patterns with billing or autoscaling trigger events (e.g., cost spikes, quota exhaustion) and identifies agentic sessions that exhibit abnormally long reasoning loops, which suggests an attempt to waste computational budget.
Detects anomalous, high-frequency enumeration of AI/ML assets such as model registries, container image repositories, and training data buckets. This activity is indicative of reconnaissance for AI supply chain attacks, specifically tracking with MITRE ATLAS AML.T0007 (Discover AI Artifacts), where an actor probes infrastructure to identify targets for model theft, data poisoning, or unauthorized access.
Detects anomalous, unauthorized access to machine learning model artifacts (e.g., .onnx, .pt, .h5) within object storage or model registries. The rule identifies suspicious patterns including high-volume file reads by non-ML team identities, lack of multi-factor authentication, or access from geolocations outside approved operating regions, indicative of potential model exfiltration or intellectual property theft.
Detects an anomalous volume of artificial intelligence model inference API calls (e.g., AWS Bedrock, Azure OpenAI, Google Vertex AI) initiated by a single IAM principal or API key within a short time window. This activity is indicative of 'LLMjacking', where compromised cloud credentials are leveraged by threat actors to perform unauthorized, high-cost AI model inference tasks.
Detects potential post-authentication persistent actions (e.g., OAuth app grants, MFA changes, mailbox rule updates) performed shortly after a successful Google Workspace login, specifically flagging actions originating from IP addresses different from the login session to identify session hijacking or token replay (AiTM) activity. Includes suppression for known corporate IP ranges, trusted OAuth application consents, and recurring user behavior.
Detects high-privilege IAM roles (roles/owner, roles/resourcemanager.organizationAdmin) being granted at the GCP organization level. This activity is indicative of the ConfigConfusion attack, where a low-privilege user exploits a Kubernetes ConfigConnector (KCC) controller's high-privilege service account to perform unauthorized IAM modifications.
Detects unauthorized or suspicious use of Google Cloud Config Connector (KCC) service accounts to apply high-privilege IAM roles ('roles/owner' or 'roles/resourcemanager.organizationAdmin') at the Organization or Folder scope. This rule monitors for 'SetIamPolicy' API calls where the principal is identified as a KCC-related service account, flagging potential exploitation of KCC's authority to grant excessive permissions.
Detects unauthorized or suspicious use of Google Cloud Config Connector (KCC) service accounts to apply high-privilege IAM roles ('roles/owner' or 'roles/resourcemanager.organizationAdmin') at the Organization or Folder scope. This rule monitors for 'SetIamPolicy' API calls where the principal is identified as a KCC-related service account, flagging potential exploitation of KCC's authority to grant excessive permissions.
Detects anomalous, high-frequency interaction with AI model endpoints characterized by a high volume of requests with a near 1:1 ratio of distinct inputs. This pattern is indicative of automated knowledge-distillation or model extraction attacks against AI services.
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