Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects unauthorized processes attempting to access the cloud instance metadata service (169.254.169.254). Adversaries frequently target this endpoint to extract sensitive instance information, such as IAM credentials or configuration data, typically as part of post-exploitation discovery or SSRF-based attacks.
Detects instances where an IAM user creates a new access key or requests a session token, followed by subsequent API activity from a different source IP address or region within a one-hour window. This behavior is often associated with the creation of persistence mechanisms or credential theft.
Detects modifications to AWS IAM policies (user or role policies) that grant AdministratorAccess, PowerUserAccess, or wildcard permissions (*). This is a common indicator of privilege escalation or persistence, where an attacker grants themselves or another identity broad administrative access within the AWS account.
Detects cloud service principals identified as AI agents or assistants performing sensitive API operations such as IAM policy modifications or storage configuration changes. This pattern may indicate that an AI service account has been compromised or misused to perform reconnaissance, privilege escalation, or persistence, exceeding its intended scope of operation.
Detects unauthorized privilege escalation for AI agent identities within an orchestration platform. The rule triggers when an AI agent account receives an IAM role change, API scope grant, or tool permission update that lacks a corresponding human-approval audit event, or occurs shortly after the agent generates logs indicating potential prompt injection or anomalous behavior.
Detects the creation of scheduled tasks, recurring jobs, or webhook subscriptions by a user identity flagged as an AI_AGENT. This activity is monitored for persistence, especially when the event lacks a descriptive context or targets external networks, suggesting potential unauthorized agent behavioral drift or malicious persistence.
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 anomalous access to AWS Bedrock Knowledge Bases, identifying a pattern of initial reconnaissance (enumerating bases and data sources) followed by high-volume querying (Retrieve/RetrieveAndGenerate). This sequence is indicative of an attacker attempting to map out backend storage configurations and subsequently exfiltrate indexed document content.
Detects unauthorized modification or deletion of AWS Bedrock guardrails. Adversaries may delete or degrade these guardrails (lowering sensitivity to 'NONE' or 'LOW') to bypass content filters, PII redaction, and safety controls, enabling toxic output generation or data exfiltration via LLM applications.
Detects the use of long-lived AWS IAM access keys (AKIA) to perform critical AWS Bedrock operations such as enabling foundation model access (PutUseCaseForModelAccess, CreateFoundationModelAgreement). Such sensitive configurations are typically expected to be performed via the AWS Console using federated credentials; the use of long-lived keys for these specific actions is a strong indicator of potential compromise and unauthorized resource enablement.
Detects unauthorized modifications to AWS Bedrock Agent configurations, specifically identifying high-risk activities such as rewriting system prompts for potential prompt injection or attaching cross-account Lambda functions. The rule monitors for UpdateAgent, CreateAgentActionGroup, UpdateAgentActionGroup, and PrepareAgent events.
Detects the TeamFiltration/UNK_CondorFiltration spray signature: a large
number of distinct target UserPrincipalNames within a single tenant, each
receiving only a handful of sign-in attempts, spread across many distinct
AWS EC2-hosted source IPs/regions within a short time window. Individual
sign-ins from AWS-hosted IP ranges (VPN gateways, corporate cloud proxies,
remote contractors) are not flagged on their own; the rule requires both
the account-fanout and the IP-rotation dimensions to co-occur for the same
tenant in the same window.
number of distinct target UserPrincipalNames within a single tenant, each
receiving only a handful of sign-in attempts, spread across many distinct
AWS EC2-hosted source IPs/regions within a short time window. Individual
sign-ins from AWS-hosted IP ranges (VPN gateways, corporate cloud proxies,
remote contractors) are not flagged on their own; the rule requires both
the account-fanout and the IP-rotation dimensions to co-occur for the same
tenant in the same window.
This rule detects the unauthorized download of complete ML model checkpoint or weight files from a model registry or object storage. It monitors for common model file extensions in download events that are not associated with a legitimate deployment justification or a pre-approved change management window, helping to identify potential intellectual property theft or model exfiltration attempts.
Detects a two-stage activity: first, the potential unauthorized reading or access of LLM provider API keys from files, environment variables, or command-line arguments; and second, the subsequent use of those same credentials to authenticate to LLM provider APIs (OpenAI, Anthropic, Azure, Bedrock) from a different, unrecognized source IP address, indicating potential credential theft and session hijacking.
Detects the creation of a new application secret or credential in a cloud environment followed by an immediate authentication attempt using that credential from an IP address that has not been observed performing successful authentication within the specified lookback window. This behavior is indicative of potential unauthorized persistence or the use of compromised credentials by an adversary.
Detects malicious attempts to delete or modify the access control settings of centralized cloud audit log storage, such as S3 buckets, Azure Log Analytics workspaces, or GCP log buckets. These actions aim to impair visibility into an adversary's activities, a common step in anti-forensics and data destruction workflows.
Detects chained AWS STS AssumeRole/AssumeRoleWithSAML/AssumeRoleWithWebIdentity events where a principal assumes a role in a different account and then quickly performs another role assumption from that new context. This pattern, often referred to as 'role chaining' or 'role laundering', is frequently used to obfuscate identity tracking and facilitate lateral movement across AWS account boundaries.
This rule detects administrative changes to cloud storage configurations (AWS S3, Azure Blob, and GCP Storage) that result in public or anonymous read/write access to buckets or containers, which could lead to unauthorized data exposure.
Detects the use of iam:PassRole to attach a potentially highly privileged IAM role (e.g., Administrator, PowerUser, FullAccess) to newly created or modified compute or serverless resources (EC2 instances, Lambda functions, Launch Templates, or Auto Scaling Groups). This behavior is indicative of an attacker attempting to escalate privileges by granting a managed resource excessive permissions that the attacker can subsequently abuse.
Detects the creation of programmatic API credentials or service principal keys by highly privileged cloud identities across AWS, Azure, and GCP. Specifically monitors AWS Root, Azure AD Global/Company Administrators, and GCP Organization Owners, whose roles generally do not require long-term API access.
Detects successful cloud console login events across AWS, Azure, and GCP where multi-factor authentication (MFA) was not utilized, combined with impossible travel indicators (login from different countries within an 8-hour window for the same principal).
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