Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
734 detections
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The analytic identifies spikes in blocked outbound network connections originating from within an AWS environment. It uses VPC Flow Logs data from CloudWatch, specifically looking for 'blocked' actions from internal IP ranges (10.0.0.0/8, 172.16.0.0/12, 192.168.0.0/16) to external destinations. A spike is determined by comparing the current number of blocked connections against a calculated baseline (average and standard deviation) over time. This detection is significant as it can indicate potential exfiltration attempts or misconfigurations leading to data leakage. If confirmed malicious, such activity could allow attackers to bypass network defenses, leading to unauthorized data transfer or communication with malicious external entities.
The following analytic detects when an EC2 snapshot is shared with an external AWS account by analyzing AWS CloudTrail events. This detection method leverages CloudTrail logs to identify modifications in snapshot permissions, specifically when the snapshot is shared outside the originating AWS account. This activity is significant as it may indicate an attempt to exfiltrate sensitive data stored in the snapshot. If confirmed malicious, an attacker could gain unauthorized access to the snapshot's data, potentially leading to data breaches or further exploitation of the compromised information.
The analytic detects the deletion of critical AWS Security Services configurations, such as CloudWatch alarms, GuardDuty detectors, and Web Application Firewall rules. It leverages Amazon Security Lake logs to identify specific API calls like "DeleteLogStream" and "DeleteDetector." This activity is significant because adversaries often use these actions to disable security monitoring and evade detection. If confirmed malicious, this could allow attackers to operate undetected, leading to potential data breaches, unauthorized access, and prolonged persistence within the AWS environment.
This analytic detects the creation of AWS KMS keys with an encryption policy accessible to everyone, including external entities. It leverages AWS CloudTrail logs from Amazon Security Lake to identify `CreateKey` or `PutKeyPolicy` events where the `kms:Encrypt` action is granted to all principals. This activity is significant as it may indicate a compromised account, allowing an attacker to misuse the encryption key to target other organizations. If confirmed malicious, this could lead to unauthorized data encryption, potentially disrupting operations and compromising sensitive information across multiple entities.
The following analytic detects the creation of AWS Network Access Control Lists (ACLs) with all ports open to a specified CIDR. It leverages AWS CloudTrail events, specifically monitoring for `CreateNetworkAclEntry` or `ReplaceNetworkAclEntry` actions with rules allowing all traffic. This activity is significant because it can expose the network to unauthorized access, increasing the risk of data breaches and other malicious activities. If confirmed malicious, an attacker could exploit this misconfiguration to gain unrestricted access to the network, potentially leading to data exfiltration, service disruption, or further compromise of the AWS environment.
The following analytic detects suspicious database file and directory discovery activities, which may signal an attacker attempt to locate and assess critical database assets on a compromised system. This behavior is often a precursor to data theft, unauthorized access, or privilege escalation, as attackers seek to identify valuable information stored in databases. By monitoring for unusual or unauthorized attempts to locate database files and directories, this analytic aids in early detection of potential reconnaissance or data breach efforts, enabling security teams to respond swiftly and mitigate the risk of further compromise.
This analytic identifies an AWS IAM account with concurrent sessions originating from more than one unique IP address within a 5-minute span. This detection leverages AWS CloudTrail logs, specifically the `DescribeEventAggregates` API call, to identify multiple IP addresses associated with the same user session. This behavior is significant as it may indicate a session hijacking attack, where an adversary uses stolen session cookies to access AWS resources from a different location. If confirmed malicious, this activity could allow unauthorized access to sensitive corporate resources, leading to potential data breaches or further exploitation.
The following analytic detects anonymous and unauthenticated requests to a Kubernetes cluster. It identifies this behavior by monitoring API calls from users who have not provided any token or password in their request, using data from `kube_audit` logs. This activity is significant for a SOC as it indicates a severe misconfiguration, allowing unfettered access to the cluster with no traceability. If confirmed malicious, an attacker could gain access to sensitive data or control over the cluster, posing a substantial security risk.
This analytic identifies more than 10 GetPasswordData API calls within a 5-minute window in an AWS account. It leverages AWS CloudTrail logs to detect this activity by counting the distinct instance IDs accessed. This behavior is significant as it may indicate an attempt to retrieve encrypted administrator passwords for running Windows instances, which is a critical security concern. If confirmed malicious, attackers could gain unauthorized access to administrative credentials, potentially leading to full control over the affected instances and further compromise of the AWS environment.
The analytic identifies users with KMS keys performing encryption operations on S3 buckets. It leverages AWS CloudTrail logs to detect the `CopyObject` event where server-side encryption with AWS KMS is specified. This activity is significant as it may indicate unauthorized or suspicious encryption of data, potentially masking exfiltration or tampering efforts. If confirmed malicious, an attacker could be encrypting sensitive data to evade detection or preparing it for exfiltration, posing a significant risk to data integrity and confidentiality.
The analytic identifies AWS console login attempts by users from a new region. It leverages AWS CloudTrail events and compares current login regions against a baseline of previously seen regions for each user. This activity is significant as it may indicate unauthorized access attempts or compromised credentials. If confirmed malicious, an attacker could gain unauthorized access to AWS resources, potentially leading to data breaches, resource manipulation, or further lateral movement within the cloud environment.
This analytic detects file transfers flagged as malware that occurred over non-standard ports (other than 80 and 443) from Cisco Secure Firewall Threat Defense logs. Adversaries may attempt to bypass protocol-based detection or use alternate ports to blend in with other traffic. This analytic identifies these non-conventional flows and surfaces potential evasion techniques. If confirmed malicious, this indicates potential malware delivery or other nefarious activity.
The following analytic detects the resetting of the master user password for an Amazon RDS DB instance. It leverages AWS CloudTrail logs from Amazon Security Lake to identify events where the `ModifyDBInstance` API call includes a new `masterUserPassword` parameter. This activity is significant because unauthorized password resets can grant attackers access to sensitive data stored in production databases, such as credit card information, PII, and healthcare data. If confirmed malicious, this could lead to data breaches, regulatory non-compliance, and significant reputational damage. Immediate investigation is required to determine the legitimacy of the password reset.
The following analytic identifies AWS console login events by users from a new country. It leverages AWS CloudTrail events and compares them against a lookup file of previously seen users and their login locations. This activity is significant because logins from new countries can indicate potential unauthorized access or compromised accounts. If confirmed malicious, this could lead to unauthorized access to AWS resources, data exfiltration, or further exploitation within the AWS environment.
The following analytic detects `spoolsv.exe` writing a `.dll` file, which is unusual behavior and may indicate exploitation of vulnerabilities like CVE-2021-34527 (PrintNightmare). This detection leverages Sysmon EventID 11 to monitor file creation events in the `\spool\drivers\x64\` directory. This activity is significant because `spoolsv.exe` typically does not write DLL files, and such behavior could signify an ongoing attack. If confirmed malicious, this could allow an attacker to execute arbitrary code, escalate privileges, or maintain persistence on the compromised system.
The analytic identifies excessive security scanning activities in AWS by detecting a high number of Describe, List, or Get API calls from a single user. It leverages AWS CloudTrail logs to count distinct event names and flags users with more than 50 such events. This behavior is significant as it may indicate reconnaissance activities by an attacker attempting to map out your AWS environment. If confirmed malicious, this could lead to unauthorized access, data exfiltration, or further exploitation of your cloud infrastructure.
The following analytic detects successful API calls to view, update, or delete the password policy in an AWS organization. It leverages AWS CloudTrail logs to identify events such as "UpdateAccountPasswordPolicy," "GetAccountPasswordPolicy," and "DeleteAccountPasswordPolicy." This activity is significant because it is uncommon for regular users to perform these actions, and such changes can indicate an adversary attempting to understand or weaken password defenses. If confirmed malicious, this could lead to compromised accounts and increased attack surface, potentially allowing unauthorized access and control over AWS resources.
The following analytic detects a suspicious registry modification aimed at disabling security audit logs by adding a specific registry entry. It leverages data from the Endpoint.Registry data model, focusing on changes to the "Control\\MiniNt" registry path. This activity is significant because it can prevent Windows from logging any events to the Security Log, effectively blinding security monitoring efforts. If confirmed malicious, this technique could allow an attacker to operate undetected, making it difficult to trace their actions and compromising the integrity of security audits.
The following analytic detects updates to the SAML provider in AWS. It leverages AWS CloudTrail logs to identify the `UpdateSAMLProvider` event, analyzing fields such as `sAMLProviderArn`, `sourceIPAddress`, and `userIdentity` details. Monitoring updates to the SAML provider is crucial as it may indicate a perimeter compromise of federated credentials or unauthorized backdoor access set by an attacker. If confirmed malicious, this activity could allow attackers to manipulate identity federation, potentially leading to unauthorized access to cloud resources and sensitive data.
The following analytic detects attempts to disable multi-factor authentication (MFA) for an AWS IAM user. It leverages AWS CloudTrail logs to identify events where MFA devices are deleted or deactivated. This activity is significant because disabling MFA can indicate an adversary attempting to weaken account security, potentially to maintain persistence using a compromised account. If confirmed malicious, this action could allow attackers to retain access to the AWS environment without detection, posing a significant risk to the security and integrity of the cloud infrastructure.
The following analytic identifies potential AWS S3 exfiltration behavior by correlating multiple risk events related to Collection and Exfiltration techniques. It leverages risk events from AWS sources, focusing on instances where two or more unique analytics and distinct MITRE ATT&CK IDs are triggered for a specific risk object. This activity is significant as it may indicate an ongoing data exfiltration attempt, which is critical for security teams to monitor. If confirmed malicious, this could lead to unauthorized access and theft of sensitive information, compromising the organization's data integrity and confidentiality.
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