Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
734 detections
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The analytic detects failed attempts to delete AWS IAM groups, triggered by access denial, conflicts, or non-existent groups. It monitors CloudTrail logs for specific error codes related to deletion failures. This behavior is significant as it may indicate unauthorized attempts to modify access controls or disrupt operations by removing groups, potentially as part of a larger attack aiming to escalate privileges or impair security protocols. Identifying these attempts allows for timely investigation and mitigation, preventing potential impact on the organization's security posture.
The following analytic identifies the creation of a new AWS IAM policy version that allows access to all resources. It detects this activity by analyzing AWS CloudTrail logs for the CreatePolicyVersion event with a policy document that grants broad permissions. This behavior is significant because it violates the principle of least privilege, potentially exposing the environment to misuse or abuse. If confirmed malicious, an attacker could gain extensive access to AWS resources, leading to unauthorized actions, data exfiltration, or further compromise of the AWS environment.
The following analytic detects the successful deletion of a group within AWS IAM, leveraging CloudTrail IAM events. This action, while not inherently malicious, can serve as a precursor to more sinister activities, such as unauthorized access or privilege escalation attempts. By monitoring for such deletions, the analytic aids in identifying potential preparatory steps towards an attack, allowing for early detection and mitigation. The identification of this behavior is crucial for a SOC to prevent the potential impact of an attack, which could include unauthorized access to sensitive resources or disruption of AWS environment operations.
The analytic identifies the creation of a login profile for one AWS user by another, followed by a console login from the same source IP. It uses AWS CloudTrail logs to correlate the `CreateLoginProfile` and `ConsoleLogin` events based on the source IP and user identity. This activity is significant as it may indicate privilege escalation, where an attacker creates a new login profile to gain unauthorized access. If confirmed malicious, this could allow the attacker to escalate privileges and maintain persistent access to the AWS environment.
This analytic detects `UpdateTrail` events in AWS CloudTrail logs where the user agent is not the AWS console and the operation is successful. This activity indicates an attempt to modify CloudTrail settings, which could be done to evade logging and hinder visibility into AWS account activities. If confirmed malicious, this could allow attackers to operate undetected, compromising the integrity and security of the AWS environment.
The analytic identifies the creation of AWS Batch jobs that could potentially abuse the AWS Bucket Replication feature on S3 buckets. It leverages AWS CloudTrail logs to detect the `JobCreated` event, analyzing job details and their status. This activity is significant because attackers can exploit this feature to exfiltrate data by creating malicious batch jobs. If confirmed malicious, this could lead to unauthorized data transfer between S3 buckets, resulting in data breaches and loss of sensitive information.
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 analytic detects an AWS account successfully authenticating from multiple unique IP addresses within a 5-minute window. It leverages AWS CloudTrail logs, specifically monitoring `ConsoleLogin` events and counting distinct source IPs. This behavior is significant as it may indicate compromised credentials, potentially from a phishing attack, being used concurrently by an adversary and a legitimate user. If confirmed malicious, this activity could allow unauthorized access to corporate resources, leading to data breaches or further exploitation within the AWS environment.
The analytic detects an AWS CloudTrail event where a user with permissions updates the login profile of another user. It leverages CloudTrail logs to identify instances where the user making the change is different from the user whose profile is being updated. This activity is significant because it can indicate privilege escalation attempts, where an attacker uses a compromised account to gain higher privileges. If confirmed malicious, this could allow the attacker to escalate their privileges, potentially leading to unauthorized access and control over sensitive resources within the AWS environment.
The analytic identifies a spike in the number of AWS Security Hub alerts for an AWS IAM User within a 4-hour interval. It leverages AWS Security Hub findings data, calculating the average and standard deviation of alerts to detect significant deviations. This activity is significant as a sudden increase in alerts for a specific user may indicate suspicious behavior or a potential security incident. If confirmed malicious, this could signify an ongoing attack, unauthorized access, or misuse of IAM credentials, potentially leading to data breaches or further exploitation.
The following analytic detects the creation of open/public S3 buckets via the AWS CLI. It leverages AWS CloudTrail logs to identify events where a user has set bucket permissions to allow access to "AuthenticatedUsers" or "AllUsers." This activity is significant because open S3 buckets can expose sensitive data to unauthorized users, leading to data breaches. If confirmed malicious, an attacker could gain unauthorized access to potentially sensitive information stored in the S3 bucket, posing a significant security risk.
This analytic detects the execution of the 'shred' command on a Linux machine, which is used to overwrite files to make them unrecoverable. It leverages data from Linux Auditd, focusing on process names and command-line arguments. This activity is significant because the 'shred' command can be used in destructive attacks, such as those seen in the Industroyer2 malware targeting energy facilities. If confirmed malicious, this activity could lead to the permanent destruction of critical files, severely impacting system integrity and data availability.
The following analytic detects the deletion of CloudWatch log groups in AWS, identified through `DeleteLogGroup` events in CloudTrail logs. This detection leverages CloudTrail data to monitor for successful log group deletions, excluding console-based actions. This activity is significant as it indicates potential attempts to evade logging and monitoring, which is crucial for maintaining visibility into AWS activities. If confirmed malicious, this could allow attackers to hide their tracks, making it difficult to detect further malicious actions or investigate incidents within the compromised AWS environment.
The following analytic identifies high-severity findings from AWS Elastic Container Registry (ECR) image scans. It detects these activities by analyzing AWS CloudTrail logs for the DescribeImageScanFindings event, specifically filtering for findings with a high severity level. This activity is significant for a SOC because high-severity vulnerabilities in container images can lead to potential exploitation if not addressed. If confirmed malicious, attackers could exploit these vulnerabilities to gain unauthorized access, execute arbitrary code, or escalate privileges within the container environment, posing a significant risk to the overall security posture.
The analytic detects the upload of a new container image to AWS Elastic Container Registry (ECR) outside of standard business hours. It leverages AWS CloudTrail logs to identify `PutImage` events occurring between 8 PM and 8 AM or on weekends. This activity is significant because container uploads outside business hours can indicate unauthorized or suspicious activity, potentially pointing to a compromised account or insider threat. If confirmed malicious, this could allow an attacker to deploy unauthorized or malicious containers, leading to potential data breaches or service disruptions.
The analytic detects an IP address with 20 or more failed authentication attempts to the AWS Web Console within a 5-minute window. This detection leverages CloudTrail logs, aggregating failed login events by IP address and time span. This activity is significant as it may indicate a brute force attack aimed at gaining unauthorized access or escalating privileges within an AWS environment. If confirmed malicious, this could lead to unauthorized access, data breaches, or further exploitation of AWS resources.
The analytic identifies high-risk activities within repositories by correlating repository data with risk scores. It leverages risk events from the Dev Sec Ops analytic stories, summing risk scores and capturing source and user information. The detection focuses on high-risk scores above 100 and sources with more than three occurrences. This activity is significant as it highlights repositories frequently targeted by threats, providing insights into potential vulnerabilities. If confirmed malicious, attackers could exploit these repositories, leading to data breaches or infrastructure compromise.
The analytic detects the upload of a new container image to AWS Elastic Container Registry (ECR) by an unknown user. It leverages AWS CloudTrail logs to identify `PutImage` events from the ECR service, filtering out known users. This activity is significant because container uploads should typically be performed by a limited set of authorized users. If confirmed malicious, this could indicate unauthorized access, potentially leading to the deployment of malicious containers, data exfiltration, or further compromise of the AWS environment.
The following analytic detects the registration of a new Multi-Factor Authentication (MFA) method for an AWS account. It leverages AWS CloudTrail logs to identify the `CreateVirtualMFADevice` event. This activity is significant because adversaries who gain unauthorized access to an AWS account may register a new MFA method to maintain persistence. If confirmed malicious, this could allow attackers to secure their access, making it difficult to detect and remove their presence, potentially leading to further unauthorized activities and data breaches.
The following analytic detects the resetting of the master user password for an Amazon RDS DB instance. It leverages AWS CloudTrail logs 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 analytic detects an AWS account experiencing more than 20 failed authentication attempts within a 5-minute window. It leverages AWS CloudTrail logs to identify multiple failed ConsoleLogin events. This behavior is significant as it may indicate a brute force attack targeting the account. If confirmed malicious, the attacker could potentially gain unauthorized access, leading to data breaches or further exploitation of the AWS environment. Security teams should consider adjusting the threshold based on their specific environment to reduce false positives.
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