Cloud Storage Object (T1530.001)
Cloud Storage Object [T1530.001]
Last updated
Cloud Storage Object [T1530.001]
Last updated
Name: Cloud Storage Object
ID: T1530.001
Tactics:
Cloud Storage Object (T1530.001) is a sub-technique within the MITRE ATT&CK framework under the broader technique of Data from Cloud Storage Object (T1530). This sub-technique specifically refers to adversaries accessing, exfiltrating, or manipulating data stored within cloud storage objects such as AWS S3 buckets, Azure Blob Storage, or Google Cloud Storage buckets. Threat actors typically exploit misconfigurations, compromised credentials, or vulnerabilities in cloud infrastructure to gain unauthorized access to sensitive data or resources stored in cloud storage services.
Adversaries targeting cloud storage objects exploit various vectors and methods, including:
Misconfigured Storage Objects: Attackers frequently discover publicly accessible cloud storage objects that have been inadvertently exposed due to misconfiguration. For example, AWS S3 buckets or Azure Blobs configured with overly permissive access controls allow anonymous users to read, write, or delete data.
Credential Compromise and Abuse: Attackers may leverage stolen or compromised cloud credentials, including API keys, access tokens, or IAM roles, to authenticate and interact with cloud storage objects directly. Credentials are often stolen via phishing, credential stuffing, or through compromised endpoints.
Privilege Escalation in Cloud Environments: Adversaries may escalate privileges within cloud environments to gain administrative or elevated permissions, enabling them to access sensitive storage objects that are otherwise restricted.
Application or API Exploitation: Attackers can exploit vulnerabilities in applications or APIs interacting with cloud storage services, using these weaknesses to bypass authentication or authorization mechanisms and directly access storage objects.
Cloud Malware and Automated Tools: Attackers may employ automation tools, scripts, or malware specifically designed to enumerate, access, and exfiltrate data from cloud storage objects.
Common technical execution steps include:
Enumeration and Discovery: Attackers perform reconnaissance to identify cloud storage objects, typically using automated enumeration tools or cloud service APIs.
Authentication and Access: Attackers authenticate using compromised credentials or exploit misconfigurations to bypass authentication entirely.
Data Exfiltration or Manipulation: After gaining access, attackers download sensitive data directly, modify stored data, or delete objects to disrupt operations.
Persistence and Stealth: Attackers may establish persistence by creating new IAM users, roles, or keys, and attempt to conceal their activities by modifying logging or monitoring configurations.
This sub-technique appears across various stages of the cyber kill-chain, including:
Reconnaissance: Attackers identify exposed cloud storage objects as low-hanging fruits for data theft or reconnaissance.
Initial Access and Execution: Attackers exploit misconfigurations or compromised credentials to gain initial entry into cloud environments and access sensitive data.
Persistence: Attackers create or modify cloud storage access permissions or IAM roles to maintain persistent access to cloud storage objects.
Data Exfiltration: Attackers typically use cloud storage objects as a source for large-scale data exfiltration, especially when sensitive or valuable information is stored.
Impact and Disruption: Attackers may manipulate or delete cloud storage objects to disrupt operations, cause data loss, or extort victims.
Typical attack scenarios include:
Opportunistic attackers scanning for misconfigured buckets to steal sensitive data.
APT groups targeting cloud storage objects to exfiltrate intellectual property or sensitive corporate information.
Ransomware operators encrypting or deleting cloud storage data to demand ransom payments.
Detection methods and tools for identifying unauthorized access or manipulation of cloud storage objects include:
Cloud-native Logging and Monitoring:
AWS CloudTrail logs, Azure Activity Logs, or Google Cloud Audit Logs to track access patterns, authentication events, and API calls related to storage objects.
Monitoring for unusual or suspicious API calls, such as mass downloads, deletions, or permission changes.
Security Information and Event Management (SIEM) Platforms:
Correlation of cloud logs with IAM activity and authentication events to detect unauthorized access or anomalous behaviors.
Alerting on unusual data access patterns, such as large data transfers or access from unfamiliar IP addresses or regions.
Cloud Security Posture Management (CSPM) Tools:
Automated scanning and continuous monitoring of cloud storage object configurations to detect misconfigurations or overly permissive access controls.
Identification of publicly accessible buckets or objects that may pose security risks.
User and Entity Behavior Analytics (UEBA):
Detection of anomalies in user behavior, such as accessing cloud storage objects outside typical working hours or from unusual geographic locations.
Specific Indicators of Compromise (IoCs):
Unusual API calls or access logs indicating data enumeration or mass downloads.
Creation of new IAM users, roles, or access keys without proper authorization.
Sudden changes in bucket permissions or access control lists (ACLs).
Logs showing access from unknown IP addresses, VPNs, or anonymizing proxies.
Large volumes of data transferred from cloud storage objects to external IP addresses or unknown locations.
Early detection of unauthorized access or manipulation of cloud storage objects is crucial due to the following impacts:
Data Breaches and Loss of Sensitive Information: Attackers can exfiltrate personally identifiable information (PII), intellectual property, trade secrets, or confidential business data stored in cloud storage objects.
Regulatory and Compliance Violations: Unauthorized access or data exposure can lead to violations of regulatory standards such as GDPR, HIPAA, or PCI DSS, resulting in substantial fines and legal penalties.
Operational Disruption: Attackers may delete or modify critical data stored in cloud objects, causing significant disruption to business operations, downtime, and potential loss of revenue.
Reputational Damage: Data breaches or unauthorized access incidents can severely damage an organization's reputation, eroding customer trust and stakeholder confidence.
Financial Impact: Breaches involving cloud storage objects can result in substantial financial losses due to remediation costs, legal settlements, regulatory fines, and loss of competitive advantage.
Early detection allows organizations to rapidly respond, mitigate impact, and prevent further unauthorized access, preserving data confidentiality, integrity, and availability.
Real-world examples involving Cloud Storage Object (T1530.001):
Capital One Data Breach (2019):
Attack Scenario: An attacker exploited a misconfigured firewall and IAM roles to access AWS S3 buckets containing sensitive customer data.
Tools and Techniques: Exploited AWS EC2 instance metadata service (IMDS) vulnerabilities, accessed IAM role credentials, and enumerated S3 buckets.
Impact: Exfiltration of personal and financial data of approximately 100 million individuals, resulting in significant financial and reputational damage.
Uber Data Breach (2016):
Attack Scenario: Attackers obtained AWS credentials from a GitHub repository and accessed sensitive data stored in AWS S3 buckets.
Tools and Techniques: Credential harvesting from publicly accessible repositories, direct access to AWS S3 buckets.
Impact: Exposed personal information of 57 million users, significant regulatory fines, and reputational harm.
Accenture Data Exposure (2017):
Attack Scenario: Misconfigured AWS S3 buckets allowed anonymous access to highly sensitive corporate data.
Tools and Techniques: Public enumeration and direct access due to misconfiguration.
Impact: Exposure of sensitive internal documents, credentials, and infrastructure details, leading to reputational damage and potential security risks.
FedEx Data Exposure (2018):
Attack Scenario: Misconfigured AWS S3 buckets left customer data publicly accessible without authentication.
Tools and Techniques: Automated scanning tools used by security researchers and attackers identified publicly accessible buckets.
Impact: Exposed personally identifiable information (PII) of thousands of customers, regulatory scrutiny, and customer trust erosion.
These examples highlight the importance of ensuring proper cloud storage configurations, rigorous monitoring, and early detection to minimize the risk and impact of unauthorized cloud storage object access.