Cloud Storage Object Discovery

Cloud Storage Object Discovery [T1619]

Information

Name: Cloud Storage Object Discovery
ID: T1619
Tactics:

Introduction

Cloud Storage Object Discovery (T1619.001) is a sub-technique within the MITRE ATT&CK framework under the Discovery tactic. It involves adversaries enumerating and discovering objects stored in cloud storage services. Attackers typically perform this action to identify sensitive data, credentials, or other valuable information stored in cloud environments. This sub-technique is crucial for adversaries aiming to escalate privileges, facilitate lateral movement, or exfiltrate sensitive data by identifying targets within cloud storage services such as Amazon S3, Azure Blob Storage, or Google Cloud Storage.

Deep Dive Into Technique

Cloud Storage Object Discovery involves various technical approaches and mechanisms to enumerate and identify objects stored in cloud storage environments. Attackers typically perform the following actions:

Enumerating Buckets or Containers:
- Attackers first attempt to discover publicly accessible or misconfigured cloud storage buckets or containers.
- Common methods include brute-forcing bucket names, leveraging naming conventions, or utilizing publicly available tools and scripts.
Listing Objects within Storage:
- Once buckets or containers are identified, attackers attempt to list or enumerate objects stored within them.
- Attack methods include using cloud provider APIs, command-line tools, or specialized enumeration scripts.
Leveraging Misconfigurations:
- Attackers exploit misconfigured permissions or weak access controls to list objects.
- Common misconfigurations include:
  - Publicly accessible buckets or blobs.
  - Improper IAM (Identity and Access Management) policies granting excessive permissions.
  - Weak or default access control settings.
Using Cloud Provider APIs and Tools:
- Attackers may use legitimate cloud CLI tools or SDKs:
  - AWS CLI (aws s3 ls s3://bucket-name)
  - Azure CLI (az storage blob list)
  - Google Cloud SDK (gsutil ls gs://bucket-name)
- Attackers also use custom scripts or automated enumeration tools (e.g., CloudBrute, Cloud_enum, Bucket Finder).
Analyzing Metadata:
- Attackers often analyze metadata associated with discovered objects to glean additional useful information (e.g., timestamps, file types, permissions).

When this Technique is Usually Used

Attackers use Cloud Storage Object Discovery across various attack scenarios and stages, including:

Initial Reconnaissance:
- Early-stage enumeration to identify publicly accessible cloud storage resources.
- Gathering intelligence on cloud infrastructure and potential entry points.
Privilege Escalation and Credential Harvesting:
- Identifying sensitive data such as credentials, API keys, or configuration files stored in cloud storage.
- Leveraging discovered sensitive information to escalate privileges or pivot to other cloud resources.
Lateral Movement:
- Identifying stored configuration files, scripts, or infrastructure definitions (e.g., Terraform, CloudFormation templates) to facilitate lateral movement within cloud environments.
Data Exfiltration:
- Identifying valuable data assets stored in cloud storage to prepare for exfiltration.
- Enumerating sensitive or proprietary data to determine the most valuable targets.
Post-Exploitation:
- Continuously monitoring cloud storage for new objects or changes, providing attackers with updated intelligence on the victim's cloud environment.

How this Technique is Usually Detected

Detection of Cloud Storage Object Discovery involves monitoring cloud environments, analyzing logs, and deploying security tools. Effective detection methods and tools include:

Cloud Provider Logging and Monitoring:
- AWS CloudTrail logs, Azure Activity Logs, Google Cloud Audit Logs can reveal unusual API calls for object listing or enumeration activities.
- Monitor for repeated failed access attempts or enumeration patterns.
Behavioral Analytics and Anomaly Detection:
- Implement anomaly detection tools (e.g., AWS GuardDuty, Azure Sentinel, Google Chronicle) to identify unusual enumeration or access patterns.
- Detect sudden spikes in API calls or object enumeration activities from unusual IP addresses or regions.
Access Control and Permission Audits:
- Regularly audit IAM policies, bucket policies, and ACL configurations to identify misconfigured permissions.
- Automated tools like AWS Config, Azure Security Center, or Google Cloud Security Command Center can help identify misconfigurations.
Network Traffic Analysis:
- Network security tools (e.g., IDS/IPS, Firewall logs) can detect enumeration traffic patterns targeting cloud storage endpoints.
- Monitor outbound network traffic to cloud storage APIs from internal or compromised hosts.
Specific Indicators of Compromise (IoCs):
- Repeated API calls to cloud storage services from unknown or suspicious IP addresses.
- Enumeration scripts or known tools executed from compromised systems (e.g., CloudBrute, Cloud_enum).
- Unusual access patterns, such as listing objects from rarely used or sensitive buckets.

Why it is Important to Detect This Technique

Early detection of Cloud Storage Object Discovery is essential due to the significant impacts it can have on organizations, including:

Data Breaches and Sensitive Data Exposure:
- Attackers can discover and exfiltrate sensitive or proprietary data stored in cloud storage, leading to severe financial and reputational damage.
Credential Theft and Privilege Escalation:
- Attackers may uncover stored credentials, API keys, or configuration files enabling unauthorized access, privilege escalation, and lateral movement.
Compliance Violations:
- Unauthorized access and enumeration of cloud storage objects can lead to regulatory compliance violations (e.g., GDPR, HIPAA), resulting in fines and legal consequences.
Operational Disruption:
- Attackers' unauthorized access and enumeration activities risk operational disruptions, causing service downtime or affecting business continuity.
Early Warning of Wider Attacks:
- Detecting enumeration activities early can serve as an early warning indicator of broader attack campaigns, allowing organizations to respond proactively and mitigate damage.

Examples

Real-world examples demonstrating Cloud Storage Object Discovery include:

Capital One Data Breach (2019):
- Attack Scenario:
  - An attacker exploited a misconfigured AWS IAM role to enumerate and access sensitive data stored in AWS S3 buckets.
- Tools and Techniques:
  - AWS CLI and API calls used to enumerate and list objects.
- Impacts:
  - Exposure of sensitive personal information of approximately 100 million individuals.
  - Significant financial and reputational damage to Capital One.
Uber Data Breach (2016):
- Attack Scenario:
  - Attackers accessed sensitive data stored in AWS S3 buckets due to weak access controls and enumerated stored objects.
- Tools and Techniques:
  - Cloud storage enumeration scripts and AWS CLI tools.
- Impacts:
  - Exposure of personal data of approximately 57 million users and drivers.
  - Resulted in regulatory fines and reputational damage.
Accenture Data Exposure (2017):
- Attack Scenario:
  - Accenture inadvertently exposed sensitive data stored in AWS S3 buckets due to misconfigured permissions.
  - Attackers enumerated and accessed stored objects.
- Tools and Techniques:
  - Publicly available enumeration tools and AWS CLI.
- Impacts:
  - Sensitive internal documentation and credentials exposed.
  - Reputational harm and potential compliance violations.
FedEx Data Exposure (2018):
- Attack Scenario:
  - Misconfigured AWS S3 bucket allowed attackers to enumerate and access sensitive customer data.
- Tools and Techniques:
  - Automated scripts and AWS CLI tools for enumeration.
- Impacts:
  - Exposure of sensitive customer data, including passport scans and driver licenses.
  - Potential regulatory fines and reputational damage.

These examples clearly illustrate the importance of detecting Cloud Storage Object Discovery activities and highlight the severe impacts resulting from successful enumeration and unauthorized access.

Last updated 1 month ago

Introduction

Deep Dive Into Technique

Enumerating Buckets or Containers:

Attackers first attempt to discover publicly accessible or misconfigured cloud storage buckets or containers.
Common methods include brute-forcing bucket names, leveraging naming conventions, or utilizing publicly available tools and scripts.

Listing Objects within Storage:

Once buckets or containers are identified, attackers attempt to list or enumerate objects stored within them.
Attack methods include using cloud provider APIs, command-line tools, or specialized enumeration scripts.

Leveraging Misconfigurations:

Attackers exploit misconfigured permissions or weak access controls to list objects.
Common misconfigurations include:
- Publicly accessible buckets or blobs.
- Improper IAM (Identity and Access Management) policies granting excessive permissions.
- Weak or default access control settings.

Using Cloud Provider APIs and Tools:

Attackers may use legitimate cloud CLI tools or SDKs:
- AWS CLI (aws s3 ls s3://bucket-name)
- Azure CLI (az storage blob list)
- Google Cloud SDK (gsutil ls gs://bucket-name)
Attackers also use custom scripts or automated enumeration tools (e.g., CloudBrute, Cloud_enum, Bucket Finder).

Analyzing Metadata:

Attackers often analyze metadata associated with discovered objects to glean additional useful information (e.g., timestamps, file types, permissions).

When this Technique is Usually Used

Attackers use Cloud Storage Object Discovery across various attack scenarios and stages, including:

Initial Reconnaissance:

Early-stage enumeration to identify publicly accessible cloud storage resources.
Gathering intelligence on cloud infrastructure and potential entry points.

Privilege Escalation and Credential Harvesting:

Identifying sensitive data such as credentials, API keys, or configuration files stored in cloud storage.
Leveraging discovered sensitive information to escalate privileges or pivot to other cloud resources.

Lateral Movement:

Identifying stored configuration files, scripts, or infrastructure definitions (e.g., Terraform, CloudFormation templates) to facilitate lateral movement within cloud environments.

Data Exfiltration:

Identifying valuable data assets stored in cloud storage to prepare for exfiltration.
Enumerating sensitive or proprietary data to determine the most valuable targets.

Post-Exploitation:

Continuously monitoring cloud storage for new objects or changes, providing attackers with updated intelligence on the victim's cloud environment.

How this Technique is Usually Detected

Detection of Cloud Storage Object Discovery involves monitoring cloud environments, analyzing logs, and deploying security tools. Effective detection methods and tools include:

Cloud Provider Logging and Monitoring:

AWS CloudTrail logs, Azure Activity Logs, Google Cloud Audit Logs can reveal unusual API calls for object listing or enumeration activities.
Monitor for repeated failed access attempts or enumeration patterns.

Behavioral Analytics and Anomaly Detection:

Implement anomaly detection tools (e.g., AWS GuardDuty, Azure Sentinel, Google Chronicle) to identify unusual enumeration or access patterns.
Detect sudden spikes in API calls or object enumeration activities from unusual IP addresses or regions.

Access Control and Permission Audits:

Regularly audit IAM policies, bucket policies, and ACL configurations to identify misconfigured permissions.
Automated tools like AWS Config, Azure Security Center, or Google Cloud Security Command Center can help identify misconfigurations.

Network Traffic Analysis:

Network security tools (e.g., IDS/IPS, Firewall logs) can detect enumeration traffic patterns targeting cloud storage endpoints.
Monitor outbound network traffic to cloud storage APIs from internal or compromised hosts.

Specific Indicators of Compromise (IoCs):

Repeated API calls to cloud storage services from unknown or suspicious IP addresses.
Enumeration scripts or known tools executed from compromised systems (e.g., CloudBrute, Cloud_enum).
Unusual access patterns, such as listing objects from rarely used or sensitive buckets.

Why it is Important to Detect This Technique

Early detection of Cloud Storage Object Discovery is essential due to the significant impacts it can have on organizations, including:

Data Breaches and Sensitive Data Exposure:

Attackers can discover and exfiltrate sensitive or proprietary data stored in cloud storage, leading to severe financial and reputational damage.

Credential Theft and Privilege Escalation:

Attackers may uncover stored credentials, API keys, or configuration files enabling unauthorized access, privilege escalation, and lateral movement.

Compliance Violations:

Unauthorized access and enumeration of cloud storage objects can lead to regulatory compliance violations (e.g., GDPR, HIPAA), resulting in fines and legal consequences.

Operational Disruption:

Attackers' unauthorized access and enumeration activities risk operational disruptions, causing service downtime or affecting business continuity.

Early Warning of Wider Attacks:

Detecting enumeration activities early can serve as an early warning indicator of broader attack campaigns, allowing organizations to respond proactively and mitigate damage.

Examples

Real-world examples demonstrating Cloud Storage Object Discovery include:

Capital One Data Breach (2019):

Attack Scenario:
- An attacker exploited a misconfigured AWS IAM role to enumerate and access sensitive data stored in AWS S3 buckets.
Tools and Techniques:
- AWS CLI and API calls used to enumerate and list objects.
Impacts:
- Exposure of sensitive personal information of approximately 100 million individuals.
- Significant financial and reputational damage to Capital One.

Uber Data Breach (2016):

Attack Scenario:
- Attackers accessed sensitive data stored in AWS S3 buckets due to weak access controls and enumerated stored objects.
Tools and Techniques:
- Cloud storage enumeration scripts and AWS CLI tools.
Impacts:
- Exposure of personal data of approximately 57 million users and drivers.
- Resulted in regulatory fines and reputational damage.

Accenture Data Exposure (2017):

Attack Scenario:
- Accenture inadvertently exposed sensitive data stored in AWS S3 buckets due to misconfigured permissions.
- Attackers enumerated and accessed stored objects.
Tools and Techniques:
- Publicly available enumeration tools and AWS CLI.
Impacts:
- Sensitive internal documentation and credentials exposed.
- Reputational harm and potential compliance violations.

FedEx Data Exposure (2018):

Attack Scenario:
- Misconfigured AWS S3 bucket allowed attackers to enumerate and access sensitive customer data.
Tools and Techniques:
- Automated scripts and AWS CLI tools for enumeration.
Impacts:
- Exposure of sensitive customer data, including passport scans and driver licenses.
- Potential regulatory fines and reputational damage.

These examples clearly illustrate the importance of detecting Cloud Storage Object Discovery activities and highlight the severe impacts resulting from successful enumeration and unauthorized access.