Executive Summary
In July 2026, a critical cloud storage attack technique known as 'bucket hijacking' was disclosed, enabling threat actors to silently redirect an organization's active cloud data streams, including audit logs and telemetry, into attacker-controlled external storage buckets across major cloud platforms. This vulnerability exploits the global uniqueness of cloud storage bucket names, allowing attackers to register a previously deleted bucket name and reroute data streams intended for the original bucket. The attack affects major cloud providers, including Google Cloud, Amazon Web Services (AWS), and Microsoft Azure, and detection is extremely challenging once deployed. (serisec.com)
This incident underscores the escalating risks associated with cloud misconfigurations and the critical need for organizations to implement robust monitoring and configuration management practices. As cloud environments become increasingly complex, the potential for such vulnerabilities to be exploited grows, emphasizing the importance of proactive security measures to safeguard sensitive data.
Why This Matters Now
The disclosure of the 'bucket hijacking' technique highlights a significant and exploitable vulnerability in cloud storage configurations. With cloud misconfigurations accounting for a substantial portion of data breaches, organizations must urgently reassess and fortify their cloud security postures to prevent potential data exfiltration and maintain compliance with regulatory standards.
Attack Path Analysis
An attacker exploited a misconfigured cloud storage bucket to gain unauthorized access, escalated privileges by modifying IAM roles, moved laterally to access additional resources, established command and control channels, exfiltrated sensitive data, and caused significant operational disruption.
Kill Chain Progression
Initial Compromise
Description
The attacker exploited a misconfigured cloud storage bucket to gain unauthorized access.
Related CVEs
CVE-2026-2473
CVSS 7.7Predictable bucket naming in Google Cloud Vertex AI Experiments allows unauthenticated remote attackers to achieve cross-tenant remote code execution via pre-creating predictably named Cloud Storage buckets.
Affected Products:
Google Cloud Vertex AI Experiments – 1.21.0 up to but not including 1.133.0
Exploit Status:
no public exploitReferences:
CVE-2026-1727
CVSS 9.1Predictable Google Cloud Storage bucket names in Google Agentspace allow attackers to engage in bucket squatting, potentially capturing sensitive information intended for legitimate service operations.
Affected Products:
Google Agentspace – All versions prior to February 13, 2026
Exploit Status:
no public exploit
MITRE ATT&CK® Techniques
Cloud Service Hijacking
Cloud Storage Object Discovery
Data from Cloud Storage
Cloud Infrastructure Discovery
Valid Accounts
Potential Compliance Exposure
Mapping incident impact across multiple compliance frameworks.
PCI DSS 4.0 – Review and Control Access to System Components
Control ID: 6.4.1
NYDFS 23 NYCRR 500 – Cybersecurity Policy
Control ID: 500.03
DORA – ICT Risk Management Framework
Control ID: Article 5
CISA ZTMM 2.0 – Identity and Access Management
Control ID: 3.1
NIS2 Directive – Security Measures
Control ID: Article 21
Sector Implications
Industry-specific impact of the vulnerabilities, including operational, regulatory, and cloud security risks.
Information Technology/IT
Cloud bucket hijacking and Windows LPE chains directly threaten IT infrastructure, requiring enhanced egress security and zero trust segmentation capabilities.
Financial Services
Global fraud operations exploit cloud misconfigurations and lateral movement vulnerabilities, demanding encrypted traffic controls and anomaly detection for compliance.
Health Care / Life Sciences
HIPAA-regulated environments face exfiltration risks from compromised cloud storage and unencrypted traffic exposing sensitive patient data flows.
Computer Software/Engineering
Software development environments vulnerable to Kubernetes security gaps and shadow AI risks requiring multicloud visibility and threat detection.
Sources
- ThreatsDay: Cloud Bucket Hijacking, Windows LPE Chain, Global Fraud Bust + 17 More Storieshttps://thehackernews.com/2026/07/threatsday-cloud-bucket-hijacking.htmlVerified
- Google Vertex AI SDK Flaw Let Attackers Hijack Model Uploads via Bucket Squattinghttps://thehackernews.com/2026/06/google-vertex-ai-sdk-flaw-let-attackers.htmlVerified
- CVE-2026-1727: Agentspace Information Disclosure Flawhttps://www.sentinelone.com/vulnerability-database/cve-2026-1727/Verified
- Cloud Threat Horizons Report H1 2026https://cloud.google.com/security/report/resources/cloud-threat-horizons-report-h1-2026Verified
Frequently Asked Questions
Cloud Native Security Fabric Mitigations and ControlsCNSF
Aviatrix Zero Trust CNSF is pertinent to this incident as it would likely limit the attacker's ability to move laterally and exfiltrate data by enforcing strict segmentation and identity-based access controls.
Control: Cloud Native Security Fabric (CNSF)
Mitigation: The attacker's initial access may have been constrained by enforcing strict access controls and continuous verification at the workload boundary.
Control: Zero Trust Segmentation
Mitigation: The attacker's ability to escalate privileges may have been limited by enforcing identity-based policies that restrict access based on verified identities.
Control: East-West Traffic Security
Mitigation: The attacker's lateral movement could have been constrained by enforcing strict east-west traffic controls that limit unauthorized inter-workload communications.
Control: Multicloud Visibility & Control
Mitigation: The attacker's command and control channels may have been detected and disrupted by providing comprehensive visibility and control over multicloud environments.
Control: Egress Security & Policy Enforcement
Mitigation: The attacker's data exfiltration efforts could have been limited by enforcing strict egress policies that monitor and control outbound data flows.
The attacker's ability to cause operational disruption may have been reduced by limiting their access to critical resources and enforcing strict access controls.
Impact at a Glance
Affected Business Functions
- Data Storage
- Machine Learning Operations
- Cloud Infrastructure Management
Estimated downtime: 3 days
Estimated loss: $500,000
Potential exposure of sensitive machine learning models and associated data.
Recommended Actions
Key Takeaways & Next Steps
- • Implement Zero Trust Segmentation to enforce least privilege access and prevent unauthorized lateral movement.
- • Utilize Multicloud Visibility & Control to monitor and manage cloud resources across multiple platforms.
- • Apply Egress Security & Policy Enforcement to control outbound traffic and prevent data exfiltration.
- • Deploy Threat Detection & Anomaly Response mechanisms to identify and respond to suspicious activities promptly.
- • Regularly audit and update IAM policies to ensure appropriate access controls are in place.



