Incident Score: Analysis & Impact (AMAWIZ1768515615)
The details regarding individual company incidents & reports gives you full view from every side.
Rankiteo Score Impact Analysis
Key Highlights From The Incident Analysis
- Timeline of Amazon Web Services (AWS)'s Vulnerability and lateral movement inside company's environment.
- Overview of affected data sets, including SSNs and PHI, and why they materially increase incident severity.
- How Rankiteo’s incident engine converts technical details into a normalized incident score.
- How this cyber incident impacts Amazon Web Services (AWS) Rankiteo cyber scoring and cyber rating.
- Rankiteo’s MITRE ATT&CK correlation analysis for this incident, with associated confidence level.
Full Incident Analysis Transcript
In this Rankiteo incident briefing, we review the Amazon Web Services (AWS) breach identified under incident ID AMAWIZ1768515615.
The analysis begins with a detailed overview of Amazon Web Services (AWS)'s information like the linkedin page: https://www.linkedin.com/company/amazon-web-services, the number of followers: 10600547, the industry type: IT Services and IT Consulting and the number of employees: 153837 employees
After the initial compromise, the video explains how Rankiteo's incident engine converts technical details into a normalized incident score. The incident score before the incident was 760 and after the incident was 758 with a difference of -2 which is could be a good indicator of the severity and impact of the incident.
In the next step of the video, we will analyze in more details the incident and the impact it had on Amazon Web Services (AWS) and their customers.
On 01 September 2025, Amazon Web Services (AWS) disclosed Supply Chain Attack issues under the banner "CodeBreach: AWS CodeBuild Misconfiguration Could Lead to Platform-Wide Compromise".
A critical misconfiguration in Amazon Web Services (AWS) CodeBuild could have allowed complete takeover of the cloud service provider's own GitHub repositories, including its AWS JavaScript SDK, putting every AWS environment at risk.
The disruption is felt across the environment, affecting AWS CodeBuild, GitHub repositories (aws-sdk-js-v3, aws-lc, amazon-corretto-crypto-provider, awslabs/open-data-registry), and exposing GitHub admin tokens, repository secrets, privileged credentials.
In response, teams activated the incident response plan, moved swiftly to contain the threat with measures like Remediation of misconfigured webhook filters, credential rotations, and began remediation that includes Anchoring regex patterns, enabling Pull Request Comment Approval build gate, using CodeBuild-hosted runners, limiting PAT permissions, while recovery efforts such as Securing build processes containing GitHub tokens or credentials in memory continue, and stakeholders are being briefed through Public advisory released by AWS and Wiz.
The case underscores how Resolved, teams are taking away lessons such as CI/CD pipeline security is critical, especially for untrusted contributions. Misconfigurations in webhook filters can lead to high-impact breaches. Anchoring regex patterns and limiting PAT permissions are essential mitigations, and recommending next steps like Enable Pull Request Comment Approval build gate for untrusted contributions, Use CodeBuild-hosted runners to manage build triggers via GitHub workflows and Ensure regex patterns in webhook filters are anchored (use ^ and $), with advisories going out to stakeholders covering AWS released an advisory detailing the misconfiguration and remediation steps.
Finally, we try to match the incident with the MITRE ATT&CK framework to see if there is any correlation between the incident and the MITRE ATT&CK framework.
The MITRE ATT&CK framework is a knowledge base of techniques and sub-techniques that are used to describe the tactics and procedures of cyber adversaries. It is a powerful tool for understanding the threat landscape and for developing effective defense strategies.
MITRE ATT&CK® Correlation Analysis
Rankiteo's analysis has identified several MITRE ATT&CK tactics and techniques associated with this incident, each with varying levels of confidence based on available evidence. Under the Initial Access tactic, the analysis identified Supply Chain Compromise: Compromise Software Supply Chain (T1195.002) with high confidence (90%), supported by evidence indicating aWS CodeBuild misconfiguration could have enabled supply chain attacks and Trusted Relationship (T1199) with moderate to high confidence (80%), supported by evidence indicating exploited predictable GitHub actor IDs to match trusted maintainers. Under the Execution tactic, the analysis identified Serverless Execution (T1648) with moderate to high confidence (80%), supported by evidence indicating attackers could trigger builds in AWS CodeBuild to execute malicious actions. Under the Credential Access tactic, the analysis identified Steal Application Access Token (T1528) with high confidence (90%), supported by evidence indicating leak GitHub admin tokens including Personal Access Token (PAT) for privileged access and Unsecured Credentials: Credentials In Files (T1552.001) with moderate to high confidence (70%), supported by evidence indicating gitHub admin tokens and repository secrets were at risk of exposure. Under the Privilege Escalation tactic, the analysis identified Valid Accounts: Cloud Accounts (T1078.004) with moderate to high confidence (80%), supported by evidence indicating pAT for aws-sdk-js-automation user granted full repository control. Under the Defense Evasion tactic, the analysis identified Modify Authentication Process: Multi-Factor Authentication (T1556.003) with moderate confidence (60%), supported by evidence indicating insecure regex patterns allowed bypass of webhook restrictions and Masquerading: Match Legitimate Name or Location (T1036.005) with moderate to high confidence (70%), supported by evidence indicating bot accounts with predictable IDs matched trusted maintainers IDs. Under the Lateral Movement tactic, the analysis identified Use Alternate Authentication Material: Application Access Token (T1550.001) with moderate to high confidence (80%), supported by evidence indicating gitHub admin tokens could enable access to multiple repositories. Under the Collection tactic, the analysis identified Data from Code Repositories (T1213.003) with high confidence (90%), supported by evidence indicating potential secrets exfiltration from AWS-managed GitHub repositories. Under the Exfiltration tactic, the analysis identified Transfer Data to Cloud Account (T1537) with moderate to high confidence (70%), supported by evidence indicating potential exfiltration of repository secrets and privileged credentials. Under the Impact tactic, the analysis identified Resource Hijacking (T1496) with moderate to high confidence (80%), supported by evidence indicating malicious code injection could affect millions of AWS environments and Network Denial of Service: Direct Network Flood (T1498.001) with moderate confidence (50%), supported by evidence indicating potential platform-wide compromise of AWS services. These correlations help security teams understand the attack chain and develop appropriate defensive measures based on the observed tactics and techniques.
Sources & References
- Amazon Web Services (AWS) Rankiteo Cyber Incident Details: https://www.rankiteo.com/company/amazon-web-services/incident/AMAWIZ1768515615
- Amazon Web Services (AWS) CyberSecurity Rating page: https://www.rankiteo.com/company/amazon-web-services
- Amazon Web Services (AWS) Rankiteo Cyber Incident Blog Article: https://blog.rankiteo.com/amawiz1768515615-amazon-web-services-wiz-vulnerability-september-2025/
- Amazon Web Services (AWS) CyberSecurity Score History: https://www.rankiteo.com/company/amazon-web-services/history
- Amazon Web Services (AWS) CyberSecurity Incident Source: https://thehackernews.com/2026/01/aws-codebuild-misconfiguration-exposed.html
- Rankiteo A.I CyberSecurity Rating methodology: https://www.rankiteo.com/Images/rankiteo_algo.pdf
- Rankiteo TPRM Scoring methodology: https://static.rankiteo.com/model/rankiteo_tprm_methodology.pdf