Incident Score: Analysis & Impact (GOOAMANPMGIT1773319158)
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 Cyber Attack 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 GOOAMANPMGIT1773319158.
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 637 and after the incident was 629 with a difference of -8 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 January 2026, Multiple Enterprises (Unspecified) disclosed Identity Compromise, AI Weaponization and Software Exploitation issues under the banner "Google’s Cloud Threat Horizons Report: Accelerating Cyber Threats and Flawed Defenses".
Google’s H1 2026 Cloud Threat Horizons Report highlights a rapidly evolving threat landscape, including unchecked identity sprawl, weaponized AI tools, and collapsing exploitation windows.
The disruption is felt across the environment, affecting Kubernetes, AWS and GitHub, and exposing Credentials, Sensitive Files (.env, .conf, .log) and Personally Identifiable Information.
In response, and began remediation that includes Automated Forensic Pipelines and AI-Native Security Architectures.
The case underscores how Ongoing, teams are taking away lessons such as Traditional security measures are insufficient against machine-speed threats. Enterprises must adopt AI-native security architectures, govern autonomous AI agents, and automate response pipelines to keep pace with adversaries, and recommending next steps like Implement identity governance for autonomous AI agents, Monitor LLM activity as a primary threat signal and Deploy automated forensic and response pipelines.
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 Valid Accounts (T1078) with high confidence (90%), supported by evidence indicating 83% of cloud intrusions in H2 2025 stemmed from identity compromise, Supply Chain Compromise: Compromise Software Dependencies and Development Tools (T1195.002) with moderate to high confidence (80%), supported by evidence indicating malicious NPM package (QUIETVAULT credential stealer), and Exploit Public-Facing Application (T1190) with moderate to high confidence (70%), supported by evidence indicating software-based initial access vectors surged from 2.9% to 44.5%. Under the Execution tactic, the analysis identified User Execution: Malicious Link (T1204.001) with moderate confidence (60%), supported by evidence indicating phishing have dominated breach vectors and Serverless Execution (T1648) with moderate to high confidence (70%), supported by evidence indicating exploited unconstrained CI/CD service accounts in Kubernetes. Under the Persistence tactic, the analysis identified Account Manipulation (T1098) with moderate to high confidence (80%), supported by evidence indicating uNC6426 leveraged a compromised GitHub token to escalate to full AWS admin access and Create Account: Cloud Account (T1136.003) with moderate confidence (50%), supported by evidence indicating overprovision access prioritizing operational convenience over security. Under the Privilege Escalation tactic, the analysis identified Valid Accounts (T1078) with high confidence (90%), supported by evidence indicating uNC6426 escalated to full AWS admin access within 72 hours and Abuse Elevation Control Mechanism: Bypass User Account Control (T1548.002) with moderate confidence (60%), supported by evidence indicating bypassing human oversight entirely via CI/CD service accounts. Under the Defense Evasion tactic, the analysis identified Use Alternate Authentication Material: Application Access Token (T1550.001) with moderate to high confidence (80%), supported by evidence indicating compromised GitHub token used for AWS admin access, Valid Accounts: Cloud Accounts (T1078.004) with high confidence (90%), supported by evidence indicating 83% of cloud intrusions stemmed from identity compromise, and Hide Artifacts: Email Hiding Rules (T1564.008) with moderate confidence (50%), supported by evidence indicating lLM process execution invisible to traditional endpoint detection. Under the Credential Access tactic, the analysis identified Steal Application Access Token (T1528) with high confidence (90%), supported by evidence indicating qUIETVAULT credential stealer embedded in malicious NPM package, Unsecured Credentials: Credentials In Files (T1552.001) with moderate to high confidence (80%), supported by evidence indicating scanned for sensitive files (.env, .conf, .log) before extracting credentials, and Brute Force: Password Guessing (T1110.001) with moderate confidence (60%), supported by evidence indicating stolen credentials and phishing have dominated breach vectors. Under the Discovery tactic, the analysis identified Account Discovery: Cloud Account (T1087.004) with moderate to high confidence (80%), supported by evidence indicating aI agents traverse environments at machine speed, File and Directory Discovery (T1083) with high confidence (90%), supported by evidence indicating hijacked local LLM to scan for sensitive files (.env, .conf, .log), and Network Service Discovery (T1046) with moderate to high confidence (70%), supported by evidence indicating automated reconnaissance engine in LLM environments. Under the Collection tactic, the analysis identified Data from Local System (T1005) with high confidence (90%), supported by evidence indicating extracted credentials from sensitive files (.env, .conf, .log) and Data from Information Repositories: Code Repositories (T1213.003) with moderate to high confidence (80%), supported by evidence indicating compromised GitHub token used for AWS admin access. Under the Command and Control tactic, the analysis identified Application Layer Protocol: Web Protocols (T1071.001) with moderate to high confidence (70%), supported by evidence indicating exfiltration over C2 channel likely via standard web protocols. Under the Exfiltration tactic, the analysis identified Exfiltration Over C2 Channel (T1041) with moderate to high confidence (80%), supported by evidence indicating data exfiltration confirmed in QUIETVAULT attack and Transfer Data to Cloud Account (T1537) with moderate to high confidence (70%), supported by evidence indicating credentials and sensitive files exfiltrated via cloud services. Under the Impact tactic, the analysis identified Resource Hijacking (T1496) with moderate to high confidence (80%), supported by evidence indicating threat actors deployed cryptocurrency miners within 48 hours of CVE disclosure and Account Access Removal (T1531) with moderate confidence (60%), supported by evidence indicating bypassed human oversight; automated exploitation. 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/GOOAMANPMGIT1773319158
- 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/gooamanpmgit1773319158-github-npm-google-aws-cyber-attack-march-2026/
- Amazon Web Services (AWS) CyberSecurity Score History: https://www.rankiteo.com/company/amazon-web-services/history
- Amazon Web Services (AWS) CyberSecurity Incident Source: https://securityboulevard.com/2026/03/83-of-cloud-breaches-start-with-identity-ai-agents-are-about-to-make-it-worse/
- 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