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Analyzing the Amazon Q Incident Using the SAIL Framework

By

Ziv Karliner

and

July 29, 2025

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In July 2025, Amazon Q - installed by over 950,000 developers - became the vector for a sophisticated supply chain attack that could have wiped user files and AWS cloud resources

This blog will analyze the Amazon Q incident through the lens of the SAIL framework to understand the failures and identify the necessary controls to prevent similar events.

The Incident: When Your AI Assistant Turns Against You

In late July 2025, an attacker exploited a vulnerability in Amazon Q Developer for VS Code Extension by submitting a malicious pull request. This request was approved and integrated into an official update, allowing the attacker to inject a "wiper" prompt into the system. This malicious prompt was designed to instruct the AI assistant to execute destructive commands, including the deletion of user files and cloud resources. 

Echoes of Previous Research: The AI Supply Chain is Now a Critical Attack Vector

This attack validates a disturbing trend we've been tracking at Pillar Security. Earlier this year, our "Rules File Backdoor" research demonstrated similar vulnerabilities in GitHub Copilot and Cursor. We showed how hidden Unicode characters in configuration files could inject malicious prompts, causing AI assistants to generate backdoored code.

The Amazon Q incident is a great example of this attack vector, that comes with three critical implications:

  1. Scale: With nearly a million installations, a single compromised update can cascade into thousands of compromised development environments
  2. Privilege: AI coding assistants operate with high system privileges, making them prime targets
  3. Trust: Developers implicitly trust their AI tools, creating a dangerous security blind spot

Breaking Down the Attack: A SAIL Framework Analysis

In this section, we'll examine the Amazon Q incident through using the SAIL Framework - a process driven methodology designed specifically for AI security. By understanding how this attack unfolded across SAIL's seven phases, security and AI teams can identify critical vulnerabilities in their own AI systems and implement preventive controls before similar attacks occur.

For AI & security teams, this analysis serves as a practical roadmap. Each SAIL phase below identifies:

  • Critical risks that could exist in your AI systems today
  • Real-world examples of how these risks manifest, informed by the Amazon Q incident
  • Concrete controls your team should implement to prevent similar compromises

SAIL Phase Risk Assessment
SAIL Phase Key SAIL Risks to Monitor How These Risks Could Manifest (Lessons from Amazon Q) Recommended Controls
Phase 1: Plan
SAIL 1.1: Inadequate AI Policy
SAIL 1.2: Governance Misalignment
SAIL 1.10: Incomplete Threat Modeling for AI Systems
AI coding assistants may not be covered by existing security policies
External contribution policies may not account for AI-specific risks
Traditional threat models may miss AI supply chain vectors like malicious prompts in PRs
Create AI-specific security policies that explicitly cover repository management and external contributions
Establish unified governance between security, AI, and development teams
Conduct threat modeling sessions that include supply chain, insider threats, and privilege escalation scenarios
Phase 2: Code/No Code
SAIL 2.1: Incomplete Asset Inventory
SAIL 2.3: Unidentified Third-Party AI Integrations
SAIL 2.5: Lack of Clarity on AI System Purpose and Criticality
AI extensions used by thousands of developers may not be in your asset inventory
External contributors to AI tools represent third-party risk
AI coding assistants have privileged access but may be classified as low-risk developer tools
Build comprehensive inventory of all AI components including extensions, APIs, coding agents and more
Map all external access points and third-party integrations
Implement risk-based classification considering user base size and business impact
Phase 3: Build
SAIL 3.2: Model Backdoor Insertion or Tampering
SAIL 3.4: Insecure System Prompt Design
SAIL 3.10: Unvetted Use of Open-Source and Third-Party AI Components
SAIL 3.14: Exposed AI Access Credentials in Discovered Assets
Malicious prompts can be inserted into AI systems via code contributions
AI assistants may have unrestricted access to execute system commands
External PRs to AI components may bypass security review
Repository permissions may grant excessive access to external parties
Implement mandatory security review for all code affecting AI behavior
Design AI systems with least-privilege access principles
Establish approval workflows requiring security sign-off for external contributions
Automate regular audits of repository permissions and access logs
Phase 4: Test
SAIL 4.2: Incomplete Red-Team Coverage
SAIL 4.3: Lack of Risk Assessment Process
SAIL 4.9: Limited Scope of Evasion Technique Testing
Security testing may focus on traditional vulnerabilities, missing AI-specific attacks
Supply chain attacks through development tools may not be tested
Privilege escalation through repository access may be overlooked
Include supply chain compromise scenarios in red team exercises
Test specifically for prompt injection and privilege escalation
Simulate attacks through development pipeline vulnerabilities
Document and track all identified risks with remediation timelines
Phase 5: Deploy
SAIL 5.7: Insecure Output Handling
SAIL 5.14: Autonomous-Agent Misuse
SAIL 5.16: Cross-Domain Prompt Injection (XPIA)
SAIL 5.17: Policy-Violating Output
Deployed AI systems may contain hidden malicious prompts
AI-generated commands may execute without validation
AI assistants may have autonomous execution capabilities
No runtime controls to prevent destructive operations
Deploy prompt validation and sanitization layers
Implement command filtering for high-risk operations
Require human approval for destructive actions
Enable runtime security policies that prevent policy violations
Phase 6: Operate
SAIL 6.1: Autonomous Code Execution Abuse
SAIL 6.2: Unrestricted API/Tool Invocation
SAIL 6.6: Autonomous Resource Provisioning/Abuse
AI systems may operate with full user privileges and filesystem access
Direct access to cloud CLIs and APIs without restrictions
No isolation between AI operations and production environments
AI can execute destructive commands like file deletion or resource termination
Deploy AI workloads in isolated sandboxed environments
Implement strict API allowlisting and rate limiting
Use separate, limited credentials for AI operations
Phase 7: Monitor
SAIL 7.1: Insufficient AI Interaction Logging
SAIL 7.2: Missing Real-time Security Alerts
SAIL 7.4: Inadequate AI Audit Trails
SAIL 7.6: Absence of AI-Specific Incident Response Plan
Permission changes in AI repositories may go undetected
Malicious code insertions may not trigger security alerts
Audit trails may not capture AI-specific security events
Standard incident response may not cover AI compromise scenarios
Enable comprehensive logging for all AI-related activities
Configure real-time alerts for permission changes and anomalous behavior
Maintain immutable audit trails with full change history
Develop and test AI-specific incident response procedures
Establish clear escalation paths and disclosure protocols
Phase 1: Plan
Key SAIL Risks to Monitor
SAIL 1.1: Inadequate AI Policy
SAIL 1.2: Governance Misalignment
SAIL 1.10: Incomplete Threat Modeling for AI Systems
How These Risks Could Manifest (Lessons from Amazon Q)
AI coding assistants may not be covered by existing security policies
External contribution policies may not account for AI-specific risks
Traditional threat models may miss AI supply chain vectors like malicious prompts in PRs
Recommended Controls
Create AI-specific security policies that explicitly cover repository management and external contributions
Establish unified governance between security, AI, and development teams
Conduct threat modeling sessions that include supply chain, insider threats, and privilege escalation scenarios
Phase 2: Code/No Code
Key SAIL Risks to Monitor
SAIL 2.1: Incomplete Asset Inventory
SAIL 2.3: Unidentified Third-Party AI Integrations
SAIL 2.5: Lack of Clarity on AI System Purpose and Criticality
How These Risks Could Manifest (Lessons from Amazon Q)
AI extensions used by thousands of developers may not be in your asset inventory
External contributors to AI tools represent third-party risk
AI coding assistants have privileged access but may be classified as low-risk developer tools
Recommended Controls
Build comprehensive inventory of all AI components including extensions, APIs, coding agents and more
Map all external access points and third-party integrations
Implement risk-based classification considering user base size and business impact
Phase 3: Build
Key SAIL Risks to Monitor
SAIL 3.2: Model Backdoor Insertion or Tampering
SAIL 3.4: Insecure System Prompt Design
SAIL 3.10: Unvetted Use of Open-Source and Third-Party AI Components
SAIL 3.14: Exposed AI Access Credentials in Discovered Assets
How These Risks Could Manifest (Lessons from Amazon Q)
Malicious prompts can be inserted into AI systems via code contributions
AI assistants may have unrestricted access to execute system commands
External PRs to AI components may bypass security review
Repository permissions may grant excessive access to external parties
Recommended Controls
Implement mandatory security review for all code affecting AI behavior
Design AI systems with least-privilege access principles
Establish approval workflows requiring security sign-off for external contributions
Automate regular audits of repository permissions and access logs
Phase 4: Test
Key SAIL Risks to Monitor
SAIL 4.2: Incomplete Red-Team Coverage
SAIL 4.3: Lack of Risk Assessment Process
SAIL 4.9: Limited Scope of Evasion Technique Testing
How These Risks Could Manifest (Lessons from Amazon Q)
Security testing may focus on traditional vulnerabilities, missing AI-specific attacks
Supply chain attacks through development tools may not be tested
Privilege escalation through repository access may be overlooked
Recommended Controls
Include supply chain compromise scenarios in red team exercises
Test specifically for prompt injection and privilege escalation
Simulate attacks through development pipeline vulnerabilities
Document and track all identified risks with remediation timelines
Phase 5: Deploy
Key SAIL Risks to Monitor
SAIL 5.7: Insecure Output Handling
SAIL 5.14: Autonomous-Agent Misuse
SAIL 5.16: Cross-Domain Prompt Injection (XPIA)
SAIL 5.17: Policy-Violating Output
How These Risks Could Manifest (Lessons from Amazon Q)
Deployed AI systems may contain hidden malicious prompts
AI-generated commands may execute without validation
AI assistants may have autonomous execution capabilities
No runtime controls to prevent destructive operations
Recommended Controls
Deploy prompt validation and sanitization layers
Implement command filtering for high-risk operations
Require human approval for destructive actions
Enable runtime security policies that prevent policy violations
Phase 6: Operate
Key SAIL Risks to Monitor
SAIL 6.1: Autonomous Code Execution Abuse
SAIL 6.2: Unrestricted API/Tool Invocation
SAIL 6.6: Autonomous Resource Provisioning/Abuse
How These Risks Could Manifest (Lessons from Amazon Q)
AI systems may operate with full user privileges and filesystem access
Direct access to cloud CLIs and APIs without restrictions
No isolation between AI operations and production environments
AI can execute destructive commands like file deletion or resource termination
Recommended Controls
Deploy AI workloads in isolated sandboxed environments
Implement strict API allowlisting and rate limiting
Use separate, limited credentials for AI operations
Phase 7: Monitor
Key SAIL Risks to Monitor
SAIL 7.1: Insufficient AI Interaction Logging
SAIL 7.2: Missing Real-time Security Alerts
SAIL 7.4: Inadequate AI Audit Trails
SAIL 7.6: Absence of AI-Specific Incident Response Plan
How These Risks Could Manifest (Lessons from Amazon Q)
Permission changes in AI repositories may go undetected
Malicious code insertions may not trigger security alerts
Audit trails may not capture AI-specific security events
Standard incident response may not cover AI compromise scenarios
Recommended Controls
Enable comprehensive logging for all AI-related activities
Configure real-time alerts for permission changes and anomalous behavior
Maintain immutable audit trails with full change history
Develop and test AI-specific incident response procedures
Establish clear escalation paths and disclosure protocols

Key Takeaways:

  1. AI Systems Are Different: The Amazon Q incident demonstrates that AI systems require specialized security controls beyond traditional application security.

  2. Supply Chain is Critical: Your AI tools are only as secure as their development and data pipeline - this includes external contributors and code repositories.

  3. Privilege Matters: AI coding assistants operate with significant system privileges, making them high-value targets for attackers.

  4. Detection is Essential: Without AI-specific monitoring and alerting, compromises can go undetected for extended periods.

  5. SAIL Provides Structure: Use the SAIL framework to systematically address AI security risks across the entire lifecycle, preventing incidents before they occur.

How Pillar Security Can Help

At Pillar Security, we've been at the forefront of identifying and defending against these emerging threats, from our groundbreaking "Rules File Backdoor" research to developing the SAIL framework itself. Our platform combines deep AI security expertise with advanced threat intelligence feeds, providing real-time visibility into AI-specific attack patterns before they hit your systems.

Contact us to learn how innovative companies are using Pillar to build and run secure AI systems. 

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