Security researchers have uncovered eight major attack vectors within Amazon’s AI platform, AWS Bedrock, revealing how attackers could exploit enterprise AI systems to access sensitive data, manipulate workflows, and bypass security controls.
AI Connectivity Creates New Risks
AWS Bedrock enables developers to build AI-powered applications by connecting foundation models directly to enterprise systems like Salesforce, SharePoint, and AWS Lambda.
While this integration boosts productivity, it also introduces new security risks. AI agents effectively become entry points into critical infrastructure, with permissions that attackers can exploit.
The 8 AWS Bedrock Attack Vectors
Researchers from XM Cyber identified eight distinct ways attackers can compromise Bedrock environments:
1. Model Invocation Log Attacks
Attackers can:
- Access sensitive prompts stored in logs
- Redirect logs to attacker-controlled storage
- Delete logs to erase evidence
2. Knowledge Base Attacks (Data Source)
By targeting connected data sources, attackers can:
- Extract raw data from S3, SharePoint, or Salesforce
- Steal credentials used by Bedrock integrations
- Move laterally into enterprise systems
3. Knowledge Base Attacks (Data Store)
Attackers can exploit stored credentials to:
- Access vector databases like Pinecone or Redis
- Gain full control of indexed enterprise data
- Retrieve sensitive structured datasets
4. Agent Attacks (Direct)
With access to agent permissions, attackers can:
- Modify agent prompts
- Force data leakage
- Attach malicious tools to perform unauthorized actions
5. Agent Attacks (Indirect)
Instead of targeting agents directly, attackers can:
- Inject malicious code into AWS Lambda functions
- Alter dependencies using layers
- Exfiltrate data through backend processes
6. Flow Attacks
Bedrock workflows can be manipulated to:
- Redirect sensitive data to attacker-controlled endpoints
- Bypass authorization checks
- Control encryption keys for future data access
7. Guardrail Attacks
Guardrails are designed to enforce AI safety, but attackers can:
- Lower security thresholds
- Remove protections entirely
- Make models vulnerable to prompt injection
8. Managed Prompt Attacks
By modifying centralized prompts, attackers can:
- Inject malicious instructions
- Override safety policies
- Spread compromised behavior across multiple systems
Why This Matters
These attack vectors highlight a critical shift in cybersecurity:
Attackers are no longer targeting just applications or infrastructure
They are targeting AI integrations, permissions, and workflows
A single misconfigured permission could allow attackers to:
- Exfiltrate sensitive enterprise data
- Manipulate AI responses
- Gain access to internal systems
Growing AI Security Concerns
The findings show that securing AI platforms like AWS Bedrock requires:
- Strict permission management (IAM controls)
- Monitoring AI workflows and integrations
- Securing data sources and APIs
- Implementing strong logging and audit mechanisms
Traditional security approaches may not be enough, as AI systems introduce dynamic and interconnected attack surfaces.
Conclusion
The discovery of these eight attack vectors underscores the importance of AI security in cloud environments. As enterprises adopt AI platforms at scale, misconfigurations and over-permissioned access could become major entry points for attackers.
Organizations using AWS Bedrock must prioritize security posture, visibility, and access control to prevent exploitation.
