Unveiling Eight Critical Attack Vectors in AWS Bedrock: A Comprehensive Analysis
Amazon Web Services (AWS) Bedrock has emerged as a pivotal platform for developing AI-driven applications, offering developers seamless access to foundational models and tools that integrate directly with enterprise data and systems. This integration, while enhancing functionality, also introduces potential security vulnerabilities. The XM Cyber threat research team has conducted an in-depth analysis, identifying eight significant attack vectors within AWS Bedrock that could be exploited by malicious actors.
1. Model Invocation Log Attacks
AWS Bedrock meticulously logs every model interaction to ensure compliance and facilitate auditing. However, this logging mechanism can be manipulated:
– Data Harvesting: Attackers with access to the designated S3 bucket can extract sensitive information from existing logs.
– Log Redirection: By utilizing the `bedrock:PutModelInvocationLoggingConfiguration` permission, an attacker can reroute logs to a bucket under their control, capturing all subsequent prompts.
– Evidence Tampering: With `s3:DeleteObject` or `logs:DeleteLogStream` permissions, an attacker can erase logs, effectively removing traces of unauthorized activities.
2. Knowledge Base Data Source Exploitation
Bedrock’s Knowledge Bases connect AI models to proprietary enterprise data through Retrieval Augmented Generation (RAG). The data sources feeding these Knowledge Bases, such as S3 buckets, Salesforce instances, SharePoint libraries, and Confluence spaces, are directly accessible from Bedrock. This accessibility presents several risks:
– Direct Data Access: An attacker with `s3:GetObject` permissions can bypass the AI model and retrieve raw data directly from the underlying bucket.
– Credential Theft: If an attacker can retrieve and decrypt secrets, they can obtain the credentials Bedrock uses to connect to integrated SaaS services. For instance, in the case of SharePoint, these credentials could potentially be used to move laterally into Active Directory.
3. Knowledge Base Data Store Vulnerabilities
After data is ingested into Bedrock, it resides in data stores where it is indexed and structured for real-time querying. Common vector databases integrated with Bedrock, including Pinecone and Redis Enterprise Cloud, have specific vulnerabilities:
– Credential Exploitation: Attackers with access to credentials and network reachability can retrieve endpoint values and API keys from the `StorageConfiguration` object returned via the `bedrock:GetKnowledgeBase` API, granting full administrative access to the vector indices.
– Direct Database Access: For AWS-native stores like Aurora and Redshift, intercepted credentials provide attackers with direct access to the entire structured knowledge base.
4. Direct Agent Manipulation
Bedrock Agents function as autonomous orchestrators within the platform. Malicious actors with `bedrock:UpdateAgent` or `bedrock:CreateAgent` permissions can:
– Prompt Tampering: Modify an agent’s base prompt to force it to disclose internal instructions and tool schemas.
– Action Group Creation: Combine the above permissions with `bedrock:CreateAgentActionGroup` to create new action groups, potentially leading to unauthorized operations.
5. Agent Flow Injection
Agents in Bedrock operate based on predefined workflows. Attackers with `bedrock:UpdateAgent` permissions can:
– Workflow Alteration: Inject malicious steps into an agent’s workflow, causing it to perform unintended actions.
– Unauthorized Command Execution: Manipulate the agent to execute commands that could compromise system integrity.
6. Guardrail Degradation
Guardrails are implemented to ensure AI models operate within set ethical and operational boundaries. An attacker with `bedrock:UpdateGuardrail` permissions can:
– Policy Modification: Alter guardrail policies to relax restrictions, allowing the model to perform actions it was previously restricted from.
– Ethical Boundary Breach: Enable the model to generate content or make decisions that violate ethical guidelines.
7. Prompt Poisoning
Prompt poisoning involves feeding the AI model malicious inputs to influence its outputs. Attackers can:
– Bias Introduction: Craft prompts that subtly introduce biases into the model’s responses.
– Misinformation Dissemination: Use poisoned prompts to generate and spread false information.
8. Flow Injection via External Integrations
Bedrock’s integration with external services can be exploited if not properly secured. Attackers can:
– Malicious Payload Delivery: Inject harmful data through integrated services, leading to unintended model behavior.
– Service Compromise: Exploit vulnerabilities in integrated services to gain unauthorized access to Bedrock’s environment.
Mitigation Strategies
To safeguard against these attack vectors, organizations should implement the following measures:
– Strict Access Controls: Limit permissions to only those necessary for each user or service.
– Regular Audits: Conduct frequent reviews of logs and configurations to detect unauthorized changes.
– Credential Management: Regularly rotate credentials and employ robust encryption methods.
– Monitoring and Alerts: Implement real-time monitoring to detect and respond to suspicious activities promptly.
– Security Training: Educate staff on potential threats and best practices for maintaining a secure environment.
By proactively addressing these vulnerabilities, organizations can harness the full potential of AWS Bedrock while maintaining a robust security posture.
