XM Cyber Maps Eight Attack Paths in AWS Bedrock

Summary
XM Cyber describes eight AWS Bedrock attack paths involving permissions, integrations, logs, knowledge bases, agents, flows, guardrails and prompts, and recommends tighter permissions and security posture controls.
Key points
- XM Cyber mapped eight attack vectors targeting Bedrock’s permissions, configurations and integrations.
- Access to model invocation logs could expose prompts; permissions could also allow logs to be redirected or deleted.
- Knowledge-base integrations could expose raw source data or credentials for connected data stores and services.
- Agent permissions or changes to underlying Lambda code and dependencies could enable malicious actions or data exposure.
- Flow changes could route data to attacker-controlled endpoints, alter business logic or change the encryption key used.
- Guardrail and managed-prompt changes could weaken safety controls or alter model behavior across applications.
- The article recommends inventorying AI workloads, reviewing permissions and mapping attack paths across cloud and on-premises systems.
Article Details
- Attack Vectors
- Redirect model invocation logs to an attacker-controlled S3 bucket using bedrock:PutModelInvocationLoggingConfiguration, exposing prompts.
- Delete S3 objects or CloudWatch log streams to remove evidence of activity.
- Read raw knowledge-base data from its underlying S3 source, bypassing the model.
- Retrieve and decrypt stored credentials for integrated services; the article says these could potentially enable lateral movement into Active Directory through SharePoint.
- Use knowledge-base credentials and network access to obtain vector-database endpoints and API keys, or use intercepted credentials to access AWS-native data stores.
- Modify or create agents, rewrite their prompts, or attach malicious action-group executors.
- Update an agent's Lambda function code or publish a Lambda layer to inject malicious code or dependencies into tool calls.
- Modify Bedrock Flows to route data through an attacker-controlled node, alter condition checks, or replace a flow's customer-managed encryption key.
- Weaken or delete Bedrock Guardrails, reducing or removing filtering controls.
- Modify managed prompt templates or select a poisoned prompt version to alter the behavior of agents and flows.
- Defensive Notes
- Apply tight permission controls to Bedrock workloads and their integrations.
- Inventory AI workloads and the permissions attached to them.
- Map attack paths across cloud and on-premises environments.
- Maintain posture controls across every component in the Bedrock stack.
MITRE ATT&CK
T1530 · Data from Cloud StorageAn attacker with S3 access can read raw data from a Bedrock Knowledge Base's underlying bucket.T1562.008 · Disable or Modify Cloud LogsThe article describes redirecting model invocation logging and deleting S3 objects or log streams to suppress or remove evidence.T1648 · Serverless ExecutionAn attacker with Lambda permissions can update function code or publish a layer to inject malicious code or dependencies into functions used by Bedrock agents.
Vendors
Products
Amazon BedrockAWS Bedrock’s connectivity to enterprise systems enables powerful AI workflows but also creates rich attack surfaces that can be exploited via permissions, configurations, and integrations.AuroraFor AWS-native stores like Aurora and Redshift, intercepted credentials give an attacker direct access to the entire structured knowledge base.AWS LambdaWhen an AI agent can query your Salesforce instance, trigger a Lambda function, or pull from a SharePoint knowledge base, it becomes a node in your infrastructure – with permissions, with reachability, and with pathsConfluenceThe data sources feeding those Knowledge Bases – S3 buckets, Salesforce instances, SharePoint libraries, Confluence spaces – are directly reachable from Bedrock.Microsoft Active DirectoryIn the case of SharePoint, they could potentially use those credentials to move laterally into Active Directory.Microsoft SharePointBedrock’s integrations to S3, Salesforce, SharePoint, and other services make AI components reachable and exploitable.PineconeFor common vector databases integrated with Bedrock, including Pinecone and Redis Enterprise Cloud, stored credentials are often the weakest link.Redis Enterprise CloudFor common vector databases integrated with Bedrock, including Pinecone and Redis Enterprise Cloud, stored credentials are often the weakest link.RedshiftFor AWS-native stores like Aurora and Redshift, intercepted credentials give an attacker direct access to the entire structured knowledge base.S3Bedrock’s integrations to S3, Salesforce, SharePoint, and other services make AI components reachable and exploitable.SalesforceBedrock’s integrations to S3, Salesforce, SharePoint, and other services make AI components reachable and exploitable.
