Listen to this Post
Introduction:
In the rapidly evolving landscape of cloud security and artificial intelligence, a new and insidious threat has emerged from the depths of serverless architectures. Security researchers have uncovered a significant blind spot within AWS Bedrock, where the generation of long-term API keys inadvertently creates “phantom” IAM users. These shadow identities retain administrative privileges indefinitely, persisting even after the original access keys are revoked or deleted. This creates a silent persistence vector for attackers, enabling a dangerous form of LLMjacking that can cost victims up to $14,000 per day per region and providing a stealthy backdoor that completely bypasses standard incident response playbooks.
Learning Objectives:
- Understand the mechanics of “phantom IAM users” in AWS Bedrock and how they create a persistence risk.
- Learn to identify, audit, and categorize orphaned identities using open-source security tooling.
- Master step-by-step procedures for emergency revocation, CloudTrail forensics, and implementing preventative controls like Service Control Policies (SCPs).
You Should Know:
- Understanding the Phantom: How AWS Bedrock Creates Orphaned Identities
When a developer or administrator generates long-term credentials (Access Key ID and Secret Access Key) for AWS Bedrock via the Console, the underlying process doesn’t just create a standard user. In specific scenarios related to foundational model invocation, the system provisions a “phantom” IAM user. This entity is detached from the original key rotation lifecycle. Even if the original keys are leaked, rotated, or deleted, this phantom user—often equipped with administrative-level permissions akin toAmazonBedrockFullAccess—remains active and undiscoverable by standard IAM user list APIs unless you know exactly where to look. This is the “stealth factor”: security teams rotate the compromised key, assume the threat is neutralized, but the backdoor remains wide open. -
Step-by-Step: Discovering Phantom IAM Users with the AWS CLI
To audit your environment for these shadow identities, you cannot rely on the standard `list-users` command alone. You must query the specific Bedrock service-linked roles and examine their attached policies and access keys. This process helps identify principals that are active but unmanaged.
Step 1: Identify Potential Phantom Principals
First, list all roles that have Bedrock permissions, as phantoms often manifest as service-linked or custom roles with long-term keys attached, which is atypical.
List roles with "bedrock" in the name or path aws iam list-roles | grep -i bedrock List access keys for a suspicious role (if it has a programmatic user attached, which is unusual) Note: This requires the role to have an IAM user entity; you might need to look for users with specific paths. aws iam list-access-keys --user-name <suspicious-role-name> --region us-east-1
Step 2: Enumerate Access Keys for All Users and Parse for Bedrock Permissions
You need to cross-reference IAM users with their attached policies to find those with Bedrock admin rights.
List all users and their keys for user in $(aws iam list-users --query "Users[].UserName" --output text); do echo "User: $user" Check if they have Bedrock admin policies aws iam list-attached-user-policies --user-name $user --query "AttachedPolicies[?PolicyName=='AdministratorAccess' || contains(PolicyName, 'Bedrock')]" --output table List their keys aws iam list-access-keys --user-name $user --output table done
If you find a user with an active `AccessKeyId` and a Bedrock admin policy, but whose key creation date is old and should have been rotated/deleted, you may have discovered a phantom.
3. Emergency Revocation and Remediation
Once a phantom identity is discovered, immediate revocation is critical. However, simply deleting the key may not delete the phantom user itself, leaving the door ajar for future key generation.
Step 1: Immediate Key Deactivation
Deactivate the key first to prevent immediate abuse while preserving it for forensics.
Deactivate the compromised access key aws iam update-access-key --access-key-id <AKIA...> --status Inactive --user-name <PhantomUserName>
Step 2: Detach Policies and Delete the Phantom
Remove the teeth from the user, then delete the user entity entirely to prevent re-activation.
Detach the Bedrock admin policy aws iam detach-user-policy --user-name <PhantomUserName> --policy-arn arn:aws:iam::aws:policy/AmazonBedrockFullAccess List and delete inline policies if any aws iam list-user-policies --user-name <PhantomUserName> aws iam delete-user-policy --user-name <PhantomUserName> --policy-name <InlinePolicyName> Finally, delete the phantom user (forces deletion of remaining keys) aws iam delete-user --user-name <PhantomUserName>
4. CloudTrail Forensics: Tracing the Phantom’s Activity
To understand the blast radius, you must query AWS CloudTrail for actions performed by the phantom user. This helps determine if the backdoor was exploited for LLMjacking.
Step 1: Search CloudTrail for Bedrock API Calls
Use the AWS CLI to filter logs for specific Bedrock invocations by the compromised user.
Search for InvokeModel actions by the phantom user in the last 7 days aws cloudtrail lookup-events --lookup-attributes AttributeKey=EventName,AttributeValue=InvokeModel --start-time "2024-05-01T00:00:00Z" --end-time "2024-05-08T00:00:00Z" --region us-east-1 --query "Events[?UserIdentity.userName=='<PhantomUserName>']"
Step 2: Identify Cost Spike and Quota Usage
LLMjacking aims to max out model quotas. Use CloudWatch to analyze metrics.
Get model invocation metrics (pseudo-code, requires CloudWatch) aws cloudwatch get-metric-statistics --namespace AWS/Bedrock --metric-name Invocations --dimensions Name=ModelId,Value=<model-id> --start-time <date> --end-time <date> --period 3600 --statistics Sum
A sudden, sustained spike in invocations coinciding with the phantom user’s activity confirms active exploitation.
5. Organizational Prevention via Service Control Policies (SCPs)
To prevent the creation of these phantom identities across all accounts in your AWS Organization, implement an SCP that denies the creation of long-term keys for Bedrock-specific roles or restricts the attachment of admin-level Bedrock policies to unauthorized principals.
Step 1: Create the SCP
This policy denies the `iam:CreateAccessKey` action if the request is coming from a Bedrock context or is attempting to create a key for a role with Bedrock permissions.
{
"Version": "2012-10-17",
"Statement": [
{
"Sid": "PreventBedrockPhantomKeys",
"Effect": "Deny",
"Action": "iam:CreateAccessKey",
"Resource": "",
"Condition": {
"ArnLike": {
"iam:PassedToService": "bedrock.amazonaws.com"
}
}
},
{
"Sid": "RestrictAdminBedrockAttachment",
"Effect": "Deny",
"Action": "iam:AttachUserPolicy",
"Resource": "arn:aws:iam:::user/",
"Condition": {
"StringEquals": {
"iam:PolicyArn": "arn:aws:iam::aws:policy/AmazonBedrockFullAccess"
}
}
}
]
}
6. Automating Audits with the Open-Source Toolkit
To continuously monitor for these threats, leverage the open-source toolkit from BeyondTrust Phantom Labs™. The toolkit automates the discovery, risk categorization, and revocation process.
Installation and Usage:
Clone the repository git clone https://github.com/BeyondTrust/bedrock-keys-security.git cd bedrock-keys-security Install dependencies (assuming Python) pip install -r requirements.txt Run the discovery module python3 phantom_hunter.py --account-id <your-aws-account-id> --region us-east-1 --output report.csv
This script will cross-reference IAM users, access key creation dates, and attached policies to generate a risk report, flagging any potential phantom identities that have outlived their key rotation schedule.
What Undercode Say:
- Identity is the new perimeter: The discovery of phantom IAM users underscores that in serverless and AI/ML environments, traditional identity lifecycle management is failing. You cannot rely on key rotation alone; you must audit the underlying identities that own the keys.
- Defense in depth for AI: Securing AI services like Bedrock requires a multi-layered approach. This includes not only network and application security but also deep IAM hygiene, proactive threat hunting via CloudTrail, and organizational guardrails like SCPs to prevent the initial misconfiguration.
- The economics of AI security: The potential cost of LLMjacking ($14,000/day) transforms a configuration oversight into a direct financial existential threat. This shifts cloud security from a compliance exercise to a core business continuity imperative, demanding automated remediation over manual periodic reviews.
Prediction:
This revelation will force major cloud providers like AWS to re-architect how long-term credentials for AI services are managed. Expect a rapid shift toward short-lived, ephemeral tokens as the only option for Bedrock and similar services, rendering “phantom users” obsolete. Furthermore, we will see the rise of specialized “AI Security Posture Management” (AI-SPM) tools that specifically hunt for these identity residues, as they become the primary vector for financially motivated cloud attacks over the next 12-18 months.
▶️ Related Video (80% Match):
🎯Let’s Practice For Free:
IT/Security Reporter URL:
Reported By: Mrcloudsec Back – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
