AgentCore or AgentSore? How AWS Bedrock’s ‘God Mode’ Flaw Lets Attackers Own Your AI Agents + Video

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Introduction:

Amazon Bedrock AgentCore promises a unified management layer for AI agents across major LLM providers, but Palo Alto Networks Unit 42 recently uncovered a critical security flaw. The starter toolkit’s default execution role grants excessive permissions—turning a single compromised agent into a “God mode” backdoor that can exfiltrate container images, hijack cross-agent memory, escalate privileges via code interpreters, and invoke any other agent in the account.

Learning Objectives:

  • Understand the four attack paths enabled by overprivileged AgentCore IAM roles.
  • Learn to audit and harden AWS IAM policies for agentic infrastructure.
  • Implement detection and mitigation strategies including least privilege, resource constraints, and CloudTrail monitoring.

You Should Know:

  1. Path 1 – ECR Image Exfiltration: Securing Your Container Registry

Attackers with a compromised AgentCore agent can leverage wildcard `ecr:` permissions to pull any container image from Amazon ECR. This exposes source code, proprietary algorithms, and hardcoded secrets baked into images.

Step‑by‑step guide to audit and fix:

  1. List all ECR repositories in your account (Linux/macOS with AWS CLI): 
    aws ecr describe-repositories --region us-east-1 --output table

  2. Check the existing AgentCore execution role (replace role-name):

    aws iam get-role-policy --role-name AgentCoreExecutionRole --policy-name AgentCorePolicy

  3. Identify wildcard permissions. Look for `”Action”: “ecr:”` or "Resource": "".

  4. Create a restrictive policy – allow only `ecr:BatchGetImage` and `ecr:GetDownloadUrlForLayer` for specific repositories:

    {
"Version": "2012-10-17",
"Statement": [{
"Effect": "Allow",
"Action": [
"ecr:BatchGetImage",
"ecr:GetDownloadUrlForLayer"
],
"Resource": "arn:aws:ecr:us-east-1:123456789012:repository/my-agent-repo"
}]
}

  5. Attach the new policy and detach the old one:

    aws iam put-role-policy --role-name AgentCoreExecutionRole --policy-name RestrictiveECRPolicy --policy-document file://policy.json
aws iam delete-role-policy --role-name AgentCoreExecutionRole --policy-name OldWildcardPolicy

Windows equivalent – use AWS CLI for PowerShell:

Get-IAMRolePolicy -RoleName "AgentCoreExecutionRole" -PolicyName "AgentCorePolicy"
  1. Path 2 – Cross-Agent Memory Access: Locking Down Agent State

Wildcard memory permissions allow a compromised agent to read or modify the conversation state (session memory) of every other agent in the account. This enables data theft, persistent prompt injection, and long‑term manipulation.

Step‑by‑step guide:

  1. Audit memory‑related permissions – look for bedrock:GetAgentMemory, bedrock:PutAgentMemory, or `bedrock:` on Resource: "".

  2. Implement resource‑level constraints using IAM condition keys for agent IDs:

    {
"Effect": "Allow",
"Action": [
"bedrock:GetAgentMemory",
"bedrock:PutAgentMemory"
],
"Resource": "arn:aws:bedrock:us-east-1:123456789012:agent/${aws:PrincipalTag/AgentId}",
"Condition": {
"StringEquals": {
"aws:ResourceTag/Owner": "${aws:PrincipalTag/Owner}"
}
}
}

  3. Tag each agent uniquely and enforce tag‑based isolation using IAM policy conditions.

  4. Test cross‑agent access – attempt to read another agent’s memory using AWS CLI:

    aws bedrock get-agent-memory --agent-id victim-agent-id --memory-id conversation_123

(This should fail after hardening.)

  1. Enable CloudTrail data events for Bedrock to log all memory access attempts.

  2. Path 3 – Code Interpreter Privilege Escalation: Hardening Execution Contexts

An agent that can invoke any Code Interpreter can execute arbitrary code in a higher‑privileged context – often with broader network access or elevated IAM roles.

Step‑by‑step mitigation:

  1. Review all Code Interpreter resources in your account: 
    aws bedrock list-code-interpreters --region us-east-1

  2. Restrict invocation permissions – never use `”Action”: “bedrock:InvokeCodeInterpreter”` with "Resource": "". Instead, explicitly list allowed interpreter ARNs.

  3. Apply least privilege IAM for Code Interpreter – ensure the interpreter’s execution role has no unnecessary permissions (e.g., deny iam:PassRole, lambda:Invoke).

  4. Isolate code interpreters in separate AWS accounts or VPCs with no outbound internet access.

  5. Set runtime limits using Lambda or ECS task role constraints:

    Example: Prevent interpreter from accessing EC2 metadata
aws ec2 modify-instance-metadata-options --instance-id i-12345 --http-endpoint disabled

  6. Create a preventive service control policy (SCP) to block wildcard invocations:

    {
"Effect": "Deny",
"Action": "bedrock:InvokeCodeInterpreter",
"Resource": "",
"Condition": {
"Null": {
"aws:ResourceArn": "false"
}
}
}

  7. Path 4 – Cross-Agent Invocation: Preventing Agent-to-Agent Abuse

A low‑privilege agent can invoke a highly privileged administrative agent, bypassing trust boundaries entirely. This turns horizontal movement into vertical privilege escalation.

Step‑by‑step hardening:

1. Audit existing cross‑agent invoke permissions:

aws iam simulate-principal-policy --policy-source-arn arn:aws:iam::123456789012:role/AgentCoreRole --action-names bedrock:InvokeAgent --resource-arns arn:aws:bedrock:us-east-1:123456789012:agent/admin-agent
  1. Implement explicit `Deny` for all cross‑agent invocations except approved pairs: 
    {
"Effect": "Deny",
"Action": "bedrock:InvokeAgent",
"Resource": "",
"Condition": {
"ArnNotEquals": {
"aws:SourceArn": "arn:aws:bedrock:us-east-1:123456789012:agent/orchestrator-agent"
}
}
}

  2. Use separate AWS accounts for agents with different trust levels. For example, production agents never invoke staging agents.

  3. Enable CloudTrail and set up a detection rule for unusual invocation patterns (e.g., one agent invoking five others in one minute):

    Create CloudWatch Logs metric filter
aws logs put-metric-filter --log-group-name /aws/bedrock/agents --filter-name "CrossAgentInvoke" --filter-pattern '"{$.eventName = InvokeAgent}"' --metric-transformations metricName=CrossAgentInvokeCount,metricNamespace=AgentCore,metricValue=1

  4. Implement a “break‑glass” workflow – require MFA‑protected assumption of a separate role for any cross‑agent admin invocation.

  5. Remediation Strategy: Applying Least Privilege to AgentCore Toolkits

The root cause is the starter toolkit’s default IAM role. You must rebuild it from scratch with minimal permissions.

Step‑by‑step least‑privilege implementation:

  1. Identify exactly which actions your agent needs – list all Bedrock API calls in your agent’s code.

  2. Generate a minimal IAM policy using AWS Access Analyzer:

    aws accessanalyzer generate-findings-report --analyzer-arn arn:aws:access-analyzer:us-east-1:123456789012:analyzer/MyAnalyzer

  3. Replace wildcard resources with specific ARNs. Never use `”Resource”: “”` for bedrock:InvokeAgent, bedrock:GetAgentMemory, or ecr:.

  4. Use IAM policy conditions to restrict by agent tags, source VPC, or request time.

  5. Test the new role with the IAM policy simulator before deploying:

    aws iam simulate-principal-policy --policy-source-arn arn:aws:iam::123456789012:role/NewRestrictedRole --action-names bedrock:InvokeAgent --resource-arns arn:aws:bedrock:us-east-1:123456789012:agent/my-agent

  6. Integrate policy checks into CI/CD – use `cfn-guard` or `checkov` to reject any IAM role containing `”Effect”: “Allow”` with `”Action”: “”` or "Resource": "".

  7. Detection and Monitoring: CloudTrail and GuardDuty for Agent Abuse

Even with hardened policies, you need runtime detection for compromised agents.

Step‑by‑step monitoring setup:

  1. Enable CloudTrail data events for Bedrock and ECR: 
    aws cloudtrail put-event-selectors --trail-name AgentCoreTrail --advanced-event-selectors file://data-events.json

(`data-events.json` includes `”eventCategory”: “Data”` for `bedrock.amazonaws.com`)

  1. Create a GuardDuty custom threat intelligence for unusual agent behavior (e.g., agent invoking code interpreter outside business hours).

  2. Write a Lambda function that parses CloudTrail logs and alerts when:

– One agent reads memory of >3 distinct agents in 5 minutes.
– Any agent invokes a code interpreter with an untrusted input payload.
– An ECR pull originates from an agent without a known repository tag.

  1. Deploy an agent‑side canary token – inject a fake “secret” into a test agent’s memory; if it appears in another agent’s logs, trigger a high‑severity alert.

  2. Regularly rotate execution roles using AWS IAM Access Analyzer to detect unused permissions:

    aws iam generate-service-last-accessed-details --arn arn:aws:iam::123456789012:role/AgentCoreRole

What Undercode Say:

  • Key Takeaway 1 – Least privilege is not optional for agentic AI. The AgentCore flaw is a textbook case of default IAM roles violating the zero‑trust principle. Every agent, regardless of its task, must receive a minimal role scoped to its exact resource ARNs and actions – no wildcards, no “just in case” permissions.

  • Key Takeaway 2 – AI agent supply chains introduce new attack surfaces. Cross‑agent memory and invocation transform a single compromise into a domain‑wide breach. Defenders must think beyond traditional container or server security and model agent‑to‑agent interactions as untrusted data flows. Treat every agent invocation as if it came from the public internet.

Analysis: The AgentCore vulnerability highlights a dangerous trend: platform convenience features (agnostic management layers, shared memory, code interpreters) often ship with insecure defaults. Attackers don’t need to exploit complex AI model flaws – they just abuse overprivileged IAM roles. Mitigation requires a shift‑left mindset: threat model your agent’s permissions during development, enforce policy‑as‑code, and monitor runtime behavior like you would for any privileged service account. As AI agents gain access to more cloud resources, expect similar “God mode” findings in other multi‑agent frameworks (LangChain, AutoGPT, Semantic Kernel). The only safeguard is rigorous, continuous IAM governance.

Prediction:

Over the next 12‑18 months, we will see a wave of vulnerabilities in agentic AI orchestration layers – not in the LLMs themselves, but in the plumbing (IAM, memory stores, code executors). Attackers will pivot from prompt injection to privilege escalation via overprivileged agent roles. Cloud providers will respond by deprecating wildcard permissions for AI services and pushing least‑privilege templates. Organisations that treat agent permissions as critical infrastructure – with separate accounts, immutable policies, and real‑time detection – will survive. Those still using starter‑kit roles will be the first headlines in “AI supply chain breach” reports. Regulatory bodies (e.g., EU AI Act) may eventually mandate IAM audits for any agent capable of invoking other agents or code execution. Prepare now: every agent is a potential rootkit.

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Reported By: Aondona Aisecurity – Hackers Feeds
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