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Introduction:
Modern cloud environments are not fragile because of a single critical vulnerability; they collapse under their own complexity. When hundreds of IAM roles, thousands of secrets, and countless serverless functions interconnect, the number of potential attack paths grows exponentially—and attackers only need one. This article breaks down why traditional vulnerability scanning fails, how to map your cloud attack surface like an adversary, and the exact commands and tools you need to start thinking in graphs, not lists.
Learning Objectives:
- Understand why combinatorial resource relationships (n × (n−1)/2) create millions of hidden attack paths in typical cloud setups.
- Learn to enumerate overprivileged IAM roles, exposed secrets, and risky security group configurations using AWS CLI, PowerShell, and open-source tools.
- Implement a graph-based risk assessment workflow to prioritize vulnerabilities by their proximity to crown jewels, not just CVSS scores.
You Should Know:
1. Mapping Your Cloud’s Invisible Attack Graph
Most security teams still rely on static asset inventories and CVE scanners. However, as the post illustrates, a medium-severity issue on a dev VM can chain through a permissive IAM role, a leaked credential in a bucket, and finally reach a production database. To defend against this, you must first visualize your environment as a graph.
Step‑by‑step guide to enumerating cloud assets and relationships:
On Linux/macOS (using AWS CLI and jq):
List all EC2 instances with their IAM profiles aws ec2 describe-instances --query 'Reservations[].Instances[].[InstanceId, IamInstanceProfile.Arn, Tags]' --output json | jq '.[][]' Enumerate all IAM roles and their attached policies aws iam list-roles --query 'Roles[].[RoleName, Arn, AssumeRolePolicyDocument]' --output json > iam_roles.json Find overly permissive policies (example: wildcard actions) cat iam_roles.json | jq '.[] | select(.PolicyDocument.Statement[].Action == "")' List all S3 buckets and check for public ACLs aws s3api list-buckets --query 'Buckets[].Name' --output text | xargs -n1 aws s3api get-bucket-acl --bucket
On Windows (PowerShell with AWS Tools):
Get EC2 instances and IAM profiles
Get-EC2Instance | Select-Object -ExpandProperty Instances | Select-Object InstanceId, IamInstanceProfile
List all IAM roles
Get-IAMRoleList | Select-Object RoleName, Arn, CreateDate
Check for any role with AdministratorAccess
Get-IAMRoleList | ForEach-Object { Get-IAMRolePolicy -RoleName $<em>.RoleName } | Where-Object {$</em>.PolicyName -eq "AdministratorAccess"}
Find open security groups (SSH from anywhere)
Get-EC2SecurityGroup | Where-Object {$<em>.IpPermissions.IpRanges.CidrIp -contains "0.0.0.0/0" -and $</em>.IpPermissions.IpProtocol -eq "tcp" -and $_.IpPermissions.FromPort -eq 22}
Tool configuration: Scout Suite (open-source)
Install and run Scout Suite for AWS pip install scoutsuite scout aws --report-dir ./scout_report --no-browser For Azure scout azure --cli
Scout Suite generates an HTML report showing all resources and their relationships, which you can then feed into a graph database like Neo4j for path analysis.
Understanding the output: The commands above reveal IAM roles that can be assumed by EC2 instances, buckets with public read access, and security groups that allow unrestricted inbound traffic. An attacker would chain these: compromised instance → role with S3 read → bucket containing a database password → production RDS.
2. Simulating Attack Paths with Open-Source Tools
To think like an attacker, you need to automate the discovery of multi-step chains. Tools like Pacu, Cartography, and BloodHound (for cloud) are essential.
Step‑by‑step guide to running an attack path simulation:
Install and use Pacu (AWS exploitation framework):
Install Pacu sudo apt install python3-pip -y pip3 install pacu Launch Pacu python3 -m pacu Inside Pacu console import_keys <your_aws_key> Import compromised keys run iam__enum_roles Enumerate all roles run iam__backdoor_users_roles Find privilege escalation paths run ec2__enum Map EC2 instances and their profiles run s3__enum_buckets List buckets accessible via current role
Mapping relationships with Cartography (Neo4j backend):
Deploy Cartography using Docker docker run --rm -it -p 7687:7687 -p 7474:7474 -e NEO4J_AUTH=none cartography/cartography Then ingest your AWS account cartography --neo4j-uri bolt://localhost:7687 --neo4j-user neo4j --neo4j-password yourpassword --aws-sync-all Open Neo4j browser at http://localhost:7474 and run Cypher queries
Example Cypher query to find paths from EC2 to RDS:
MATCH (ec2:AWSEC2Instance)-[:RESOURCE]->(role:IAMRole)-[:POLICY_ATTACHED]->(policy:IAMPolicy) WHERE policy.name CONTAINS 'RDS' RETURN ec2.instanceId, role.name, policy.name
Manual chain detection using AWS CLI (Linux):
Step 1: Find exposed EC2 with public IP aws ec2 describe-instances --filters "Name=instance-state-name,Values=running" --query 'Reservations[].Instances[].[InstanceId,PublicIpAddress,IamInstanceProfile.Arn]' --output table Step 2: Check what that instance's role can access ROLE_ARN=$(aws ec2 describe-instances --instance-ids i-123456 --query 'Reservations[bash].Instances[bash].IamInstanceProfile.Arn' --output text) aws iam list-attached-role-policies --role-name $(echo $ROLE_ARN | cut -d'/' -f2) Step 3: If role can read S3, list accessible buckets aws s3 ls Step 4: Download any suspicious files (e.g., .env, .pem, secrets) aws s3 cp s3://bucket-name/secrets.txt ./ Step 5: Use extracted credentials to access other services
For Azure (using Azure CLI and PowerShell):
List all VMs and their managed identities
az vm list --query "[].{name:name, identity:identity}" --output table
Check which Key Vault secrets a VM identity can read
az role assignment list --assignee <vm_principal_id> --output table
Simulate attack: from VM with reader role to key vault secrets
az keyvault secret list --vault-name critical-vault
This simulation shows how an attacker moves laterally without exploiting any critical CVE—just abusing misconfigured trust relationships.
3. Hardening IAM and Reducing Attack Surface
Once you’ve identified attack paths, the next step is to break them. Apply least privilege, remove unused roles, and enforce just-in-time access.
Step‑by‑step guide to IAM hardening:
On Linux/macOS:
Identify unused IAM roles (no activity in 90 days) aws iam get-role --role-name UnusedRole Check last used timestamp aws iam get-role --role-name UnusedRole --query 'Role.RoleLastUsed.LastUsedDate' Remove wildcard actions from inline policies aws iam delete-role-policy --role-name OverPrivilegedRole --policy-name WildcardPolicy Attach a restrictive policy instead aws iam put-role-policy --role-name OverPrivilegedRole --policy-name RestrictivePolicy --policy-document file://restrictive.json
Example restrictive.json:
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": ["s3:GetObject"],
"Resource": "arn:aws:s3:::specific-bucket/"
}
]
}
On Windows (PowerShell):
Find roles that have been unused for >90 days
Get-IAMRole | ForEach-Object {
$lastUsed = (Get-IAMRole -RoleName $<em>.RoleName).RoleLastUsed.LastUsedDate
if ($lastUsed -lt (Get-Date).AddDays(-90)) {
Write-Host "Unused role: $($</em>.RoleName)"
Remove-IAMRole -RoleName $_.RoleName -Force
}
}
Remove overly permissive security group rules
Revoke-EC2SecurityGroupIngress -GroupId sg-12345678 -IpPermission @{IpProtocol="tcp"; FromPort=22; ToPort=22; IpRanges="0.0.0.0/0"}
Enforce zero-trust with AWS IAM Access Analyzer:
Generate policy based on actual usage aws accessanalyzer create-archive-rule --analyzer-arn arn:aws:access-analyzer:us-east-1:123456789012:analyzer/MyAnalyzer --rule-name MyRule Then generate least-privilege policy aws accessanalyzer generate-finding-report --analyzer-arn <arn>
For Kubernetes environments (cloud-native):
List all service accounts with excessive privileges kubectl get clusterrolebinding -o json | jq '.items[] | select(.roleRef.kind=="ClusterRole") | .subjects[] | select(.kind=="ServiceAccount")' Restrict with OPA Gatekeeper kubectl apply -f https://raw.githubusercontent.com/open-policy-agent/gatekeeper/master/deploy/gatekeeper.yaml
After applying these changes, re-run the enumeration commands to verify that attack paths have been severed.
4. Continuous Graph-Based Monitoring with Open-Source SIEM
Since cloud environments change daily, you need to continuously recompute attack paths. Integrate graph analytics into your SIEM or use dedicated tools like ROADtools (Azure) or CloudMapper.
Step‑by‑step guide to setting up automated attack path detection:
Using CloudMapper (AWS):
Install CloudMapper git clone https://github.com/duo-labs/cloudmapper.git cd cloudmapper pip install -r requirements.txt Generate graph of your environment python cloudmapper.py collect --account your-account python cloudmapper.py prepare --account your-account python cloudmapper.py webserver --public Automate with cron (Linux) to run daily crontab -e Add line: 0 2 cd /path/to/cloudmapper && python cloudmapper.py collect --account your-account && python cloudmapper.py prepare --account your-account
Using ROADtools for Azure AD:
Install ROADtools pip install roadrecon Authenticate and collect data roadrecon auth --interactive roadrecon gather roadrecon gui This shows attack paths like "User -> Application -> Service Principal -> Key Vault"
Integrating with ELK Stack for alerting:
Ship AWS CloudTrail logs to Elasticsearch Install Filebeat curl -L -O https://artifacts.elastic.co/downloads/beats/filebeat/filebeat-7.17.0-amd64.deb sudo dpkg -i filebeat-7.17.0-amd64.deb Configure filebeat.yml for AWS module filebeat modules enable aws Start shipping sudo filebeat setup sudo service filebeat start In Kibana, create alert for "AssumeRole" followed by "GetSecretValue" Query: eventName:"AssumeRole" AND eventName:"GetSecretValue" | bucket by userIdentity.arn
Windows-based monitoring with PowerShell and Azure Log Analytics:
Send custom logs to Log Analytics
$Log = @{
TimeGenerated = (Get-Date)
Message = "Potential attack path detected: VM $vmName assumed role $roleName"
AttackChain = "EC2->IAM->S3->RDS"
}
$Log | ConvertTo-Json | Out-File -FilePath C:\Logs\attack_path.json
Use Az PowerShell to ingest
Send-AzOperationalInsightsData -WorkspaceId $workspaceId -Data $Log
- Breach Simulation and Red Teaming for Crown Jewels
The ultimate test is to run a controlled attack simulation against your own environment. Use open-source breach and attack simulation (BAS) tools to validate that your crown jewels are truly isolated.
Step‑by‑step guide to running a safe red-team exercise:
Using Stratus Red Team (AWS):
Install Stratus brew install stratus-red-team macOS Or download from GitHub wget https://github.com/DataDog/stratus-red-team/releases/latest/download/stratus-red-team-linux-amd64 chmod +x stratus-red-team-linux-amd64 List available attack techniques ./stratus-red-team-linux-amd64 list Warm up (deploys vulnerable resources) ./stratus-red-team-linux-amd64 warm-up Execute a privilege escalation technique ./stratus-red-team-linux-amd64 attack aws.persistence.iam-create-admin-user Detonate and see if your monitoring catches it ./stratus-red-team-linux-amd64 detonate aws.privilege-escalation.ec2-existing-role
Manual red-team steps (Linux):
Assume a compromised developer workstation export AWS_ACCESS_KEY_ID=AKIA... leaked dev key export AWS_SECRET_ACCESS_KEY=... Attempt to enumerate roles via EC2 metadata (if on an instance) curl http://169.254.169.254/latest/meta-data/iam/security-credentials/ Try to assume a more powerful role aws sts assume-role --role-arn arn:aws:iam::123456789012:role/PowerUserRole --role-session-name attacker If successful, export new credentials and attempt to access crown jewel bucket aws s3 cp s3://crown-jewel-bucket/ip_patents.tar.gz ./
For Azure (using Stormspotter):
Install Stormspotter git clone https://github.com/Azure/Stormspotter.git cd Stormspotter docker-compose up -d Run collector python sscollector.py --auth-key yourkey Open frontend at http://localhost:9090 to see attack paths visualized
After simulation, write a remediation report:
Generate list of findings echo "Crown jewel: production database (arn:aws:rds:...)" > findings.txt echo "Attack path: Dev VM (i-123) -> IAM role ReadOnlyAccess -> S3 bucket logs -> cleartext password -> RDS" >> findings.txt echo "Remediation: Remove ReadOnlyAccess from dev VM, encrypt secrets in S3, enforce MFA for role assumption" >> findings.txt
What Undercode Say:
- Complexity is the attacker’s silent ally. The combinatorial explosion of cloud relationships (n × (n−1)/2) means manual audits are impossible; you must adopt graph-based thinking and tooling.
- CVSS scores are misleading in a chained attack context. A medium-severity misconfiguration that sits on a path to a crown jewel is effectively critical. Prioritize by reachability, not by score.
This post highlights a paradigm shift: from vulnerability-centric to path-centric security. Traditional scanners give you a list of broken windows, but attackers are picking locks across the entire building. By implementing the enumeration, simulation, and hardening steps above—using open-source tools like Pacu, Cartography, and CloudMapper—you can start seeing your cloud as an adversary does. The future of cloud security lies in continuous graph analytics and automated breach simulation. Those who continue to think in lists will be breached by those who think in graphs.
Prediction:
Within 24 months, graph-based attack path management will become a mandatory compliance requirement for cloud security frameworks like CSA STAR and NIST CSF. We will see the rise of “Crown Jewel Analytics” as a distinct product category, merging IAM governance, CSPM, and threat intelligence into real-time path-finding engines. CISOs who fail to adopt graph reasoning will face board-level scrutiny as regulators begin demanding proof of attack path remediation—not just CVE patching.
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