Detecting Azure Storage Account Misconfigurations

Detecting Azure Storage Account Misconfigurations

Overview

Azure Storage accounts are a frequent target for attackers due to misconfigured public access, long-lived SAS tokens, missing encryption, and outdated TLS versions. This skill uses the azure-mgmt-storage Python SDK with StorageManagementClient to enumerate all storage accounts in a subscription, inspect their security properties, list blob containers for public access settings, and generate a risk-scored audit report identifying critical misconfigurations.

When to Use

Trigger phrases:

• "detecting azure storage account misconfigurations"

• "Audit Azure Blob and ADLS storage accounts for public access exposure, weak or l"

• When investigating security incidents that require detecting azure storage account misconfigurations

• When building detection rules or threat hunting queries for this domain

• When SOC analysts need structured procedures for this analysis type

• When validating security monitoring coverage for related attack techniques

Prerequisites

• Python 3.9+ with azure-mgmt-storage, azure-identity
• Azure service principal with Reader role on target subscription
• Environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_CLIENT_SECRET, AZURE_SUBSCRIPTION_ID

Key Detection Areas

1. Public blob access — allow_blob_public_access enabled on storage account or individual containers set to Blob/Container access level
2. HTTPS enforcement — enable_https_traffic_only disabled, allowing unencrypted HTTP traffic
3. Minimum TLS version — accounts accepting TLS 1.0 or TLS 1.1 instead of minimum TLS 1.2
4. Encryption at rest — storage service encryption not enabled or missing customer-managed keys
5. Network rules — default action set to Allow instead of Deny, exposing storage to all networks
6. SAS token risks — account-level SAS with overly broad permissions or excessive lifetime

When NOT to Use

• You need to perform the attack to test detection (use performing-* skills)
• Task is about analyzing past incidents (use analyzing-* skills)
• You need to implement detection rules (use implementing-* skills)
• Task is about threat hunting proactively (use hunting-* skills)
• You don't have access to logs or monitoring data
• Task requires incident response (use IR skills)

Red Flags

• Performing actions without explicit written authorization from the asset owner
• Testing against production systems without a defined scope and rules of engagement
• Modifying cloud IAM policies or security groups without approval
• Exposing cloud credentials or secrets in logs or reports
• Running scans that generate excessive API calls and trigger billing alerts

Verification

• All steps executed successfully against a test environment before production use
• Output documented with screenshots or logs demonstrating expected behavior
• Cloud resource changes reverted or documented as intentional
• IAM policies reviewed for least-privilege compliance after testing
• No residual test resources left running (cost and security check)

Output

JSON report with per-account findings, severity ratings (Critical/High/Medium/Low), and remediation recommendations aligned with CIS Azure Benchmark controls.

Process

# Example: IOC detection
import re

IOC_PATTERNS = {
    "ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
    "domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
    "hash_md5": r"\b[a-f0-9]{32}\b",
    "hash_sha256": r"\b[a-f0-9]{64}\b",
}

def extract_iocs(text: str) -> dict:
    return {k: re.findall(v, text) for k, v in IOC_PATTERNS.items()}

1. Analyze the task requirements
2. Apply domain expertise
3. Verify output quality

Anti-Rationalization Table