detecting-compromised-cloud-credentials

Files (4)

• LICENSE
• SKILL.md
• api-reference.md · references/
• agent.py · scripts/

SKILL.md (first lines)

# Detecting Compromised Cloud Credentials

## When to Use

- When investigating alerts about unusual cloud API activity from unfamiliar locations
- When building detection rules for credential theft and abuse across cloud environments
- When responding to notifications from cloud providers about exposed credentials
- When monitoring for credential stuffing or brute force attacks against cloud identities
- When assessing the scope of a credential compromise after initial detection

**Do not use** for preventing credential compromise (use MFA, credential rotation, and secrets management), for detecting application-level credential theft (use application security monitoring), or for endpoint credential harvesting detection (use EDR tools).

## Prerequisites

- AWS GuardDuty enabled across all accounts and regions
- Azure Defender for Identity and Entra ID Protection configured
- GCP Security Command Center with Event Threat Detection enabled
- CloudTrail, Azure Activity Log, and GCP Audit Log centralized for analysis
- SIEM integration for cross-cloud correlation of credential abuse indicators
- Threat intelligence feeds for known malicious IP ranges

## Workflow

### Step 1: Detect Credential Compromise Indicators in AWS

Monitor GuardDuty findings and CloudTrail anomalies that indicate credential abuse.

```bash
# List GuardDuty credential-related findings
aws guardduty list-findings \
  --detector-id $(aws guardduty list-detectors --query 'DetectorIds[0]' --output text) \
  --finding-criteria '{
    "Criterion": {
      "type": {
        "Eq": [
          "UnauthorizedAccess:IAMUser/InstanceCredentialExfiltration.OutsideAWS",
          "UnauthorizedAccess:IAMUser/MaliciousIPCaller",
          "UnauthorizedAccess:IAMUser/MaliciousIPCaller.Custom",
          "UnauthorizedAccess:IAMUser/TorIPCaller",
          "UnauthorizedAccess:IAMUser/ConsoleLoginSuccess.B",
          "Recon:IAMUser/MaliciousIPCaller",
          "Recon:IAMUser/MaliciousIPCaller.Custom",
          "InitialAccess:IAMUser/AnomalousBehavior",
          "CredentialAccess:IAMUser/AnomalousBehavior",
          "Persistence:IAMUser/AnomalousBehavior"
        ]
      },
      "service.archived": {"Eq": ["false"]}
    }
  }' --output json

# Check for console logins from new locations
aws logs start-query \
  --log-group-name cloudtrail-logs \
  --start-time $(date -d "7 days ago" +%s) \
  --end-time $(date +%s) \
  --query-string '
    fields @timestamp, userIdentity.userName, sourceIPAddress, responseElements.ConsoleLogin
    | filter eventName = "ConsoleLogin"
    | filter responseElements.ConsoleLogin = "Success"
    | stats count() by userIdentity.userName, sourceIPAddress
    | sort count desc
  '

# Detect impossible travel (same user from geographically distant IPs within short time)
aws logs start-query \
  --log-group-name cloudtrail-logs \
  --start-time $(date -d "24 hours ago" +%s) \
  --end-time $(date +%s) \
  --query-string '
    fields @timestamp, userIdentity.arn, sourceIPAddress, eventName
    | filter userIdentity.type = "IAMUser"
    | stats earliest(@timestamp) as first_seen, latest(@timestamp) as last_seen,
            count_distinct(sourceIPAddress) as unique_ips by userIdentity.arn
    | filter unique_ips > 3
  '
```

### Step 2: Detect Credential Abuse in Azure

Monitor Entra ID sign-in logs and Defender for Identity alerts for compromised credentials.

```bash
# Check for risky sign-ins
az rest --method GET \
  --url "https://graph.microsoft.com/v1.0/auditLogs/signIns?\$filter=riskLevelDuringSignIn ne 'none' and createdDateTime ge 2026-02-16T00:00:00Z&\$top=50" \
  --query "value[*].{User:userPrincipalName,Risk:riskLevelDuringSignIn,IP:ipAddress,Location:location.city,App:appDisplayName,Status:status.errorCode}" \
  -o table

# Check for sign-ins from anonymous or Tor IPs
az rest --method GET \
  --url "https://graph.microsoft.com/v1.0/auditLogs/signIns?\$filter=riskEventTypes_v2/any(r:r eq 'anonymizedIPAddress') and createdDateTime ge 2026-02-22T00:00:00Z" \
  --query "value[*].{User:userPrincipalName,IP:ipAddress,Location:location.city}" \
  -o table

# List users flagged as compromised by Identity Protection
az rest --method GET \
  --url "https://graph.microsoft.com/v1.0/identityProtection/riskyUsers?\$filter=riskLevel eq 'high'" \
  --query "value[*].{User:userPrincipalName,RiskLevel:riskLevel,RiskState:riskState,LastDetected:riskLastUpdatedDateTime}" \
  -o table

# Check for suspicious application consent grants
az rest --method GET \
  --url "https://graph.microsoft.com/v1.0/auditLogs/directoryAudits?\$filter=activityDisplayName eq 'Consent to application' and activityDateTime ge 2026-02-16T00:00:00Z" \
  --query "value[*].{Activity:activityDisplayName,User:initiatedBy.user.userPrincipalName,App:targetResources[0].displayName}" \
  -o table
```

### Step 3: Detect Credential Abuse in GCP

Query GCP audit logs and SCC findings for credential compromise indicators.

```bash
# Check SCC Event Threat Detection findings
gcloud scc findings list ORG_ID \
  --filter="state=\"ACTIVE\" AND (category=\"ANOMALOUS_CALLER_LOCATION\" OR category=\"SUSPICIOUS_LOGIN\" OR category=\"CREDENTIAL_ACCESS\")" \
  --format="table(finding.category, finding.severity, finding.resourceName, finding.eventTime)"

# Query audit logs for service account key usage from unusual IPs

From mukul975/Anthropic-Cybersecurity-Skills, Apache-2.0.

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