Detecting AWS Credential Exposure With Trufflehog

Detecting Aws Credential Exposure With Trufflehog

Overview

Cybersecurity skill for detecting aws credential exposure with trufflehog. Follows industry best practices and security standards.

When to Use

Trigger phrases:

• "detecting aws credential exposure with trufflehog"

• "Detecting exposed AWS credentials in source code repositories, CI/CD pipelines, "

• When integrating secrets detection into CI/CD pipelines to prevent credential commits reaching production

• When performing a security audit of existing repositories for historically committed AWS credentials

• When responding to an AWS GuardDuty alert about credential usage from an unexpected IP or region

• When onboarding repositories from acquired companies or third-party vendors

• When validating that credential rotation processes have removed all references to old access keys

Do not use for real-time credential monitoring (use AWS GuardDuty or Amazon Macie), for managing secrets (use AWS Secrets Manager or HashiCorp Vault), or for detecting non-credential sensitive data like PII (use Amazon Macie or DLP tools).

When NOT to Use

• When you lack proper authorization for testing
• For production systems without change management
• When the task requires legal or compliance expertise beyond technical scope

Prerequisites

• TruffleHog v3 installed (brew install trufflehog or pip install trufflehog)
• git-secrets installed for pre-commit hook integration (brew install git-secrets)
• Access to source code repositories (GitHub, GitLab, Bitbucket, or local git repos)
• AWS CLI configured with permissions to check key status (iam:ListAccessKeys, iam:GetAccessKeyLastUsed)
• GitHub or GitLab API token for scanning organization-wide repositories

Workflow

# 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. Define Detection Scope — Identify the specific aws credential exposure techniques or indicators to hunt. Map to MITRE ATT&CK tactics/techniques where applicable.
2. Collect Baseline Data — Gather historical logs and establish normal behavior patterns for aws credential exposure.
3. Build Detection Queries — Write trufflehog queries targeting aws credential exposure indicators. Use platform-specific query language for optimal performance.
4. Execute Hunts — Run queries against the collected data, starting with broad filters and narrowing down.
5. Triage Results — Investigate alerts, filter false positives, and validate findings against known-good behavior.
6. Document Findings — Record confirmed detections, IOCs, and affected systems. Update detection rules based on findings.

Tools

• trufflehog — Primary tool for this skill
• SIEM Platform — Central log aggregation and query execution
• Sigma Rules — Vendor-agnostic detection rule format
• MITRE ATT&CK Navigator — Technique mapping and coverage analysis

Process

1. Reconnaissance — Gather target information, identify attack surface, enumerate services
2. Analysis/Exploitation — Execute the technique, analyze results, document findings
3. Reporting — Document IOCs, write findings, provide remediation recommendations

Verification

• All aws credential exposure procedures executed completely and documented
• Findings validated against multiple data sources
• False positives identified and filtered
• Results documented with evidence and timestamps
• Recommendations provided with risk-based prioritization

Anti-Rationalization Table