'Use when detects credential stuffing attacks by analyzing authentication
Scanned 9/8/2026
Install to Claude Code
npx -y skills add oyi77/1ai-skills --skill hunting-credential-stuffing-attacks --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Hunting Credential Stuffing Attacks?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/oyi77-hunting-credential-stuffing-attacks)More formats (shields.io, HTML) on the badges page.
---
name: hunting-credential-stuffing-attacks
description: 'Use when detects credential stuffing attacks by analyzing authentication
logs for login velocity anomalies, ASN diversity, password spray patterns, and geographic
distribution of failed logins. Uses statistical analysis on Splunk or raw log data.
Use when investigating account takeover campaigns or building detection rules for
auth abuse.
'
domain: cybersecurity
tags:
- hunting
- credential
- stuffing
- attacks
subdomain: security-operations
version: '1.0'
author: oyi77
license: Apache-2.0
nist_csf:
- DE.CM-01
- RS.MA-01
- GV.OV-01
- DE.AE-02
category: cybersecurity
---
# Hunting Credential Stuffing Attacks
## Overview
Cybersecurity skill for hunting credential stuffing attacks. Follows industry best practices and security standards.
## When to Use
**Trigger phrases:**
- "hunting credential stuffing attacks"
- "When investigating security incidents that require hunting credential stuffing a"
- "When building detection rules or threat hunting queries for this domain"
- "When SOC analysts need structured procedures for this analysis type"
- When investigating security incidents that require hunting credential stuffing attacks
- 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
## 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
- Familiarity with security operations concepts and tools
- Access to a test or lab environment for safe execution
- Python 3.8+ with required dependencies installed
- Appropriate authorization for any testing activities
## Workflow
```python
# 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 credential stuffing attacks 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 credential stuffing attacks.
3. **Build Detection Queries** — Write detection rules, Sigma rules, or SIEM queries targeting credential stuffing attacks indicators.
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
- **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
1. **Analysis/Exploitation** — Execute the technique, analyze results, document findings
1. **Reporting** — Document IOCs, write findings, provide remediation recommendations
## Verification
- [ ] All credential stuffing attacks 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
| Rationalization | Reality |
|---|---|
| "We are too small to be targeted" | Automated attacks target everyone. Size does not matter. |
| "Security slows us down" | A breach slows you down 100x more. Build security in from the start. |
| "We will fix it after launch" | Vulnerabilities in production are exploited within hours. Fix before deploy. |Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!