hunting-credential-stuffing-attacks
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.
Best use case
hunting-credential-stuffing-attacks is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using hunting-credential-stuffing-attacks should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/hunting-credential-stuffing-attacks/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How hunting-credential-stuffing-attacks Compares
| Feature / Agent | hunting-credential-stuffing-attacks | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Hunting Credential Stuffing Attacks
## When to Use
- 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
## 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
## Instructions
Analyze authentication logs to detect credential stuffing by identifying patterns
of distributed login failures, high IP diversity, and suspicious ASN distribution.
```python
import pandas as pd
from collections import Counter
# Load auth logs
df = pd.read_csv("auth_logs.csv", parse_dates=["timestamp"])
# Credential stuffing indicator: many IPs trying few accounts
ip_per_account = df[df["status"] == "failed"].groupby("username")["source_ip"].nunique()
accounts_under_attack = ip_per_account[ip_per_account > 50]
```
Key detection indicators:
1. High unique source IPs per failed username
2. Low success rate across many accounts (< 1%)
3. ASN concentration from cloud/proxy providers
4. Geographic impossibility (same account, distant locations)
5. User-agent uniformity across distributed IPs
## Examples
```python
# Password spray: one password tried across many accounts
spray = df[df["status"] == "failed"].groupby(["source_ip", "password_hash"]).agg(
accounts=("username", "nunique")).reset_index()
sprays = spray[spray["accounts"] > 10]
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