---
name: hunting-credential-stuffing-attacks
description: >
  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
subdomain: security-operations
tags: [hunting, credential, stuffing, attacks]
version: "1.0"
author: mahipal
license: MIT
---

# Hunting Credential Stuffing Attacks

## 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]
```