Imported from SaiVatsal/Skill-I-Use (Anthropic-Cybersecurity-Skills-main/skills/hunting-credential-stuffing-attacks/SKILL.md). Install upstream with npx skills add SaiVatsal/Skill-I-Use --skill hunting-credential-stuffing-attacks. Copyright stays with the author (Apache-2.0).

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.

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

# 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]