Anthropic-Cybersecurity-Skills/skills/hunting-credential-stuffing-attacks/SKILL.md

name, description, domain, subdomain, tags, version, author, license, nist_csf, mitre_attack, mitre_f3

name	description	domain	subdomain	tags	version	author	license	nist_csf	mitre_attack	mitre_f3
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.	cybersecurity	security-operations	credential-stuffing authentication-logs login-anomaly asn-analysis threat-hunting account-takeover	1.0	mahipal	Apache-2.0	DE.CM-01 RS.MA-01 GV.OV-01 DE.AE-02	T1078 T1190 T1059 T1003 T1110	version tactics techniques 1.1 initial-access positioning id name tactic source T1110.004 Brute Force: Credential Stuffing initial-access attack id name tactic source T1110.003 Brute Force: Password Spraying initial-access attack id name tactic source F1006.002 Account Takeover: Exposed Login Credential initial-access f3 id name tactic source F1006 Account Takeover initial-access f3

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]