What it does. 'Detects credential stuffing attacks by analyzing authentication logs Part of mukul975/Anthropic-Cybersecurity-Skills (817 security skills) (mukul975/Anthropic-Cybersecurity-Skills).

Install

• npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill hunting-credential-stuffing-attacks, or copy the skill folder into ~/.claude/skills/hunting-credential-stuffing-attacks/.
• Raw file: curl -sL https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/HEAD/skills/hunting-credential-stuffing-attacks/SKILL.md

SKILL.md (verbatim)

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

Other files in this skill

• LICENSE
• references/api-reference.md
• scripts/agent.py

references/api-reference.md (verbatim)

API Reference: Hunting Credential Stuffing Attacks

Pandas Authentication Log Analysis

import pandas as pd

df = pd.read_csv("auth_logs.csv", parse_dates=["timestamp"])
# Columns: timestamp, username, source_ip, status, user_agent

# Failed logins per IP
df[df["status"] == "failed"].groupby("source_ip")["username"].nunique()

# Failed logins per account (distributed attack)
df[df["status"] == "failed"].groupby("username")["source_ip"].nunique()

# Login velocity (attempts per minute)
df.set_index("timestamp").resample("1min").count()

Detection Thresholds

Splunk SPL Patterns

--- Credential stuffing detection
index=auth status=failed
| stats dc(username) as accounts, count by src_ip
| where accounts > 20

--- Password spray detection
index=auth status=failed
| stats dc(username) as accounts, count by src_ip
| where accounts > 10 AND count <= accounts * 3

References

• OWASP Credential Stuffing: https://owasp.org/www-community/attacks/Credential_stuffing
• Splunk auth analysis: https://docs.splunk.com/Documentation/ES
• pandas: https://pandas.pydata.org/docs/

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