mirror of
https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git
synced 2026-07-11 10:03:42 +03:00
mukul975
cb8d79e068
- Add validated mitre_attack frontmatter to all 754 skills (286 distinct techniques), verified against MITRE ATT&CK v19.1 via the official mitreattack-python library: 0 revoked, deprecated, or invalid IDs - Curate precise per-skill technique IDs for forensics, malware-analysis, threat-intel, and red-team skills (e.g. DCSync -> T1003.006, Kerberoasting -> T1558.003, Pass-the-Ticket -> T1550.003) - Reconcile v19.1 tactic restructuring: Defense Evasion split into Stealth (TA0005) and Defense Impairment (TA0112); revoked T1562.* family and T1070.001/.002 remapped to active equivalents (T1685.*) - Normalize word-split tags across 35 skills (remove filename-derived stopword tags, add semantic cybersecurity tags) - Add api-reference.md for 3 skills that were missing it - Update README ATT&CK section with accurate v19.1 tactic distribution
2.4 KiB
2.4 KiB
name, description, domain, subdomain, tags, version, author, license, nist_csf, mitre_attack
| name | description | domain | subdomain | tags | version | author | license | nist_csf | mitre_attack | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 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 |
|
1.0 | mahipal | 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:
- High unique source IPs per failed username
- Low success rate across many accounts (< 1%)
- ASN concentration from cloud/proxy providers
- Geographic impossibility (same account, distant locations)
- 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]