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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
84 lines
2.4 KiB
Markdown
84 lines
2.4 KiB
Markdown
---
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name: hunting-credential-stuffing-attacks
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description: 'Detects credential stuffing attacks by analyzing authentication logs
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for login velocity anomalies, ASN diversity, password spray patterns, and geographic
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distribution of failed logins. Uses statistical analysis on Splunk or raw log data.
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Use when investigating account takeover campaigns or building detection rules for
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auth abuse.
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'
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domain: cybersecurity
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subdomain: security-operations
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tags:
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- credential-stuffing
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- authentication-logs
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- login-anomaly
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- asn-analysis
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- threat-hunting
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- account-takeover
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version: '1.0'
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author: mahipal
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license: Apache-2.0
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nist_csf:
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- DE.CM-01
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- RS.MA-01
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- GV.OV-01
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- DE.AE-02
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mitre_attack:
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- T1078
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- T1190
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- T1059
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- T1003
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- T1110
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---
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# Hunting Credential Stuffing Attacks
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## When to Use
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- When investigating security incidents that require hunting credential stuffing attacks
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- When building detection rules or threat hunting queries for this domain
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- When SOC analysts need structured procedures for this analysis type
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- When validating security monitoring coverage for related attack techniques
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## Prerequisites
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- Familiarity with security operations concepts and tools
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- Access to a test or lab environment for safe execution
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- Python 3.8+ with required dependencies installed
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- Appropriate authorization for any testing activities
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## Instructions
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Analyze authentication logs to detect credential stuffing by identifying patterns
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of distributed login failures, high IP diversity, and suspicious ASN distribution.
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```python
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import pandas as pd
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from collections import Counter
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# Load auth logs
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df = pd.read_csv("auth_logs.csv", parse_dates=["timestamp"])
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# Credential stuffing indicator: many IPs trying few accounts
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ip_per_account = df[df["status"] == "failed"].groupby("username")["source_ip"].nunique()
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accounts_under_attack = ip_per_account[ip_per_account > 50]
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```
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Key detection indicators:
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1. High unique source IPs per failed username
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2. Low success rate across many accounts (< 1%)
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3. ASN concentration from cloud/proxy providers
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4. Geographic impossibility (same account, distant locations)
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5. User-agent uniformity across distributed IPs
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## Examples
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```python
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# Password spray: one password tried across many accounts
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spray = df[df["status"] == "failed"].groupby(["source_ip", "password_hash"]).agg(
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accounts=("username", "nunique")).reset_index()
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sprays = spray[spray["accounts"] > 10]
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```
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