mirror of
https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
synced 2026-08-01 08:30:20 +00:00
24f816b6a3
Place useful parts of the surrounding repos into sica-fondt by layer, per the
body model (Ada = membrane; brain/endocrine/capabilities/knowledge non-Ada):
- brain/ LLM reasoning + providers (dapr, hermes, MoMoA)
- capabilities/ REPRAG sidecars: hermes tools/skills, dapr tools, parallel
dispatch, A51 channels, and the OSINT cluster
- knowledge/ LORAG corpus: 754 cyber-skills, agency personas, secure-coding,
MITRE ATT&CK data
- reference/ defensive threat-reference (C3, shhbruh doc) + AdaYaml parser
License handling: AGPL sources (worldosint, advanced_evolution, mercury,
Reticulum) and GPL DeTTECT are SPEC-only clean-room/port descriptions — no
copyleft code copied. MIT/Apache/data parts copied as working trees.
Safety: shhbruh escape/persistence material and C3 covert-C2 kept as reference
only, not wired into the running organism. See CONSOLIDATION.md.
https://claude.ai/code/session_01UehUqEXXJJCsHoA4voCU5c
74 lines
2.2 KiB
Markdown
74 lines
2.2 KiB
Markdown
---
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name: hunting-credential-stuffing-attacks
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description: 'Detects credential stuffing attacks by analyzing authentication logs for login velocity anomalies, ASN diversity,
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password spray patterns, and geographic 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 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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- hunting
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- credential
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- stuffing
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- attacks
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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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---
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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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