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https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
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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
65 lines
2.1 KiB
Markdown
65 lines
2.1 KiB
Markdown
---
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name: detecting-sql-injection-via-waf-logs
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description: Analyze WAF (ModSecurity/AWS WAF/Cloudflare) logs to detect SQL injection attack campaigns. Parses ModSecurity
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audit logs and JSON WAF event logs to identify SQLi patterns (UNION SELECT, OR 1=1, SLEEP(), BENCHMARK()), tracks attack
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sources, correlates multi-stage injection attempts, and generates incident reports with OWASP classification.
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domain: cybersecurity
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subdomain: security-operations
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tags:
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- detecting
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- sql
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- injection
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- via
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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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# Detecting SQL Injection via WAF Logs
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## When to Use
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- When investigating security incidents that require detecting sql injection via waf logs
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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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1. Install dependencies: `pip install requests`
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2. Collect WAF logs (ModSecurity audit log, AWS WAF JSON logs, or Cloudflare firewall events).
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3. Run the agent to parse and analyze:
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- Detect SQLi payloads via 15+ regex patterns
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- Classify attacks by OWASP injection type (classic, blind, time-based, UNION-based)
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- Identify persistent attackers by IP clustering
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- Correlate multi-request injection campaigns
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- Calculate attack success probability based on response codes
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```bash
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python scripts/agent.py --log-file /var/log/modsec_audit.log --format modsecurity --output sqli_report.json
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```
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## Examples
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### ModSecurity SQLi Detection
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```
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Rule 942100 triggered: SQL Injection Attack Detected via libinjection
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URI: /api/users?id=1' UNION SELECT username,password FROM users--
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Source IP: 203.0.113.42 (47 requests in 5 minutes)
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Classification: UNION-based SQLi campaign
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```
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