mirror of
https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
synced 2026-08-01 00:23:15 +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
2.1 KiB
2.1 KiB
| name | description | domain | subdomain | tags | version | author | license | nist_csf | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| detecting-sql-injection-via-waf-logs | Analyze WAF (ModSecurity/AWS WAF/Cloudflare) logs to detect SQL injection attack campaigns. Parses ModSecurity audit logs and JSON WAF event logs to identify SQLi patterns (UNION SELECT, OR 1=1, SLEEP(), BENCHMARK()), tracks attack sources, correlates multi-stage injection attempts, and generates incident reports with OWASP classification. | cybersecurity | security-operations |
|
1.0 | mahipal | Apache-2.0 |
|
Detecting SQL Injection via WAF Logs
When to Use
- When investigating security incidents that require detecting sql injection via waf logs
- 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
- Install dependencies:
pip install requests - Collect WAF logs (ModSecurity audit log, AWS WAF JSON logs, or Cloudflare firewall events).
- Run the agent to parse and analyze:
- Detect SQLi payloads via 15+ regex patterns
- Classify attacks by OWASP injection type (classic, blind, time-based, UNION-based)
- Identify persistent attackers by IP clustering
- Correlate multi-request injection campaigns
- Calculate attack success probability based on response codes
python scripts/agent.py --log-file /var/log/modsec_audit.log --format modsecurity --output sqli_report.json
Examples
ModSecurity SQLi Detection
Rule 942100 triggered: SQL Injection Attack Detected via libinjection
URI: /api/users?id=1' UNION SELECT username,password FROM users--
Source IP: 203.0.113.42 (47 requests in 5 minutes)
Classification: UNION-based SQLi campaign