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mukul975
efca3ec611
Mapped every skill to NIST CSF 2.0 subcategory IDs (GV/ID/PR/DE/RS/RC functions) based on subdomain and content analysis. Restores 11 skills corrupted during prior rebase, re-enriching with ATLAS, D3FEND, NIST AI RMF, and CSF 2.0 fields. All 754 skills now carry structured mappings for all 5 security frameworks: - MITRE ATT&CK (in tags) - MITRE ATLAS v5.5 (atlas_techniques) - MITRE D3FEND v1.3 (d3fend_techniques) - NIST AI RMF 1.0 (nist_ai_rmf) - NIST CSF 2.0 (nist_csf)
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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