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.
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Installation Guide
How to use detecting-sql-injection-via-waf-logs on Cursor
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Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your machine
- ›Node.js 16+ with npm — verify with
node --version - ›Active project directory where you want to add
detecting-sql-injection-via-waf-logs
Run the install command
Execute the skills CLI command in your project's root directory to begin installation:
Fetches detecting-sql-injection-via-waf-logs from mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate detecting-sql-injection-via-waf-logs. Access via /detecting-sql-injection-via-waf-logs in your agent's command palette.
Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Documentation
| name | detecting-sql-injection-via-waf-logs |
| description | 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. |
| domain | cybersecurity |
| subdomain | security-operations |
| tags | - detecting - sql - injection - via |
| version | '1.0' |
| author | mahipal |
| license | Apache-2.0 |
| nist_csf | - DE.CM-01 - RS.MA-01 - GV.OV-01 - DE.AE-02 |
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
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Use Cases
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Steps
- 1Install skill using provided installation command
- 2Test with simple use case relevant to your work
- 3Evaluate output quality and relevance
- 4Iterate on prompts to improve results
- 5Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
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Reviews
- LLuis Rao★★★★★Dec 28, 2024
detecting-sql-injection-via-waf-logs reduced setup friction for our internal harness; good balance of opinion and flexibility.
- KKwame Desai★★★★★Dec 24, 2024
detecting-sql-injection-via-waf-logs is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- YYuki Chawla★★★★★Dec 24, 2024
detecting-sql-injection-via-waf-logs reduced setup friction for our internal harness; good balance of opinion and flexibility.
- CCamila Perez★★★★★Dec 24, 2024
We added detecting-sql-injection-via-waf-logs from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- EEvelyn Mensah★★★★★Dec 16, 2024
Registry listing for detecting-sql-injection-via-waf-logs matched our evaluation — installs cleanly and behaves as described in the markdown.
- DDiego Flores★★★★★Dec 12, 2024
I recommend detecting-sql-injection-via-waf-logs for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- EEmma Farah★★★★★Dec 12, 2024
Useful defaults in detecting-sql-injection-via-waf-logs — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- KKiara Thomas★★★★★Dec 12, 2024
Keeps context tight: detecting-sql-injection-via-waf-logs is the kind of skill you can hand to a new teammate without a long onboarding doc.
- SSophia Thomas★★★★★Dec 8, 2024
Keeps context tight: detecting-sql-injection-via-waf-logs is the kind of skill you can hand to a new teammate without a long onboarding doc.
- LLuis Perez★★★★★Nov 19, 2024
I recommend detecting-sql-injection-via-waf-logs for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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