bilibili-web-security-ai-llm

Organize web penetration testing and AI security assessment knowledge for vulnerability analysis workflows.

Updated Feb 21, 2026

Search Tags:#llm #web-security #ctf #penetration-testing #ai-security #vulnerability-analysis

Ready to run

npx skills add https://github.com/Gitnapp/Skills --skill bilibili-web-security-ai-llm

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: bilibili-web-security-ai-llm
Source: https://github.com/Gitnapp/Skills/tree/main/ctf/bilibili-web-security-ai-llm
Command: npx skills add https://github.com/Gitnapp/Skills --skill bilibili-web-security-ai-llm

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, and includes scripts (resource) components.

What problem does it solve?

This Skill provides a structured knowledge base for analyzing web vulnerabilities and AI security risks, helping security practitioners organize penetration testing techniques and adversarial AI concepts.

Core Features & Use Cases

Web Security Knowledge: Covers vulnerability analysis workflows for SQL injection, XSS, file upload, CSRF, SSRF, RCE, XXE, deserialization, access control issues, and login security testing.
AI Security Testing: Provides concepts and methods for evaluating prompt injection, jailbreak attempts, prompt leakage, and large language model security behavior.
CTF and Pentest Reference: Includes practical testing workflows, common tools, and reusable Python templates for authorized security exercises and competitions.

Quick Start

Use the bilibili-web-security-ai-llm skill to review web penetration testing techniques and AI security assessment methods for an authorized CTF challenge.

Frequently Asked Questions about bilibili-web-security-ai-llm

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I test for prompt injection and jailbreak vulnerabilities in large language models?▼

To test for prompt injection and jailbreak vulnerabilities in large language models, you apply structured AI security assessment concepts that evaluate prompt leakage and model safety behavior during authorized testing workflows.

What is the best way to structure web penetration testing workflows for a CTF competition?▼

The best way to structure web penetration testing workflows for a CTF competition is to use organized vulnerability analysis references and reusable Python templates covering SQL injection, XSS, and RCE testing techniques.

Can I use Python scripts to automate vulnerability analysis for web security testing?▼

Yes, you can use Python scripts to automate vulnerability analysis for web security testing by leveraging reusable templates designed for authorized security exercises and penetration testing tasks.

Does this approach cover both traditional web vulnerabilities and AI security risks?▼

Yes, this approach covers both traditional web vulnerabilities and AI security risks by organizing penetration testing techniques for issues like SSRF and deserialization alongside large language model safety evaluations.

Do I need the requests library to run automation templates for security analysis tasks?▼

Yes, you need the requests library installed in your Python environment to run the automation templates and scripts provided for executing authorized security analysis tasks.

What types of login security reviews are supported by structured web vulnerability references?▼

Structured web vulnerability references support login security reviews by providing testing workflows for access control issues, authentication bypasses, and session management vulnerabilities during authorized penetration testing.