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βœ”οΈ Table of Content

πŸ“˜ Course Introduction
πŸ” How API works with Web application
βš–οΈ Types of APIs and their advantages/disadvantages
πŸ”Ž Analysing HTTP request and response headers
πŸ›‘οΈ API Hacking methodologies
πŸ“„ Enumerate web pages and analyse functionalities
πŸ•΅οΈ API passive reconnaissance Strategies
πŸš€ API active reconnaissance (Kite runner)
πŸ”§ Introduction to POSTMAN
πŸ” Testing for Excessive data exposure
πŸ“‚ Directory indexing / brute force
πŸ”‘ Password mutation
🎯 Password spray attacks against web application
πŸ›‘οΈ Introduction to JSON Web Token
πŸ•΅οΈ Hunting for JWT authentication vulnerabilities
πŸ’£ Exploiting JWT unverified signature
πŸ”“ Cracking JWT secret keys
🚫 Bypass JWT removing signature
πŸ’‰ Exploit jku header injection
πŸ”§ Exploit KID in JSON web tokens
πŸ” Attacking 0Auth 2.0
πŸ“Š Introduction to OWASP TOP 10 API
βš”οΈ Hunting and exploiting XXS in API
πŸ•΅οΈ Testing for the ReDOS attack in the API web application
πŸ’₯ Exploiting XML vulnerabilities
πŸ”§ WordPress XML-RPC attack
🌐 Exploiting WSDL/SOAP to RFI
πŸ€– API Automated Vulnerability scanning
πŸ’‰ Testing SQL/NoSQL Injection in an API
πŸ”“ Exploiting object-level access control
πŸ”§ Exploiting Function level access control
πŸ“‘ Testing in-band SSRF vulnerabilities in an API
🌍 Testing out-band SSRF vulnerabilities in an API
βš™οΈ Testing OS Command Injection
β˜• Exploiting Java deserialization vulnerabilities
πŸ—‚οΈ Testing for improper assets management
πŸ“¦ Testing for Mass assignment vulnerabilities
🚧 Bypass filter, space, and blacklisted characters
πŸ” Bypass Captcha and MFA
πŸ“‹ Remediations and Reporting
❀1
IPv4 vs IPv6
❀1
CLI Tools for Linux Admin
❀2
Home Network Security Tips
❀2
Azure Cloud Servics
Password manger for Linux
Bug Bounty Training Program (Online)

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βœ”οΈ Table of Content
πŸš€ Introduction to WAPT & OWASP Top 10
πŸ›  Pentest Lab Setup
πŸ” Information Gathering & Reconnaissance
πŸ’» Netcat for Pentester
βš™οΈ Configuration Management Testing
πŸ” Cryptography
πŸ”‘ Authentication
πŸ•’ Session Management
πŸ“‚ Local File Inclusion
🌐 Remote File Inclusion
πŸ“ Path Traversal
πŸ’£ OS Command Injection
πŸ”€ Open Redirect
πŸ“€ Unrestricted File Upload
🐚 PHP Web Shells
πŸ“ HTML Injection
🌟 Cross-Site Scripting (XSS)
πŸ”„ Client-Side Request Forgery
πŸ›‘ SQL Injection
πŸ“œ XXE Injection
🎁 Bonus Section
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Gined Access
Credential Dumping
Emerging Neurotech Risks Explained Through Restaurant Analogies

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Understand neurotechnology’s ethical and security risks with bite-sized restaurant scenarios:

☒ NT01 - Mental Privacy Violation
Waiter eavesdrops on diners’ thoughts β†’ Sells preferences to advertisers
Risk: Unauthorized neural data harvesting for targeted ads.

☒ NT02 - Brain Hacking (BCI Exploits)
Chef remotely alters a dish order β†’ Customer forced to eat spam
Risk: Hackers hijack brain-computer interfaces (BCIs) to manipulate actions.

☒ NT03 - Cognitive Bias Exploitation
Menu prices flash in red β†’ Diners overpay without realizing
Risk: Neurostimulation subtly influences decisions (e.g., purchases, voting).

☒ NT04 - Deep Brain "Chef" Tampering
Kitchen AI overrides chef’s recipes β†’ Diners addicted to salty food
Risk: Implanted devices hacked to alter behaviors or cravings.

☒ NT05 - Neuro-Discrimination
VIPs get premium brain upgrades β†’ Others stuck with slow service
Risk: Socioeconomic divides widen with cognitive enhancement tech.
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🧠 LLM Architecture
πŸ” LLM Security Principles
πŸ—„οΈ Data Security in AI Systems
πŸ›‘οΈ Model Security
πŸ—οΈ Infrastructure Security
πŸ“œ OWASP Top 10 for LLMs
βš™οΈ LLM Installation and Deployment
πŸ“‘ Model Context Protocol (MCP)
πŸš€ Publishing Your Model Using Ollama
πŸ” Introduction to Retrieval-Augmented Generation (RAG)
🌐 Making Your AI Application Public
πŸ“Š Types of Enumeration Using AI
🎯 Prompt Injection Attacks
🐞 Exploiting LLM APIs: Real-World Bug Scenarios
πŸ”‘ Password Leakage via AI Models
🎭 Indirect Prompt Injection Techniques
⚠️ Misconfigurations in LLM Deployments
πŸ‘‘ Exploitation of LLM APIs with Excessive Privileges
πŸ“ Content Manipulation in LLM Outputs
πŸ“€ Data Extraction Attacks on LLMs
πŸ”’ Securing AI Systems
🧾 System Prompts and Their Security Implications
πŸ€– Automated Penetration Testing with AI
6 Most Common API Security Issues
πŸ‘1
iOS Vuln Assesment
0Auth 2.0
❀2
SAIL Framework: Secure AI Risks Explained Through Restaurant Scenarios
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Understand AI security risks with bite-sized restaurant analogies from the SAIL Framework (Secure AI Lifecycle):

☒ SAIL 1.1 - Inadequate AI Policy
Chef ignores food safety rules β†’ Kitchen fails health inspection
Risk: No AI governance = unchecked vulnerabilities.

☒ SAIL 3.1 - Data Poisoning
Rival sabotages ingredients β†’ Diners get sick
Risk: Corrupt training data skews AI decisions.

☒ SAIL 4.1 - Untested Model
New dish served without taste-test β†’ Allergic reactions
Risk: Unvalidated AI models cause harmful outputs.

☒ SAIL 5.3 - Direct Prompt Injection
Customer writes "Add poison" on order β†’ Chef obeys
Risk: Malicious prompts hijack AI behavior.

☒ SAIL 6.5 - Indirect Prompt Injection
Hidden note in delivery box β†’ Kitchen follows rogue recipe
Risk: External data manipulates AI covertly.

☒ SAIL 7.2 - Missing Real-time Alerts
Food spoils silently β†’ No one notices
Risk: Undetected model drift or attacks.
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