Is AI-ML-Free-Resources-for-Security-and-Prompt-Injection open source?

Yes — anmolksachan/AI-ML-Free-Resources-for-Security-and-Prompt-Injection is an open-source project tracked on heatdrop.

Where can I find AI-ML-Free-Resources-for-Security-and-Prompt-Injection?

anmolksachan/AI-ML-Free-Resources-for-Security-and-Prompt-Injection is on GitHub at https://github.com/anmolksachan/AI-ML-Free-Resources-for-Security-and-Prompt-Injection.

← all repositories

anmolksachan/AI-ML-Free-Resources-for-Security-and-Prompt-Injection

A syllabus for breaking LLMs, not building them

It turns the internet's scattered free AI/ML security resources into a single, coherent self-study syllabus.

★605 stars Learning Domain Apps

View on GitHub ↗ Homepage ↗

AI-ML-Free-Resources-for-Security-and-Prompt-Injection

Collecting fresh signals — velocity needs a few days of history.

collecting data…

star history

What it does

This repository is a curated syllabus — not a tool — that maps free courses, papers, videos, and CTFs for developers moving from general security into AI/ML pentesting. It structures the material into seven phases, starting with Python and ML fundamentals and ending with real-world bug bounties and agentic AI attacks. Think of it as a reading list with a rigorous table of contents.

The interesting bit

The 2026 edition treats AI security as an extension of web pentesting rather than an academic niche, folding in brand-new attack surfaces like MCP server exploitation, AI IDE attacks, and agent-to-agent protocol abuse alongside classic prompt injection. It cites recent research and real-world disclosures, giving the roadmap a surprisingly current edge for a static list.

Key highlights

Seven-phase progression from “zero to practitioner” with explicit prerequisites like PortSwigger, Python, and HTTP fundamentals
Heavy emphasis on zero-cost resources: Coursera audits, fast.ai, Harvard CS50P, Kaggle, and OWASP guides
Dedicated 2026 section on agentic AI and MCP security, covering tool poisoning, conversation hijacking, and AI IDE attacks
Curated attack taxonomy including indirect prompt injection, RAG poisoning, multi-turn jailbreaks, and model extraction
Includes practical next steps: CTFs, bug bounty programs, academic papers, and community channels

Caveats

This is a curated index, not software — expect to do all the homework yourself
The README is massive and link-heavy; finding specific sections requires relying on the table of contents
Some statistics, such as a cited “92% success rate” for multi-turn attacks, are presented without inline sourcing or verification

Verdict

Worth bookmarking if you’re a pentester or appsec engineer pivoting into LLM red-teaming. Skip it if you need a drop-in scanner or automated test suite — this is a study guide, not a framework.

Frequently asked

What is anmolksachan/AI-ML-Free-Resources-for-Security-and-Prompt-Injection?: It turns the internet's scattered free AI/ML security resources into a single, coherent self-study syllabus.
Is AI-ML-Free-Resources-for-Security-and-Prompt-Injection open source?: Yes — anmolksachan/AI-ML-Free-Resources-for-Security-and-Prompt-Injection is an open-source project tracked on heatdrop.
How popular is AI-ML-Free-Resources-for-Security-and-Prompt-Injection?: anmolksachan/AI-ML-Free-Resources-for-Security-and-Prompt-Injection has 605 stars on GitHub.
Where can I find AI-ML-Free-Resources-for-Security-and-Prompt-Injection?: anmolksachan/AI-ML-Free-Resources-for-Security-and-Prompt-Injection is on GitHub at https://github.com/anmolksachan/AI-ML-Free-Resources-for-Security-and-Prompt-Injection.