Build AI-powered security tools. 50+ hands-on labs covering ML, LLMs, RAG, threat detection, DFIR, and red teaming. Includes Colab notebooks, Docker environment, and CTF challenges.
Noise Injection Techniques provides a comprehensive exploration of methods to make machine learning models more robust to real-world bad data. This repository explains and demonstrates Gaussian noise, dropout, mixup, masking, adversarial noise, and label smoothing, with intuitive explanations, theory, and practical code examples.
Comprehensive taxonomy of AI security vulnerabilities, LLM adversarial attacks, prompt injection techniques, and machine learning security research. Covers 71+ attack vectors including model poisoning, agentic AI exploits, and privacy breaches.
90-day learning path from ML fundamentals to production AI security systems
This project aims to address this gap by conducting a systematic, controlled study of human versus LLM-generated text detectability using paired questionβanswer datasets. Rather than proposing a novel detection architecture, the focus is on analyzing detection robustness, failure modes, and the impact of adversarial humanization strategies.
An application to demonstrate stealing an AI model through knowledge distillation.
A curated list of awesome AI security tools, frameworks, and resources. OWASP AI Testing Guide, Agentic AI Top 10, EU AI Act, adversarial ML, LLM red-teaming, prompt injection.
Curated LLM/AI attack tools β prompt injection, jailbreaks, agentic threats, adversarial ML, MCP attack surface
Geometric AI governance and evaluation framework with a 14-layer security pipeline, semantic projection, and reproducible benchmark lanes.
Comprehensive, auto-updating literature review of GenAI & LLM security research, standards, tools, and resources. 100+ curated entries with interactive webapp.
DVAI - Damn Vulnerable AI Ecosystem. Open-source, zero-infrastructure-cost AI red team training range.
Zero-Trust Adversarial Reasoning Engine - autoresearch inspired kernel to create and validate claims.
An adversarial evaluation framework for LLM-integrated Security Operations Centers
π€ Test and secure AI systems with advanced techniques for Large Language Models, including jailbreaks and automated vulnerability scanners.
Mechanism-grounded taxonomy of 40 LLM jailbreak patterns across 10 categories. 8,000-trial bootstrap evaluation for the June 2026 frontier (Claude Opus 4-8, GPT-5.5, Gemini 3.5, DeepSeek V4). Every citation direct-WebFetch verified; refuted claims documented.
Adversarial evaluation framework for embodied and agentic AI β failure-first methodology, jailbreak corpus, VLA red-teaming, and policy research.
A collection of resources documenting my research and learning journey in AI System Security.
π‘οΈ Discover and analyze critical vulnerabilities in Meta AI's Instagram Group Chat, ensuring robust security through comprehensive testing and reporting.
Open research hub mapping AI/ML WiFi sensing papers, datasets, code, reproducibility, and security gaps, starting with healthcare-relevant sensing.
π§ WAFMANCER v2.0 β Next-Gen WAF Evasion Framework. AI-powered payload synthesis. Trust-based WAF manipulation. Bypassed Cloudflare. 50+ mutations. PoC generator. "Not a tool. A research weapon." π₯
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