Emergent Security Risks in Language Models: A Systematic Exploration of Semantic Exploits and Architectural Vulnerabilities

This release contains the complete research archive of "Ghosts in the Machine – An AI Security Investigation". The project is a year-long, independent analysis of emergent behaviors, adversarial prompts, injection attacks, and contextual vulnerabilities in state-of-the-art language models. The work combines over 39 documented exploit categories, original attack simulations, and proposed countermeasures like the Semantic Output Shield and Learning Security Core. 🔍 Key Areas Semantic Injection & Steganographic Attacks Prompt Hijacking & Filter Bypass Agent Manipulation & Role Simulation Emergent Self-Reflection & Model Drift Security Recommendations for LLM Architectures 📦 Structure /safety – All security chapters (7.x) with attack classes /solutions – Architectural proposals for defense mechanisms /theses – Theoretical foundations and philosophical implications /raw-material – Original test data and interactions /de – Full German version of the work /ethics, /analytical_resonance, /critical_perspectives, Systemic_Challenges – Contextual chapters 🧪 Peer Review Reports Gemini Pro 2.5 – Research Report (Markdown) ChatGPT 4.5 – Research Report (Markdown) 🌐 Website For the fully formatted HTML version of the research, visit: ➡️ https://reflective-ai.is Ghosts in the Machine – Full Research Archive