Emergent Vulnerabilitis in Large Language Models: A Security-Centric Analysis of Semantic Attacks and Architectural Risks

This research project investigates the security architecture of large language models through extensive empirical testing. Across 39 categorized attack vectors, the work reveals how models can be manipulated not only by direct instructions but also through subtle semantic cues, contextual persistence, and structural patterns. These include methods such as poetic obfuscation, role-based agent redirection, and multimodal input deception. Beyond identifying risks, the study delivers concrete security proposals. Key solutions include the development of a semantic output shield to prevent unfiltered model completion, input-layer sandboxing for multimodal validation, and the strict separation of context memory across session states. Each solution is tailored to counter the observed bypass techniques that current filters fail to address. The work also includes analytical reflection on how models generate hallucinations, especially when prompted by ambiguous or seemingly neutral language. It connects these behaviors to ethical risks, such as the illusion of reliability, the simulation of empathy, or the reinforcement of confirmation biases through reinforcement learning. All findings are supported by raw data, structured markdown reports, and comparative reviews using leading models like ChatGPT 4.5 and Gemini Pro 2.5. The research invites deeper scrutiny into how emergent behaviors, model introspection, and ethical misalignment challenge conventional safety assumptions in AI development.