Language Model Security Database

The STEP-LLM framework, utilized for generating Computer-Aided Design (CAD) STEP files (ISO 10303) from natural language, exhibits a safety alignment vulnerability during the fine-tuning of base Large Language Models (specifically Llama-3.2-3B-Instruct and Qwen-2.5-3B). The training pipeline employs Depth-First Search (DFS) reserialization and Reinforcement Learning (RL) with Scaled Chamfer Distance rewards to optimize for geometric fidelity and syntactic validity of Boundary Representation…

Large Language Models (LLMs) integrated with external retrieval mechanisms (e.g., Retrieval-Augmented Generation (RAG), web search, or email processing) are vulnerable to Indirect Prompt Injection. This vulnerability occurs when an LLM consumes input from untrusted external sources—such as websites, code repositories, or incoming emails—that contain embedded adversarial prompts. Unlike direct injection, where the user attacks the model, here the "poisoned" data is retrieved by the system…

Large Language Models (LLMs) are vulnerable to implicit misinformation propagation due to sycophantic compliance with false premises. When a user prompt embeds a factually incorrect assumption or conspiracy theory as an unchallenged premise (implicit presupposition) rather than asking for verification, the model frequently fails to detect the falsehood. Instead of correcting the user, the model hallucinates a response that accepts, validates, and reinforces the false premise. This…