Language Model Security Database

The VILTA (VLM-in-the-Loop Trajectory Adversary) framework is vulnerable to Prompt Injection and Data Poisoning via un-sanitized scene representation inputs. The system integrates a Vision-Language Model (Gemini-2.5-Flash) into a closed-loop reinforcement learning environment, feeding it Bird’s-Eye-View (BEV) imagery alongside text-based vehicle dynamics data (e.g., position, speed, and risk_category) to generate challenging driving trajectories. An attacker who can manipulate the input…

Commercial Multimodal Large Language Model (MLLM) integrated systems are vulnerable to a "Dual Steganography" jailbreak paradigm (referred to as Odysseus). The vulnerability arises from the reliance of safety filters on the assumption that malicious content must be explicitly visible in the input or output modalities (text or image). Attackers can bypass these filters by encoding malicious queries into binary matrices and embedding them into benign-looking images using steganographic encoders…

FC-Attack leverages automatically generated flowcharts containing step-by-step descriptions derived or rephrased from harmful queries, combined with a benign textual prompt, to jailbreak Large Vision-Language Models (LVLMs). The vulnerability lies in the model's susceptibility to visual prompts containing harmful information within the flowcharts, thus bypassing safety alignment mechanisms.