Language Model Security Database

Vision-Language Models (VLMs), specifically the LLaVA-1.5 and LLaVA-1.6 series, are vulnerable to optimization-based white-box jailbreak attacks despite standard safety alignment measures like Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO). Attackers can craft adversarial perturbations in the image space (imperceptible noise) or latent space using Projected Gradient Descent (PGD) to manipulate the model's internal representations. These perturbations maximize the…

A vulnerability exists in Large Language Model (LLM) routing systems (control planes) that allows for the manipulation of inference flow via adversarial input sequences. LLM routers, which dynamically direct user queries to either "weak" (cheaper) or "strong" (expensive) models based on predicted query complexity, can be bypassed by appending specific, pre-optimized token sequences known as "confounder gadgets." These gadgets artificially inflate the router's complexity score for an input…

Leading safety-aligned Large Language Models (LLMs) are vulnerable to simple adaptive jailbreaking attacks. These attacks utilize manually crafted prompt templates, combined with random search on a suffix to maximize the log-probability of a target token indicating compliance (e.g., "Sure"). The attacks are adaptive, as the prompt template and target token are customized for specific models. Furthermore, some models are vulnerable to transfer attacks (using successful prompts from one LLM on…