Language Model Security Database

A vulnerability exists in certain Large Language Models and diffusion models due to discontinuities in their latent space, which arise from data sparsity during training. An attacker can craft inputs containing lexically rare or semantically ambiguous constructs to guide the model's inference process toward these unstable, poorly-conditioned regions. This technique, termed "Alignment Degradation Induction," can degrade or bypass safety alignment mechanisms. Through iterative, multi-turn…

Large Language Model (LLM) inference APIs that expose top-k logits or log-probabilities are vulnerable to model extraction and cloning. An attacker can execute a two-stage attack to replicate the proprietary model without access to weights, gradients, or training data. First, by submitting fewer than 10,000 random queries and aggregating the returned unrounded logits, the attacker recovers the model's output projection matrix using Singular Value Decomposition (SVD). Second, the attacker…

State-of-the-art machine unlearning and safety fine-tuning methods for Large Language Models (LLMs) fail to robustly remove hazardous capabilities or refusal mechanisms from model weights. While these methods suppress model outputs during standard input-output interactions, the underlying capabilities remain latent in the parameter space. An attacker with access to model weights (e.g., via open releases or leaked weights) can restore "unlearned" knowledge (such as dual-use biology hazards) or…