Updated 7/21/2026

Language Model Security Database

Explore primary-source AI security research, evaluated models, attack techniques, and defensive evidence.

959 research findings · 1077 evaluated models

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51 entries

Matches every word across titles, descriptions, sources, affected systems, and models.

LLM Causal Neuron Attack

Updated 12/28/2024

Large Language Models (LLMs) such as Llama 2 and Vicuna exhibit a vulnerability where specific layers (e.g., layer 3 in Llama2-13B, layer 1 in Llama2-7B and Vicuna-13B) overfit to harmful prompts, resulting in a disproportionate influence on the model's output for such prompts. This overfitting creates a narrow "safety" mechanism easily bypassed by adversarial prompts designed to avoid triggering these specific layers. Additionally, a single neuron (e.g., neuron 2100 in Llama2 and Vicuna)…

Causality analysis for evaluating the security of large language models

Affects: GPT-3.5 Turbo, GPT-NeoX, Llama 2-13B-chat-hf +2 more

Source: arXiv

Research methodology

Entries summarize publicly available primary-source security research. Model names reflect only systems explicitly evaluated by the cited paper, and measurements are research-reported unless independent verification is stated.