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

Two-Frame Infilling Jailbreak

Updated 4/10/2026

A temporal trajectory infilling vulnerability in Text-to-Video (T2V) generative models allows attackers to bypass input and output safety filters to generate policy-violating content. The vulnerability is exploited using a fragmented prompting technique known as Two Frames Matter (TFM). An attacker submits a prompt that specifies only sparse boundary conditions (the start and end frames) using semantically suggestive but lexically benign alternatives, entirely omitting the intermediate action…

Two Frames Matter: A Temporal Attack for Text-to-Video Model Jailbreaking

Source: arXiv

Single Word Video Corruption

Updated 1/14/2026

Text-to-Video (T2V) diffusion models are vulnerable to black-box adversarial prompt attacks that degrade output quality regarding semantic fidelity and temporal dynamics. This vulnerability is exploited via the T2VAttack framework, which utilizes two primary vector strategies: T2VAttack-S (Substitution) and T2VAttack-I (Insertion). T2VAttack-S leverages a greedy search to identify key semantic tokens and replaces them with high-similarity synonyms defined in lexical databases (e.g., WordNet)…

T2VAttack: Adversarial Attack on Text-to-Video Diffusion Models

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.