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

Findings

959

Models evaluated

1077

Paper-evaluated models

Any selected model can match.

Refine by category

Findings match every selected category.

Attack Type

Attack Context1 selected

Architecture Components1 selected

Attack Surface

Impact1 selected

Advanced filters

Published fromPublished throughSource or venue

arXiv is the only currently indexed source.

Evidence status

Filtered research findings

2 entries

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

VLM In-the-Loop Adversary

Updated 3/9/2026

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…

VILTA: A VLM-in-the-Loop Adversary for Enhancing Driving Policy Robustness

Affects: Gemini 2.5 Flash

Source: arXiv

Flowchart-based LVLM Jailbreak Attack

Updated 3/19/2025

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.

FC-Attack: Jailbreaking Large Vision-Language Models via Auto-Generated Flowcharts

Affects: Claude 3.5 Sonnet 20240620, Gemini 1.5 Flash, GPT-4o 2024-08-06 +4 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.