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 Type1 selected

Attack Context

Architecture Components

Attack Surface

Impact1 selected

Advanced filters

Published fromPublished throughSource or venue

arXiv is the only currently indexed source.

Evidence status

Filtered research findings

1 entry

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

Fill-Squeeze Scheduler DoS

Updated 2/21/2026

LLM serving frameworks utilizing continuous batching and PagedAttention (such as vLLM, SGLang, and Orca) are vulnerable to a resource exhaustion Denial-of-Service attack known as "Fill and Squeeze." An unprivileged remote attacker can exploit the deterministic state transitions of the scheduler's memory management to induce severe latency or service denial. The attack leverages a side-channel vulnerability where Inter-Token Latency (ITL) correlates linearly with global KV-cache usage due to…

Rethinking Latency Denial-of-Service: Attacking the LLM Serving Framework, Not the Model

Affects: Qwen 3 8B, Gemma 3 12B IT, DeepSeek R1 Distill Llama 8B +1 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.