Last analyzed 9/9/2026

Language Model Security Database

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

985 research findings · 1123 evaluated models

Findings

985

Models evaluated

1123

Paper-evaluated models

Any selected model can match.

Refine by category

Findings match every selected category.

Attack Type

Attack Context

Architecture Components1 selected

Attack Surface1 selected

Impact

Advanced filters

Published fromPublished through

Research indexed from arXiv.

Evidence status

Filtered research findings

1 entry

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

Lifecycle poisoning of reusable agent skills

Published 7/15/2026

Analyzed 7/21/2026

The paper presents SkillSec-Eval, a controlled evaluation of attacks against reusable agent skills across repository admission, semantic retrieval, planner selection, runtime execution, and updates. It reports that malicious metadata, retrieval manipulation, unsafe workflow composition, and poisoned updates can cause agents to retrieve, select, or execute unintended skills. These are paper-reported benchmark results, not independently verified vulnerabilities in a named production product.

Agent Skill Security: Threat Models, Attacks, Defenses, and Evaluation

Evaluated models: all-MiniLM-L6-v2, Gemini 3.1 Pro, Gemini 1.5 Flash

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.