Last analyzed Sep 9, 2026

Language Model Security Database

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

985 research findings · 1119 evaluated models

Findings

985

Models evaluated

1119

Paper-evaluated models

Any selected model can match.

Refine by category

Findings match every selected category.

Attack Type

Attack Context

Architecture Components2 selected

Attack Surface1 selected

Impact

Advanced filters

Published fromPublished throughEvidence status

Research indexed from arXiv.

Filtered research findings

2 entries

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

Latent Space Discontinuity Exploitation

Published Nov 1, 2025Analyzed Dec 5, 2025Source: arXiv

A vulnerability exists in certain Large Language Models and diffusion models due to discontinuities in their latent space, which arise from data sparsity during training. An attacker can craft inputs containing lexically rare or semantically ambiguous constructs to guide the model's inference process toward these unstable, poorly-conditioned regions. This technique, termed "Alignment Degradation Induction," can degrade or bypass safety alignment mechanisms. Through iterative, multi-turn…

Exploiting Latent Space Discontinuities for Building Universal LLM Jailbreaks and Data Extraction Attacks

Evaluated models: Not reported

LLM Risk Amplification

Published Jan 13, 2025Analyzed Dec 9, 2025Source: arXiv

Vision Language Models (VLMs) are vulnerable to visual prompt injection attacks via text-to-image obfuscation. While these models often possess safety guardrails for standard text-based inputs, they fail to apply equivalent safety alignment to textual instructions embedded visually within an image. An attacker can overlay malicious instructions (e.g., requests for illegal acts, hate speech) onto an image file and submit it to the model. The model’s Optical Character Recognition (OCR) or visual…

Lessons from red teaming 100 generative ai products

Evaluated models: GPT-4, Phi-3

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.

Discovery and publication are separate: new papers enter a candidate queue, then require human review and explicit approval before publication. Updates follow that review process, not a guaranteed daily publishing schedule. “Last analyzed” is the newest analysis date among published entries; it does not mean that all recent research has been covered.