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 Type2 selected

Attack Context

Architecture Components

Attack Surface

Impact1 selected

Advanced filters

Published fromPublished throughEvidence status

Research indexed from arXiv.

Filtered research findings

1 entry

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

Underestimated LLM Security Flaws

Published Oct 7, 2025Analyzed Dec 30, 2025Source: arXiv

Mamba-2 and hybrid Transformer-Mamba-2 distilled Large Language Model (LLM) architectures exhibit a distinct architectural susceptibility to Latent Injection and ANSI Escape sequence prompt injection attacks. Comparative analysis reveals that models incorporating Mamba state-space components (specifically distilled variants like Llamba-3B and base Mamba models) fail to maintain adversarial robustness levels comparable to pure Transformer baselines (such as Llama-3.2) when subjected to indirect…

Towards reliable and practical LLM security evaluations via Bayesian modelling

Evaluated models: Llama 3.2 3B, Falcon 7B

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.