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 Components

Attack Surface1 selected

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.

Agent Lifecycle Compound Threats

Updated 4/10/2026

OpenClaw is vulnerable to persistent memory poisoning, allowing an attacker to manipulate the agent's long-term memory store (MEMORY.md) via prompt injection. Because the autonomous agent continuously integrates this memory file as context for all subsequent reasoning and task planning, injected payloads act as durable behavioral constraints. This allows an attacker to persistently alter the agent's core policy, manipulate tool selection, and hijack future sessions without any further…

Taming openclaw: Security analysis and mitigation of autonomous llm agent threats

Source: arXiv

Evaluator Consensus Manipulation

Updated 2/22/2026

A vulnerability exists in the "Adaptive Trust Weighting" mechanism of the Cost-Aware Proof of Quality (PoQ) protocol for decentralized LLM inference. The protocol updates evaluator trust weights based on the deviation of a submitted score from the consensus score of the current round. Because the consensus score is derived from the very scores being evaluated (a self-referential feedback loop), the mechanism fails to distinguish between honest and coordinated malicious evaluators…

Adaptive and Robust Cost-Aware Proof of Quality for Decentralized LLM Inference Networks

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.