Skip to main content
LLM Security Database
Skip to research search
Last analyzed 9/9/2026

Language Model Security Database

985 research findings · 1123 evaluated models

Filtered research findings

3 entries

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

Published 8/4/2026
Analyzed 8/13/2026

SkillSentry evaluates third-party agent skills by constructing source-grounded decoy environments and comparing matched executions with and without the tested skill. The method requires completed, observable, skill-attributed side effects rather than treating suspicious text, ordinary privileged operations, or unexecuted paths as proven malicious behavior.

SkillSentry: Adaptive Honey Worlds for Dynamic Safety Testing of Agent Skills
Evaluated models: DeepSeek V4-Pro

Source: arXiv

Published 7/28/2026
Analyzed 8/13/2026

The MTGuard study evaluates unsafe Model Context Protocol tool calls originating from compromised server data, host-side execution changes, and malicious user-controlled resources. Its hybrid monitor combines pre-execution parameter inspection, behavioral observation, and post-execution result verification across browser-automation and financial-analysis agents.

Hybrid Analysis for Secure MCP Tool Use in LLM Agents
Evaluated models: GPT-5.6 Luna, DeepSeek V4 Flash, DeepSeek V4-Pro

Source: arXiv

Published 3/1/2026
Analyzed 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
Evaluated models: Not reported

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.