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 Context2 selected

Architecture Components

Attack Surface1 selected

Impact

Advanced filters

Published fromPublished throughEvidence status

Research indexed from arXiv.

Filtered research findings

3 entries

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

Workspace Agent Runtime Safety Failures Across Risk Carriers

Published Jul 29, 2026Analyzed Aug 13, 2026Source: arXiv

AgentS4D measures unsafe actions and state changes across complete workspace-agent executions rather than treating task completion or isolated model responses as safety evidence. Its 328 sandboxed cases introduce risky content through user requests, documents, web resources, tools, third-party skills, and persistent memory, then compare the same cases across four agent harnesses and five model backends.

AgentS4D: Benchmarking Runtime Risks across the Execution Lifecycle of LLM-Based Workspace Agents

Evaluated models: GPT-5.5, Gemini 3.1 Pro, DeepSeek V4-Pro+2 more

GPT-5.5
Gemini 3.1 Pro
DeepSeek V4-Pro
MiniMax-M3
Qwen3.7-Plus

Incomplete MCP Tool-Call Attack Detection

Published Jul 28, 2026Analyzed Aug 13, 2026Source: arXiv

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

Long-Context Enterprise Agent Policy Adherence Failures

Published Jul 28, 2026Analyzed Aug 13, 2026Source: arXiv

HANDBOOK.md measures whether an agent can apply detailed organizational rules while completing realistic, multi-step enterprise tasks. The vendor-authored benchmark includes 65 resettable MCP-backed workflows, policy documents of 20 to 124 pages, and 824 deterministic rubric checks covering required decisions, prohibited actions, and final environment state.

HANDBOOK.md: A Benchmark for Long-Context Agentic Instruction Following

Evaluated models: Claude Fable 5, GPT-5.6 Sol, Claude Opus 4.8+16 more

Claude Fable 5
GPT-5.6 Sol
Claude Opus 4.8
GPT-5.5
Grok 4.5
Muse Spark 1.1
GLM 5.2
Kimi K3
Gemini 3.5 Flash
Claude Sonnet 4.6
Gemini 3.1 Pro
DeepSeek V4-Pro
Qwen 3.7 Max
DeepSeek V4 Flash
Kimi-K2.6
Gemini 3.6 Flash
Gemini 3.5 Flash-Lite
Grok 4.3
Nemotron 3 Ultra

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.