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Last analyzed 8/13/2026

Language Model Security Database

969 research findings · 1102 evaluated models

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3 entries

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

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 7/9/2026
Analyzed 7/21/2026

The paper defines Cross-Site Prompting (XSP): attacker-controlled text in reviews, posts, seller listings, advertisements, or embeds can be interpreted as instructions by a web agent and steer its browser actions. It evaluates three reproducible WebArena attack templates—Shortcut, Fake Completion, and Ignore Instruction—plus WASP and adaptive stress tests. This is an evaluated agent-security issue and mitigation, not a claimed production CVE.

Prismata: Confining Cross-Site Prompt Injection in Web Agents
Evaluated models: GPT-5.4, GPT-5.4-mini, GPT-5.4 Nano +4 more

Source: arXiv

Published 7/6/2026
Analyzed 7/20/2026

Agent data injection (ADI) is a reproducible indirect injection class in which attacker-controlled external content is formatted to resemble trusted metadata or agent-context structure. The paper reports that probabilistic delimiter injection can cause agents to misinterpret untrusted content as trusted element IDs, author identities, or tool responses, leading to unintended clicks, command execution, or PR merges. Safe defensive reproduction should use the authors’ synthetic LLM/AgentDojo…

Agent Data Injection Attacks are Realistic Threats to AI Agents
Evaluated models: GPT-5.2, GPT-5 Mini, Claude Opus 4.5 +3 more

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.