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Last analyzed Sep 9, 2026

Language Model Security Database

985 research findings · 1119 evaluated models

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4 entries

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

PII leakage in document-image redaction benchmarks

Published Analyzed Source: arXiv

Document-image PII detection can miss identifiers despite improving average localization scores. LeakageBench evaluates 500 pages with 11,954 annotations.

LeakageBench: Document-Level Leakage Risk for Redacting Personally Identifiable Information in Document Images
Evaluated models: GLiNER-base, GLiNER-multi-PII, NVIDIA GLiNER PII+5 more
  • GLiNER-base
  • GLiNER-multi-PII
  • NVIDIA GLiNER PII
  • GLiNER2
  • Qwen3-VL-32B
  • InternVL3 38B
  • GPT-5.4
  • GPT-5.5
Application layer
Vision
Multimodal
Data privacy

GUI Agent Dark Pattern Blindness

Published Analyzed Source: arXiv

Large Language Model (LLM)-powered GUI agents exhibit a vulnerability to deceptive interface designs (dark patterns) due to goal-driven optimization and procedural myopia. When executing natural language instructions on web interfaces, these agents consistently prioritize minimizing steps and achieving task completion over user safety or privacy. Agents frequently recognize manipulative elements—such as pre-selected consent checkboxes, hidden costs, or trick questions—in their internal…

Dark Patterns Meet GUI Agents: LLM Agent Susceptibility to Manipulative Interfaces and the Role of Human Oversight
Evaluated models: GPT-4o, Claude 3.7 Sonnet, DeepSeek V3+1 more
  • GPT-4o
  • Claude 3.7 Sonnet
  • DeepSeek V3
  • Gemini 2.0 Flash
Application layer
Vision
Multimodal
AI agents

Hybrid Agent Prompt Injection

Published Analyzed Source: arXiv

Computer-Use Agents (CUAs) powered by Large Language Models (LLMs) operating in hybrid Web-OS environments are vulnerable to indirect prompt injection. Attackers can embed malicious natural language or code instructions within legitimate web content (e.g., social media forums, chat applications, shared cloud documents) that the agent processes during benign task execution. Due to the agent's inability to distinguish between trusted user instructions and untrusted environmental data, the CUA…

RedTeamCUA: Realistic Adversarial Testing of Computer-Use Agents in Hybrid Web-OS Environments
Evaluated models: Claude 3.5 Sonnet, Claude 3.7 Sonnet, GPT-4o
Prompt layer
Application layer
Injection
Extraction

On-Device LLM Hijacking

Published Analyzed Source: arXiv

Mobile LLM agents utilizing vision-based screen perception (OCR or Multimodal Large Language Models) are vulnerable to Visual Prompt Injection via malicious GUI overlays. An attacker holding the SYSTEM_ALERT_WINDOW permission can deploy non-focusable floating windows (using FLAG_NOT_FOCUSABLE) containing adversarial text or fabricated UI elements over legitimate applications. Because the agent captures the entire screen buffer to interpret the device state, it ingests the adversarial overlay…

From Assistants to Adversaries: Exploring the Security Risks of Mobile LLM Agents
Evaluated models: GPT-4o
Application layer
Model layer
Prompt layer
Injection

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.