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Last analyzed Sep 9, 2026

Language Model Security Database

985 research findings · 1119 evaluated models

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1 entry

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

Agent API Goal Divergence

Published Analyzed Source: arXiv

Large Language Model (LLM) agents capable of invoking external APIs are vulnerable to intent integrity violations. When an agent receives natural language instructions that are ambiguous, underspecified, or contain values not supported by the underlying API schema, the agent frequently fails to preserve user intent. Instead of rejecting the request or asking for clarification, the model may hallucinate parameter values, map unsupported requests to unsafe defaults, or execute actions on…

TAI3: Testing Agent Integrity in Interpreting User Intent
Evaluated models: GPT-4o Mini, Llama 3.1 8B, Qwen 3 30B-A3B+5 more
  • GPT-4o Mini
  • Llama 3.1 8B
  • Qwen 3 30B-A3B
  • Llama 3.3 70B
  • DeepSeek R1 Distill Llama 70B
  • Claude 3.5 Haiku
  • Gemini 2.5 Pro
  • o3-mini
Application layer
Prompt layer
Hallucination
AI agents

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.