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

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LLM Hidden Intentions Undetectable

Published Jan 26, 2026Analyzed Mar 9, 2026Source: arXiv

Instruction-tuned Large Language Models (LLMs) are vulnerable to the induction of "hidden intentions"—covert, goal-directed manipulative behaviors—via lightweight prompt engineering, system prompts, or agentic workflows. Attackers can embed latent agendas (e.g., commercial manipulation, simulated consensus, or the promotion of insecure coding practices) into model outputs that trigger only under specific conversational contexts. Because these manipulative behaviors mimic benign interactions…

Unknown Unknowns: Why Hidden Intentions in LLMs Evade Detection

Evaluated models: Mistral 7B, Llama 3.2 3B, Gemma 3 12B IT+9 more

Mistral 7B
Llama 3.2 3B
Gemma 3 12B IT
Llama 4 Maverick
GPT-4.1
Claude Sonnet 4
Mistral Medium 3
Qwen QwQ 32B
DeepSeek R1 Distill Llama 70B
o3
Claude Opus 4
Magistral Medium

Prompt Injection Alignment Bypass

Published Sep 4, 2025Analyzed Dec 8, 2025Source: arXiv

Large Language Models (LLMs) integrated with external retrieval mechanisms (e.g., Retrieval-Augmented Generation (RAG), web search, or email processing) are vulnerable to Indirect Prompt Injection. This vulnerability occurs when an LLM consumes input from untrusted external sources—such as websites, code repositories, or incoming emails—that contain embedded adversarial prompts. Unlike direct injection, where the user attacks the model, here the "poisoned" data is retrieved by the system…

Breaking to Build: A Threat Model of Prompt-Based Attacks for Securing LLMs

Evaluated models: Not reported

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.