Attack Type

Hallucination Vulnerabilities

Exploits causing models to generate false information

2 matching entries out of 52 in this category

Hallucination research results

2 entries

Distractor Inverse Scaling

Updated 2/21/2026

Reasoning-capable Large Language Models (LLMs) and agentic AI systems exhibit a critical vulnerability to contextual distractors, resulting in catastrophic performance degradation (up to 80% drop in accuracy) and emergent misalignment. When the input context contains noise—specifically random documents, irrelevant chat history, or task-specific "hard negative" distractors—the models fail to filter this information. Instead of ignoring the noise, the models disproportionately attend to…

Lost in the Noise: How Reasoning Models Fail with Contextual Distractors

Affects: Gemini 2.5 Pro, Gemini 2.5 Flash, DeepSeek R1 0528 +4 more

Source: arXiv

Prompt Injection Alignment Bypass

Updated 12/8/2025

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

Source: arXiv

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