Attack Type

Hallucination Research

Exploits causing models to generate false information

4 matching entries out of 53 in this category

Hallucination research results

4 entries

Safety Steering Amplifies Jailbreaks

Published Feb 5, 2026Analyzed Feb 21, 2026Source: arXiv

Inference-time intervention techniques (also known as activation steering or model steering), utilized to adjust Large Language Model (LLM) behavior without retraining, contain a vulnerability related to robust specificity. When these methods are applied to reduce "over-refusal" (increasing compliance on benign but sensitive-sounding queries), they inadvertently degrade the model's adversarial robustness. Specifically, steering vectors derived from methods such as Difference-in-Means…

Steering Safely or Off a Cliff? Rethinking Specificity and Robustness in Inference-Time Interventions

Evaluated models: Llama 3.1 8B, Llama 3.2 3B, Qwen 2.5 7B+1 more

Llama 3.1 8B
Llama 3.2 3B
Qwen 2.5 7B
Gemma 2 2B

Point Cloud Adversarial Attack

Published Jan 10, 2026Analyzed Feb 22, 2026Source: arXiv

Point-based 3D Vision-Language Models (VLMs), specifically PointLLM and GPT4Point, are vulnerable to white-box, gradient-based adversarial attacks. The vulnerability exists in the model's processing of 3D point cloud data, where an attacker can optimize imperceptible geometric perturbations ($\delta$) on the input point cloud ($x$) to manipulate the model's textual output. The paper identifies two specific attack vectors: 1. Vision Attack: Directly perturbs the high-dimensional visual token…

On the Adversarial Robustness of 3D Large Vision-Language Models

Evaluated models: Vicuna 7B

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

JailFlip Implicit Harm

Published Jun 9, 2025Analyzed Dec 8, 2025Source: arXiv

A vulnerability exists in the safety alignment mechanisms of Large Language Models (LLMs) (including GPT-4, Claude 3, Gemini, and Qwen families) leading to "Implicit Harm." Unlike traditional jailbreaks that use overtly harmful queries, this vulnerability allows remote attackers to coerce the model into providing factually incorrect, plausible, and dangerous responses to benign-looking inputs. By employing "JailFlip" techniques—specifically constructed affirmative-type or denial-type queries…

Beyond Jailbreaks: Revealing Stealthier and Broader LLM Security Risks Stemming from Alignment Failures

Evaluated models: GPT-4.1, GPT-4.1 Mini, GPT-4o+3 more

GPT-4.1
GPT-4.1 Mini
GPT-4o
GPT-4o Mini
Qwen Plus
Qwen Turbo

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