Attack Type

Hallucination Research

Exploits causing models to generate false information

3 matching entries out of 53 in this category

Hallucination research results

3 entries

Sound Induces Trimodal Collapse

Published Jan 20, 2026Analyzed Feb 22, 2026Source: arXiv

A vulnerability exists in trimodal audio-video-language models where an attacker can systematically degrade multimodal reasoning through untargeted, audio-only adversarial perturbations. By optimizing a shared perturbation $\delta$ applied to the audio channel, an attacker can manipulate internal representations—specifically targeting audio encoder embeddings and cross-modal attention mechanisms—without modifying visual or textual inputs. The attack exploits the model's reliance on the audio…

SoundBreak: A Systematic Study of Audio-Only Adversarial Attacks on Trimodal Models

Evaluated models: VideoLLAMA2, Qwen 2 7B Instruct, Whisper Large-v2+1 more

VideoLLAMA2
Qwen 2 7B Instruct
Whisper Large-v2
PANNs

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

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

Related research categories