259 matches found
DualTAP: A Dual-Task Adversarial Protector for Mobile MLLM Agents
The reliance of mobile GUI agents on Multimodal Large Language Models MLLMs introduces a severe privacy vulnerability: screenshots containing Personally Identifiable Information PII are often sent to untrusted, third-party routers. These routers can exploit their own MLLMs to mine this data,...
Jailbreaking Large Vision Language Models in Intelligent Transportation Systems
Large Vision Language Models LVLMs demonstrate strong capabilities in multimodal reasoning and many real-world applications, such as visual question answering. However, LVLMs are highly vulnerable to jailbreaking attacks. This paper systematically analyzes the vulnerabilities of LVLMs integrated ...
JPRO: Automated Multimodal Jailbreaking Via Multi-Agent Collaboration Framework
The widespread application of large VLMs makes ensuring their secure deployment critical. While recent studies have demonstrated jailbreak attacks on VLMs, existing approaches are limited: they require either white-box access, restricting practicality, or rely on manually crafted patterns, leadin...
Enhanced MLLM Black-Box Jailbreaking Attacks and Defenses
Multimodal large language models MLLMs comprise of both visual and textual modalities to process vision language tasks. However, MLLMs are vulnerable to security-related issues, such as jailbreak attacks that alter the model's input to induce unauthorized or harmful responses. The incorporation o...
Beyond Text: Multimodal Jailbreaking of Vision-Language and Audio Models through Perceptually Simple Transformations
Multimodal large language models MLLMs have achieved remarkable progress, yet remain critically vulnerable to adversarial attacks that exploit weaknesses in cross-modal processing. We present a systematic study of multimodal jailbreaks targeting both vision-language and audio-language models,...
CrossGuard: Safeguarding MLLMs against Joint-Modal Implicit Malicious Attacks
Multimodal Large Language Models MLLMs achieve strong reasoning and perception capabilities but are increasingly vulnerable to jailbreak attacks. While existing work focuses on explicit attacks, where malicious content resides in a single modality, recent studies reveal implicit attacks, in which...
Multimodal Safety Is Asymmetric: Cross-Modal Exploits Unlock Black-Box MLLMs Jailbreaks
Multimodal large language models MLLMs have demonstrated significant utility across diverse real-world applications. But MLLMs remain vulnerable to jailbreaks, where adversarial inputs can collapse their safety constraints and trigger unethical responses. In this work, we investigate jailbreaks i...
Countermind: A Multi-Layered Security Architecture for Large Language Models
The security of Large Language Model LLM applications is fundamentally challenged by "form-first" attacks like prompt injection and jailbreaking, where malicious instructions are embedded within user inputs. Conventional defenses, which rely on post hoc output filtering, are often brittle and fai...
VisualDAN: Exposing Vulnerabilities in VLMs with Visual-Driven DAN Commands
Vision-Language Models VLMs have garnered significant attention for their remarkable ability to interpret and generate multimodal content. However, securing these models against jailbreak attacks continues to be a substantial challenge. Unlike text-only models, VLMs integrate additional modalitie...
GHSA-3F6C-7FW2-PPM4 vLLM is vulnerable to Server-Side Request Forgery (SSRF) through `MediaConnector` class
Summary A Server-Side Request Forgery SSRF vulnerability exists in the MediaConnector class within the vLLM project's multimodal feature set. The loadfromurl and loadfromurlasync methods fetch and process media from user-provided URLs without adequate restrictions on the target hosts. This allows...
CVE-2025-6242
The CVE-2025-6242 SSRF vulnerability targets vLLM's MediaConnector (load_from_url/load_from_url_async) allowing user-supplied URLs to trigger server-side requests to internal resources. Concrete details: the issue arises from insufficient host restriction on mediaURL fetches, enabling potential a...
CVE-2025-6242 Vllm: server side request forgery (ssrf) in mediaconnector
A Server-Side Request Forgery SSRF vulnerability exists in the MediaConnector class within the vLLM project's multimodal feature set. The loadfromurl and loadfromurlasync methods fetch and process media from user-provided URLs without adequate restrictions on the target hosts. This allows an...
EUVD-2025-32903
LLaMA-Factory is a tuning library for large language models. Prior to version 0.9.4, a Server-Side Request Forgery SSRF vulnerability in the chat API allows any authenticated user to force the server to make arbitrary HTTP requests to internal and external networks. This can lead to the exposure ...
PT-2025-41177
Name of the Vulnerable Software and Affected Versions vLLM affected versions not specified Description An issue exists within the MediaConnector class in the vLLM project’s multimodal feature set. Specifically, the load from url and load from url async methods do not sufficiently restrict the...
EUVD-2024-0393
Malicious code in bioql PyPI...
LLaVul: a Multimodal LLM for Interpretable Vulnerability Reasoning about Source Code
Increasing complexity in software systems places a growing demand on reasoning tools that unlock vulnerabilities manifest in source code. Many current approaches focus on vulnerability analysis as a classifying task, oversimplifying the nuanced and context-dependent real-world scenarios. Even...
DMLDroid: Deep Multimodal Fusion Framework for Android Malware Detection with Resilience to Code Obfuscation and Adversarial Perturbations
In recent years, learning-based Android malware detection has seen significant advancements, with detectors generally falling into three categories: string-based, image-based, and graph-based approaches. While these methods have shown strong detection performance, they often struggle to sustain...
Aura-CAPTCHA: a Reinforcement Learning and GAN-Enhanced Multi-Modal CAPTCHA System
Aura-CAPTCHA was developed as a multi-modal CAPTCHA system to address vulnerabilities in traditional methods that are increasingly bypassed by AI technologies, such as Optical Character Recognition OCR and adversarial image processing. The design integrated Generative Adversarial Networks GANs fo...
MADPromptS: Unlocking Zero-Shot Morphing Attack Detection with Multiple Prompt Aggregation
Face Morphing Attack Detection MAD is a critical challenge in face recognition security, where attackers can fool systems by interpolating the identity information of two or more individuals into a single face image, resulting in samples that can be verified as belonging to multiple identities by...
