Directory Traversal

Overview nemo-toolkit is a NeMo - a toolkit for Conversational AI Affected versions of this package are vulnerable to Directory Traversal via the model loading process. An attacker can execute arbitrary code and tamper with data by supplying a .nemo file containing maliciously crafted metadata...

CVE-2025-23304

CVE-2025-23304 affects the NVIDIA NeMo library (model loading component). The vulnerability arises from loading .nemo files with maliciously crafted metadata, enabling code injection that may lead to remote code execution and data tampering. Affected: NVIDIA NeMo library (model loading). Exploita...

CVE-2025-23304

NVIDIA NeMo library for all platforms contains a vulnerability in the model loading component, where an attacker could cause code injection by loading .nemo files with maliciously crafted metadata. A successful exploit of this vulnerability may lead to remote code execution and data tampering...

CVE-2025-23304

NVIDIA NeMo library for all platforms contains a vulnerability in the model loading component, where an attacker could cause code injection by loading .nemo files with maliciously crafted metadata. A successful exploit of this vulnerability may lead to remote code execution and data tampering...

CVE-2025-23304

NVIDIA NeMo library for all platforms contains a vulnerability in the model loading component, where an attacker could cause code injection by loading .nemo files with maliciously crafted metadata. A successful exploit of this vulnerability may lead to remote code execution and data tampering...

CVE-2025-54382 Cherry Studio RCE Vulnerability Disclosure

Cherry Studio is a desktop client that supports for multiple LLM providers. In version 1.5.1, a remote code execution RCE vulnerability exists in the Cherry Studio platform when connecting to streamableHttp MCP servers. The issue arises from the server’s implicit trust in the oauth auth redirecti...

CVE-2025-8747

A safe mode bypass vulnerability in the Model.loadmodel method in Keras versions 3.0.0 through 3.10.0 allows an attacker to achieve arbitrary code execution by convincing a user to load a specially crafted .keras model archive...

CVE-2025-45146

ModelCache for LLM through v0.2.0 was discovered to contain an deserialization vulnerability via the component /manager/datamanager.py. This vulnerability allows attackers to execute arbitrary code via supplying crafted data...

Apple macOS Sequoia code execution vulnerability (CNVD-2025-19511)

Apple macOS Sequoia is an operating system from the American company Apple Apple. Apple macOS Sequoia suffers from a code execution vulnerability that is caused due to an error in the model I/O component when opening a specially crafted file. An attacker can exploit the vulnerability to execute...

NVIDIA NeMo library 路径遍历漏洞

NVIDIA NeMo library is a library of deep learning tools from NVIDIA. The NVIDIA NeMo library suffers from a path traversal vulnerability, which originates in the model loading component, that can be exploited by an attacker to obtain sensitive files by accessing locations outside of a restricted...

KuWFi 5G01-X55 安全漏洞

KuWFi 5G01-X55 is a WiFi router from KuWFi China. A security vulnerability exists in KuWFi 5G01-X55 FL2020V0.0.12, which originates from an unauthenticated API endpoint could lead to the disclosure of sensitive configuration data...

Apple macOS Sequoia code execution vulnerability

Apple macOS Sequoia is an operating system from the American company Apple Apple. A code execution vulnerability exists in Apple macOS Sequoia, which is caused due to an error in the model I/O component when opening a specially crafted file, and can be exploited by an attacker to execute arbitrar...

CVE-2025-43988

KuWFi 5G01-X55 FL2020V0.0.12 devices expose an unauthenticated API endpoint ajaxget.cgi, allowing remote attackers to retrieve sensitive configuration data, including admin credentials...

Amazon Nova AI Challenge -- Trusted AI: Advancing Secure, AI-Assisted Software Development

AI systems for software development are rapidly gaining prominence, yet significant challenges remain in ensuring their safety. To address this, Amazon launched the Trusted AI track of the Amazon Nova AI Challenge, a global competition among 10 university teams to drive advances in secure AI. In...

CVE-2025-43986

CVE-2025-43986 affects KuWFi GC111 GC111-GL-LM321_V3.0_20191211. The TELNET service is enabled by default and exposed over WAN with no authentication, per multiple sources (NVD/Red Hat/CNNVD/CVE list). This creates a network-accessible backdoor risk with potential for unauthorized access to devic...

Keras vulnerable to CVE-2025-1550 bypass via reuse of internal functionality

Summary It is possible to bypass the mitigation introduced in response to CVE-2025-1550, when an untrusted Keras v3 model is loaded, even when “safemode” is enabled, by crafting malicious arguments to built-in Keras modules. The vulnerability is exploitable on the default configuration and does n...

Security update for amber-cli

This update for amber-cli fixes the following issues: Update to version 1.13.1+git20250329.c2e3bb8: CVE-2025-30204: Fixed jwt-go excessive memory allocation during header parsing bsc1240511 jwt version upgrade 174 Update policy size limit to 20k 173 Update tenant user model with latest changes 17...

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...

IAG: Input-Aware Backdoor Attack on VLMs for Visual Grounding

Vision-language models VLMs have shown significant advancements in tasks such as visual grounding, where they localize specific objects in images based on natural language queries and images. However, security issues in visual grounding tasks for VLMs remain underexplored, especially in the conte...

Shadow in the Cache: Unveiling and Mitigating Privacy Risks of KV-Cache in LLM Inference

The Key-Value KV cache, which stores intermediate attention computations Key and Value pairs to avoid redundant calculations, is a fundamental mechanism for accelerating Large Language Model LLM inference. However, this efficiency optimization introduces significant yet underexplored privacy risk...