ffmpeg 安全漏洞

FFmpeg is a Rust library developed by a single developer. Versions 8.0 and 8.0.1 of FFMPEG contain security vulnerabilities, which stem from an oversight in the validation of quantization parameters. These vulnerabilities may lead to out-of-bounds reads, memory leaks, or system crashes...

CVE-2025-69693

Out-of-bounds read in FFmpeg 8.0 and 8.0.1 RV60 video decoder libavcodec/rv60dec.c. The quantization parameter qp validation at line 2267 only checks the lower bound qp 0 but is missing upper bound validation. The qp value can reach 65 base value 63 from 6-bit frame header + offset +2 from...

CVE-2025-69693

Out-of-bounds read in FFmpeg 8.0 and 8.0.1 RV60 video decoder libavcodec/rv60dec.c. The quantization parameter qp validation at line 2267 only checks the lower bound qp 0 but is missing upper bound validation. The qp value can reach 65 base value 63 from 6-bit frame header + offset +2 from...

Ubuntu: Security Advisory (USN-8039-1)

The remote host is missing an update for the SPDX-FileCopyrightText: 2026 Greenbone AG Some text descriptions might be excerpted from a referenced sources, and are Copyright C by the respective right holders. SPDX-License-Identifier: GPL-2.0-only ifdescription...

Ubuntu 22.04 LTS / 24.04 LTS / 25.10 : libpng vulnerability (USN-8039-1)

The remote Ubuntu 22.04 LTS / 24.04 LTS / 25.10 host has packages installed that are affected by a vulnerability as referenced in the USN-8039-1 advisory. It was discovered that the libpng simplified API incorrectly handled quantizing RGB images. If a user or automated system were tricked into...

USN-8039-1 libpng1.6 vulnerability

It was discovered that the libpng simplified API incorrectly handled quantizing RGB images. If a user or automated system were tricked into opening a specially crafted PNG file, an attacker could use this issue to cause libpng to crash, resulting in a denial of service...

SASER: Stego Attacks on Open-Source LLMs

Open-source large language models LLMs have demonstrated considerable dominance over proprietary LLMs in resolving neural processing tasks, thanks to the collaborative and sharing nature. Although full access to source codes, model parameters, and training data lays the groundwork for transparenc...

EUVD-2021-0408

Malware in sbrugna...

EUVD-2021-0371

Malware in sbrugna...

EUVD-2021-0277

Malware in sbrugna...

EUVD-2022-3254

Malicious code in bioql PyPI...

Hierarchical Graph Neural Network for Compressed Speech Steganalysis

Steganalysis methods based on deep learning DL often struggle with computational complexity and challenges in generalizing across different datasets. Incorporating a graph neural network GNN into steganalysis schemes enables the leveraging of relational data for improved detection accuracy and...

Sparse Regression Codes for Secret Key Agreement: Achieving Strong Secrecy and Near-Optimal Rates for Gaussian Sources

Secret key agreement from correlated physical layer observations is a cornerstone of information-theoretic security. This paper proposes and rigorously analyzes a complete, constructive protocol for secret key agreement from Gaussian sources using Sparse Regression Codes SPARCs. Our protocol...

CompLeak: Deep Learning Model Compression Exacerbates Privacy Leakage

Model compression is crucial for minimizing memory storage and accelerating inference in deep learning DL models, including recent foundation models like large language models LLMs. Users can access different compressed model versions according to their resources and budget. However, while existi...

Semi-Fragile Watermarking of Remote Sensing Images Using DWT, Vector Quantization and Automatic Tiling

A semi-fragile watermarking scheme for multiple band images is presented in this article. We propose to embed a mark into remote sensing images applying a tree-structured vector quantization approach to the pixel signatures instead of processing each band separately. The signature of the...

TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs

Chip manufacturing is a complex process, and to achieve a faster time to market, an increasing number of untrusted third-party tools and designs from around the world are being utilized. The use of these untrusted third party intellectual properties IPs and tools increases the risk of adversaries...

Side-Channel Extraction of Dataflow AI Accelerator Hardware Parameters

Dataflow neural network accelerators efficiently process AI tasks on FPGAs, with deployment simplified by ready-to-use frameworks and pre-trained models. However, this convenience makes them vulnerable to malicious actors seeking to reverse engineer valuable Intellectual Property IP through...

An Efficient Digital Watermarking Technique for Small Scale Devices

In the age of IoT and mobile platforms, ensuring that content stay authentic whilst avoiding overburdening limited hardware is a key problem. This study introduces hybrid Fast Wavelet Transform & Additive Quantization index Modulation FWT-AQIM scheme, a lightweight watermarking approach that...

Mind the Gap: a Practical Attack on GGUF Quantization

With the increasing size of frontier LLMs, post-training quantization has become the standard for memory-efficient deployment. Recent work has shown that basic rounding-based quantization schemes pose security risks, as they can be exploited to inject malicious behaviors into quantized models tha...

CVE-2022-29212

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, certain TFLite models that were created using TFLite model converter would crash when loaded in the TFLite interpreter. The culprit is that during quantization the scale of values could b...