PYSEC-2026-948 TensorFlow vulnerable to segfault in `QuantizedBiasAdd`

Impact If QuantizedBiasAdd is given mininput, maxinput, minbias, maxbias tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack. python import tensorflow as tf outtype = tf.qint32 input = tf.constant85,170,255, shape=3, dtype=tf.quint8 bias =...

PYSEC-2026-995 TensorFlow vulnerable to segfault in `QuantizedInstanceNorm`

Impact If QuantizedInstanceNorm is given xmin or xmax tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack. python import tensorflow as tf outputrangegiven = False givenymin = 0 givenymax = 0 varianceepsilon = 1e-05 minseparation = 0.001 x =...

PYSEC-2026-1038 TensorFlow vulnerable to segfault in `QuantizedRelu` and `QuantizedRelu6`

Impact If QuantizedRelu or QuantizedRelu6 are given nonscalar inputs for minfeatures or maxfeatures, it results in a segfault that can be used to trigger a denial of service attack. python import tensorflow as tf outtype = tf.quint8 features = tf.constant28, shape=4,2, dtype=tf.quint8 minfeatures...

PYSEC-2026-959 TensorFlow vulnerable to segfault in `QuantizedMatMul`

Impact If QuantizedMatMul is given nonscalar input for: - mina - maxa - minb - maxb It gives a segfault that can be used to trigger a denial of service attack. python import tensorflow as tf Toutput = tf.qint32 transposea = False transposeb = False Tactivation = tf.quint8 a = tf.constant7,...

PYSEC-2026-949 TensorFlow vulnerable to segfault in `QuantizedAvgPool`

Impact If QuantizedAvgPool is given mininput or maxinput tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack. python import tensorflow as tf ksize = 1, 2, 2, 1 strides = 1, 2, 2, 1 padding = "SAME" input = tf.constant1, shape=1,4,4,2,...

PYSEC-2026-950 Segfault due to missing support for quantized types

Impact There is a potential for segfault / denial of service in TensorFlow by calling tf.compat.v1. ops which don't yet have support for quantized types added after migration to TF 2.x: python import numpy as np import tensorflow as tf...

BIT-PYTORCH-2025-2149 PyTorch Quantized Sigmoid Module nnq_Sigmoid initialization

A vulnerability was found in PyTorch 2.6.0+cu124. It has been rated as problematic. Affected by this issue is the function nnqSigmoid of the component Quantized Sigmoid Module. The manipulation of the argument scale/zeropoint leads to improper initialization. The attack needs to be approached...

USN-8035-1 libpng1.6 vulnerabilities

It was discovered that the libpng simplified API incorrectly processed palette PNG images with partial transparency and gamma correction. 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...

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-2025-7523

Malicious code in bioql PyPI...

EUVD-2022-7469

Malicious code in bioql PyPI...

Linux Distros Unpatched Vulnerability : CVE-2025-2149

The Linux/Unix host has one or more packages installed that are impacted by a vulnerability without a vendor supplied patch available. - A vulnerability was found in PyTorch 2.6.0+cu124. It has been rated as problematic. Affected by this issue is the function nnqSigmoid of the component Quantized...

Phishing Detection in the Gen-AI Era: Quantized LLMs Vs Classical Models

Phishing attacks are becoming increasingly sophisticated, underscoring the need for detection systems that strike a balance between high accuracy and computational efficiency. This paper presents a comparative evaluation of traditional Machine Learning ML, Deep Learning DL, and quantized...

CVE-2023-25670

TensorFlow is an open source platform for machine learning. Versions prior to 2.12.0 and 2.11.1 have a null point error in QuantizedMatMulWithBiasAndDequantize with MKL enabled. A fix is included in TensorFlow version 2.12.0 and version 2.11.1...

CVE-2022-41889

TensorFlow is an open source platform for machine learning. If a list of quantized tensors is assigned to an attribute, the pywrap code fails to parse the tensor and returns a nullptr, which is not caught. An example can be seen in tf.compat.v1.extractvolumepatches by passing in quantized tensors...

Quantized Approximate Signal Processing (QASP): Towards Homomorphic Encryption for Audio

Audio and speech data are increasingly used in machine learning applications such as speech recognition, speaker identification, and mental health monitoring. However, the passive collection of this data by audio listening devices raises significant privacy concerns. Fully homomorphic encryption...

LiteLMGuard: Seamless and Lightweight On-Device Prompt Filtering for Safeguarding Small Language Models against Quantization-Induced Risks and Vulnerabilities

The growing adoption of Large Language Models LLMs has influenced the development of their lighter counterparts-Small Language Models SLMs-to enable on-device deployment across smartphones and edge devices. These SLMs offer enhanced privacy, reduced latency, server-free functionality, and improve...

CVE-2025-2149

A vulnerability was found in PyTorch 2.6.0+cu124. It has been rated as problematic. Affected by this issue is the function nnqSigmoid of the component Quantized Sigmoid Module. The manipulation of the argument scale/zeropoint leads to improper initialization. The attack needs to be approached...

The vulnerability of the nnq_Sigmoid() function in the Quantized Sigmoid Module framework of the PyTorch machine learning library allows attackers to exploit it to compromise the integrity of protected information.

The vulnerability of the nnqSigmoid function in the Quantized Sigmoid Module of the PyTorch machine learning framework is related to incorrect initialization of resources when processing parameters scale and zeropoint. Exploiting this vulnerability can allow attackers to compromise the integrity ...

PyTorch: Manipulation of the argument scale/zero_point leads to improper initialization via Quantized Sigmoid Module

A vulnerability was found in PyTorch 2.6.0+cu124. It has been rated as problematic. Affected by this issue is the function nnqSigmoid of the component Quantized Sigmoid Module. The manipulation of the argument scale/zeropoint leads to improper initialization. The attack needs to be approached...