dga_predict

Prédiction des algorithmes de génération de domaines à l'aide de LSTM Ce dépôt contient un code très simple pour classer les domaines comme DGA ou bénins. Ce code illustre nos résultats dans notre article arXiv disponible ici : https://arxiv.org/abs/1611.00791. Une différence réside dans les jeux...

LSTM-MI

إطار عمل قائم على LSTM للتعامل مع imbalance متعدد الفئات في كشف بوتنت DGA. يحتوي هذا المستودع على كود بسيط جداً لتصنيف النطاقات على أنها DGA أو حميدة. يوضح هذا الكود نتائجنا في بحثنا المنشور هنا: https://www.sciencedirect.com/science/article/pii/S0925231217317320. يقدم هذا البحث خوارزمية LSTM.MI...

DeepPass

DeepPass Application dockerisée qui analyse des documents à la recherche de candidats de mots de passe. Le billet de blog "DeepPass — Trouver des mots de passe avec le Deep Learning" donne plus de détails sur l'approche et le développement du modèle. Pour lancer: docker-compose up Cela exposera...

CVE-2026-73066

Tesseract is an open source OCR engine. Prior to 5.5.3, a crafted .traineddata LSTM model component loaded through Tesseract's deserializer can cause an unchecked signed integer multiplication in Convolve::DeSerialize in src/lstm/convolve.cpp to wrap the convolution output-channel count,...

CVE-2026-73066

Tesseract is an open source OCR engine. Prior to 5.5.3, a crafted .traineddata LSTM model component loaded through Tesseract's deserializer can cause an unchecked signed integer multiplication in Convolve::DeSerialize in src/lstm/convolve.cpp to wrap the convolution output-channel count,...

UBUNTU-CVE-2026-73066

Tesseract is an open source OCR engine. Prior to 5.5.3, a crafted .traineddata LSTM model component loaded through Tesseract's deserializer can cause an unchecked signed integer multiplication in Convolve::DeSerialize in src/lstm/convolve.cpp to wrap the convolution output-channel count,...

CVE-2026-73066

Tesseract prior to 5.5.3 is affected by a heap out-of-bounds write in LSTM Convolve via a crafted .traineddata loaded through the deserializer. The root cause is an unchecked signed integer multiplication that wraps the convolution output-channel count, undersizing the forward-pass output buffer ...

EUVD-2026-56181

Tesseract is an open source OCR engine. Prior to 5.5.3, a crafted .traineddata LSTM model component loaded through Tesseract's deserializer can cause an unchecked signed integer multiplication in Convolve::DeSerialize in src/lstm/convolve.cpp to wrap the convolution output-channel count,...

CVE-2026-73066 Tesseract: Heap out-of-bounds write in LSTM Convolve layer via crafted .traineddata

Tesseract is an open source OCR engine. Prior to 5.5.3, a crafted .traineddata LSTM model component loaded through Tesseract's deserializer can cause an unchecked signed integer multiplication in Convolve::DeSerialize in src/lstm/convolve.cpp to wrap the convolution output-channel count,...

CVE-2026-73066 Tesseract: Heap out-of-bounds write in LSTM Convolve layer via crafted .traineddata

Tesseract is an open source OCR engine. Prior to 5.5.3, a crafted .traineddata LSTM model component loaded through Tesseract's deserializer can cause an unchecked signed integer multiplication in Convolve::DeSerialize in src/lstm/convolve.cpp to wrap the convolution output-channel count,...

CVE-2026-73066 Tesseract: Heap out-of-bounds write in LSTM Convolve layer via crafted .traineddata

Tesseract is an open source OCR engine. Prior to 5.5.3, a crafted .traineddata LSTM model component loaded through Tesseract's deserializer can cause an unchecked signed integer multiplication in Convolve::DeSerialize in src/lstm/convolve.cpp to wrap the convolution output-channel count,...

PT-2026-70224

Name of the Vulnerable Software and Affected Versions Tesseract versions prior to 5.5.3 Description A heap out-of-bounds write can occur during OCR recognition when a crafted .traineddata LSTM model component is loaded through the deserializer. This happens because an unchecked signed integer...

PYSEC-2026-3318 TensorFlow vulnerable to segfault in `BlockLSTMGradV2`

Impact The implementation of BlockLSTMGradV2 does not fully validate its inputs. - wci, wcf, wco, b must be rank 1 - w, csprev, hprev must be rank 2 - x must be rank 3 This results in a a segfault that can be used to trigger a denial of service attack. python import tensorflow as tf usepeephole =...

PYSEC-2026-3267 Missing validation causes denial of service via `LSTMBlockCell`

Impact The implementation of tf.rawops.LSTMBlockCell does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack: python import tensorflow as tf tf.rawops.LSTMBlockCell x=tf.constant0.837607, shape=28,29, dtype=tf.float32,...

PYSEC-2026-3089 Missing validation causes denial of service via `LSTMBlockCell`

Impact The implementation of tf.rawops.LSTMBlockCell does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack: python import tensorflow as tf tf.rawops.LSTMBlockCell x=tf.constant0.837607, shape=28,29, dtype=tf.float32,...

PYSEC-2026-988 TensorFlow vulnerable to segfault in `BlockLSTMGradV2`

Impact The implementation of BlockLSTMGradV2 does not fully validate its inputs. - wci, wcf, wco, b must be rank 1 - w, csprev, hprev must be rank 2 - x must be rank 3 This results in a a segfault that can be used to trigger a denial of service attack. python import tensorflow as tf usepeephole =...

PYSEC-2026-944 Missing validation causes denial of service via `LSTMBlockCell`

Impact The implementation of tf.rawops.LSTMBlockCell does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack: python import tensorflow as tf tf.rawops.LSTMBlockCell x=tf.constant0.837607, shape=28,29, dtype=tf.float32,...

Categorical Robustness Assessment for Machine Learning Based Network Intrusion Detection Systems

Network Intrusion Detection Systems NIDS heavily utlize Machine Learning ML but ML models can be manipulated via adversarial attacks. These attacks add carefully crafted perturbations to network traffic data that leads to misclassifications. While prior work has demonstrated adversarial...

Rethinking IoT Intrusion Detection: Augmenting Routing Metrics with Radio Features

Machine learning-based intrusion detection systems IDS for RPL-based IoT networks often rely solely on routing layer features, which provide only a partial view of network behaviour. In this work, we investigate whether incorporating Transmit TX and Receive RX radio features alongside the standar...

SE-Enhanced ViT and BiLSTM-Based Intrusion Detection for Secure IIoT and IoMT Environments

With the rapid growth of interconnected devices in Industrial and Medical Internet of Things IIoT and MIoT ecosystems, ensuring timely and accurate detection of cyber threats has become a critical challenge. This study presents an advanced intrusion detection framework based on a hybrid...