54 matches found
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,...
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...
Incremental Federated Learning for Intrusion Detection in IoT Networks under Evolving Threat Landscape
The expansion of Internet of Things IoT devices has increased the attack surface of networks, necessitating a robust and adaptive intrusion detection systems. Machine learning based systems have been considered promising in enhancing the detection performance. Federated learning settings enabled ...
A Lightweight Defense Mechanism against Next Generation of Phishing Emails Using Distilled Attention-Augmented BiLSTM
The current generation of large language models produces sophisticated social-engineering content that bypasses standard text screening systems in business communication platforms. Our proposed solution for mail gateway and endpoint deception detection operates in a privacy-protective manner whil...
PyTorch torch.lstm_cell memory corruption
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Hybrid Ensemble Method for Detecting Cyber-Attacks in Water Distribution Systems Using the BATADAL Dataset
The cybersecurity of Industrial Control Systems that manage critical infrastructure such as Water Distribution Systems has become increasingly important as digital connectivity expands. BATADAL benchmark data is a good source of testing intrusion detection techniques, but it presents several...
New Machine Learning Approaches for Intrusion Detection in ADS-B
With the growing reliance on the vulnerable Automatic Dependent Surveillance-Broadcast ADS-B protocol in air traffic management ATM, ensuring security is critical. This study investigates emerging machine learning models and training strategies to improve AI-based intrusion detection systems IDS...