Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection

Malicious anomalous activity detection is a fundamental challenge for cyber security systems. Both tensor decomposition under statistical framework with CANDECOMP-PARAFAC alternating Poisson regression CP-APR and normalizing flows have proven to be powerful unsupervised machine learning methods...

Overflow in `tf.keras.losses.poisson`

Impacttf.keras.losses.poisson receives a ypred and ytrue that are passed through functor::mul in BinaryOp. If the resulting dimensions overflow an int32, TensorFlow will crash due to a size mismatch during broadcast assignment.pythonimport numpy as npimport tensorflow as tftruevalue =...

PYSEC-2026-3136 Overflow in `tf.keras.losses.poisson`

Impact tf.keras.losses.poisson receives a ypred and ytrue that are passed through functor::mul in BinaryOp. If the resulting dimensions overflow an int32, TensorFlow will crash due to a size mismatch during broadcast assignment. python import numpy as np import tensorflow as tf truevalue =...

Overflow in `tf.keras.losses.poisson`

Impacttf.keras.losses.poisson receives a ypred and ytrue that are passed through functor::mul in BinaryOp. If the resulting dimensions overflow an int32, TensorFlow will crash due to a size mismatch during broadcast assignment.pythonimport numpy as npimport tensorflow as tftruevalue =...

PYSEC-2026-3297 Overflow in `tf.keras.losses.poisson`

Impact tf.keras.losses.poisson receives a ypred and ytrue that are passed through functor::mul in BinaryOp. If the resulting dimensions overflow an int32, TensorFlow will crash due to a size mismatch during broadcast assignment. python import numpy as np import tensorflow as tf truevalue =...

PYSEC-2026-3311 TensorFlow vulnerable to `CHECK` fail in `RandomPoissonV2`

Impact When RandomPoissonV2 receives large input shape and rates, it gives a CHECK fail that can trigger a denial of service attack. python import tensorflow as tf arg0=tf.random.uniformshape=4,, dtype=tf.int32, maxval=65536 arg1=tf.random.uniformshape=4, 4, 4, 4, 4, dtype=tf.float32, maxval=None...

TensorFlow vulnerable to `CHECK` fail in `RandomPoissonV2`

ImpactWhen RandomPoissonV2 receives large input shape and rates, it gives a CHECK fail that can trigger a denial of service attack.pythonimport tensorflow as tfarg0=tf.random.uniformshape=4,, dtype=tf.int32, maxval=65536arg1=tf.random.uniformshape=4, 4, 4, 4, 4, dtype=tf.float32,...

PYSEC-2026-3162 TensorFlow vulnerable to `CHECK` fail in `RandomPoissonV2`

Impact When RandomPoissonV2 receives large input shape and rates, it gives a CHECK fail that can trigger a denial of service attack. python import tensorflow as tf arg0=tf.random.uniformshape=4,, dtype=tf.int32, maxval=65536 arg1=tf.random.uniformshape=4, 4, 4, 4, 4, dtype=tf.float32, maxval=None...

TensorFlow vulnerable to `CHECK` fail in `RandomPoissonV2`

ImpactWhen RandomPoissonV2 receives large input shape and rates, it gives a CHECK fail that can trigger a denial of service attack.pythonimport tensorflow as tfarg0=tf.random.uniformshape=4,, dtype=tf.int32, maxval=65536arg1=tf.random.uniformshape=4, 4, 4, 4, 4, dtype=tf.float32,...

PT-2026-59753

Impact tf.keras.losses.poisson receives a y pred and y true that are passed through functor::mul in BinaryOp. If the resulting dimensions overflow an int32, TensorFlow will crash due to a size mismatch during broadcast assignment. python import numpy as np import tensorflow as tf true value =...

PT-2026-59779

Impact When RandomPoissonV2 receives large input shape and rates, it gives a CHECK fail that can trigger a denial of service attack. python import tensorflow as tf arg 0=tf.random.uniformshape=4,, dtype=tf.int32, maxval=65536 arg 1=tf.random.uniformshape=4, 4, 4, 4, 4, dtype=tf.float32, maxval=No...

PT-2026-59925

Impact When RandomPoissonV2 receives large input shape and rates, it gives a CHECK fail that can trigger a denial of service attack. python import tensorflow as tf arg 0=tf.random.uniformshape=4,, dtype=tf.int32, maxval=65536 arg 1=tf.random.uniformshape=4, 4, 4, 4, 4, dtype=tf.float32, maxval=No...

PT-2026-59911

Impact tf.keras.losses.poisson receives a y pred and y true that are passed through functor::mul in BinaryOp. If the resulting dimensions overflow an int32, TensorFlow will crash due to a size mismatch during broadcast assignment. python import numpy as np import tensorflow as tf true value =...

PYSEC-2026-970 Overflow in `tf.keras.losses.poisson`

Impact tf.keras.losses.poisson receives a ypred and ytrue that are passed through functor::mul in BinaryOp. If the resulting dimensions overflow an int32, TensorFlow will crash due to a size mismatch during broadcast assignment. python import numpy as np import tensorflow as tf truevalue =...

Overflow in `tf.keras.losses.poisson`

Impacttf.keras.losses.poisson receives a ypred and ytrue that are passed through functor::mul in BinaryOp. If the resulting dimensions overflow an int32, TensorFlow will crash due to a size mismatch during broadcast assignment.pythonimport numpy as npimport tensorflow as tftruevalue =...

PYSEC-2026-982 TensorFlow vulnerable to `CHECK` fail in `RandomPoissonV2`

Impact When RandomPoissonV2 receives large input shape and rates, it gives a CHECK fail that can trigger a denial of service attack. python import tensorflow as tf arg0=tf.random.uniformshape=4,, dtype=tf.int32, maxval=65536 arg1=tf.random.uniformshape=4, 4, 4, 4, 4, dtype=tf.float32, maxval=None...

Modeling Sparse and Bursty Vulnerability Sightings: Forecasting under Data Constraints

Understanding and anticipating vulnerability-related activity is a major challenge in cyber threat intelligence. This work investigates whether vulnerability sightings, such as proof-of-concept releases, detection templates, or online discussions, can be forecast over time. Building on our earlie...

EUVD-2022-7281

Malicious code in bioql PyPI...

Linux Distros Unpatched Vulnerability : CVE-2019-2212

The Linux/Unix host has one or more packages installed that are impacted by a vulnerability without a vendor supplied patch available. - In poissondistribution of random, there is an out of bounds read. This could lead to local information disclosure with no additional execution privileges needed...

Hush! Protecting Secrets during Model Training: an Indistinguishability Approach

We consider the problem of secret protection, in which a business or organization wishes to train a model on their own data, while attempting to not leak secrets potentially contained in that data via the model. The standard method for training models to avoid memorization of secret information i...