GRAD: Graph-Structured Antibody Defense for LLM Agents

Memory poisoning and decomposition attacks distribute malicious evidence across the operations of large language model agents. Existing request-level defenses can miss such attacks because they assess individual inputs without connecting earlier information to later actions. Moreover, detectors...

autotrain-advanced (>=0.6.95, <=0.7.1), dialogy (>=0.10.4, <=2.2.5) +10 more potentially affected by CVE-2026-9856 via transformers (4.38.0)

transformers PYPI version 4.38.0 is affected by a known vulnerability. The following packages have a transitive dependency on transformers and may be impacted: - autotrain-advanced =0.6.95, =0.10.4, =1.7.0, =0.0.1, =0.10.9, =0.1.0, =0.1.17, =0.1.20 - profanity-sanitizer 0.1.4 - team-comm-tools...

Malicious code in @zalastax/nolb-grad (npm)

The package @zalastax/nolb-grad was found to contain malicious code. --- Credit: OpenSSF source...

A Comparison of Malware Image Transformations Using Grad-CAM and Hybrid Learning Models

Recent studies have shown that binary-to-image representations can enable effective machine learning-based results for malware detection and classification. However, performance can vary significantly, depending on the technique used to convert binaries to images. Furthermore, the explainability...

autotrain-advanced (>=0.6.95, <=0.7.1), dialogy (>=0.10.4, <=2.2.5) +10 more potentially affected by CVE-2026-9856 via transformers (4.38.0)

transformers PYPI version 4.38.0 is affected by a known vulnerability. The following packages have a transitive dependency on transformers and may be impacted: - autotrain-advanced =0.6.95, =0.10.4, =1.7.0, =0.0.1, =0.10.9, =0.1.0, =0.1.17, =0.1.20 - profanity-sanitizer 0.1.4 - team-comm-tools...

TensorFlow has Floating Point Exception in AvgPoolGrad with XLA

ImpactIf the stride and window size are not positive for tf.rawops.AvgPoolGrad, it can give an FPE.pythonimport tensorflow as tfimport numpy as [email protected]=Truedef test: y = tf.rawops.AvgPoolGradoriginputshape=1,0,0,0, grad=0.39117979, ksize=1,0,0,0, strides=1,0,0,0, padding="SAME",...

TensorFlow vulnerable to Out-of-Bounds Read in GRUBlockCellGrad

ImpactOut of bounds read in GRUBlockCellGradpythonfunc = tf.rawops.GRUBlockCellGradpara = 'x': 21.1, 156.2, 83.3, 115.4, 'hprev': array136.5, 136.6, 'wru': array26.7, 0.8, 47.9, 26.1, 26.2, 26.3, 'wc': array 0.4, 31.5, 0.6, 'bru': array0.1, 0.2 , dtype=float32, 'bc': 0x41414141, 'r': array0.3, 0....

PYSEC-2026-3286 TensorFlow vulnerable to Out-of-Bounds Read in GRUBlockCellGrad

Impact Out of bounds read in GRUBlockCellGrad python func = tf.rawops.GRUBlockCellGrad para = 'x': 21.1, 156.2, 83.3, 115.4, 'hprev': array136.5, 136.6, 'wru': array26.7, 0.8, 47.9, 26.1, 26.2, 26.3, 'wc': array 0.4, 31.5, 0.6, 'bru': array0.1, 0.2 , dtype=float32, 'bc': 0x41414141, 'r': array0.3...

TensorFlow vulnerable to Out-of-Bounds Read in GRUBlockCellGrad

ImpactOut of bounds read in GRUBlockCellGradpythonfunc = tf.rawops.GRUBlockCellGradpara = 'x': 21.1, 156.2, 83.3, 115.4, 'hprev': array136.5, 136.6, 'wru': array26.7, 0.8, 47.9, 26.1, 26.2, 26.3, 'wc': array 0.4, 31.5, 0.6, 'bru': array0.1, 0.2 , dtype=float32, 'bc': 0x41414141, 'r': array0.3, 0....

Overflow in `ResizeNearestNeighborGrad`

ImpactWhen tf.rawops.ResizeNearestNeighborGrad is given a large size input, it overflows.import tensorflow as tfaligncorners = Truehalfpixelcenters = Falsegrads = tf.constant1, shape=1,8,16,3, dtype=tf.float16size = tf.constant1879048192,1879048192, shape=2,...

Overflow in `ResizeNearestNeighborGrad`

ImpactWhen tf.rawops.ResizeNearestNeighborGrad is given a large size input, it overflows.import tensorflow as tfaligncorners = Truehalfpixelcenters = Falsegrads = tf.constant1, shape=1,8,16,3, dtype=tf.float16size = tf.constant1879048192,1879048192, shape=2,...

PYSEC-2026-3091 Overflow in `ResizeNearestNeighborGrad`

Impact When tf.rawops.ResizeNearestNeighborGrad is given a large size input, it overflows. import tensorflow as tf aligncorners = True halfpixelcenters = False grads = tf.constant1, shape=1,8,16,3, dtype=tf.float16 size = tf.constant1879048192,1879048192, shape=2, dtype=tf.int32...

PYSEC-2026-3340 `CHECK` fail via inputs in `SparseFillEmptyRowsGrad`

Impact If SparseFillEmptyRowsGrad is given empty inputs, TensorFlow will crash. python import tensorflow as tf tf.rawops.SparseFillEmptyRowsGrad reverseindexmap=, gradvalues=, name=None Patches We have patched the issue in GitHub commit af4a6a3c8b95022c351edae94560acc61253a1b8. The fix will be...

PYSEC-2026-3201 `CHECK` fail via inputs in `SparseFillEmptyRowsGrad`

Impact If SparseFillEmptyRowsGrad is given empty inputs, TensorFlow will crash. python import tensorflow as tf tf.rawops.SparseFillEmptyRowsGrad reverseindexmap=, gradvalues=, name=None Patches We have patched the issue in GitHub commit af4a6a3c8b95022c351edae94560acc61253a1b8. The fix will be...

PYSEC-2026-3165 `FractionalMaxPoolGrad` Heap out of bounds read

Impact If FractionMaxPoolGrad is given outsize inputs rowpoolingsequence and colpoolingsequence, TensorFlow will crash. python import tensorflow as tf tf.rawops.FractionMaxPoolGrad originput = 1, 1, 1, 1, 1, origoutput = 1, 1, 1, outbackprop = 3, 3, 6, rowpoolingsequence = -0x4000000, 1, 1,...

PYSEC-2026-3313 `FractionalMaxPoolGrad` Heap out of bounds read

Impact If FractionMaxPoolGrad is given outsize inputs rowpoolingsequence and colpoolingsequence, TensorFlow will crash. python import tensorflow as tf tf.rawops.FractionMaxPoolGrad originput = 1, 1, 1, 1, 1, origoutput = 1, 1, 1, outbackprop = 3, 3, 6, rowpoolingsequence = -0x4000000, 1, 1,...

PYSEC-2026-3250 TensorFlow vulnerable to `CHECK` fail in `FractionalMaxPoolGrad`

Impact FractionalMaxPoolGrad validates its inputs with CHECK failures instead of with returning errors. If it gets incorrectly sized inputs, the CHECK failure can be used to trigger a denial of service attack: python import tensorflow as tf overlapping = True originput = tf.constant.453409232,...

TensorFlow vulnerable to `CHECK` fail in `FractionalMaxPoolGrad`

ImpactFractionalMaxPoolGrad validates its inputs with CHECK failures instead of with returning errors. If it gets incorrectly sized inputs, the CHECK failure can be used to trigger a denial of service attack:pythonimport tensorflow as tfoverlapping = Trueoriginput = tf.constant.453409232,...

PYSEC-2026-3375 TensorFlow vulnerable to `CHECK` fail in `FractionalMaxPoolGrad`

Impact FractionalMaxPoolGrad validates its inputs with CHECK failures instead of with returning errors. If it gets incorrectly sized inputs, the CHECK failure can be used to trigger a denial of service attack: python import tensorflow as tf overlapping = True originput = tf.constant.453409232,...

TensorFlow vulnerable to `CHECK` fail in `FractionalMaxPoolGrad`

ImpactFractionalMaxPoolGrad validates its inputs with CHECK failures instead of with returning errors. If it gets incorrectly sized inputs, the CHECK failure can be used to trigger a denial of service attack:pythonimport tensorflow as tfoverlapping = Trueoriginput = tf.constant.453409232,...