gradient-untangler

gradient-untangler A local research harness that searches for the exact tokens that make an open-weight language model start its answer the way you specify. Open-weight models ship as ordinary files: config.json, a tokenizer, and one or more .safetensors or .bin shards. A Hugging Face id is only ...

MGASA-2026-0315 Updated libvncserver packages fix security vulnerabilities

The updated packages fix security vulnerabilities: Heap Out-of-Bounds Read in HandleUltraZipBPP due to unchecked subrectangle count. CVE-2026-32853 NULL pointer dereferences in httpd proxy handlers via malformed CONNECT/GET requests. CVE-2026-32854 LibVNCClient Tight Gradient decoding allows...

MGASA-2026-0259 Updated libreoffice packages fix security vulnerabilities

The updated packages fix security vulnerabilities: Heap buffer overflow in DXF polyline import. CVE-2026-6039 Heap use-after-free in ODF number-format blank-width parsing. CVE-2026-6040 Heap buffer overflow in EMF+ gradient brush import. CVE-2026-6045 Stack buffer overflow in PPT presentation...

Updated libreoffice packages fix security vulnerabilities

The updated packages fix security vulnerabilities: Heap buffer overflow in DXF polyline import. CVE-2026-6039 Heap use-after-free in ODF number-format blank-width parsing. CVE-2026-6040 Heap buffer overflow in EMF+ gradient brush import. CVE-2026-6045 Stack buffer overflow in PPT presentation...

PYSEC-2026-3366 TensorFlow has Floating Point Exception in AvgPoolGrad with XLA

Impact If the stride and window size are not positive for tf.rawops.AvgPoolGrad, it can give an FPE. python import tensorflow as tf import numpy as np @tf.functionjitcompile=True def test: y = tf.rawops.AvgPoolGradoriginputshape=1,0,0,0, grad=0.39117979, ksize=1,0,0,0, strides=1,0,0,0,...

PYSEC-2026-3268 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-2026-3338 TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsPerChannelGradient`

Impact When tf.quantization.fakequantwithminmaxvarsperchannelgradient receives input min or max of rank other than 1, it gives a CHECK fail that can trigger a denial of service attack. python import tensorflow as tf arg0=tf.random.uniformshape=1,1, dtype=tf.float32, maxval=None...

PYSEC-2026-3083 TensorFlow vulnerable to `CHECK` fail in `AvgPoolGrad`

Impact The implementation of AvgPoolGrad does not fully validate the input originputshape. This results in a CHECK failure which 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 = "VALID" dataformat = "NHWC"...

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-2026-3257 TensorFlow vulnerable to `CHECK` failures in `AvgPool3DGrad`

Impact The implementation of AvgPool3DGradOp does not fully validate the input originputshape. This results in an overflow that results in a CHECK failure which can be used to trigger a denial of service attack: python import tensorflow as tf ksize = 1, 1, 1, 1, 1 strides = 1, 1, 1, 1, 1 padding ...

PYSEC-2026-3364 TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsGradient`

Impact When tf.quantization.fakequantwithminmaxvarsgradient receives input min or max that is nonscalar, it gives a CHECK fail that can trigger a denial of service attack. python import tensorflow as tf import numpy as np arg0=tf.constantvalue=np.random.randomsize=2, 2, shape=2, 2, dtype=tf.float...

PT-2026-59927

Impact If FractionMaxPoolGrad is given outsize inputs row pooling sequence and col pooling sequence, TensorFlow will crash. python import tensorflow as tf tf.raw ops.FractionMaxPoolGrad orig input = 1, 1, 1, 1, 1, orig output = 1, 1, 1, out backprop = 3, 3, 6, row pooling sequence = -0x4000000, 1...

PYSEC-2026-3247 Out of bounds read in Tensorflow

Impact The implementation of FractionalAvgPoolGrad does not consider cases where the input tensors are invalid allowing an attacker to read from outside of bounds of heap: python import tensorflow as tf @tf.function def test: y = tf.rawops.FractionalAvgPoolGrad originputtensorshape=2,2,2,2,...

`FractionalMaxPoolGrad` Heap out of bounds read

ImpactIf FractionMaxPoolGrad is given outsize inputs rowpoolingsequence and colpoolingsequence, TensorFlow will crash.pythonimport tensorflow as tftf.rawops.FractionMaxPoolGrad originput = 1, 1, 1, 1, 1, origoutput = 1, 1, 1, outbackprop = 3, 3, 6, rowpoolingsequence = -0x4000000, 1, 1,...

PYSEC-2026-1006 TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsPerChannelGradient`

Impact When tf.quantization.fakequantwithminmaxvarsperchannelgradient receives input min or max of rank other than 1, it gives a CHECK fail that can trigger a denial of service attack. python import tensorflow as tf arg0=tf.random.uniformshape=1,1, dtype=tf.float32, maxval=None...

PYSEC-2026-1031 TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsGradient`

Impact When tf.quantization.fakequantwithminmaxvarsgradient receives input min or max that is nonscalar, it gives a CHECK fail that can trigger a denial of service attack. python import tensorflow as tf import numpy as np arg0=tf.constantvalue=np.random.randomsize=2, 2, shape=2, 2, dtype=tf.float...

PYSEC-2026-1002 Missing validation crashes `QuantizeAndDequantizeV4Grad`

Impact The implementation of tf.rawops.QuantizeAndDequantizeV4Grad 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.QuantizeAndDequantizeV4Grad gradients=tf.constant1,...

CVE-2026-44311

Fabric.js is a Javascript HTML5 canvas library. Prior to 7.4.0, a potential Cross-Site Scripting XSS vulnerability exists in Fabric.js due to improper escaping of user-controlled input during SVG serialization via the toSVG method. Specifically, the color field within the colorStops array of a...

CVE-2026-44311

CVE-2026-44311 (Fabric.js) describes an XSS in which the color value in colorStops of a fabric.Gradient is not properly escaped when serializing to SVG via toSVG(), allowing injected HTML/SVG to be executed if the SVG is rendered into the DOM. Documents confirm the issue affects Fabric.js prior t...

CVE-2026-44311

Fabric.js is a Javascript HTML5 canvas library. Prior to 7.4.0, a potential Cross-Site Scripting XSS vulnerability exists in Fabric.js due to improper escaping of user-controlled input during SVG serialization via the toSVG method. Specifically, the color field within the colorStops array of a...