How to Make AI Tools Work Reliably for Growing Teams

Learn how growing teams make AI tools reliable with clear workflows, shared rules, secure systems, and repeatable processes that improve quality and speed daily...

PYSEC-2026-976 TensorFlow vulnerable to `CHECK` fail in `QuantizeAndDequantizeV3`

Impact If QuantizeAndDequantizeV3 is given a nonscalar numbits input tensor, it results in a CHECK fail that can be used to trigger a denial of service attack. python import tensorflow as tf signedinput = True rangegiven = False narrowrange = False axis = -1 input = tf.constant-3.5, shape=1,...

EUVD-2023-59663

Malicious code in bioql PyPI...

Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents

Whitepaper called Open Challenges In Multi-Agent Security: Towards Secure Systems Of Interacting AI Agents...

MAL-2025-1085 Malicious code in cs-icons (npm)

--- -= Per source details. Do not edit below this line.=- Source: ghsa-malware 481dd64f1bb368951028f0c7cb211c53754e51217edc6a8d89a31e8c9cc8ad9b Any computer that has this package installed or running should be considered fully compromised. All secrets and keys stored on that computer should be...

CVE-2023-7279

A vulnerability has been found in Secure Systems Engineering Connaisseur up to 3.3.0 and classified as problematic. This vulnerability affects unknown code of the file connaisseur/res/targetsschema.json of the component Delegation Name Handler. The manipulation leads to inefficient regular...

CVE-2023-7279 Secure Systems Engineering Connaisseur Delegation Name targets_schema.json redos

A vulnerability has been found in Secure Systems Engineering Connaisseur up to 3.3.0 and classified as problematic. This vulnerability affects unknown code of the file connaisseur/res/targetsschema.json of the component Delegation Name Handler. The manipulation leads to inefficient regular...

CVE-2023-7279

CVE-2023-7279 affects Secure Systems Engineering Connaisseur up to version 3.3.0, with the issue localized to the file connaisseur/res/targets_schema.json in the Delegation Name Handler. The vulnerability causes inefficient regular expression complexity; the reported attack complexity is high and...

CVE-2023-7279 Secure Systems Engineering Connaisseur Delegation Name targets_schema.json redos

A vulnerability has been found in Secure Systems Engineering Connaisseur up to 3.3.0 and classified as problematic. This vulnerability affects unknown code of the file connaisseur/res/targetsschema.json of the component Delegation Name Handler. The manipulation leads to inefficient regular...

Overflow in `ImageProjectiveTransformV2`

Impact When tf.rawops.ImageProjectiveTransformV2 is given a large output shape, it overflows. python import tensorflow as tf interpolation = "BILINEAR" fillmode = "REFLECT" images = tf.constant0.184634328, shape=2,5,8,3, dtype=tf.float32 transforms = tf.constant0.378575385, shape=2,8,...

Overflow in `FusedResizeAndPadConv2D`

Impact When tf.rawops.FusedResizeAndPadConv2D is given a large tensor shape, it overflows. python import tensorflow as tf mode = "REFLECT" strides = 1, 1, 1, 1 padding = "SAME" resizealigncorners = False input = tf.constant147, shape=3,3,1,1, dtype=tf.float16 size =...

TensorFlow vulnerable to `CHECK` fail in `Save` and `SaveSlices`

Impact If Save or SaveSlices is run over tensors of an unsupported dtype, it results in a CHECK fail that can be used to trigger a denial of service attack. python import tensorflow as tf filename = tf.constant"" tensornames = tf.constant"" Save data = tf.casttf.random.uniformshape=1,...

TensorFlow vulnerable to `CHECK` fail in `LRNGrad`

Impact If LRNGrad is given an outputimage input tensor that is not 4-D, it results in a CHECK fail that can be used to trigger a denial of service attack. python import tensorflow as tf depthradius = 1 bias = 1.59018219 alpha = 0.117728651 beta = 0.404427052 inputgrads = tf.random.uniformshape=4,...

TensorFlow vulnerable to segfault in `RaggedBincount`

Impact If RaggedBincount is given an empty input tensor splits, it results in a segfault that can be used to trigger a denial of service attack. python import tensorflow as tf binaryoutput = True splits = tf.random.uniformshape=0, minval=-10000, maxval=10000, dtype=tf.int64, seed=-7430 values =...

TensorFlow vulnerable to segfault in `SparseBincount`

Impact If SparseBincount is given inputs for indices, values, and denseshape that do not make a valid sparse tensor, it results in a segfault that can be used to trigger a denial of service attack. python import tensorflow as tf binaryoutput = True indices = tf.random.uniformshape=, minval=-10000...

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 segfault in `QuantizeDownAndShrinkRange`

Impact If QuantizeDownAndShrinkRange is given nonscalar inputs for inputmin or inputmax, it results in a segfault that can be used to trigger a denial of service attack. python import tensorflow as tf outtype = tf.quint8 input = tf.constant1, shape=3, dtype=tf.qint32 inputmin = tf.constant,...

TensorFlow vulnerable to segfault in `QuantizedMatMul`

Impact If QuantizedMatMul is given nonscalar input for: - mina - maxa - minb - maxb It gives a segfault that can be used to trigger a denial of service attack. python import tensorflow as tf Toutput = tf.qint32 transposea = False transposeb = False Tactivation = tf.quint8 a = tf.constant7,...

TensorFlow vulnerable to segfault in `QuantizedBiasAdd`

Impact If QuantizedBiasAdd is given mininput, maxinput, minbias, maxbias tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack. python import tensorflow as tf outtype = tf.qint32 input = tf.constant85,170,255, shape=3, dtype=tf.quint8 bias =...

TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVars`

Impact If FakeQuantWithMinMaxVars is given min or max tensors of a nonzero rank, it results in a CHECK fail that can be used to trigger a denial of service attack. python import tensorflow as tf numbits = 8 narrowrange = False inputs = tf.constant0, shape=2,3, dtype=tf.float32 min = tf.constant0,...