Detection-struts-cve-2017-5638-detector

🚨 Echtzeit-Anomalieerkennung für Apache Struts CVE-2017-5638 Zero-Day-Exploit-Erkennung basierend auf Streaming-Analytik & probabilistischen Datenstrukturen 📌 Projektbeschreibung Dieses Projekt ist ein Echtzeit-Anomalieerkennungssystem , das entwickelt wurde, um RCE-Exploits Remote Code Execution...

Astra Linux – Vulnerability found in Linux 5.10, Linux 6.1, and Linux 5.15

In the Linux kernel, the following vulnerability has been resolved: Squashfs: check the return result of sbminblocksize Syzkaller reports a bug named “UBSAN: shift-out-of-bounds in squashfsbioread”. Syzkaller forks multiple processes. After mounting the Squashfs filesystem, it issues an...

CVE-2026-19980

A security flaw has been discovered in GL.iNet A1300, AX1800, AXT1800, BE1400, BE3600, BE6500, BE9300, BE10000, E5800, MT2500, MT3000, MT3600BE, MT5000, MT6000, X2000, X3000 and XE3000 up to 4.8.x. Affected by this issue is the function ui.updatelangs of the component Language Update. Performing ...

CVE-2026-68254

The Linux kernel component drm/i915/vrr was updated to enforce that EDID-provided min_vfreq and max_vfreq are valid for VRR. The root cause was inadequate validation of VRR frequencies, with a potential division-by-zero in intel_vrr_compute_vmax if min_vfreq was zero. The fix explicitly validates...

Astra Linux – Vulnerability found in Linux 6.12, Linux 6.1

In the Linux kernel, the following vulnerability has been resolved: hwmon: The macro FANFROMREG evaluates its arguments multiple times. When used in lockless contexts involving shared driver data, this leads to Time-of-Check to Time-of-Use TOCTOU race conditions, potentially causing divide-by-zer...

Malicious code in log-min (npm)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 5dc78815e8c40947273929a7224aa04230de911771e62580045fb8bb213468c9 The package publishes as 'log-min' with a description claiming it is a JavaScript library for the Theta Blockchain, while its README identifies the...

EUVD-2026-46353

Data::SortedSet::Shared versions before 0.03 for Perl allow an out-of-bounds read via unvalidated node indices in the rank and min/max query paths. The attach-time validator ssvalidateheader bounds only the root index against the node pool nodecapacity. The order-statistics and min/max queries th...

CVE-2026-59140

Data::SortedSet::Shared versions before 0.03 for Perl allow an out-of-bounds read via unvalidated node indices in the rank and min/max query paths. The attach-time validator ssvalidateheader bounds only the root index against the node pool nodecapacity. The order-statistics and min/max queries th...

CVE-2026-59140 Data::SortedSet::Shared versions before 0.03 for Perl allow an out-of-bounds read via unvalidated node indices in the rank and min/max query paths

Data::SortedSet::Shared versions before 0.03 for Perl allow an out-of-bounds read via unvalidated node indices in the rank and min/max query paths. The attach-time validator ssvalidateheader bounds only the root index against the node pool nodecapacity. The order-statistics and min/max queries th...

CVE-2026-59140

CVE-2026-59140 affects Data::SortedSet::Shared

PT-2026-62043

Data::SortedSet::Shared versions before 0.03 for Perl allow an out-of-bounds read via unvalidated node indices in the rank and min/max query paths. The attach-time validator ss validate header bounds only the root index against the node pool node capacity. The order-statistics and min/max queries...

GHSA-XJ96-63GP-2GMR Pillow: Heap out-of-bounds write in `ImageFilter.RankFilter` via integer overflow in `ImagingExpand`

Summary Pillow's public rank-filter API can trigger a native heap out-of-bounds write when given a very large odd filter size. Minimal public API trigger: python from PIL import Image, ImageFilter im = Image.new"L", 3, 3, 128 im.filterImageFilter.MedianFilter4294967295 ImageFilter.RankFilter.filt...

SUSE CVE-2026-63815

In the Linux kernel, the following vulnerability has been resolved: f2fs: bound iinlinexattrsize for non-inline-xattr inodes When the flexibleinlinexattr feature is enabled, doreadinode loads the on-disk iinlinexattrsize unconditionally: if f2fssbhasflexibleinlinexattrsbi fi-iinlinexattrsize =...

PYSEC-2026-3245 TensorFlow vulnerable to segfault in `QuantizedRelu` and `QuantizedRelu6`

Impact If QuantizedRelu or QuantizedRelu6 are given nonscalar inputs for minfeatures or maxfeatures, it results in a segfault that can be used to trigger a denial of service attack. python import tensorflow as tf outtype = tf.quint8 features = tf.constant28, shape=4,2, dtype=tf.quint8 minfeatures...

PYSEC-2026-3372 TensorFlow vulnerable to segfault in `QuantizedRelu` and `QuantizedRelu6`

Impact If QuantizedRelu or QuantizedRelu6 are given nonscalar inputs for minfeatures or maxfeatures, it results in a segfault that can be used to trigger a denial of service attack. python import tensorflow as tf outtype = tf.quint8 features = tf.constant28, shape=4,2, dtype=tf.quint8 minfeatures...

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-2026-3199 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-2026-3273 TensorFlow vulnerable to segfault in `QuantizedAvgPool`

Impact If QuantizedAvgPool is given mininput or maxinput 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 ksize = 1, 2, 2, 1 strides = 1, 2, 2, 1 padding = "SAME" input = tf.constant1, shape=1,4,4,2,...

PYSEC-2026-3234 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-2026-3364 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...