1728 matches found
kernel: drm/gem: Fix inconsistent plane dimension calculation in drm_gem_fb_init_with_funcs()
A flaw was found in the Linux kernel's Direct Rendering Manager DRM Graphics Execution Manager GEM component. This vulnerability arises from an inconsistent calculation of plane dimensions, which can lead to incorrect memory allocation checks. A local attacker could exploit this by creating a...
kernel: drm/gem: Fix inconsistent plane dimension calculation in drm_gem_fb_init_with_funcs()
A flaw was found in the Linux kernel's Direct Rendering Manager DRM Graphics Execution Manager GEM component. This vulnerability arises from an inconsistent calculation of plane dimensions, which can lead to incorrect memory allocation checks. A local attacker could exploit this by creating a...
kernel: drm/gem: Fix inconsistent plane dimension calculation in drm_gem_fb_init_with_funcs()
A flaw was found in the Linux kernel's Direct Rendering Manager DRM Graphics Execution Manager GEM component. This vulnerability arises from an inconsistent calculation of plane dimensions, which can lead to incorrect memory allocation checks. A local attacker could exploit this by creating a...
CVE-2026-59942
Dompdf is an HTML to PDF converter for PHP. Versions 3.15 and prior are vulnerable to a Denial of Service DoS attack via resource exhaustion. An attacker can crash the PHP process by providing a specially crafted HTML document containing a single image with massive dimensions e.g., 30,000x30,000...
CVE-2026-59942
Dompdf is an HTML to PDF converter for PHP. Versions 3.15 and prior are vulnerable to a Denial of Service DoS attack via resource exhaustion. An attacker can crash the PHP process by providing a specially crafted HTML document containing a single image with massive dimensions e.g., 30,000x30,000...
CVE-2026-59942
Dompdf (PHP HTML-to-PDF library) versions before 3.16 are vulnerable to a DoS via resource exhaustion. A crafted HTML string with a high-entropy, oversized image (e.g., 30000x30000) bypasses dimension checks and triggers heavy bitmap processing during rendering, causing 100% CPU usage and memory ...
CVE-2026-65703 FFmpeg 2.7 - 8.1.2 Out-of-Bounds Write in TDSC Video Decoder
FFmpeg versions 2.7 through 8.1.2 contain an out-of-bounds write vulnerability in the TDSC video decoder that allows remote attackers to cause heap corruption by supplying a crafted AVI file that changes frame dimensions across TDSF frames. The tdscparsetdsf function fails to unreference the...
CVE-2026-65703
FFmpeg versions 2.7 through 8.1.2 contain an out-of-bounds write vulnerability in the TDSC video decoder that allows remote attackers to cause heap corruption by supplying a crafted AVI file that changes frame dimensions across TDSF frames. The tdscparsetdsf function fails to unreference the...
PYSEC-2026-3488 ms-swift: Image Cache Hash Collision via Missing Dimension Metadata
A security vulnerability has been detected in modelscope ms-swift up to 4.2.0. This affects the function Template.savepilimage of the file swift/template/base.py of the component PIL Image Cache Key Handler. The manipulation leads to use of weak hash. An attack has to be approached locally. A hig...
CVE-2026-33328
libvips is a fast image processing library with low memory needs. On 32-bit systems in versions before and including 8.18.0, the gifload operation could incorrectly determine dimensions leading to an integer overflow. This has been patched in version 8.18.1...
CVE-2026-33327
libvips is a fast image processing library with low memory needs. The vipsload operation in versions before and including 8.18.0 could incorrectly determine image dimensions leading to an integer overflow and a subsequent heap-based buffer overflow. This has been patched in version 8.18.1...
CVE-2026-33327
CVE-2026-33327 affects the libvips image processing library. The vulnerability is in the vipsload operation for versions
PYSEC-2026-3345 Seg fault in `ndarray_tensor_bridge` due to zero and large inputs
Impact If a numpy array is created with a shape such that one element is zero and the others sum to a large number, an error will be raised. E.g. the following raises an error: python np.ones0, 231, 231 An example of a proof of concept: python import numpy as np import tensorflow as tf inputval =...
CVE-2026-60090
PraisonAI before 4.6.78 fails to validate the caller-controlled dimension argument in the PGVector and Cassandra knowledge-store createcollection backends. Although schema, keyspace, and collection-name identifiers are validated, the dimension value declared as int but not enforced at runtime is...
EUVD-2026-43175
PraisonAI before 4.6.78 fails to validate the caller-controlled dimension argument in the PGVector and Cassandra knowledge-store createcollection backends. Although schema, keyspace, and collection-name identifiers are validated, the dimension value declared as int but not enforced at runtime is...
CVE-2026-60090 PraisonAI before 4.6.78 SQL/CQL Injection via vector dimension
PraisonAI before 4.6.78 fails to validate the caller-controlled dimension argument in the PGVector and Cassandra knowledge-store createcollection backends. Although schema, keyspace, and collection-name identifiers are validated, the dimension value declared as int but not enforced at runtime is...
CVE-2026-60090
CVE-2026-60090 affects PraisonAI prior to 4.6.78, exposing a SQL/CQL injection risk via the vector dimension in the PGVector and Cassandra knowledge-store backends. The root cause is that the caller-controlled dimension value is interpolated into the generated CREATE TABLE DDL without runtime enf...
CVE-2026-60090 PraisonAI before 4.6.78 SQL/CQL Injection via vector dimension
PraisonAI before 4.6.78 fails to validate the caller-controlled dimension argument in the PGVector and Cassandra knowledge-store createcollection backends. Although schema, keyspace, and collection-name identifiers are validated, the dimension value declared as int but not enforced at runtime is...
PYSEC-2026-3121 Out of bounds read in Tensorflow
Impact The implementation of shape inference for ReverseSequence does not fully validate the value of batchdim and can result in a heap OOB read: python import tensorflow as tf @tf.function def test: y = tf.rawops.ReverseSequence input = 'aaa','bbb', seqlengths = 1,1,1, seqdim = -10, batchdim = -...
