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Search Results (47408 CVEs found)
| CVE | Vendors | Products | Updated | CVSS v3.1 |
|---|---|---|---|---|
| CVE-2022-30673 | 3 Adobe, Apple, Microsoft | 3 Indesign, Macos, Windows | 2025-04-23 | 5.5 Medium |
| Adobe InDesign versions 16.4.2 (and earlier) and 17.3 (and earlier) are affected by an out-of-bounds read vulnerability that could lead to disclosure of sensitive memory. An attacker could leverage this vulnerability to bypass mitigations such as ASLR. Exploitation of this issue requires user interaction in that a victim must open a malicious file. | ||||
| CVE-2022-38414 | 3 Adobe, Apple, Microsoft | 3 Indesign, Macos, Windows | 2025-04-23 | 7.8 High |
| Adobe InDesign versions 16.4.2 (and earlier) and 17.3 (and earlier) are affected by a Heap-based Buffer Overflow vulnerability that could result in arbitrary code execution in the context of the current user. Exploitation of this issue requires user interaction in that a victim must open a malicious file. | ||||
| CVE-2022-30675 | 3 Adobe, Apple, Microsoft | 3 Indesign, Macos, Windows | 2025-04-23 | 5.5 Medium |
| Adobe InDesign versions 16.4.2 (and earlier) and 17.3 (and earlier) are affected by an out-of-bounds read vulnerability that could lead to disclosure of sensitive memory. An attacker could leverage this vulnerability to bypass mitigations such as ASLR. Exploitation of this issue requires user interaction in that a victim must open a malicious file. | ||||
| CVE-2022-38415 | 3 Adobe, Apple, Microsoft | 3 Indesign, Macos, Windows | 2025-04-23 | 7.8 High |
| Adobe InDesign versions 16.4.2 (and earlier) and 17.3 (and earlier) are affected by a Heap-based Buffer Overflow vulnerability that could result in arbitrary code execution in the context of the current user. Exploitation of this issue requires user interaction in that a victim must open a malicious file. | ||||
| CVE-2022-38413 | 3 Adobe, Apple, Microsoft | 3 Indesign, Macos, Windows | 2025-04-23 | 7.8 High |
| Adobe InDesign versions 16.4.2 (and earlier) and 17.3 (and earlier) are affected by a Heap-based Buffer Overflow vulnerability that could result in arbitrary code execution in the context of the current user. Exploitation of this issue requires user interaction in that a victim must open a malicious file. | ||||
| CVE-2022-30676 | 3 Adobe, Apple, Microsoft | 3 Indesign, Macos, Windows | 2025-04-23 | 5.5 Medium |
| Adobe InDesign versions 16.4.2 (and earlier) and 17.3 (and earlier) are affected by an out-of-bounds read vulnerability that could lead to disclosure of sensitive memory. An attacker could leverage this vulnerability to bypass mitigations such as ASLR. Exploitation of this issue requires user interaction in that a victim must open a malicious file. | ||||
| CVE-2022-38416 | 3 Adobe, Apple, Microsoft | 3 Indesign, Macos, Windows | 2025-04-23 | 7.8 High |
| Adobe InDesign versions 16.4.2 (and earlier) and 17.3 (and earlier) are affected by an out-of-bounds read vulnerability when parsing a crafted file, which could result in a read past the end of an allocated memory structure. An attacker could leverage this vulnerability to execute code in the context of the current user. Exploitation of this issue requires user interaction in that a victim must open a malicious file. | ||||
| CVE-2022-38417 | 3 Adobe, Apple, Microsoft | 3 Indesign, Macos, Windows | 2025-04-23 | 7.8 High |
| Adobe InDesign versions 16.4.2 (and earlier) and 17.3 (and earlier) are affected by an out-of-bounds read vulnerability when parsing a crafted file, which could result in a read past the end of an allocated memory structure. An attacker could leverage this vulnerability to execute code in the context of the current user. Exploitation of this issue requires user interaction in that a victim must open a malicious file. | ||||
| CVE-2022-35938 | 1 Google | 1 Tensorflow | 2025-04-23 | 7 High |
| TensorFlow is an open source platform for machine learning. The `GatherNd` function takes arguments that determine the sizes of inputs and outputs. If the inputs given are greater than or equal to the sizes of the outputs, an out-of-bounds memory read or a crash is triggered. This issue has been patched in GitHub commit 4142e47e9e31db481781b955ed3ff807a781b494. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue. | ||||
| CVE-2022-35937 | 1 Google | 1 Tensorflow | 2025-04-23 | 7 High |
| TensorFlow is an open source platform for machine learning. The `GatherNd` function takes arguments that determine the sizes of inputs and outputs. If the inputs given are greater than or equal to the sizes of the outputs, an out-of-bounds memory read is triggered. This issue has been patched in GitHub commit 595a65a3e224a0362d7e68c2213acfc2b499a196. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue. | ||||
| CVE-2022-35940 | 1 Google | 1 Tensorflow | 2025-04-23 | 5.9 Medium |
| TensorFlow is an open source platform for machine learning. The `RaggedRangOp` function takes an argument `limits` that is eventually used to construct a `TensorShape` as an `int64`. If `limits` is a very large float, it can overflow when converted to an `int64`. This triggers an `InvalidArgument` but also throws an abort signal that crashes the program. We have patched the issue in GitHub commit 37cefa91bee4eace55715eeef43720b958a01192. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue. | ||||
| CVE-2022-36015 | 1 Google | 1 Tensorflow | 2025-04-23 | 5.9 Medium |
| TensorFlow is an open source platform for machine learning. When `RangeSize` receives values that do not fit into an `int64_t`, it crashes. We have patched the issue in GitHub commit 37e64539cd29fcfb814c4451152a60f5d107b0f0. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue. | ||||
| CVE-2022-35705 | 3 Adobe, Apple, Microsoft | 3 Bridge, Macos, Windows | 2025-04-23 | 7.8 High |
| Adobe Bridge version 12.0.2 (and earlier) and 11.1.3 (and earlier) are affected by an out-of-bounds read vulnerability when parsing a crafted file, which could result in a read past the end of an allocated memory structure. An attacker could leverage this vulnerability to execute code in the context of the current user. Exploitation of this issue requires user interaction in that a victim must open a malicious file. | ||||
| CVE-2022-35703 | 3 Adobe, Apple, Microsoft | 3 Bridge, Macos, Windows | 2025-04-23 | 7.8 High |
| Adobe Bridge version 12.0.2 (and earlier) and 11.1.3 (and earlier) are affected by an out-of-bounds read vulnerability when parsing a crafted file, which could result in a read past the end of an allocated memory structure. An attacker could leverage this vulnerability to execute code in the context of the current user. Exploitation of this issue requires user interaction in that a victim must open a malicious file. | ||||
| CVE-2022-35707 | 3 Adobe, Apple, Microsoft | 3 Bridge, Macos, Windows | 2025-04-23 | 7.8 High |
| Adobe Bridge version 12.0.2 (and earlier) and 11.1.3 (and earlier) are affected by an out-of-bounds read vulnerability when parsing a crafted file, which could result in a read past the end of an allocated memory structure. An attacker could leverage this vulnerability to execute code in the context of the current user. Exploitation of this issue requires user interaction in that a victim must open a malicious file. | ||||
| CVE-2022-35702 | 3 Adobe, Apple, Microsoft | 3 Bridge, Macos, Windows | 2025-04-23 | 7.8 High |
| Adobe Bridge version 12.0.2 (and earlier) and 11.1.3 (and earlier) are affected by an out-of-bounds read vulnerability when parsing a crafted file, which could result in a read past the end of an allocated memory structure. An attacker could leverage this vulnerability to execute code in the context of the current user. Exploitation of this issue requires user interaction in that a victim must open a malicious file. | ||||
| CVE-2022-35706 | 3 Adobe, Apple, Microsoft | 3 Bridge, Macos, Windows | 2025-04-23 | 7.8 High |
| Adobe Bridge version 12.0.2 (and earlier) and 11.1.3 (and earlier) are affected by a Heap-based Buffer Overflow vulnerability that could result in arbitrary code execution in the context of the current user. Exploitation of this issue requires user interaction in that a victim must open a malicious file. | ||||
| CVE-2022-35708 | 3 Adobe, Apple, Microsoft | 3 Bridge, Macos, Windows | 2025-04-23 | 7.8 High |
| Adobe Bridge version 12.0.2 (and earlier) and 11.1.3 (and earlier) are affected by a Heap-based Buffer Overflow vulnerability that could result in arbitrary code execution in the context of the current user. Exploitation of this issue requires user interaction in that a victim must open a malicious file. | ||||
| CVE-2022-35951 | 2 Fedoraproject, Redis | 2 Fedora, Redis | 2025-04-23 | 7 High |
| Redis is an in-memory database that persists on disk. Versions 7.0.0 and above, prior to 7.0.5 are vulnerable to an Integer Overflow. Executing an `XAUTOCLAIM` command on a stream key in a specific state, with a specially crafted `COUNT` argument may cause an integer overflow, a subsequent heap overflow, and potentially lead to remote code execution. This has been patched in Redis version 7.0.5. No known workarounds exist. | ||||
| CVE-2022-36025 | 1 Linuxfoundation | 1 Besu | 2025-04-23 | 9.1 Critical |
| Besu is a Java-based Ethereum client. In versions newer than 22.1.3 and prior to 22.7.1, Besu is subject to an Incorrect Conversion between Numeric Types. An error in 32 bit signed and unsigned types in the calculation of available gas in the CALL operations (including DELEGATECALL) results in incorrect gas being passed into called contracts and incorrect gas being returned after call execution. Where the amount of gas makes a difference in the success or failure, or if the gas is a negative 64 bit value, the execution will result in a different state root than expected, resulting in a consensus failure in networks with multiple EVM implementations. In networks with a single EVM implementation this can be used to execute with significantly more gas than then transaction requested, possibly exceeding gas limitations. This issue is patched in version 22.7.1. As a workaround, reverting to version 22.1.3 or earlier will prevent incorrect execution. | ||||