Total
13378 CVE
| CVE | Vendors | Products | Updated | CVSS v3.1 |
|---|---|---|---|---|
| CVE-2020-15350 | 1 Riot-os | 1 Riot | 2024-11-21 | 9.8 Critical |
| RIOT 2020.04 has a buffer overflow in the base64 decoder. The decoding function base64_decode() uses an output buffer estimation function to compute the required buffer capacity and validate against the provided buffer size. The base64_estimate_decode_size() function calculates the expected decoded size with an arithmetic round-off error and does not take into account possible padding bytes. Due to this underestimation, it may be possible to craft base64 input that causes a buffer overflow. | ||||
| CVE-2020-15266 | 1 Google | 1 Tensorflow | 2024-11-21 | 3.7 Low |
| In Tensorflow before version 2.4.0, when the `boxes` argument of `tf.image.crop_and_resize` has a very large value, the CPU kernel implementation receives it as a C++ `nan` floating point value. Attempting to operate on this is undefined behavior which later produces a segmentation fault. The issue is patched in eccb7ec454e6617738554a255d77f08e60ee0808 and TensorFlow 2.4.0 will be released containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved. | ||||
| CVE-2020-15254 | 1 Crossbeam Project | 1 Crossbeam | 2024-11-21 | 8.1 High |
| Crossbeam is a set of tools for concurrent programming. In crossbeam-channel before version 0.4.4, the bounded channel incorrectly assumes that `Vec::from_iter` has allocated capacity that same as the number of iterator elements. `Vec::from_iter` does not actually guarantee that and may allocate extra memory. The destructor of the `bounded` channel reconstructs `Vec` from the raw pointer based on the incorrect assumes described above. This is unsound and causing deallocation with the incorrect capacity when `Vec::from_iter` has allocated different sizes with the number of iterator elements. This has been fixed in crossbeam-channel 0.4.4. | ||||
| CVE-2020-15213 | 1 Google | 1 Tensorflow | 2024-11-21 | 4 Medium |
| In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code. | ||||
| CVE-2020-15207 | 2 Google, Opensuse | 2 Tensorflow, Leap | 2024-11-21 | 8.7 High |
| In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, to mimic Python's indexing with negative values, TFLite uses `ResolveAxis` to convert negative values to positive indices. However, the only check that the converted index is now valid is only present in debug builds. If the `DCHECK` does not trigger, then code execution moves ahead with a negative index. This, in turn, results in accessing data out of bounds which results in segfaults and/or data corruption. The issue is patched in commit 2d88f470dea2671b430884260f3626b1fe99830a, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | ||||
| CVE-2020-15205 | 2 Google, Opensuse | 2 Tensorflow, Leap | 2024-11-21 | 9 Critical |
| In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `data_splits` argument of `tf.raw_ops.StringNGrams` lacks validation. This allows a user to pass values that can cause heap overflow errors and even leak contents of memory In the linked code snippet, all the binary strings after `ee ff` are contents from the memory stack. Since these can contain return addresses, this data leak can be used to defeat ASLR. The issue is patched in commit 0462de5b544ed4731aa2fb23946ac22c01856b80, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | ||||
| CVE-2020-15198 | 1 Google | 1 Tensorflow | 2024-11-21 | 5.4 Medium |
| In Tensorflow before version 2.3.1, the `SparseCountSparseOutput` implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the `indices` tensor has the same shape as the `values` one. The values in these tensors are always accessed in parallel. Thus, a shape mismatch can result in accesses outside the bounds of heap allocated buffers. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1. | ||||
| CVE-2020-15196 | 1 Google | 1 Tensorflow | 2024-11-21 | 8.5 High |
| In Tensorflow version 2.3.0, the `SparseCountSparseOutput` and `RaggedCountSparseOutput` implementations don't validate that the `weights` tensor has the same shape as the data. The check exists for `DenseCountSparseOutput`, where both tensors are fully specified. In the sparse and ragged count weights are still accessed in parallel with the data. But, since there is no validation, a user passing fewer weights than the values for the tensors can generate a read from outside the bounds of the heap buffer allocated for the weights. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1. | ||||
| CVE-2020-15195 | 2 Google, Opensuse | 2 Tensorflow, Leap | 2024-11-21 | 8.5 High |
| In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the implementation of `SparseFillEmptyRowsGrad` uses a double indexing pattern. It is possible for `reverse_index_map(i)` to be an index outside of bounds of `grad_values`, thus resulting in a heap buffer overflow. The issue is patched in commit 390611e0d45c5793c7066110af37c8514e6a6c54, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | ||||
| CVE-2020-15173 | 1 Accel-ppp | 1 Accel-ppp | 2024-11-21 | 8.2 High |
| In ACCEL-PPP (an implementation of PPTP/PPPoE/L2TP/SSTP), there is a buffer overflow when receiving an l2tp control packet ith an AVP which type is a string and no hidden flags, length set to less than 6. If your application is used in open networks or there are untrusted nodes in the network it is highly recommended to apply the patch. The problem was patched with commit 2324bcd5ba12cf28f47357a8f03cd41b7c04c52b As a workaround changes of commit 2324bcd5ba12cf28f47357a8f03cd41b7c04c52b can be applied to older versions. | ||||
| CVE-2020-15158 | 1 Mz-automation | 1 Libiec61850 | 2024-11-21 | 7.7 High |
| In libIEC61850 before version 1.4.3, when a message with COTP message length field with value < 4 is received an integer underflow will happen leading to heap buffer overflow. This can cause an application crash or on some platforms even the execution of remote code. If your application is used in open networks or there are untrusted nodes in the network it is highly recommend to apply the patch. This was patched with commit 033ab5b. Users of version 1.4.x should upgrade to version 1.4.3 when available. As a workaround changes of commit 033ab5b can be applied to older versions. | ||||
| CVE-2020-15065 | 1 Digitus | 2 Da-70254, Da-70254 Firmware | 2024-11-21 | 6.5 Medium |
| DIGITUS DA-70254 4-Port Gigabit Network Hub 2.073.000.E0008 devices allow an attacker on the same network to denial-of-service the device via long input values. | ||||
| CVE-2020-15061 | 1 Lindy-international | 2 42633, 42633 Firmware | 2024-11-21 | 6.5 Medium |
| Lindy 42633 4-Port USB 2.0 Gigabit Network Server 2.078.000 devices allow an attacker on the same network to denial-of-service the device via long input values. | ||||
| CVE-2020-15057 | 1 Tp-link | 2 Tl-ps310u, Tl-ps310u Firmware | 2024-11-21 | 6.5 Medium |
| TP-Link USB Network Server TL-PS310U devices before 2.079.000.t0210 allow an attacker on the same network to denial-of-service the device via long input values. | ||||
| CVE-2020-14968 | 2 Jsrsasign Project, Netapp | 2 Jsrsasign, Max Data | 2024-11-21 | 9.8 Critical |
| An issue was discovered in the jsrsasign package before 8.0.17 for Node.js. Its RSASSA-PSS (RSA-PSS) implementation does not detect signature manipulation/modification by prepending '\0' bytes to a signature (it accepts these modified signatures as valid). An attacker can abuse this behavior in an application by creating multiple valid signatures where only one signature should exist. Also, an attacker might prepend these bytes with the goal of triggering memory corruption issues. | ||||
| CVE-2020-14967 | 2 Jsrsasign Project, Netapp | 2 Jsrsasign, Max Data | 2024-11-21 | 9.8 Critical |
| An issue was discovered in the jsrsasign package before 8.0.18 for Node.js. Its RSA PKCS1 v1.5 decryption implementation does not detect ciphertext modification by prepending '\0' bytes to ciphertexts (it decrypts modified ciphertexts without error). An attacker might prepend these bytes with the goal of triggering memory corruption issues. | ||||
| CVE-2020-14664 | 2 Netapp, Oracle | 15 7-mode Transition Tool, Active Iq Unified Manager, Cloud Backup and 12 more | 2024-11-21 | 8.3 High |
| Vulnerability in the Java SE product of Oracle Java SE (component: JavaFX). The supported version that is affected is Java SE: 8u251. Difficult to exploit vulnerability allows unauthenticated attacker with network access via multiple protocols to compromise Java SE. Successful attacks require human interaction from a person other than the attacker and while the vulnerability is in Java SE, attacks may significantly impact additional products. Successful attacks of this vulnerability can result in takeover of Java SE. Note: This vulnerability applies to Java deployments, typically in clients running sandboxed Java Web Start applications or sandboxed Java applets, that load and run untrusted code (e.g., code that comes from the internet) and rely on the Java sandbox for security. This vulnerability does not apply to Java deployments, typically in servers, that load and run only trusted code (e.g., code installed by an administrator). CVSS 3.1 Base Score 8.3 (Confidentiality, Integrity and Availability impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:C/C:H/I:H/A:H). | ||||
| CVE-2020-14404 | 4 Canonical, Debian, Libvnc Project and 1 more | 15 Ubuntu Linux, Debian Linux, Libvncserver and 12 more | 2024-11-21 | 5.4 Medium |
| An issue was discovered in LibVNCServer before 0.9.13. libvncserver/rre.c allows out-of-bounds access via encodings. | ||||
| CVE-2020-14403 | 4 Canonical, Debian, Libvnc Project and 1 more | 15 Ubuntu Linux, Debian Linux, Libvncserver and 12 more | 2024-11-21 | 5.4 Medium |
| An issue was discovered in LibVNCServer before 0.9.13. libvncserver/hextile.c allows out-of-bounds access via encodings. | ||||
| CVE-2020-14402 | 4 Canonical, Debian, Libvnc Project and 1 more | 15 Ubuntu Linux, Debian Linux, Libvncserver and 12 more | 2024-11-21 | 5.4 Medium |
| An issue was discovered in LibVNCServer before 0.9.13. libvncserver/corre.c allows out-of-bounds access via encodings. | ||||