| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| Adobe InDesign versions 16.4.2 (and earlier) and 17.3 (and earlier) are affected by by an out-of-bounds write 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. |
| 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. |
| 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. |
| 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. |
| TensorFlow is an open source platform for machine learning. The `ScatterNd` function takes an input argument that determines the indices of of the output tensor. An input index greater than the output tensor or less than zero will either write content at the wrong index or trigger a crash. We have patched the issue in GitHub commit b4d4b4cb019bd7240a52daa4ba61e3cc814f0384. 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. |
| Adobe Bridge version 12.0.2 (and earlier) and 11.1.3 (and earlier) are affected by an out-of-bounds write 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. |
| Adobe Bridge version 12.0.2 (and earlier) and 11.1.3 (and earlier) are affected by an out-of-bounds write 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. |
| Adobe Bridge version 12.0.2 (and earlier) and 11.1.3 (and earlier) are affected by an out-of-bounds write 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. |
| Adobe ColdFusion versions Update 14 (and earlier) and Update 4 (and earlier) are affected by a Stack-based Buffer Overflow vulnerability that could result in arbitrary code execution in the context of the current user. Exploitation of this issue does not require user interaction, the vulnerability is triggered when a crafted network packet is sent to the server. |
| Adobe ColdFusion versions Update 14 (and earlier) and Update 4 (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 does not require user interaction, the vulnerability is triggered when a crafted network packet is sent to the server. |
| Adobe ColdFusion versions Update 14 (and earlier) and Update 4 (and earlier) are affected by a Stack-based Buffer Overflow vulnerability that could result in arbitrary code execution in the context of the current user. Exploitation of this issue does not require user interaction, the vulnerability is triggered when a crafted network packet is sent to the server. |
| Adobe ColdFusion versions Update 14 (and earlier) and Update 4 (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 does not require user interaction, the vulnerability is triggered when a crafted network packet is sent to the server. |
| Adobe Acrobat Reader versions 22.002.20212 (and earlier) and 20.005.30381 (and earlier) are affected by a Stack-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. |
| Adobe Acrobat Reader versions 22.002.20212 (and earlier) and 20.005.30381 (and earlier) are affected by a Stack-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. |
| GitHub Actions Runner is the application that runs a job from a GitHub Actions workflow. The actions runner invokes the docker cli directly in order to run job containers, service containers, or container actions. A bug in the logic for how the environment is encoded into these docker commands was discovered in versions prior to 2.296.2, 2.293.1, 2.289.4, 2.285.2, and 2.283.4 that allows an input to escape the environment variable and modify that docker command invocation directly. Jobs that use container actions, job containers, or service containers alongside untrusted user inputs in environment variables may be vulnerable. The Actions Runner has been patched, both on `github.com` and hotfixes for GHES and GHAE customers in versions 2.296.2, 2.293.1, 2.289.4, 2.285.2, and 2.283.4. GHES and GHAE customers may want to patch their instance in order to have their runners automatically upgrade to these new runner versions. As a workaround, users may consider removing any container actions, job containers, or service containers from their jobs until they are able to upgrade their runner versions. |
| Wasmtime is a standalone runtime for WebAssembly. Prior to version 2.0.2, there is a bug in Wasmtime's implementation of its pooling instance allocator when the allocator is configured to give WebAssembly instances a maximum of zero pages of memory. In this configuration, the virtual memory mapping for WebAssembly memories did not meet the compiler-required configuration requirements for safely executing WebAssembly modules. Wasmtime's default settings require virtual memory page faults to indicate that wasm reads/writes are out-of-bounds, but the pooling allocator's configuration would not create an appropriate virtual memory mapping for this meaning out of bounds reads/writes can successfully read/write memory unrelated to the wasm sandbox within range of the base address of the memory mapping created by the pooling allocator. This bug is not applicable with the default settings of the `wasmtime` crate. This bug can only be triggered by setting `InstanceLimits::memory_pages` to zero. This is expected to be a very rare configuration since this means that wasm modules cannot allocate any pages of linear memory. All wasm modules produced by all current toolchains are highly likely to use linear memory, so it's expected to be unlikely that this configuration is set to zero by any production embedding of Wasmtime. This bug has been patched and users should upgrade to Wasmtime 2.0.2. This bug can be worked around by increasing the `memory_pages` allotment when configuring the pooling allocator to a value greater than zero. If an embedding wishes to still prevent memory from actually being used then the `Store::limiter` method can be used to dynamically disallow growth of memory beyond 0 bytes large. Note that the default `memory_pages` value is greater than zero. |
| Wasmtime is a standalone runtime for WebAssembly. Prior to version 2.0.2, there is a bug in Wasmtime's C API implementation where the definition of the `wasmtime_trap_code` does not match its declared signature in the `wasmtime/trap.h` header file. This discrepancy causes the function implementation to perform a 4-byte write into a 1-byte buffer provided by the caller. This can lead to three zero bytes being written beyond the 1-byte location provided by the caller. This bug has been patched and users should upgrade to Wasmtime 2.0.2. This bug can be worked around by providing a 4-byte buffer casted to a 1-byte buffer when calling `wasmtime_trap_code`. Users of the `wasmtime` crate are not affected by this issue, only users of the C API function `wasmtime_trap_code` are affected. |
| Contiki-NG is an open-source, cross-platform operating system for Next-Generation IoT devices. Versions prior to 4.9 are vulnerable to an Out-of-bounds read. While processing the L2CAP protocol, the Bluetooth Low Energy stack of Contiki-NG needs to map an incoming channel ID to its metadata structure. While looking up the corresponding channel structure in get_channel_for_cid (in os/net/mac/ble/ble-l2cap.c), a bounds check is performed on the incoming channel ID, which is meant to ensure that the channel ID does not exceed the maximum number of supported channels.However, an integer truncation issue leads to only the lowest byte of the channel ID to be checked, which leads to an incomplete out-of-bounds check. A crafted channel ID leads to out-of-bounds memory to be read and written with attacker-controlled data. The vulnerability has been patched in the "develop" branch of Contiki-NG, and will be included in release 4.9. As a workaround, Users can apply the patch in Contiki-NG pull request 2081 on GitHub. |
| Sourcegraph is a code intelligence platform. In versions prior to 4.1.0 a command Injection vulnerability existed in the gitserver service, present in all Sourcegraph deployments. This vulnerability was caused by a lack of input validation on the host parameter of the `/list-gitolite` endpoint. It was possible to send a crafted request to gitserver that would execute commands inside the container. Successful exploitation requires the ability to send local requests to gitserver. The issue is patched in version 4.1.0. |
| TensorFlow is an open source platform for machine learning. The function MakeGrapplerFunctionItem 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. We have patched the issue in GitHub commit a65411a1d69edfb16b25907ffb8f73556ce36bb7. The fix will be included in TensorFlow 2.11.0. We will also cherrypick this commit on TensorFlow 2.8.4, 2.9.3, and 2.10.1. |
