Total
2183 CVE
| CVE | Vendors | Products | Updated | CVSS v3.1 |
|---|---|---|---|---|
| CVE-2025-27780 | 1 Applio | 1 Applio | 2025-08-01 | 9.8 Critical |
| Applio is a voice conversion tool. Versions 3.2.8-bugfix and prior are vulnerable to unsafe deserialization in model_information.py. `model_name` in model_information.py takes user-supplied input (e.g. a path to a model) and pass that value to the `run_model_information_script` and later to `model_information` function, which loads that model with `torch.load` in rvc/train/process/model_information.py (on line 16 in 3.2.8-bugfix), which is vulnerable to unsafe deserialization. The issue can lead to remote code execution. A patch is available in the `main` branch of the repository. | ||||
| CVE-2025-27781 | 1 Applio | 1 Applio | 2025-08-01 | 9.8 Critical |
| Applio is a voice conversion tool. Versions 3.2.8-bugfix and prior are vulnerable to unsafe deserialization in inference.py. `model_file` in inference.py as well as `model_file` in tts.py take user-supplied input (e.g. a path to a model) and pass that value to the `change_choices` and later to `get_speakers_id` function, which loads that model with `torch.load` in inference.py (line 326 in 3.2.8-bugfix), which is vulnerable to unsafe deserialization. The issue can lead to remote code execution. A patch is available on the `main` branch of the repository. | ||||
| CVE-2025-27778 | 1 Applio | 1 Applio | 2025-08-01 | 9.8 Critical |
| Applio is a voice conversion tool. Versions 3.2.8-bugfix and prior are vulnerable to unsafe deserialization in `infer.py`. The issue can lead to remote code execution. As of time of publication, a fix is available on the `main` branch of the Applio repository but not attached to a numbered release. | ||||
| CVE-2025-27779 | 1 Applio | 1 Applio | 2025-08-01 | 9.8 Critical |
| Applio is a voice conversion tool. Versions 3.2.8-bugfix and prior are vulnerable to unsafe deserialization in `model_blender.py` lines 20 and 21. `model_fusion_a` and `model_fusion_b` from voice_blender.py take user-supplied input (e.g. a path to a model) and pass that value to the `run_model_blender_script` and later to `model_blender` function, which loads these two models with `torch.load` in `model_blender.py (on lines 20-21 in 3.2.8-bugfix), which is vulnerable to unsafe deserialization. The issue can lead to remote code execution. A patch is available on the `main` branch of the Applio repository. | ||||
| CVE-2025-30165 | 1 Vllm | 1 Vllm | 2025-07-31 | 8 High |
| vLLM is an inference and serving engine for large language models. In a multi-node vLLM deployment using the V0 engine, vLLM uses ZeroMQ for some multi-node communication purposes. The secondary vLLM hosts open a `SUB` ZeroMQ socket and connect to an `XPUB` socket on the primary vLLM host. When data is received on this `SUB` socket, it is deserialized with `pickle`. This is unsafe, as it can be abused to execute code on a remote machine. Since the vulnerability exists in a client that connects to the primary vLLM host, this vulnerability serves as an escalation point. If the primary vLLM host is compromised, this vulnerability could be used to compromise the rest of the hosts in the vLLM deployment. Attackers could also use other means to exploit the vulnerability without requiring access to the primary vLLM host. One example would be the use of ARP cache poisoning to redirect traffic to a malicious endpoint used to deliver a payload with arbitrary code to execute on the target machine. Note that this issue only affects the V0 engine, which has been off by default since v0.8.0. Further, the issue only applies to a deployment using tensor parallelism across multiple hosts, which we do not expect to be a common deployment pattern. Since V0 is has been off by default since v0.8.0 and the fix is fairly invasive, the maintainers of vLLM have decided not to fix this issue. Instead, the maintainers recommend that users ensure their environment is on a secure network in case this pattern is in use. The V1 engine is not affected by this issue. | ||||
| CVE-2024-11041 | 2 Vllm, Vllm-project | 2 Vllm, Vllm | 2025-07-31 | N/A |
| vllm-project vllm version v0.6.2 contains a vulnerability in the MessageQueue.dequeue() API function. The function uses pickle.loads to parse received sockets directly, leading to a remote code execution vulnerability. An attacker can exploit this by sending a malicious payload to the MessageQueue, causing the victim's machine to execute arbitrary code. | ||||
| CVE-2025-49841 | 1 Rvc-boss | 1 Gpt-sovits-webui | 2025-07-30 | 9.8 Critical |
| GPT-SoVITS-WebUI is a voice conversion and text-to-speech webUI. In versions 20250228v3 and prior, there is an unsafe deserialization vulnerability in process_ckpt.py. The SoVITS_dropdown variable takes user input and passes it to the load_sovits_new function in process_ckpt.py. In load_sovits_new, the user input, here sovits_path is used to load a model with torch.load, leading to unsafe deserialization. At time of publication, no known patched versions are available. | ||||
| CVE-2025-49840 | 1 Rvc-boss | 1 Gpt-sovits-webui | 2025-07-30 | 9.8 Critical |
| GPT-SoVITS-WebUI is a voice conversion and text-to-speech webUI. In versions 20250228v3 and prior, there is an unsafe deserialization vulnerability in inference_webui.py. The GPT_dropdown variable takes user input and passes it to the change_gpt_weights function. In change_gpt_weights, the user input, here gpt_path is used to load a model with torch.load, leading to unsafe deserialization. At time of publication, no known patched versions are available. | ||||
| CVE-2025-49838 | 1 Rvc-boss | 1 Gpt-sovits-webui | 2025-07-30 | 9.8 Critical |
| GPT-SoVITS-WebUI is a voice conversion and text-to-speech webUI. In versions 20250228v3 and prior, there is an unsafe deserialization vulnerability in vr.py AudioPreDeEcho. The model_choose variable takes user input (e.g. a path to a model) and passes it to the uvr function. In uvr, a new instance of AudioPreDeEcho class is created with the model_path attribute containing the aforementioned user input (here called locally model_name). Note that in this step the .pth extension is added to the path. In the AudioPreDeEcho class, the user input, here called model_path, is used to load the model on that path with torch.load, which can lead to unsafe deserialization. At time of publication, no known patched versions are available. | ||||
| CVE-2025-49839 | 1 Rvc-boss | 1 Gpt-sovits-webui | 2025-07-30 | 9.8 Critical |
| GPT-SoVITS-WebUI is a voice conversion and text-to-speech webUI. In versions 20250228v3 and prior, there is an unsafe deserialization vulnerability in bsroformer.py. The model_choose variable takes user input (e.g. a path to a model) and passes it to the uvr function. In uvr, a new instance of Roformer_Loader class is created with the model_path attribute containing the aformentioned user input (here called locally model_name). Note that in this step the .ckpt extension is added to the path. In the Roformer_Loader class, the user input, here called model_path, is used to load the model on that path with torch.load, which can lead to unsafe deserialization. At time of publication, no known patched versions are available. | ||||
| CVE-2025-49837 | 1 Rvc-boss | 1 Gpt-sovits-webui | 2025-07-30 | 9.8 Critical |
| GPT-SoVITS-WebUI is a voice conversion and text-to-speech webUI. In versions 20250228v3 and prior, there is an unsafe deserialization vulnerability in vr.py AudioPre. The model_choose variable takes user input (e.g. a path to a model) and passes it to the uvr function. In uvr, a new instance of AudioPre class is created with the model_path attribute containing the aforementioned user input (here called locally model_name). Note that in this step the .pth extension is added to the path. In the AudioPre class, the user input, here called model_path, is used to load the model on that path with torch.load, which can lead to unsafe deserialization. At time of publication, no known patched versions are available. | ||||
| CVE-2025-2244 | 1 Bitdefender | 1 Gravityzone | 2025-07-30 | 9.8 Critical |
| A vulnerability in the sendMailFromRemoteSource method in Emails.php as used in Bitdefender GravityZone Console unsafely uses php unserialize() on user-supplied input without validation. By crafting a malicious serialized payload, an attacker can trigger PHP object injection, perform a file write, and gain arbitrary command execution on the host system. | ||||
| CVE-2024-5998 | 1 Langchain | 1 Langchain | 2025-07-30 | 7.8 High |
| A vulnerability in the FAISS.deserialize_from_bytes function of langchain-ai/langchain allows for pickle deserialization of untrusted data. This can lead to the execution of arbitrary commands via the os.system function. The issue affects the latest version of the product. | ||||
| CVE-2024-24725 | 1 Gibbonedu | 1 Gibbon | 2025-07-29 | 8.8 High |
| Gibbon through 26.0.00 allows remote authenticated users to conduct PHP deserialization attacks via columnOrder in a POST request to the modules/System%20Admin/import_run.php&type=externalAssessment&step=4 URI. | ||||
| CVE-2025-30065 | 1 Apache | 2 Parquet, Parquet Java | 2025-07-28 | 9.8 Critical |
| Schema parsing in the parquet-avro module of Apache Parquet 1.15.0 and previous versions allows bad actors to execute arbitrary code Users are recommended to upgrade to version 1.15.1, which fixes the issue. | ||||
| CVE-2016-15044 | 2025-07-25 | N/A | ||
| A remote code execution vulnerability exists in Kaltura versions prior to 11.1.0-2 due to unsafe deserialization of user-controlled data within the keditorservices module. An unauthenticated remote attacker can exploit this issue by sending a specially crafted serialized PHP object in the kdata GET parameter to the redirectWidgetCmd endpoint. Successful exploitation leads to execution of arbitrary PHP code in the context of the web server process. | ||||
| CVE-2025-4393 | 2025-07-25 | 6.5 Medium | ||
| Medtronic MyCareLink Patient Monitor has an internal service that deserializes data, which allows a local attacker to interact with the service by crafting a binary payload to crash the service or elevate privileges. This issue affects MyCareLink Patient Monitor models 24950 and 24952: before June 25, 2025 | ||||
| CVE-2025-26397 | 2025-07-25 | 7.8 High | ||
| SolarWinds Observability Self-Hosted is susceptible to Deserialization of Untrusted Data Local Privilege Escalation vulnerability. An attacker with low privileges can escalate privileges to run malicious files copied to a permission-protected folder. This vulnerability requires authentication from a low-level account and local access to the host server. | ||||
| CVE-2024-8375 | 1 Google | 1 Reverb | 2025-07-22 | 7.8 High |
| There exists a use after free vulnerability in Reverb. Reverb supports the VARIANT datatype, which is supposed to represent an arbitrary object in C++. When a tensor proto of type VARIANT is unpacked, memory is first allocated to store the entire tensor, and a ctor is called on each instance. Afterwards, Reverb copies the content in tensor_content to the previously mentioned pre-allocated memory, which results in the bytes in tensor_content overwriting the vtable pointers of all the objects which were previously allocated. Reverb exposes 2 relevant gRPC endpoints: InsertStream and SampleStream. The attacker can insert this stream into the server’s database, then when the client next calls SampleStream they will unpack the tensor into RAM, and when any method on that object is called (including its destructor) the attacker gains control of the Program Counter. We recommend upgrading past git commit https://github.com/google-deepmind/reverb/commit/6a0dcf4c9e842b7f999912f792aaa6f6bd261a25 | ||||
| CVE-2025-20276 | 1 Cisco | 1 Unified Contact Center Express | 2025-07-22 | 3.8 Low |
| A vulnerability in the web-based management interface of Cisco Unified CCX could allow an authenticated, remote attacker to execute arbitrary code on an affected device. To exploit this vulnerability, the attacker must have valid administrative credentials. This vulnerability is due to insecure deserialization of Java objects by the affected software. An attacker could exploit this vulnerability by sending a crafted Java object to an affected device. A successful exploit could allow the attacker to execute arbitrary code on the underlying operating system of an affected device as a low-privilege user. A successful exploit could also allow the attacker to undertake further actions to elevate their privileges to root. | ||||