| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| The ObjectSerializationDecoder in Apache MINA uses Java’s native deserialization protocol to process
incoming serialized data but lacks the necessary security checks and defenses. This vulnerability allows
attackers to exploit the deserialization process by sending specially crafted malicious serialized data,
potentially leading to remote code execution (RCE) attacks.
This issue affects MINA core versions 2.0.X, 2.1.X and 2.2.X, and will be fixed by the releases 2.0.27, 2.1.10 and 2.2.4.
It's also important to note that an application using MINA core library will only be affected if the IoBuffer#getObject() method is called, and this specific method is potentially called when adding a ProtocolCodecFilter instance using the ObjectSerializationCodecFactory class in the filter chain. If your application is specifically using those classes, you have to upgrade to the latest version of MINA core library.
Upgrading will not be enough: you also need to explicitly allow the classes the decoder will accept in the ObjectSerializationDecoder instance, using one of the three new methods:
/**
* Accept class names where the supplied ClassNameMatcher matches for
* deserialization, unless they are otherwise rejected.
*
* @param classNameMatcher the matcher to use
*/
public void accept(ClassNameMatcher classNameMatcher)
/**
* Accept class names that match the supplied pattern for
* deserialization, unless they are otherwise rejected.
*
* @param pattern standard Java regexp
*/
public void accept(Pattern pattern)
/**
* Accept the wildcard specified classes for deserialization,
* unless they are otherwise rejected.
*
* @param patterns Wildcard file name patterns as defined by
* {@link org.apache.commons.io.FilenameUtils#wildcardMatch(String, String) FilenameUtils.wildcardMatch}
*/
public void accept(String... patterns)
By default, the decoder will reject *all* classes that will be present in the incoming data.
Note: The FtpServer, SSHd and Vysper sub-project are not affected by this issue. |
| The Friends plugin for WordPress is vulnerable to PHP Object Injection in version 3.5.1 via deserialization of untrusted input of the query_vars parameter This makes it possible for authenticated attackers, with subscriber-level access and above, to inject a PHP Object. No known POP chain is present in the vulnerable software, which means this vulnerability has no impact unless another plugin or theme containing a POP chain is installed on the site. If a POP chain is present via an additional plugin or theme installed on the target system, it may allow the attacker to perform actions like delete arbitrary files, retrieve sensitive data, or execute code depending on the POP chain present. This requires access to the sites SALT_NONCE and and SALT_KEY to exploit. |
| Retrieval-based-Voice-Conversion-WebUI is a voice changing framework based on VITS. Versions 2.2.231006 and prior are vulnerable to unsafe deserialization. The ckpt_path1 variable takes user input (e.g. a path to a model) and passes it to the show_info function in process_ckpt.py, which uses it to load the model on that path with torch.load, which can lead to unsafe deserialization and remote code execution. As of time of publication, no known patches exist. |
| Retrieval-based-Voice-Conversion-WebUI is a voice changing framework based on VITS. Versions 2.2.231006 and prior are vulnerable to unsafe deserialization. The ckpt_path2 variable takes user input (e.g. a path to a model) and passes it to the extract_small_model function in process_ckpt.py, which uses it to load the model on that path with torch.load, which can lead to unsafe deserialization and remote code execution. As of time of publication, no known patches exist. |
| Retrieval-based-Voice-Conversion-WebUI is a voice changing framework based on VITS. Versions 2.2.231006 and prior are vulnerable to unsafe deserialization. The ckpt_path0 variable takes user input (e.g. a path to a model) and passes it to the change_info function in process_ckpt.py, which uses it to load the model on that path with torch.load, which can lead to unsafe deserialization and remote code execution. As of time of publication, no known patches exist. |
| Retrieval-based-Voice-Conversion-WebUI is a voice changing framework based on VITS. Versions 2.2.231006 and prior are vulnerable to unsafe deserialization. The ckpt_a and cpkt_b variables take user input (e.g. a path to a model) and pass it to the merge function in process_ckpt.py, which uses them to load the models on those paths with torch.load, which can lead to unsafe deserialization and remote code execution. As of time of publication, no known patches exist. |
| Retrieval-based-Voice-Conversion-WebUI is a voice changing framework based on VITS. Versions 2.2.231006 and prior are vulnerable to unsafe deserialization. The ckpt_dir variable takes user input (e.g. a path to a model) and passes it to the change_info function in export.py, which uses it to load the model on that path with torch.load, which can lead to unsafe deserialization and remote code execution. As of time of publication, no known patches exist. |
| Retrieval-based-Voice-Conversion-WebUI is a voice changing framework based on VITS. Versions 2.2.231006 and prior are vulnerable to unsafe deserialization. The model_choose variable takes user input (e.g. a path to a model) and passes it to the uvr function in vr.py. In uvr , a new instance of AudioPre class is created with the model_path attribute containing the aformentioned user input. In the AudioPre class, the user input, is used to load the model on that path with torch.load, which can lead to unsafe deserialization and remote code execution. As of time of publication, no known patches exist. |
| Retrieval-based-Voice-Conversion-WebUI is a voice changing framework based on VITS. Versions 2.2.231006 and prior are vulnerable to unsafe deserialization. The model_choose variable takes user input (e.g. a path to a model) and passes it to the uvr function in vr.py. In uvr , if model_name contains the string "DeEcho", a new instance of AudioPreDeEcho class is created with the model_path attribute containing the aforementioned user input. In the AudioPreDeEcho class, the user input is used to load the model on that path with torch.load, which can lead to unsafe deserialization and remote code execution. As of time of publication, no known patches exist. |
| 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. |
| 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. |
| 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. |
| 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. |
| 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. |
| 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. |
| 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. |
| 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. |
| 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. |
| 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. |
| 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. |
