Total
1928 CVE
| CVE | Vendors | Products | Updated | CVSS v2 | CVSS v3 |
|---|---|---|---|---|---|
| CVE-2025-7504 | 1 Alex.kirk | 1 Friends | 2025-08-02 | N/A | 7.5 HIGH |
| 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. | |||||
| CVE-2025-50472 | 2025-08-01 | N/A | 9.8 CRITICAL | ||
| The modelscope/ms-swift library thru 2.6.1 is vulnerable to arbitrary code execution through deserialization of untrusted data within the `load_model_meta()` function of the `ModelFileSystemCache()` class. Attackers can execute arbitrary code and commands by crafting a malicious serialized `.mdl` payload, exploiting the use of `pickle.load()` on data from potentially untrusted sources. This vulnerability allows for remote code execution (RCE) by deceiving victims into loading a seemingly harmless checkpoint during a normal training process, thereby enabling attackers to execute arbitrary code on the targeted machine. Note that the payload file is a hidden file, making it difficult for the victim to detect tampering. More importantly, during the model training process, after the `.mdl` file is loaded and executes arbitrary code, the normal training process remains unaffected'meaning the user remains unaware of the arbitrary code execution. | |||||
| CVE-2025-50460 | 2025-08-01 | N/A | 9.8 CRITICAL | ||
| A remote code execution (RCE) vulnerability exists in the ms-swift project version 3.3.0 due to unsafe deserialization in tests/run.py using yaml.load() from the PyYAML library (versions = 5.3.1). If an attacker can control the content of the YAML configuration file passed to the --run_config parameter, arbitrary code can be executed during deserialization. This can lead to full system compromise. The vulnerability is triggered when a malicious YAML file is loaded, allowing the execution of arbitrary Python commands such as os.system(). It is recommended to upgrade PyYAML to version 5.4 or higher, and to use yaml.safe_load() to mitigate the issue. | |||||
| CVE-2025-8227 | 1 Yanyutao0402 | 1 Chancms | 2025-08-01 | 6.5 MEDIUM | 6.3 MEDIUM |
| A vulnerability was found in yanyutao0402 ChanCMS up to 3.1.2. It has been declared as critical. Affected by this vulnerability is an unknown functionality of the file /collect/getArticle. The manipulation of the argument taskUrl leads to deserialization. The attack can be launched remotely. The exploit has been disclosed to the public and may be used. Upgrading to version 3.1.3 is able to address this issue. The patch is named 33d9bb464353015aaaba84e27638ac9a3912795d. It is recommended to upgrade the affected component. | |||||
| CVE-2025-43846 | 1 Rvc-project | 1 Retrieval-based-voice-conversion-webui | 2025-08-01 | N/A | 9.8 CRITICAL |
| 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. | |||||
| CVE-2025-43847 | 1 Rvc-project | 1 Retrieval-based-voice-conversion-webui | 2025-08-01 | N/A | 9.8 CRITICAL |
| 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. | |||||
| CVE-2025-43848 | 1 Rvc-project | 1 Retrieval-based-voice-conversion-webui | 2025-08-01 | N/A | 9.8 CRITICAL |
| 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. | |||||
| CVE-2025-43849 | 1 Rvc-project | 1 Retrieval-based-voice-conversion-webui | 2025-08-01 | N/A | 9.8 CRITICAL |
| 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. | |||||
| CVE-2025-43850 | 1 Rvc-project | 1 Retrieval-based-voice-conversion-webui | 2025-08-01 | N/A | 9.8 CRITICAL |
| 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. | |||||
| CVE-2025-43851 | 1 Rvc-project | 1 Retrieval-based-voice-conversion-webui | 2025-08-01 | N/A | 9.8 CRITICAL |
| 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. | |||||
| CVE-2025-43852 | 1 Rvc-project | 1 Retrieval-based-voice-conversion-webui | 2025-08-01 | N/A | 9.8 CRITICAL |
| 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. | |||||
| CVE-2025-27780 | 1 Applio | 1 Applio | 2025-08-01 | N/A | 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 | N/A | 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 | N/A | 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 | N/A | 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-2025-49083 | 2025-07-31 | N/A | N/A | ||
| CVE-2025-49083 is a vulnerability in the management console of Absolute Secure Access after version 12.00 and prior to version 13.56. Attackers with administrative access to the console can cause unsafe content to be deserialized and executed in the security context of the console. The attack complexity is low and there are no attack requirements. Privileges required are high and there is no user interaction required. The impact to confidentiality is low, impact to integrity is high and there is no impact to availability. The impact to the confidentiality and integrity of subsequent systems is low and there is no subsequent system impact to availability. | |||||
| CVE-2025-25692 | 2025-07-31 | N/A | 6.5 MEDIUM | ||
| A PHAR deserialization vulnerability in the _getHeaders function of PrestaShop v8.2.0 allows attackers to execute arbitrary code via a crafted POST request. | |||||
| CVE-2025-25691 | 2025-07-31 | N/A | 6.5 MEDIUM | ||
| A PHAR deserialization vulnerability in the component /themes/import of PrestaShop v8.2.0 allows attackers to execute arbitrary code via a crafted POST request. | |||||
| CVE-2025-30165 | 1 Vllm | 1 Vllm | 2025-07-31 | N/A | 8.0 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 | 1 Vllm | 1 Vllm | 2025-07-31 | N/A | 9.8 CRITICAL |
| 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. | |||||