| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| Successfully using libcurl to do a transfer over a specific HTTP proxy
(`proxyA`) with **Digest** authentication and then changing the proxy host to
a second one (`proxyB`) for a second transfer, reusing the same handle, makes
libcurl wrongly pass on the `Proxy-Authorization:` header field meant for
`proxyA`, to `proxyB`. |
| The Avada Builder plugin for WordPress is vulnerable to Arbitrary File Read in all versions up to, and including, 3.15.2 via the 'fusion_get_svg_from_file' function with the 'custom_svg' parameter of the 'fusion_section_separator' shortcode. This makes it possible for authenticated attackers, with Subscriber-level access and above, to read the contents of arbitrary files on the server, which can contain sensitive information. The vulnerability was partially patched in version 3.15.2 and fully patched in version 3.15.3. |
| The Hostinger Reach – AI-Powered Email Marketing for WordPress plugin for WordPress is vulnerable to unauthorized modification of data due to a missing capability check on the 'handle_ajax_action' function in all versions up to, and including, 1.3.8. This makes it possible for authenticated attackers, with Subscriber-level access and above, to use the 'hostinger_reach_connection_notice_action' action to update the API key value stored in the database. This vulnerability can only be exploited when the plugin is not connected to a site and no API key value exists in the database. |
| In Thruk Monitoring through 2.46.3, the login field of the login form is vulnerable to reflected XSS. This vulnerability can be exploited by unauthenticated remote attackers to target users of the monitoring interface. |
| RayVentory Scan Engine through 12.6 Update 8 allows attackers to gain privileges if they control the value of the PATH environment variable. NOTE: this is disputed because ability of an attacker to control the environment is a site-specific misconfiguration. |
| Certain GL.iNet devices with 4.x firmware allow authentication bypass (resulting in administrative control of the device) via a username that is both a valid SQL statement and a valid regular expression. For example, this affects version 4.3.7 on GL-MT3000 GL-AR300M GL-B1300 GL-AX1800 GL-AR750S GL-MT2500 GL-AXT1800 GL-X3000 and GL-SFT1200. |
| nanoMODBUS through v1.22.0 has a stack-based buffer overflow in recv_read_registers_res() in nanomodbus.c. When a client calls nmbs_read_holding_registers() or nmbs_read_input_registers(), the library writes register data from the server response to the caller-provided buffer based on the response's byte_count field before validating that byte_count matches the requested quantity. A malicious Modbus TCP server can send a response with byte_count=250 (125 registers) regardless of the requested quantity, causing up to 248 bytes of attacker-controlled data to overflow the buffer, potentially allowing remote code execution. |
| lwjson 1.8.1 contains an improper input validation vulnerability in the streaming JSON parser (lwjson_stream.c). The end-of-string detection logic incorrectly identifies escaped quote characters by only checking the immediately preceding character rather than counting consecutive backslashes, causing valid JSON strings ending with an escaped backslash (like "\\") to never terminate parsing. A remote attacker can send well-formed JSON to cause applications using lwjson_stream_parse() to hang indefinitely, resulting in denial of service. |
| PySyft (Syft Datasite/Server) versions 0.9.5 and earlier are vulnerable to remote code execution due to insufficient validation and sandboxing of user-submitted code. The system allows low-privileged users to submit Python functions (via @sy.syft_function()) for remote execution on the server. While a code approval mechanism exists, the submitted code undergoes no security checks for dangerous operations (e.g., file access, command execution). Once approved, the code is executed within the server process using exec() and eval() functions without proper isolation. A remote attacker can leverage this to execute arbitrary Python code on the server, leading to complete compromise of the server environment. |
| The superduper project thru v0.10.0 contains a critical remote code execution vulnerability in its query parsing component. The _parse_op_part() function in query.py uses the unsafe eval() function to dynamically evaluate user-supplied query operands without proper sanitization or restriction. Although the function attempts to limit the execution context by providing a restricted global namespace, it does not block access to dangerous built-in functions. A remote attacker can exploit this by submitting a specially crafted query string containing Python code that imports modules (e.g., os) and executes arbitrary system commands, leading to complete compromise of the server. |
| The TinyZero project thru commit 6652a63c57fa7e5ccde3fc9c598c7176ff15b839 (2025-58-24) contains a critical command injection vulnerability (CWE-78) in its HDFS file operation utilities. The vulnerability arises from the unsafe construction and execution of shell commands via os.system() without proper input sanitization or escaping. User-controlled input (such as file paths) is directly interpolated into shell command strings using f-strings within the _copy() function. An attacker can inject arbitrary OS commands by supplying a specially crafted path parameter through the Hydra configuration framework. This leads to remote code execution with the privileges of the user running the TinyZero training process. |
| The Adversarial Robustness Toolbox (ART) thru 1.20.1 contains a remote code execution vulnerability in its Kubeflow component. The robustness evaluation function for PyTorch models uses the unsafe eval() function to dynamically evaluate user-supplied strings for the LossFn and Optimizer parameters without any sanitization or security restrictions. An attacker can exploit this by providing a specially crafted string that contains arbitrary Python code, which will be executed when eval() is called, leading to complete compromise of the system running the ART evaluation. |
| The Adversarial Robustness Toolbox (ART) thru 1.20.1 contains an insecure deserialization vulnerability (CWE-502) in its Kubeflow component's model loading functionality. When loading model weights from a file (e.g., model.pt) during robustness evaluation, the code uses torch.load() without the security-restrictive weights_only=True parameter. This allows the deserialization of arbitrary Python objects via the Pickle module. An attacker can exploit this by uploading a maliciously crafted model file to an object storage location referenced by the pipeline, or by controlling the model_id parameter to point to such a file. When the pipeline loads the model, the malicious payload is executed, leading to remote code execution. |
| The Adversarial Robustness Toolbox (ART) thru 1.20.1 contains a command-line argument injection vulnerability in its Kubeflow component (robustness_evaluation_fgsm_pytorch.py). The script uses the unsafe eval() function to parse string values provided via the --clip_values and --input_shape command-line arguments. This allows an attacker to inject arbitrary Python code into these arguments, which will be executed when eval() is called. The vulnerability can be exploited remotely if an attacker can control these arguments (e.g., through pipeline configuration or automated scripts), leading to arbitrary code execution on the system running the ART evaluation. |
| Cognee thru v0.4.0 contains a critical remote code execution vulnerability in its notebook cell execution API endpoint. The endpoint is designed to execute arbitrary Python code provided by the user, but it does so using the unsafe exec() function without any sandboxing, validation, or security controls. An attacker can exploit this by sending a specially crafted POST request containing malicious Python code to the execution endpoint. This leads to arbitrary code execution on the Cognee server with the privileges of the server process, allowing complete compromise of the system. |
| The imgaug library thru 0.4.0 contains an insecure deserialization vulnerability in its BackgroundAugmenter class within the multicore.py module. The class uses Python's pickle module to deserialize data received via a multiprocessing queue in the _augment_images_worker() method without any safety checks. An attacker who can influence the data placed into this queue (e.g., through social engineering, malicious input scripts, or a compromised shared queue) can provide a malicious pickle payload. When deserialized, this payload can execute arbitrary code in the context of the worker process, leading to remote or local code execution depending on the deployment scenario. |
| The llm CLI tool thru 0.27.1 contains a critical code injection vulnerability via its --functions command-line argument. This argument is intended to allow users to provide custom Python function definitions. However, the tool directly executes the provided code using the unsafe exec() function without any sanitization, sandboxing, or security restrictions. An attacker can exploit this by crafting a malicious llm command with arbitrary Python code in the --functions argument and using social engineering to trick a victim into running it. This leads to arbitrary code execution on the victim's system, potentially granting the attacker full control. |
| The Ludwig framework thru 0.10.4 is vulnerable to insecure deserialization (CWE-502) through its predict() method. When a user provides a dataset file path to the predict() method, the framework automatically determines the file format. If the file is a pickle (.pkl) file, it is loaded using pandas.read_pickle() without any validation or security restrictions. This allows the deserialization of arbitrary Python objects via the unsafe pickle module. A remote attacker can exploit this by providing a maliciously crafted pickle file, leading to arbitrary code execution on the system running the Ludwig prediction. |
| The Ludwig framework thru 0.10.4 is vulnerable to insecure deserialization (CWE-502) in its model serving component. When starting a model server with the ludwig serve command, the framework loads model weight files using torch.load() without enabling the security-restrictive weights_only=True parameter. This default behavior allows the deserialization of arbitrary Python objects via the pickle module. An attacker can exploit this by providing a maliciously crafted PyTorch model file, leading to arbitrary code execution on the system hosting the Ludwig model server. |
| The mamba language model framework thru 2.2.6 is vulnerable to insecure deserialization (CWE-502) when loading pre-trained models from HuggingFace Hub. The MambaLMHeadModel.from_pretrained() method uses torch.load() to load the pytorch_model.bin weight file without enabling the security-restrictive weights_only=True parameter. This allows the deserialization of arbitrary Python objects via the pickle module. An attacker can exploit this by publishing a malicious model repository on HuggingFace Hub. When a victim loads a model from this repository, arbitrary code is executed on the victim's system in the context of the mamba process. |
