CVEs

SAP TAF_APPLAUNCHER within Business Server Pages allows an unauthenticated attacker to craft malicious links that, when clicked by a victim, redirects them to attacker?controlled sites, potentially exposing or altering sensitive information in the victim�s browser. This results in a low impact on confidentiality and integrity, with no impact on the availability of the application.

SAP Financial Consolidation allows an authenticated attacker to disconnect other users by terminating their sessions temporarily preventing access. However, the application itself cannot be compromised resulting in a low impact on availability. There is no impact on confidentiality and integrity of the data

An OS Command Injection vulnerability exists in the SAP NetWeaver Application Server for ABAP and ABAP Platform that allows an authenticated attacker with administrative access to execute specially crafted shell commands on the server, bypassing the logging mechanism. This allows the execution of unintended OS commands without detection, potentially impacting the integrity and availability of the application, with no impact on confidentiality.

Due to insufficient authorization checks in the SAP Incentive and Commission Management application, authenticated users could invoke a remote-enabled function module to perform table update operations. This vulnerability has a low impact on integrity with no impact on confidentiality and availability of the application.

Due to missing authorization check in SAP S/4HANA Condition Maintenance, an authenticated attacker could gain unauthorized access to view and modify condition table records, resulting in low impact on the confidentiality and integrity of the data. Additionally, this vulnerability may prevent the legitimate user from accessing the records, causing low impact on application availability.

Due to missing authorization check in SAP Strategic Enterprise Management (Scorecard Wizard in Business Server Pages), an authenticated attacker could access information that they are otherwise unauthorized to view. This vulnerability also enables the attacker to change the default settings and modify value fields, which will mislead risk evaluations and falsely lower assessed risk levels. This results in a low impact on the confidentiality and integrity of the data. There is no impact on the application�s availability.

SQL injection vulnerability exists in @sap/hdi-deploy package, where SQL queries are dynamically constructed using user input without proper parameterization or prepared statements. Successful exploitation could allow the high privileged users to alter the SELECT statements impacting confidentiality and availability of the application. There is no impact on integrity.

Due to a Code Injection vulnerability in SAP Application Server ABAP for SAP NetWeaver and ABAP Platform, an authenticated attacker could send specially crafted inputs to the application. If processed by the application, this input could be delivered to users subscribed to the channel and result in execution. Successful exploitation could enable the attacker to execute arbitrary code for other users, resulting in a low impact on the integrity, with no impact to the confidentiality and availability of the system.

Due to improper Spring Security configuration, SAP Commerce Cloud allows an unauthenticated user to perform malicious input injection, resulting in arbitrary server-side code execution, leading to high impact on Confidentiality, Integrity, and Availability of the application.

SAP S/4HANA (SAP Enterprise Search for ABAP) contains a SQL injection vulnerability that allows an authenticated attacker to inject malicious SQL statements through user-controlled input. The application directly concatenates this malicious user input into SQL queries, which are then passed to the underlying database without proper validation or sanitization. Upon successful exploitation, an attacker may gain unauthorized access to sensitive database information and could potentially crash the application. This vulnerability has a high impact on the confidentiality and availability of the application, while integrity remains unaffected.

Due to an OS Command Execution vulnerability in SAP Forecasting & Replenishment, an authenticated attacker with administrative authorizations could abuse a non-remote-enabled function to execute arbitrary operating system commands. Successful exploitation could allow the attacker to read or modify any system data or shut down the system, resulting in a complete compromise of confidentiality, integrity, and availability.

SAPUI5 (Search UI) allows an unauthenticated attacker to manipulate specific URL parameters on the Search UI to include malicious content. Successful exploitation may mislead victim users into clicking and accessing attacker-controlled pages rendered by the application. This vulnerability has a low impact on confidentiality with no effect on the integrity and availability of the application.

Due to a reflected cross-site scripting (XSS) vulnerability in SAP NetWeaver Application Server ABAP (Applications based on Business Server Pages), an unauthenticated attacker could craft a URL that exploits an unprotected URL parameter to embed a malicious script. If a victim clicks the link, the injected input is processed during web page generation, resulting in the execution of malicious content in the victim�s browser context. This could allow the attacker to access and/or modify information, impacting the confidentiality and integrity of the application, with no impact to availability.

Due to insufficient CSRF protection in SAP BusinessObjects Business Intelligence Platform ,an authenticated user could be tricked by an attacker to send unintended requests to the web server. This has low impact on integrity and availability of the application. There is no impact on confidentiality of the data.

Reserved. Details will be published at disclosure.

Reserved. Details will be published at disclosure.

Reserved. Details will be published at disclosure.

Sangoma Switchvox before 8.4 places cleartext SIP authentication credentials in a backup file.

On 2026-05-11, between approximately 19:20 and 19:26 UTC, 84 malicious versions across 42 @tanstack/* packages were published to the npm registry. The publishes were authenticated via the legitimate GitHub Actions OIDC trusted-publisher binding for TanStack/router, but the publish workflow itself was not modified. The attacker chained three known vulnerability classes — a pull_request_target "Pwn Request" misconfiguration, GitHub Actions cache poisoning across the fork↔base trust boundary, and runtime memory extraction of the OIDC token from the Actions runner process — to publish credential-stealing malware under a trusted identity. Each affected package received exactly two malicious versions, published a few minutes apart.

Exim before 4.99.3, in certain GnuTLS configurations, has a remotely reachable use-after-free in the BDAT body parsing path. It is triggered when a client sends a TLS close_notify mid-body during a CHUNKING transfer, followed by a final cleartext byte on the same TCP connection. This can lead to heap corruption. An unauthenticated network attacker exploiting this vulnerability could execute arbitrary code.

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.

The torch-checkpoint-shrink.py script in the ml-engineering project in commit 0099885db36a8f06556efe1faf552518852cb1e0 (2025-20-27) contains an insecure deserialization vulnerability (CWE-502). The script uses torch.load() to process PyTorch checkpoint files (.pt) without enabling the security-restrictive weights_only=True parameter. This oversight allows the deserialization of arbitrary Python objects via the pickle module. A remote attacker can exploit this by providing a maliciously crafted checkpoint file, leading to arbitrary code execution in the context of the user running the script.

The nexent v1.7.5.2 backend service contains an unauthorized arbitrary file deletion vulnerability in its ElasticSearch service interface. The DELETE /{index_name}/documents endpoint lacks proper authentication and authorization controls and does not validate the user-supplied path_or_url parameter. This allows unauthenticated remote attackers to send crafted requests that trigger the deletion of arbitrary documents from ElasticSearch indices and corresponding files from the MinIO storage system. Successful exploitation leads to data destruction and denial of service.

The nexent v1.7.5.2 backend service contains an unauthorized arbitrary storage file deletion vulnerability in its file management API. The DELETE /storage/{object_name:path} endpoint lacks authentication, authorization, and input validation mechanisms. Unauthenticated remote attackers can send crafted requests with a user-controlled object_name path parameter to delete arbitrary files from the underlying MinIO storage system. Successful exploitation leads to data loss and denial of service.

The _load_model() function in the neural_magic_training.py script of the optimate project in commit a6d302f912b481c94370811af6b11402f51d377f (2024-07-21) allows arbitrary code execution. When a user supplies a directory path via the --model command-line argument, the function reads a module.py file from that directory and executes its contents directly using Python's exec() function. This design does not validate or sanitize the file's content, allowing an attacker who controls the input directory to execute arbitrary Python code in the context of the process running the script.

The _load_model() function in the neural_magic_training.py script of the optimate project in commit a6d302f912b481c94370811af6b11402f51d377f (2024-07-21) is vulnerable to insecure deserialization (CWE-502). When a user provides a single model file path (e.g., .pt or .pth) via the --model command-line argument, the function loads the file using torch.load() without enabling the weights_only=True security parameter. This allows the deserialization of arbitrary Python objects through the Pickle module. A remote attacker can exploit this by providing a maliciously crafted model file, leading to arbitrary code execution during deserialization on the victim's system.

The _load_model() function in the neural_magic_training.py script of the optimate project in commit a6d302f912b481c94370811af6b11402f51d377f (2024-07-21) is vulnerable to insecure deserialization (CWE-502). When loading a model state dictionary from a state_dict.pt file via torch.load(), the function does not enable the weights_only=True security parameter. This allows the deserialization of arbitrary Python objects through the Pickle module. A remote attacker can exploit this by providing a maliciously crafted state_dict.pt file within a directory specified via the --model argument, leading to arbitrary code execution during the deserialization process on the victim's system.

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.

PyTorch-Lightning versions 2.6.0 and earlier contain an insecure deserialization vulnerability (CWE-502) in the checkpoint loading mechanism. The LightningModule.load_from_checkpoint() method, which is commonly used to load saved model states, internally calls torch.load() without setting the security-restrictive weights_only=True parameter. This default behavior allows the deserialization of arbitrary Python objects via the Pickle module. A remote attacker can exploit this by providing a maliciously crafted checkpoint file, leading to arbitrary code execution on the victim's system when the file is loaded.

The snorkel library thru v0.10.0 contains an insecure deserialization vulnerability (CWE-502) in the Trainer.load() method of the Trainer class. The method loads model checkpoint 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. A remote attacker can exploit this by providing a maliciously crafted model file, leading to arbitrary code execution on the victim's system when the file is loaded via the vulnerable method.

The snorkel library thru v0.10.0 contains a critical insecure deserialization vulnerability (CWE-502) in the BaseLabeler.load() method of the BaseLabeler class. The method loads serialized labeler models using the unsafe pickle.load() function on user-supplied file paths without any validation or security controls. Python's pickle module is inherently dangerous for deserializing untrusted data, as it can execute arbitrary code during the deserialization process. A remote attacker can exploit this by providing a maliciously crafted pickle file, leading to arbitrary code execution on the victim's system when the file is loaded via the vulnerable method.

The snorkel library thru v0.10.0 contains an insecure deserialization vulnerability (CWE-502) in the MultitaskClassifier.load() method of the MultitaskClassifier class. The method 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. A remote attacker can exploit this by providing a maliciously crafted model file, leading to arbitrary code execution on the victim's system when the file is loaded via the vulnerable method.

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.

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 CosyVoice project thru commit 6e01309e01bc93bbeb83bdd996b1182a81aaf11e (2025-30-21) contains an insecure deserialization vulnerability (CWE-502) in its model loading process. When loading model files (.pt) from a user-specified directory (via the --model_dir argument), 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 providing a maliciously crafted model directory containing .pt files with embedded pickle payloads. When a victim loads this directory using CosyVoice's web interface, the malicious payload is executed, leading to remote code execution on the victim's system.

Guardrails AI thru 0.6.7 contains a code injection vulnerability (CWE-94) in its Hub package installation mechanism. When installing validator packages via guardrails hub install, the system retrieves a manifest from the Guardrails Hub and dynamically executes a script specified in the post_install field. The script path is constructed from untrusted manifest data and executed without proper validation or sanitization, allowing remote code execution. An attacker who can publish malicious packages to the Hub can inject arbitrary code that will be executed on any system where a victim installs the malicious package.

Horovod thru 0.28.1 contains an insecure deserialization vulnerability (CWE-502) in its KVStore HTTP server component. The KVStore server, used for distributed task coordination, lacks authentication and authorization controls, allowing any remote attacker to write arbitrary data via HTTP PUT requests. When a Horovod worker reads data from the KVStore (via HTTP GET), it deserializes the data using cloudpickle.loads() without verifying its source or integrity. An attacker can exploit this by sending a malicious pickle payload to the server before the legitimate data is written, causing the victim worker to deserialize and execute arbitrary code, leading to remote code execution.

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) 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.

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 mem0 1.0.0 server lacks authentication and authorization controls for its memory management API endpoints. Critical functions such as updating memory records (PUT /memories/{memory_id}) are exposed without any verification of the requester's identity or permissions. A remote attacker can exploit this by sending unauthenticated requests to modify, overwrite, or delete arbitrary memory records, leading to unauthorized data manipulation and potential data loss.

The mem0 1.0.0 server lacks authentication and authorization controls for its memory deletion API endpoint (DELETE /memories). The endpoint allows unauthenticated users to delete memory records by specifying arbitrary user identifiers (e.g., user_id, run_id, agent_id) in the request query parameters. A remote attacker can exploit this by sending unauthenticated DELETE requests to erase memory data for any user, leading to unauthorized data loss and denial of service.

The mem0 v1.0.0 server lacks authentication and authorization controls for its memory reset functionality accessible via the DELETE /memories endpoint. An unauthenticated attacker can send a DELETE request that triggers a reset operation, leading to the execution of a DROP TABLE SQL statement. This results in the deletion of the entire memory database table, causing catastrophic data loss and a complete denial of service for all users of the service.

The mem0 1.0.0 server lacks authentication and authorization controls for its memory reset and table re-creation functionality accessible via the DELETE /memories endpoint. An unauthenticated attacker can send a DELETE request that triggers a reset operation, leading to the execution of a CREATE TABLE SQL statement. This can cause unexpected table re-creation, schema disruption, potential data loss, and denial of service for the memory management service.

The mem0 1.0.0 server lacks authentication and authorization controls for its memory deletion API endpoint (DELETE /memories/{memory_id}). The endpoint allows unauthenticated users to delete arbitrary memory records without verifying their identity or permissions. A remote attacker can exploit this by sending unauthenticated DELETE requests to remove any memory entry from the database, leading to unauthorized data loss and potential denial of service.