Use after free in Windows Kernel-Mode Drivers allows an authorized attacker to execute code over a network. CVSSv3.1 8.0 (HIGH)
Heap-based buffer overflow in Windows Message Queuing allows an unauthorized attacker to execute code over an adjacent network. CVSSv3.1 8.8 (HIGH)
Improper neutralization of special elements in output used by a downstream component ('injection') in Azure Machine Learning allows an unauthorized attacker to perform spoofing over a network. CVSSv3.1 8.2 (HIGH)
Improper authentication in Azure SDK allows an unauthorized attacker to bypass a security feature over a network. CVSSv3.1 9.1 (CRITICAL)
Deserialization of untrusted data in Microsoft Office SharePoint allows an authorized attacker to execute code over a network. CVSSv3.1 8.8 (HIGH)
Deserialization of untrusted data in Microsoft Office SharePoint allows an authorized attacker to execute code over a network. CVSSv3.1 8.8 (HIGH)
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. CVSSv3.1 9.1 (CRITICAL)
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 reposit CVSSv3.1 9.8 (CRITICAL)
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 mo CVSSv3.1 9.8 (CRITICAL)
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 explo CVSSv3.1 9.8 (CRITICAL)
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 CVSSv3.1 9.8 (CRITICAL)
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 malici CVSSv3.1 9.8 (CRITICAL)
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 CVSSv3.1 9.8 (CRITICAL)
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 publi CVSSv3.1 9.8 (CRITICAL)
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 pr CVSSv3.1 8.8 (HIGH)
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 CVSSv3.1 9.8 (CRITICAL)
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 remot CVSSv3.1 9.8 (CRITICAL)
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 malicio CVSSv3.1 9.8 (CRITICAL)
Insufficient ownership checks in `clientarea.php` allow an authenticated client area user to submit requests using another user’s `addonId` without any ownership validation leading to unauthorized access to the victim's resources and their cPanel account. CVSSv3.1 10.0 (CRITICAL)
A missing authorization vulnerability in Fortinet FortiSandbox 5.0.0 through 5.0.1, FortiSandbox 4.4.0 through 4.4.8, FortiSandbox Cloud 5.0.2 through 5.0.5, FortiSandbox PaaS 23.4 all versions, FortiSandbox PaaS 23.3 all versions, FortiSandbox PaaS 23.1 all versions, FortiSandbox PaaS 22.2 all versions, FortiSandbox PaaS 22.1 all versions, FortiSandbox PaaS 21.4 all versions, FortiSandbox PaaS 21.3 all versions, FortiSandbox PaaS 5.0.0 through 5.0.1, FortiSandbox PaaS 4.4.5 CVSSv3.1 9.8 (CRITICAL)
A out-of-bounds write vulnerability in Fortinet FortiOS 7.6.0 through 7.6.3, FortiOS 7.4.0 through 7.4.8, FortiOS 7.2.0 through 7.2.11 allows attacker to execute unauthorized code or commands via specially crafted packets. CVSSv3.1 8.8 (HIGH)
An access issue was addressed with additional sandbox restrictions. This issue is fixed in macOS Sequoia 15.7.7, macOS Sonoma 14.8.7, macOS Tahoe 26.2. An app may be able to break out of its sandbox. CVSSv3.1 8.8 (HIGH)
JunoClaw is an agentic AI platform built on Juno Network. Prior to 0.x.y-security-1, the WAVS bridge's computeDataVerify called fetch() on agent-supplied URLs without validating scheme, port, or resolved IP, resulting in an SSRF vulnerability. This vulnerability is fixed in 0.x.y-security-1. CVSSv3.1 8.2 (HIGH)
JunoClaw is an agentic AI platform built on Juno Network. Prior to 0.x.y-security-1, every MCP write tool (send_tokens, execute_contract, instantiate_contract, upload_wasm, ibc_transfer, etc.) accepted 'mnemonic: string' as an explicit tool-call parameter. The BIP-39 seed was consequently embedded in the LLM tool-call JSON, exposing it to any transport, log, or telemetry surface in the path between the LLM provider and the MCP process. This vulnerability is fixed in 0.x.y-sec CVSSv3.1 9.8 (CRITICAL)
JunoClaw is an agentic AI platform built on Juno Network. Prior to 0.x.y-security-1, substring-based blocklist in plugin-shell's command-safety check could be bypassed by adversarial argument constructions, allowing unauthorized command execution on the host when combined with the companion advisory. Pre-patch, the check was applied to the raw command string rather than the parsed first token. This vulnerability is fixed in 0.x.y-security-1. CVSSv3.1 8.4 (HIGH)