| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| n8n-MCP is an MCP server that provides AI assistants access to n8n node documentation, properties, and operations. Prior to version 2.47.11, when n8n-mcp runs in HTTP transport mode, incoming requests to the POST /mcp endpoint had their request metadata written to server logs regardless of the authentication outcome. In deployments where logs are collected, forwarded to external systems, or viewable outside the request trust boundary (shared log storage, SIEM pipelines, support/ops access), this can result in disclosure of: bearer tokens from the Authorization header, per-tenant API keys from the, x-n8n-key header in multi-tenant setups, JSON-RPC request payloads sent to the MCP endpoint. Access control itself was not bypassed — unauthenticated requests were correctly rejected with 401 Unauthorized — but sensitive values from those rejected requests could still be persisted in logs. This issue has been patched in version 2.47.11. |
| A vulnerability exists in iControl SOAP where an authenticated attacker with the Resource Administrator or Administrator role can download sensitive files. Note: Software versions which have reached End of Technical Support (EoTS) are not evaluated. |
| 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. |
| An authenticated attacker with the Resource Administrator or Administrator role can modify configuration objects through iControl SOAP resulting in privilege escalation. Note: Software versions which have reached End of Technical Support (EoTS) are not evaluated. |
| An improper sanitization vulnerability exists in the BIG-IP QKView utility that allows a low-privileged attacker to read sensitive information from a QKView file.
Note: Software versions which have reached End of Technical Support (EoTS) are not evaluated |
| 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 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. |
| ciguard is a static security auditor for CI/CD pipelines. From 0.8.0 to 0.8.1 , the discover_pipeline_files() function in src/ciguard/discovery.py walks a directory tree following symlinks, with cycle protection via tracking visited resolved paths. An attacker who can plant a symlink in a directory the user (or AI agent) scans can cause discovery to walk into the symlink target and return paths to pipeline-shaped files outside the requested root. This vulnerability is fixed in 0.8.2. |
| 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-security-1. |
| Improper neutralization of input during web page generation ('cross-site scripting') in Visual Studio Code allows an unauthorized attacker to bypass a security feature locally. |
| LWP::UserAgent versions before 6.83 for Perl leak Authorization and Proxy-Authorization headers on cross-origin redirects.
On a 3xx response, the redirect handler strips only Host and Cookie before issuing the follow-up request. Caller-supplied Authorization and Proxy-Authorization headers are sent unchanged to the redirect target, including across scheme, host, or port changes.
A redirect to an attacker controlled host therefore discloses the caller's credentials to that host. |
| When schema validation is enabled on a collection and an update or insert would violate the collection's schema, the local server log message generated may not have all user data redacted.
This issue impacts MongoDB Server v7.0 versions prior to 7.0.34, v8.0 versions prior to 8.0.23, v8.2 versions prior to 8.2.9 and v8.3 versions prior to 8.3.2. |
| The Elasticsearch logging provider, when configured with a `host` URL that embeds credentials (for example `https://user:password@server.example.com:9200`), wrote the full host URL — including the embedded credentials — into task logs. Any user with task-log read permission could harvest the backend credentials. Users are advised to upgrade to `apache-airflow-providers-elasticsearch` 6.5.3 or later and, as a defense-in-depth measure, configure the backend credentials via a secret backend rather than embedding them in the `[elasticsearch] host` URL. |
| Files or directories accessible to external parties in Microsoft Office Word allows an unauthorized attacker to disclose information locally. |
| The OpenSearch logging provider, when configured with a `host` URL that embeds credentials (for example `https://user:password@server.example.com:9200`), wrote the full host URL — including the embedded credentials — into task logs. Any user with task-log read permission could harvest the backend credentials. Users are advised to upgrade to `apache-airflow-providers-opensearch` 1.9.1 or later and, as a defense-in-depth measure, configure the backend credentials via a secret backend rather than embedding them in the `[opensearch] host` URL. |
| 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 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. |
