Exposure of Sensitive Information via Arbitrary System Path Lookup in h2oai/h2o-3
In h2oai/h2o-3 version 3.40.0.4, an exposure of sensitive information vulnerability exists due to an arbitrary system path lookup feature. This vulnerability allows any remote user to view full paths in the entire file system where h2o-3 is hosted. Specifically, the issue resides in the Typeahead API call, which when requested with a typeahead lookup of '/', exposes the root filesystem including directories such as /home, /usr, /bin, among others. This vulnerability could allow attackers to explore the entire filesystem, and when combined with a Local File Inclusion (LFI) vulnerability, could make exploitation of the server trivial.
A clickjacking vulnerability exists in zenml-io/zenml versions up to and including 0.55.5 due to the application's failure to set appropriate X-Frame-Options or Content-Security-Policy HTTP headers. This vulnerability allows an attacker to embed the application UI within an iframe on a malicious page, potentially leading to unauthorized actions by tricking users into interacting with the interface under the attacker's control. The issue was addressed in version 0.56.3.
A path traversal vulnerability exists in the parisneo/lollms application, affecting version 9.4.0 and potentially earlier versions, but fixed in version 5.9.0. The vulnerability arises due to improper validation of file paths between Windows and Linux environments, allowing attackers to traverse beyond the intended directory and read any file on the Windows system. Specifically, the application fails to adequately sanitize file paths containing backslashes (`\`), which can be exploited to access the root directory and read, or even delete, sensitive files. This issue was discovered in the context of the `/user_infos` endpoint, where a crafted
Denial of Service and Data Model Poisoning via URL Encoding in mlflow/mlflow
A vulnerability in mlflow/mlflow version 2.11.1 allows attackers to create multiple models with the same name by exploiting URL encoding. This flaw can lead to Denial of Service (DoS) as an authenticated user might not be able to use the intended model, as it will open a different model each time. Additionally, an attacker can exploit this vulnerability to perform data model poisoning by creating a model with the same name, potentially causing an authenticated user to become a victim by using the poisoned model. The issue stems from inadequate validation of model names, allowing for the creation of models with URL-encoded names that are treat
Server-Side Request Forgery (SSRF) in gradio-app/gradio
A Server-Side Request Forgery (SSRF) vulnerability exists in the gradio-app/gradio version 4.21.0, specifically within the `/queue/join` endpoint and the `save_url_to_cache` function. The vulnerability arises when the `path` value, obtained from the user and expected to be a URL, is used to make an HTTP request without sufficient validation checks. This flaw allows an attacker to send crafted requests that could lead to unauthorized access to the local network or the AWS metadata endpoint, thereby compromising the security of internal servers.
Local File Inclusion in JSON component in gradio-app/gradio
A local file inclusion vulnerability exists in the JSON component of gradio-app/gradio version 4.25. The vulnerability arises from improper input validation in the `postprocess()` function within `gradio/components/json_component.py`, where a user-controlled string is parsed as JSON. If the parsed JSON object contains a `path` key, the specified file is moved to a temporary directory, making it possible to retrieve it later via the `/file=..` endpoint. This issue is due to the `processing_utils.move_files_to_cache()` function traversing any object passed to it, looking for a dictionary with a `path` key, and then copying the specified file to
RCE via Property/Class Pollution in lightning-ai/pytorch-lightning
A remote code execution (RCE) vulnerability exists in the lightning-ai/pytorch-lightning library version 2.2.1 due to improper handling of deserialized user input and mismanagement of dunder attributes by the `deepdiff` library. The library uses `deepdiff.Delta` objects to modify application state based on frontend actions. However, it is possible to bypass the intended restrictions on modifying dunder attributes, allowing an attacker to construct a serialized delta that passes the deserializer whitelist and contains dunder attributes. When processed, this can be exploited to access other modules, classes, and instances, leading to arbitrary
Jupyter server on Windows discloses Windows user password hash
The Jupyter Server provides the backend for Jupyter web applications. Jupyter Server on Windows has a vulnerability that lets unauthenticated attackers leak the NTLMv2 password hash of the Windows user running the Jupyter server. An attacker can crack this password to gain access to the Windows machine hosting the Jupyter server, or access other network-accessible machines or 3rd party services using that credential. Or an attacker perform an NTLM relay attack without cracking the credential to gain access to other network-accessible machines. This vulnerability is fixed in 2.14.1.
### Impact
`kyber512`, `kyber768`, and `kyber1024` on Mac OS \(or when compiled with clang\) only: An attacker able to submit many decapsulation requests against a single private key, and to gain timing information about the decapsulation, could recover the private key. Proof-of-concept exploit exists for a local attacker.
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:L/A:N/E:P/RL:U/RC:C
### Patches
No patch is currently available / pending upstream [PQClean#556](https://github.com/PQClean/PQClean/issues/556).
### Workarounds
No workarounds have been reported. The 0.0.7 -> 0.0.7.1 upgrade, when available, should be a drop-in replacement<!--; it
An out-of-bounds read in the 'bson' module of PyMongo 4.6.2 or earlier allows deserialization of malformed BSON provided by a Server to raise an exception which may contain arbitrary application memory.
Arbitrary JavaScript execution due to using outdated libraries
### Summary
gradio-pdf projects with dependencies on the pdf.js library are vulnerable to CVE-2024-4367, which allows arbitrary JavaScript execution.
### PoC
1. Generate a pdf file with a malicious script in the fontmatrix. (This will run `alert(‘XSS’)`.)
[poc.pdf](https://github.com/user-attachments/files/15516798/poc.pdf)
2. Run the app. In this PoC, I've used the demo for a simple proof.
3. Upload a PDF file containing the script.
Deserialization of untrusted data can occur in versions 0.6 or newer of the skops python library, enabling a maliciously crafted model to run arbitrary code on an end user's system when loaded.
Deseriliazation of untrusted data can occur in versions 3.7.0 or newer of Ydata's ydata-profiling open-source library, enabling a maliciously crafted dataset to run arbitrary code on an end user's system when loaded.
A cross-site scripting (XSS) vulnerability in versions 3.7.0 or newer of Ydata's ydata-profiling open-source library allows for payloads to be run when a maliocusly crafted report is viewed in the browser.
Deserialization of untrusted data can occur in versions 3.7.0 or newer of Ydata's ydata-profiling open-source library, enabling a malicously crafted report to run arbitrary code on an end user's system when loaded.
Remote Code Execution can occur in versions of the MLflow platform running version 1.11.0 or newer, enabling a maliciously crafted MLproject to execute arbitrary code on an end user’s system when run.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.27.0 or newer, enabling a maliciously crafted Recipe to execute arbitrary code on an end user’s system when run.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.5.0 or newer, enabling a maliciously uploaded PyTorch model to run arbitrary code on an end user’s system when interacted with.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 2.5.0 or newer, enabling a maliciously uploaded Langchain AgentExecutor model to run arbitrary code on an end user’s system when interacted with.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 2.0.0rc0 or newer, enabling a maliciously uploaded Tensorflow model to run arbitrary code on an end user’s system when interacted with.