ghsa

### Impact _What kind of vulnerability is it? Who is impacted?_ **Description:** This vulnerability affects confidential client applications, including daemons, web apps, and web APIs. Under specific circumstances, sensitive information such as client secrets or certificate details may be exposed in the service logs of these applications. Service logs are intended to be handled securely. **Impact:** The vulnerability impacts service logs that meet the following criteria: - **Logging Level:** Logs are generated at the information level. - **Credential Descriptions:** containing: - Local file paths with passwords. - Base64 encoded values. - Client secret. Additionally, logs of services using Base64 encoded certificates or certificate paths with password credential descriptions are also affected if the certificates are invalid or expired, regardless of the log level. Note that these credentials are not usable due to their invalid or expired status. If your service log...

Insertion of Sensitive Information into Log File vulnerability in Apache ActiveMQ Artemis. All the values of the broker properties are logged when the org.apache.activemq.artemis.core.config.impl.ConfigurationImpl logger has the debug level enabled. This issue affects Apache ActiveMQ Artemis: from 1.5.1 before 2.40.0. It can be mitigated by restricting log access to only trusted users. Users are recommended to upgrade to version 2.40.0, which fixes the issue.

### Impact Currently the default settings for double-opt-in allow for mass unsolicited newsletter sign-ups without confirmation. Default settings are: Newsletter: Double Opt-in - active Newsletter: Double opt-in for registered customers - disabled Log-in & sign-up: Double opt-in on sign-up - disabled With these settings, anyone can register an account on the shop using any e-mail-address and then check the check-box in the account page to sign up for the newsletter. The recipient will receive two mails confirming registering and signing up for the newsletter, no confirmation link needed to be clicked for either. In the backend the recipient is set to “instantly active”. ### Patches Update to Shopware 6.6.10.3 or 6.5.8.17 ### Workarounds For older versions of 6.4, corresponding security measures are also available via a plugin. For the full range of functions, we recommend updating to the latest Shopware version.

## Impact wallabag versions prior to 2.6.11 were discovered to contain multiple Cross-Site Request Forgery (CSRF) vulnerabilities across several endpoints. An attacker could craft a malicious link or page that, if visited by a logged-in wallabag user, could trick the user's browser into performing unintended actions within their wallabag account without their consent. Additionally, one endpoint affects the login page locale setting. The affected endpoints allow attackers to potentially perform actions such as: * **Manage API Tokens:** * `/generate-token` * `/revoke-token` * **Manage User Rules:** * `/tagging-rule/delete/{taggingRule}` * `/ignore-origin-user-rule/delete/{ignoreOriginUserRule}` * **Modify User Configuration:** * `/config/view-mode` * **Manage Individual Entries:** * `/reload/{id}` * `/archive/{id}` * `/star/{id}` * `/delete/{id}` * `/share/{id}` * `/share/delete/{id}` * **Manage Tags:** * `/remove-tag/{entry}/{tag}` ...

### Summary Xgrammar includes a cache for compiled grammars to increase performance with repeated use of the same grammar. This cache is held in memory. Since the cache is unbounded, a system making use of xgrammar can be abused to fill up a host's memory and case a denial of service. For example, sending many small requests to an LLM inference server with unique JSON schemas would eventually cause this denial of service to occur. ### Details The fix is to add a limit to the cache size. This was done in https://github.com/mlc-ai/xgrammar/pull/243 An example of making use of the new cache size limit can be found in vLLM here: https://github.com/vllm-project/vllm/pull/16283 ### Impact Any system making use of Xgrammar and taking requests as input from potentially untrusted parties would be vulnerable to this denial of service issue.

Name: ISA-2025-003: Malicious validator can spoof votes from other validators Component: tendermint-rs Criticality: High (Catastrophic Impact; Rare Likelihood per [ACMv1.2](https://github.com/interchainio/security/blob/main/resources/CLASSIFICATION_MATRIX.md)) Affected versions: <= v0.40.2 Affected users: Everyone ### Description tendermint-rs contains a critical vulnerability in its light client implementation due to insecure handling of corrupted validator sets. Because it doesn't check that the validator address is correctly derived from the validator's public key when counting votes, it is possible to spoof votes from other validators. The result is being able to construct the malicious block and cheat the light client. The light client will accept such a block, seemingly signed by 2/3+ majority. ### Patches The new tendermint-rs release [v0.40.3](https://github.com/informalsystems/tendermint-rs/releases/tag/v0.40.3) fixes this issue. Unreleased code in the main branch is pat...

### Summary In koa < 2.16.1 and < 3.0.0-alpha.5, passing untrusted user input to ctx.redirect() even after sanitizing it, may execute javascript code on the user who use the app. ### Patches This issue is patched in 2.16.1 and 3.0.0-alpha.5. ### PoC https://gist.github.com/linhnph05/03d677b183636af206ff781bdd19701a ### Impact 1. Redirect user to another phishing site 2. Make request to another endpoint of the application based on user's cookie 3. Steal user's cookie

### Summary There was an insecure deserialization in BentoML's runner server. By setting specific headers and parameters in the POST request, it is possible to execute any unauthorized arbitrary code on the server, which will grant the attackers to have the initial access and information disclosure on the server. ### PoC - First, create a file named **model.py** to create a simple model and save it ``` import bentoml import numpy as np class mymodel: def predict(self, info): return np.abs(info) def __call__(self, info): return self.predict(info) model = mymodel() bentoml.picklable_model.save_model("mymodel", model) ``` - Then run the following command to save this model ``` python3 model.py ``` - Next, create **bentofile.yaml** to build this model ``` service: "service.py" description: "A model serving service with BentoML" python: packages: - bentoml - numpy models: - tag: MyModel:latest include: - "*.py" ``` - Then, create **service.p...

A bypass has been identified for the previously known vulnerability CVE-2017-0929, allowing unauthenticated attackers to execute arbitrary GET requests against target systems, including internal or adjacent networks. ### Impact This vulnerability facilitates a semi-blind SSRF attack, allowing attackers to make the target server send requests to internal or external URLs without viewing the full responses. Potential impacts include internal network reconnaissance, bypassing firewalls.

### Impact The buffer created for parsing metadata for PNG and WebP images was only bounded by their input data type, which could lead to potentially large memory allocation, and unreasonably high for image metadata. Before `v0.11.0`, If you didn't trust the input images, this could be abused to construct denial-of-service attacks. ### Patches `v0.11.0` added a 10 MB upper limit.