#git

### Summary This vulnerability affects JUnit's support for writing Open Test Reporting XML files which is an opt-in feature of `junit-platform-reporting`. If a repository is cloned using a GitHub token or other credentials in its URL, for example: ```bash git clone https://${GH_APP}:${GH_TOKEN}@github.com/example/example.git ``` The credentials are captured by `OpenTestReportGeneratingListener` which produces (trimmed for brevity): ```xml <infrastructure> <git:repository originUrl="https://username:token@github.com/example/example.git" /> </infrastructure> ``` ### Details https://github.com/junit-team/junit5/blob/6b7764dac92fd35cb348152d1b37f8726875a4e0/junit-platform-reporting/src/main/java/org/junit/platform/reporting/open/xml/OpenTestReportGeneratingListener.java#L183 I think this should be configurable in some way to exclude select git information or exclude it entirely. ### PoC 1. Clone a repo using a GitHub token as shown above. 2. Enable the listener `junit.platfor...

### Summary Certs generated by v4 contain their private key. ## Details ### Background Recently, I encountered an API in Go that’s easy to misuse: sha512.Sum384 and sha512.New384().Sum look very similar and behave very differently. https://go.dev/play/p/kDCqqoYk84k demonstrates this. I want to discuss extending static analysis to detect this case with the go community, but before I do that, I want to make a best-effort pass at open-source projects to fix the existing bugs. I figured that if there were any vulnerabilities out there, they would be easy to find once that discussion begins, so it’s better to address them early. This work is a hobby project and has no affiliation with my employer, so I may be slow to respond due to existing commitments. ### PoC https://go.dev/play/p/vSW0U3Hq4qk ### Impact [This code](https://github.com/juju/utils/blob/0141ef0ee74a0cac603c5c9e4aff194008722f41/cert/cert.go#L120) (cert.NewLeaf) generates certs with the SubjectKeyId set to `sha512.Ne...

There is a heap buffer overflow when writing a sufficiently large (>64k encoded with default settings) image in the DDS format due to writing into a buffer without checking for available space. This only affects users who save untrusted data as a compressed DDS image. * Unclear how large the potential write could be. It is likely limited by process segfault, so it's not necessarily deterministic. It may be practically unbounded. * Unclear if there's a restriction on the bytes that could be emitted. It's likely that the only restriction is that the bytes would be emitted in chunks of 8 or 16. This was introduced in Pillow 11.2.0 when the feature was added.

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View CSAF 1. EXECUTIVE SUMMARY CVSS v3 9.8 ATTENTION: Exploitable remotely/low attack complexity Vendor: FESTO Equipment: CODESYS Vulnerabilities: Partial String Comparison, Uncontrolled Resource Consumption, Memory Allocation with Excessive Size Value 2. RISK EVALUATION Successful exploitation of these vulnerabilities could allow an attacker to block legitimate user connections, crash the application, or authenticate without proper credentials. 3. TECHNICAL DETAILS 3.1 AFFECTED PRODUCTS FESTO reports that the following products are affected: FESTO CODESYS Gateway Server V2: All versions FESTO CODESYS Gateway Server V2: prior to V2.3.9.38 3.2 VULNERABILITY OVERVIEW 3.2.1 PARTIAL STRING COMPARISON CWE-187 In CODESYS Gateway Server V2 for versions prior to V2.3.9.38 only part of the specified password is being compared to the real CODESYS Gateway password. An attacker may perform authentication by specifying a small password that matches the corresponding part of the longer real CODESYS ...

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Model Context Protocol (MCP) is a powerful protocol from Anthropic that defines how to connect large language models (LLMs) to external tools. It has quickly gained traction due to its ease of use and the benefits it adds in our use of AI. In this article we'll cover some of the potential security risks you'll encounter with MCP and how you can approach mitigating them.How MCP worksMCP does not directly connect LLMs with tools. The MCP client component accesses the LLM, and the MCP server component accesses the tools. One MCP client has access to one or more MCP servers. Users may connect any

### Impact Graylog users can gain elevated privileges by creating and using API tokens for the local Administrator or any other user for whom the malicious user knows the ID. For the attack to succeed, the attacker needs a user account in Graylog. They can then proceed to issue hand-crafted requests to the Graylog REST API and exploit a weak permission check for token creation. ### Workarounds In Graylog version `6.2.0` and above, regular users can be restricted from creating API tokens. The respective configuration can be found in `System > Configuration > Users > "Allow users to create personal access tokens"`. This option should be *Disabled*, so that only administrators are allowed to create tokens. ### Recommended Actions After upgrading Graylog from a vulnerable version to a patched version, administrators are advised to perform the following steps to ensure the integrity of their system: #### Review API tokens An overview of all existing API tokens is available at `Syste...