protobufjs compiles protobuf definitions into JavaScript (JS) functions. Prior to 7.5.6 and 8.0.2, protobufjs includes a minimal UTF-8 decoder that accepted overlong UTF-8 byte sequences and decoded them to their canonical characters instead of replacing them. An attacker who can provide protobuf binary data decoded through the affected UTF-8 path may be able to bypass application-level checks that inspect raw bytes before protobuf string decoding. For example, bytes that do not contain certain ASCII characters could decode to strings containing those characters. This vulnerability is fixed in 7.5.6 and 8.0.2.
protobufjs compiles protobuf definitions into JavaScript (JS) functions. Prior to 7.5.6 and 8.0.2, protobufjs could recurse without a depth limit while decoding nested protobuf data. This affected both skipping unknown group fields and generated decoding of nested message fields. A crafted protobuf binary payload could cause the JavaScript call stack to be exhausted during decoding. This vulnerability is fixed in 7.5.6 and 8.0.2.
protobufjs compiles protobuf definitions into JavaScript (JS) functions. Prior to 7.5.6 and 8.0.2, protobufjs allowed certain schema option paths to traverse through inherited object properties while applying options. A crafted protobuf schema or JSON descriptor could cause option handling to write to properties on global JavaScript constructors, corrupting process-wide built-in functionality. This vulnerability is fixed in 7.5.6 and 8.0.2.
protobufjs compiles protobuf definitions into JavaScript (JS) functions. Prior to 7.5.6 and 8.0.2, protobufjs used plain objects with inherited prototypes for internal type lookup tables used by generated encode and decode functions. If Object.prototype had already been polluted, those lookup tables could resolve attacker-controlled inherited properties as valid protobuf type information. This could cause attacker-controlled strings to be emitted into generated JavaScript code. This vulnerability is fixed in 7.5.6 and 8.0.2.
jupyterlab is an extensible environment for interactive and reproducible computing, based on the Jupyter Notebook Architecture. Prior to 4.5.7, JupyterLab's HTML sanitizer allowlists data-commandlinker-command and data-commandlinker-args on button elements, while CommandLinker listens for all click events on document.body and executes the named command without checking whether the element came from trusted JupyterLab UI. A notebook with a pre-saved HTML cell output containing a deceptive button can trigger arbitrary JupyterLab commands - including arbitrary code execution - on a single user click, without any code being submitted for execution by the user. This vulnerability is fixed in 4.5.7.
JupyterLab is an extensible environment for interactive and reproducible computing, based on the Jupyter Notebook Architecture. From 4.0.0 to 4.5.6, the allow-list of extensions that can be installed from PyPI Extension Manager (allowed_extensions_uris) is not correctly enforced by JupyterLab. The PyPI Extension Manager was not contained to packages listed on the default PyPI index. This vulnerability is fixed in 4.5.7.
protobufjs-cli is the command line add-on for protobuf.js. Prior to 1.2.1 and 2.0.2, pbts invoked JSDoc by building a shell command string from input file paths and executing it through child_process.exec. File paths containing shell metacharacters could therefore be interpreted by the shell instead of being passed to JSDoc as plain arguments. This vulnerability is fixed in 1.2.1 and 2.0.2.
A path injection vulnerability exists in OpenPLC v3 (2c82b0e79c53f8c1f1458eee15fec173400d6e1a) as the binary program compiled from glue_generator.cpp does not perform any validation on the file path parameters passed via the command line. The user-controlled input parameters are directly passed to the underlying file operation functions (fopen/ifstream/ofstream) for file reading and writing. An attacker can exploit this vulnerability by constructing a malicious path to read arbitrary readable files.
AutoGPT is a platform that allows users to create, deploy, and manage continuous artificial intelligence agents that automate complex workflows. In AutoGPT, the execution process is recorded to the console (stdout/stderr), and deployed in container mode, which is automatically captured by Docker and stored as "container logs". However, prior to 0.6.32, there is no limit on the log size when the container is deployed. When the number of user accesses is too large, the log on the server disk will be too large, causing disk resource exhaustion and eventually causing DoS. autogpt-platform-beta-v0.6.32 fixes the issue.
Crypt::Argon2 versions from 0.017 before 0.031 for Perl perform a heap out-of-bounds read in argon2_verify on empty encoded input.
The auto-detect form of argon2_verify passes encoded_len - 1 as the length argument to memchr without checking that encoded_len is non-zero. When the encoded string is empty, the size_t subtraction underflows to SIZE_MAX and memchr scans adjacent heap memory looking for a '$' separator byte.
A caller that invokes argon2_verify against a stored hash that may legitimately be empty (for example a placeholder row or a NULL column materialised as an empty string) reads out-of-bounds heap memory, which can crash the process or leak the position of an adjacent '$' byte into subsequent parsing.