VAITP

SQLBot is an intelligent Text-to-SQL system based on large language models and RAG. Prior to 1.9.0, the POST /api/v1/datasource/parseExcel endpoint in backend/apps/datasource/api/datasource.py uses attacker-controlled multipart filename data when selecting where uploaded content is stored, writes the content before spreadsheet parsing and validation finish, and can transform a double-extension filename into a Python source file. An attacker able to submit a crafted multipart upload can use these behaviors to place attacker-controlled content in /opt/sqlbot/app/alembic/versions/ even when a spreadsheet parsing failure after the file write causes the endpoint to return an error. The planted file remains on disk, and subsequent SQLBot startup or migration processing causes Alembic to import the module and execute its module-level statements in the SQLBot application runtime. This issue is fixed in version 1.9.0.

NetBox Device Type Library is a collection of community-sourced device type definitions for import into NetBox. The absence of tests/init.py and the lack of --import-mode=importlib cause pytest prepend import mode to place the tests directory at the front of sys.path during collection. An unauthenticated contributor can add a module such as tests/git.py that shadows GitPython when tests/definitions_test.py executes from git import Git, Repo, or add tests/conftest.py for automatic collection-time execution. Python imports and runs the pull-request module before any test function, allowing arbitrary code execution on the GitHub Actions runner, test-result tampering, and access to tokens or network resources exposed to the workflow. This module-shadowing path is independent of the earlier pickle deserialization flaw and the separately tracked NETBOX_DT_LIBRARY_URL issue. This vulnerability is fixed by commit b0d9a3dadd0a0a9d3c93b0b2777559fd4bad1037.

Improper validation of certificate with host mismatch in the MQTT client TLS connection layer in AWS IoT Device SDK for Python 1.5.3 through 1.6.0 on Python 3.7 and later might allow an adversary-in-the-middle actor to impersonate the AWS IoT Core endpoint, read device telemetry, and inject arbitrary MQTT messages that the device processes as authentic, via a certificate issued for an unrelated hostname by a certificate authority present in the device trust store. To remediate this issue, users should upgrade to version 1.6.1.

Soup Sieve is a CSS selector library designed to be used with Beautiful Soup 4. Prior to 2.9, the selector parser in src/soupsieve/css_parser.py defines IDENTIFIER with adjacent quantified groups over overlapping character classes, and VALUE embeds IDENTIFIER for attribute selectors. When an attacker-controlled selector contains a long identifier or unquoted attribute-value run followed by input that makes the overall match fail, the regular expression engine explores quadratically many splits between the overlapping groups. User-controlled selectors can reach this path through soupsieve.compile(), soupsieve.select(), or BeautifulSoup.select(), while applications using only hard-coded selectors are unaffected. The resulting CPU consumption can hold the Python GIL, exhaust application workers, and stall a service; successful plain identifier matches are linear, and the issue does not cause memory corruption or code execution. The issue is fixed in version 2.9.

Soup Sieve is a CSS selector library designed to be used with Beautiful Soup 4. Prior to 2.9, selector_iter in src/soupsieve/css_parser.py trims the raw selector with RE_WS_END, an end-anchored WSC whitespace-and-comment expression used with search(), so the regular expression engine retries a greedy scan at every starting offset. An attacker-controlled valid selector containing a long internal whitespace run, or a selector containing a long CSS comment run followed by another token, causes quadratic CPU work before tokenization. User-controlled selectors can reach the path through soupsieve.compile() and BeautifulSoup.select(), while applications using only hard-coded selectors are unaffected. This root cause is separate from the IDENTIFIER and VALUE backtracking vulnerability because the cost occurs in RE_WS_END.search during trimming rather than token matching. The resulting CPU consumption can hold the Python GIL, exhaust workers, and stall a service without causing memory corruption or code execution. The issue is fixed in version 2.9.

Sanic is an opensource python web server/framework. In version 25.12.0, Sanic's core HTTP/1.1 chunked-body handling does not fully consume the trailer-part after the terminating zero chunk before reusing the keep-alive connection buffer. A remote unauthenticated client can place attacker-controlled bytes in that trailer region, causing Sanic to parse and route them as a hidden second request after the outer request. This breaks HTTP request-boundary integrity and can provide a request-smuggling primitive when Sanic is deployed behind intermediaries. This issue is fixed in version 25.12.1.

Sanic is an opensource python web server/framework. Prior to version 24.12.1, and in version 25.12.0, the HTTP/1.1 response pipeline in sanic/response/types.py serializes response header names and values without rejecting carriage-return or line-feed characters. Applications that place attacker-controlled data in response.headers, file(..., filename=...), or cookie path and domain attributes can therefore emit injected headers and may split responses. Depending on application and proxy behavior, this can enable session fixation through injected cookies, cache poisoning, or security-header corruption. This issue is fixed in versions 24.12.1 and 25.12.1.

djust provides Phoenix LiveView-style reactive server-side rendering for Django with Rust-powered performance. Prior to version 1.0.7, the djust live transport resolves the LiveView to mount from a client-supplied dotted path by calling `__import__(module_path, ...)`. The module is imported — running its top-level code (import side effects) — before the framework checks that the resolved object is a `LiveView` subclass and before any per-view authentication. The `LIVEVIEW_ALLOWED_MODULES` allowlist that should contain this is fail-open (`if allowed_modules:` — skipped when the setting is unset, the framework default) and uses loose `startswith` matching. An unauthenticated WebSocket client (the WS handshake does not require auth; per-view auth runs only after import + instantiate) can therefore send a `mount` / `live_redirect_mount` / `url_change` frame (or an SSE mount) with `view = "<any.importable.module>.AnyName"` and cause the server to import — and execute the top-level code of — any importable Python module by name. Version 1.0.7 fixes the issue with a fail-closed resolution gate (`djust._view_resolution.is_view_import_allowed`): a client view path resolves only if (a) its module is already loaded (`sys.modules` — so resolving runs no new code; URL-routed views loaded by URLconf at startup keep working with zero config) or (b) it matches `LIVEVIEW_ALLOWED_MODULES` on a module-segment boundary (explicit opt-in for lazily-imported views). The gate runs before `__import__` at all three sinks (+ defense-in-depth inside `_instantiate_view`). As a workaround, set `LIVEVIEW_ALLOWED_MODULES` to the narrow list of modules that contain your mountable LiveView classes. (Note: pre-patch the allowlist is `startswith`-matched and the import still precedes the subclass check, so this is mitigation, not a complete fix.)

AsyncSSH is a Python package which provides an asynchronous client and server implementation of the SSHv2 protocol on top of the Python asyncio framework. Prior to 2.24.0, _process_channel_open and _process_channel_open_confirmation in asyncssh/connection.py accept a peer-supplied send_pktsize value of zero. When channel data reaches SSHChannel._flush_send_buf in asyncssh/channel.py, the zero value causes each loop iteration to slice and remove zero bytes without reducing the send window, leaving the synchronous loop permanently true with no await point. A malicious SSH server can trigger the client path through SSH_MSG_CHANNEL_OPEN_CONFIRMATION before the first channel write, while an authenticated client can trigger the server path through SSH_MSG_CHANNEL_OPEN and freeze every current and future connection handled by the process. This vulnerability is fixed in 2.24.0.

Stanza is a Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages. Prior to 1.14.0, stanza.resources.common.unzip in stanza/resources/common.py passes downloaded model and resource archives to zipfile.ZipFile.extractall without validating member paths, and the vulnerable extraction path is reachable through stanza.download and stanza.install_corenlp. A malicious archive containing parent-directory traversal entries can write outside the intended model directory, allowing files writable by the Stanza process to be overwritten and potentially enabling code execution through modified shell configuration, SSH authorization data, Python packages, or executable scripts. This issue is fixed in version 1.14.0.