Jeudi 24 septembre 2026

kap-sh zapros

Zapros, a Python HTTP client, prior to version 0.14.0 is vulnerable to denial of service via memory exhaustion. The issue affects all callers who streamed compressed responses relying on the chunk size — explicit (`iter_bytes(chunk_size=...)`) or the default — to bound memory. The decoder ignored that bound, so a chunk could be far larger than requested and a single compressed response could overflow memory. Version 0.14.0 contains a patch. Some workarounds are available. Read the still-compressed body with `Response.iter_raw()` / `Response.async_iter_raw()`, which bypass the built-in decoders, and decompress it yourself with an explicit output-size bound (e.g. `zlib`'s `max_length`), aborting once a configured limit is exceeded. Where feasible, send `Accept-Encoding: identity` to disable response compression so bodies are not decompressed client-side. Avoid decoding response bodies from untrusted servers.

InternLM lmdeploy

LMDeploy is a toolkit for compressing, deploying, and serving large language models. Versions 012.1 through 0.12.2 contain a code injection vulnerability in `lmdeploy/pytorch/config.py` line 620 that allows an attacker to execute arbitrary Python code by publishing a malicious HuggingFace model with a crafted `quantization_config.quant_dtype` value. When a user loads the model with lmdeploy, the `quant_dtype` is passed to `eval(f'torch.{quant_dtype}')` without any validation. Version 0.12.3 contains a patch.

netbox-community devicetype-library

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.

sanic-org sanic

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.

SepineTam mcp-for-stata

MCP-for-Stata is a MCP server for integrating Stata into agent loops with a safety-first design. Prior to version 1.19.0, the ado_package_install MCP tool in stata-mcp concatenates user-controlled input directly into a Stata command string without any validation or sanitization. An attacker who can invoke the MCP tool or the equivalent Python API can embed newline characters in the package argument to inject arbitrary Stata commands. Because Stata supports a shell escape command, this leads to full OS-level arbitrary command execution (RCE) under the account running the Stata-MCP server. The tool is registered in the default all profile, so no non-default configuration is required. This issue has been patched in version 1.19.0.

conda

Conda is a system-level binary package and environment manager that runs on major operating systems and platforms. Prior to 26.5.2, parse_entry_point_def in conda/common/path/python.py accepted an unvalidated entry-point command from a noarch:python package's info/link.json metadata. CreatePythonEntryPointAction in conda/core/path_actions.py interpolated that command into target_short_path, and PrefixPathAction.target_full_path joined it to the installation prefix without verifying that the result remained under the intended bin or Scripts directory. create_python_entry_point in conda/gateways/disk/create.py then wrote an executable wrapper to the resulting path. A malicious package could use path separators, traversal segments, or an absolute command path to write outside the prefix or overwrite another in-prefix entry point during default install and environment transactions. Out-of-prefix writes require the target parent directory to exist, while an overwritten entry point can execute attacker-controlled Python when later invoked with the installing user's privileges. This issue is fixed in version 26.5.2.

webpy web.py 0.76 is vulnerable to server-side template injection (SSTI). The template engine can be tricked into executing attacker-controlled template code that built-in security checks are designed to reject. When an application precompiles templates from a directory the attacker can write to and later renders them through the precompiled template loader, the sandbox is bypassed and the attacker's code runs, resulting in arbitrary Python code execution and OS command execution on the server.

wandb

Weights & Biases wandb before 0.29.0 fails to validate the file name from server responses in the File.download function, allowing path traversal attacks. Attackers controlling the backend can supply file names with directory traversal sequences to write files outside the intended download directory, potentially enabling code execution through modification of shell startup files or Python import paths.

speechbrain

SpeechBrain before 1.1.1 contains an arbitrary code execution vulnerability that allows attackers to execute arbitrary code by supplying a crafted CKPT.yaml checkpoint metadata file parsed with PyYAML's unsafe loader during candidate enumeration in Checkpointer.recover_if_possible(). Attackers can embed malicious Python object construction tags such as !!python/object/apply in any CKPT.yaml file within the configured checkpoint path to trigger code execution during candidate discovery, even if the malicious checkpoint is never selected for recovery.

Andyyyy64 whichllm

whichllm before 0.5.16 contains a code injection vulnerability in the run and snippet commands that allows a remote attacker who controls a HuggingFace repository to achieve arbitrary code execution by crafting a malicious GGUF filename containing double quotes or other special characters. The script generation function in cli.py interpolates HuggingFace-derived values, including GGUF variant filenames from the Hub API siblings rfilename field, directly into Python source code without escaping, allowing the crafted filename to break out of the generated string literal and execute injected code on the user's machine before any model download occurs.

Agno AGI Agno

Agno's PythonTools in libs/agno/agno/tools/python.py contains a path traversal vulnerability that allows attackers to read, write, or execute arbitrary files by supplying parent-directory traversal sequences in the file_name argument passed to read_file, save_to_file, or run_python_file tool actions. Attackers can inject traversal sequences such as '../../../../../../etc/passwd' through direct tool invocation or via prompt injection embedded in agent-processed content to escape the intended base_dir boundary and achieve arbitrary file read, arbitrary file write, or arbitrary Python code execution within the process user's authority.

Red Hat Ansible Automation Platform 2.5 for RHEL 8 · Red Hat Ansible Automation Platform 2.5 for RHEL 9 · Red Hat Ansible Automation Platform 2.6 for RHEL 9 · Red Hat Ansible Automation Platform 2.6

A flaw was found in Red Hat Ansible Automation Platform's automation- controller. The setting that formats the log message emitted for API 4XX errors is an administrator-controlled Python format-string template that is rendered with a live user object as an argument. Because Python string formatting permits attribute and item traversal on its arguments, an administrator can craft a template that walks from the user object into the application settings and reads the Django secret key and the database password. The formatted message is written to a logger that can be forwarded to an external log aggregator, whose destination is also administrator-controlled, allowing the secrets to be sent off the host. An authenticated administrator can thereby obtain the master encryption key used to protect all stored credentials and the database service password, enabling offline decryption of every stored credential, forgery of user sessions, and direct access to the controller database.