Semaine du 11 mai au 17 mai 2026

open-webui · Openwebui open webui

Open WebUI is a self-hosted artificial intelligence platform designed to operate entirely offline. Prior to 0.8.12, the /api/v1/utils/code/execute endpoint executes arbitrary Python code via Jupyter for any verified user, even when the admin has set ENABLE_CODE_EXECUTION=false. The feature gate is not enforced on the API endpoint — the configuration says "disabled" but code still executes. This vulnerability is fixed in 0.8.12.

open-webui · Openwebui open webui

Open WebUI is a self-hosted artificial intelligence platform designed to operate entirely offline. Prior to 0.9.5, the tool update endpoint (POST /api/v1/tools/id/{id}/update) is missing the workspace.tools permission check that is present on the tool create endpoint. This allows a user who has been explicitly denied tool management capabilities ( and who the administrator considers untrusted for code execution ) to replace a tool's server-side Python content and trigger execution, bypassing the intended workspace.tools security boundary. This vulnerability is fixed in 0.9.5.

universal-tool-calling-protocol python-utcp

python-utcp is the python implementation of UTCP. Prior to 1.1.3, _prepare_environment() in cli_communication_protocol.py passes a full copy of os.environ to every CLI subprocess. When combined with CVE-2026-45369, an attacker can exfiltrate all process-level secrets in a single tool call. This vulnerability is fixed in 1.1.3.

universal-tool-calling-protocol python-utcp

python-utcp is the python implementation of UTCP. Prior to 1.1.3, the _substitute_utcp_args method in cli_communication_protocol.py inserts user-controlled tool_args values directly into shell command strings without any sanitization or escaping. These commands are then executed via /bin/bash -c (Unix) or powershell.exe -Command (Windows), allowing an attacker to inject arbitrary shell commands. This vulnerability is fixed in 1.1.3.

AWS Amazon SageMaker Python SDK

Missing integrity verification in the Triton inference handler in Amazon SageMaker Python SDK v2 before v2.257.2 and v3 before v3.8.0 might allow a remote authenticated actor to achieve code execution in inference containers via replacement of model artifacts in S3 with a specially crafted pickle payload that is deserialized without verification. This issue requires a remote authenticated actor with S3 write access to the model artifact path. To remediate this issue, we recommend upgrading to Amazon SageMaker Python SDK v2.257.2 or v3.8.0 and rebuild any Triton models previously created with ModelBuilder using the updated SDK.

AWS Amazon SageMaker Python SDK

Cleartext storage of sensitive information in the ModelBuilder/Serve component in Amazon SageMaker Python SDK before v2.257.2 and v3 before v3.8.0 might allow a remote authenticated actor to extract the HMAC signing key from SageMaker API responses and forge valid integrity signatures for specially crafted model artifacts, achieving code execution in inference containers. This issue requires a remote authenticated actor with permissions to call SageMaker describe APIs and S3 write access to the model artifact path. To remediate this issue, we recommend upgrading to Amazon SageMaker Python SDK v2.257.2 or v3.8.0 and rebuild any models previously created with ModelBuilder using the updated SDK.

huggingface diffusers

Diffusers is the a library for pretrained diffusion models. Prior to 0.38.0, diffusers 0.37.0 allows remote code execution without the trust_remote_code=True safeguard when loading pipelines from Hugging Face Hub repositories. The _resolve_custom_pipeline_and_cls function in pipeline_loading_utils.py performs string interpolation on the custom_pipeline parameter using f"{custom_pipeline}.py". When custom_pipeline is not supplied by the user, it defaults to None, which Python interpolates as the literal string "None.py". If an attacker publishes a Hub repository containing a file named None.py with a class that subclasses DiffusionPipeline, the file is automatically downloaded and executed during a standard DiffusionPipeline.from_pretrained() call with no additional keyword arguments. The trust_remote_code check in DiffusionPipeline.download() is bypassed because it evaluates custom_pipeline is not None as False (since the kwarg was never supplied), while the downstream code path that actually loads the module resolves the None value into a valid filename. An attacker can achieve silent arbitrary code execution by publishing a malicious model repository with a None.py file and a standard-looking model_index.json that references a legitimate pipeline class name, requiring only that a victim calls from_pretrained on the repository. This vulnerability is fixed in 0.38.0.

Kludex python-multipart · Red Hat Ansible Automation Platform 2.6 · Red Hat Ansible Automation Platform 2.7 · Red Hat Migration Toolkit for Applications 8.2

Python-Multipart is a streaming multipart parser for Python. Prior to 0.0.27, python-multipart has a denial of service vulnerability in multipart part header parsing. When parsing multipart/form-data, MultipartParser previously had no limit on the number of part headers or the size of an individual part header. An attacker could send a request with either many repeated headers without terminating the header block or a single very large header value, causing excessive CPU work before request rejection or completion. This vulnerability is fixed in 0.0.27.

twisted · Twisted twisted

Twisted is an event-based framework for internet applications, supporting Python 3.6+. Prior to 26.4.0rc2, the twisted.names module is vulnerable to a Denial of Service (DoS) attack via resource exhaustion during DNS name decompression. A remote, unauthenticated attacker can exploit this by sending a crafted TCP DNS packet containing deeply chained compression pointers. This flaw bypasses previous loop-prevention logic, causing the single-threaded Twisted reactor to hang while processing millions of recursive lookups, effectively freezing the server. This vulnerability is fixed in 26.4.0rc2.

urllib3 · 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

urllib3 is an HTTP client library for Python. From 2.6.0 to before 2.7.0, urllib3 could decompress the whole response instead of the requested portion (1) during the second HTTPResponse.read(amt=N) call when the response was decompressed using the official Brotli library or (2) when HTTPResponse.drain_conn() was called after the response had been read and decompressed partially (compression algorithm did not matter here). These issues could cause urllib3 to fully decode a small amount of highly compressed data in a single operation. This could result in excessive resource consumption (high CPU usage and massive memory allocation for the decompressed data) on the client side. This vulnerability is fixed in 2.7.0.

heymrun heym

Heym before 0.0.21 contains a sandbox escape vulnerability in the custom Python tool executor that allows authenticated workflow authors to bypass sandbox restrictions by using object-graph introspection primitives. Attackers can use Python introspection techniques to recover the unrestricted __import__ function, import blocked modules such as os and subprocess, and access inherited backend environment variables containing database credentials and encryption keys to execute arbitrary host commands as the backend service user.

sqlalchemy mako

Mako is a template library written in Python. Prior to 1.3.12, on Windows, a URI using backslash traversal (e.g. \..\..\ secret.txt) bypasses the directory traversal check in Template.__init__ and the posixpath-based normalization in TemplateLookup.get_template(), allowing reads of files outside the configured template directory. This vulnerability is fixed in 1.3.12.

Netflix lemur

Lemur manages TLS certificate creation. Prior to 1.9.0, Lemur's LDAP authentication module (lemur/auth/ldap.py) constructs LDAP search filters using unsanitized user input via Python string interpolation. An authenticated LDAP user can inject LDAP filter metacharacters through the username field to manipulate group membership queries and escalate their privileges to administrator. This vulnerability is fixed in 1.9.0.

emmett-framework granian

Granian is a Rust HTTP server for Python applications. From 1.2.0 to 2.7.4, Granian aborts a worker process when an unauthenticated client sends a WebSocket upgrade request whose Sec-WebSocket-Protocol header contains non-ASCII bytes. The crash happens in Granian's WebSocket scope construction path, before the ASGI application is invoked. This vulnerability is fixed in 2.7.4.

The CosyVoice project thru commit 6e01309e01bc93bbeb83bdd996b1182a81aaf11e (2025-30-21) contains an insecure deserialization vulnerability (CWE-502) in its model loading process. When loading model files (.pt) from a user-specified directory (via the --model_dir argument), the code uses torch.load() without the security-restrictive weights_only=True parameter. This allows the deserialization of arbitrary Python objects via the Pickle module. An attacker can exploit this by providing a maliciously crafted model directory containing .pt files with embedded pickle payloads. When a victim loads this directory using CosyVoice's web interface, the malicious payload is executed, leading to remote code execution on the victim's system.

The superduper project thru v0.10.0 contains a critical remote code execution vulnerability in its query parsing component. The _parse_op_part() function in query.py uses the unsafe eval() function to dynamically evaluate user-supplied query operands without proper sanitization or restriction. Although the function attempts to limit the execution context by providing a restricted global namespace, it does not block access to dangerous built-in functions. A remote attacker can exploit this by submitting a specially crafted query string containing Python code that imports modules (e.g., os) and executes arbitrary system commands, leading to complete compromise of the server.

Snorkel snorkel

The snorkel library thru v0.10.0 contains an insecure deserialization vulnerability (CWE-502) in the MultitaskClassifier.load() method of the MultitaskClassifier class. The method loads model weight files using torch.load() without enabling the security-restrictive weights_only=True parameter. This default behavior allows the deserialization of arbitrary Python objects via the Pickle module. A remote attacker can exploit this by providing a maliciously crafted model file, leading to arbitrary code execution on the victim's system when the file is loaded via the vulnerable method.

Snorkel snorkel

The snorkel library thru v0.10.0 contains a critical insecure deserialization vulnerability (CWE-502) in the BaseLabeler.load() method of the BaseLabeler class. The method loads serialized labeler models using the unsafe pickle.load() function on user-supplied file paths without any validation or security controls. Python's pickle module is inherently dangerous for deserializing untrusted data, as it can execute arbitrary code during the deserialization process. A remote attacker can exploit this by providing a maliciously crafted pickle file, leading to arbitrary code execution on the victim's system when the file is loaded via the vulnerable method.

Snorkel snorkel

The snorkel library thru v0.10.0 contains an insecure deserialization vulnerability (CWE-502) in the Trainer.load() method of the Trainer class. The method loads model checkpoint files using torch.load() without enabling the security-restrictive weights_only=True parameter. This default behavior allows the deserialization of arbitrary Python objects via the Pickle module. A remote attacker can exploit this by providing a maliciously crafted model file, leading to arbitrary code execution on the victim's system when the file is loaded via the vulnerable method.

Lightningai pytorch lightning

PyTorch-Lightning versions 2.6.0 and earlier contain an insecure deserialization vulnerability (CWE-502) in the checkpoint loading mechanism. The LightningModule.load_from_checkpoint() method, which is commonly used to load saved model states, internally calls torch.load() without setting the security-restrictive weights_only=True parameter. This default behavior allows the deserialization of arbitrary Python objects via the Pickle module. A remote attacker can exploit this by providing a maliciously crafted checkpoint file, leading to arbitrary code execution on the victim's system when the file is loaded.

The _load_model() function in the neural_magic_training.py script of the optimate project in commit a6d302f912b481c94370811af6b11402f51d377f (2024-07-21) is vulnerable to insecure deserialization (CWE-502). When a user provides a single model file path (e.g., .pt or .pth) via the --model command-line argument, the function loads the file using torch.load() without enabling the weights_only=True security parameter. This allows the deserialization of arbitrary Python objects through the Pickle module. A remote attacker can exploit this by providing a maliciously crafted model file, leading to arbitrary code execution during deserialization on the victim's system.

The _load_model() function in the neural_magic_training.py script of the optimate project in commit a6d302f912b481c94370811af6b11402f51d377f (2024-07-21) is vulnerable to insecure deserialization (CWE-502). When loading a model state dictionary from a state_dict.pt file via torch.load(), the function does not enable the weights_only=True security parameter. This allows the deserialization of arbitrary Python objects through the Pickle module. A remote attacker can exploit this by providing a maliciously crafted state_dict.pt file within a directory specified via the --model argument, leading to arbitrary code execution during the deserialization process on the victim's system.

Python Software Foundation CPython · Python python

`xml.parsers.expat` and `xml.etree.ElementTree` use insufficient entropy for Expat hash-flooding protection, which allows a crafted XML document to trigger hash flooding.\r\n\r\nFully mitigating this vulnerability requires both updating libexpat to 2.8.0 or later and applying this patch.

pyload · Pyload-ng project pyload-ng

pyLoad is a free and open-source download manager written in Python. Prior to 0.5.0b3.dev100, the set_config_value() API method (@permission(Perms.SETTINGS)) in src/pyload/core/api/__init__.py gates security-sensitive options behind a hand-maintained allowlist ADMIN_ONLY_CORE_OPTIONS. The allowlist contains ("proxy", "username") and ("proxy", "password") — which protect the proxy credentials — but it does not include ("proxy", "enabled"), ("proxy", "host"), ("proxy", "port"), or ("proxy", "type"). Any authenticated user with the non-admin SETTINGS permission can enable proxying and point pyload at any host they control. From that point, every outbound download, captcha fetch, update check, and plugin HTTP call is transparently routed through the attacker. This is a direct continuation of the fix family CVE-2026-33509 / CVE-2026-35463 / CVE-2026-35464 / CVE-2026-35586, each of which patched a different missed option in the same allowlist. This vulnerability is fixed in 0.5.0b3.dev100.

The flash-attention project thru commit e724e2588cbe754beb97cf7c011b5e7e34119e62 (2025-13-04) contains a code injection vulnerability (CWE-94) in its training script. The script registers the Python eval() function as a Hydra configuration resolver under the name eval. This allows configuration files to execute arbitrary Python code via the ${eval:...} syntax. An attacker can exploit this by providing a malicious configuration file, leading to arbitrary code execution when the training script is run with that configuration.

The flash-attention training framework thru commit e724e2588cbe754beb97cf7c011b5e7e34119e62 (2025-13-04) contains an insecure deserialization vulnerability (CWE-502) in its checkpoint loading mechanism. The load_checkpoint() function in checkpoint.py and the checkpoint loading code in eval.py use torch.load() without enabling the security-restrictive weights_only=True parameter. This allows the deserialization of arbitrary Python objects via the pickle module. An attacker can exploit this by providing a maliciously crafted checkpoint file. When a victim loads this checkpoint during model warmstarting or evaluation, arbitrary code is executed on the victim's system.

CosyVoice thru commit 6e01309e01bc93bbeb83bdd996b1182a81aaf11e (2025-30-21) contains an insecure deserialization vulnerability (CWE-502) in its gRPC server component. When the server starts, it loads the speech synthesis model from a user-specified directory using torch.load() without enabling the weights_only=True security parameter. This allows the deserialization of arbitrary Python objects via the pickle module. An attacker can exploit this by providing malicious model files within a directory. When a victim starts the gRPC server pointing to this directory, arbitrary code is executed on the victim's system during server initialization.

CosyVoice thru commit 6e01309e01bc93bbeb83bdd996b1182a81aaf11e (2025-30-21) contains an insecure deserialization vulnerability (CWE-502) in its average_model.py model averaging tool. The script loads PyTorch checkpoint files (epoch_*.pt) for model averaging using torch.load() without enabling the weights_only=True security parameter. This allows the deserialization of arbitrary Python objects via the pickle module. An attacker can exploit this by providing malicious checkpoint files within a directory. When a victim uses the tool to average models from this directory, arbitrary code is executed on the victim's system.

CosyVoice thru commit 6e01309e01bc93bbeb83bdd996b1182a81aaf11e (2025-30-21) contains an insecure deserialization vulnerability (CWE-502) in its make_parquet_list.py data processing tool. The script loads PyTorch .pt files (utterance embeddings, speaker embeddings, speech tokens) using torch.load() without enabling the weights_only=True security parameter. This allows the deserialization of arbitrary Python objects via the pickle module. An attacker can exploit this by providing malicious .pt files within a data directory. When a victim processes this directory using the tool, arbitrary code is executed on the victim's system.

pgadmin.org pgAdmin 4 · Pgadmin pgadmin 4

Deserialization of untrusted data (CWE-502) in pgAdmin 4 FileBackedSessionManager. The session manager performed unsafe deserialization of session-file contents (using Python's standard object-serialization module) before performing any HMAC integrity check. Any file dropped into the sessions directory was deserialized unconditionally. An authenticated user with write access to the sessions directory (whether by misconfiguration or in combination with another path-traversal flaw) could plant a crafted serialized payload to achieve operating-system level remote code execution under the pgAdmin process identity. Fix prepends a 64-byte hex SHA-256 HMAC over the session body, computed with SECRET_KEY, and verifies it via hmac.compare_digest before any deserialization. The check is raised (rather than asserted) on empty SECRET_KEY so it is not stripped under -O. This issue affects pgAdmin 4: before 9.15.