Mai 2026

pyload

pyLoad is a free and open-source download manager written in Python. Prior to 0.5.0b3.dev100, the packages.js template at src/pyload/webui/app/themes/modern/templates/js/packages.js:172 interpolates a stored link URL into a template literal inside single-quoted HTML and then writes the result to the DOM via $(div).html(html). No escaping runs between the API value and innerHTML. An attacker (Alice) who can submit a package link puts a single quote plus event handler into the URL, breaks out of the attribute, and executes JavaScript in every operator's browser that opens the downloads view. The theme does not set a Content Security Policy that restricts inline script or event handlers. This vulnerability is fixed in 0.5.0b3.dev100.

openreplay

OpenReplay is a self-hosted session replay suite. Prior to 1.26.0, OpenReplay's Python API exposes several app_apikey routes that trust a caller-provided projectKey after validating only that the API key itself is valid and that the target projectKey exists. The authorization flow does not verify that the authenticated API key and the requested project belong to the same tenant. Because the public tracker design exposes projectKey to browser-side code, an attacker who owns any valid API key for their own tenant can target another tenant's project by reusing that public projectKey. The vulnerable routes allow the attacker to enumerate victim user sessions and then retrieve sensitive session event data across the tenant boundary. This vulnerability is fixed in 1.26.0.

jpadilla pyjwt · 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

PyJWT is a JSON Web Token implementation in Python. Prior to 2.13.0, when the verifier is decoding JSON Web Tokens, while supporting both asymmetric and HMAC algorithms, the library does not validate use of JSON Web Keys in HMAC algorithm, allowing attacker to use the issuer public key as the secret key for HMAC algorithm. This vulnerability is fixed in 2.13.0.

jg-rp liquid · Jg-rp python liquid

Python Liquid is a Python engine for the Liquid template language. Prior to 2.2.0, the built-in FileSystemLoader and CachingFileSystemLoader do not guard against reading files outside their search paths when given an absolute path to resolve. This allows malicious template authors to load and render arbitrary files via the {% include %} and {% render %} tags. Targeted files would need to contain valid Liquid markup and be readable by the application process. This vulnerability is fixed in 2.2.0.

cnighswonger claude-code-cache-fix

claude-code-cache-fix is a cache optimization proxy for Claude Code. From 3.5.0 to before 3.5.2, tools/quota-statusline.sh (introduced in v3.5.0) interpolates Claude Code's hook stdin payload directly into a Python triple-quoted string literal. A ''' byte sequence in any user-controlled field of the payload closes the literal early and lets following bytes execute as Python in the user's Claude Code process. This vulnerability is fixed in 3.5.2.

ultrajson · Ultrajson project ultrajson

UltraJSON is a fast JSON encoder and decoder written in pure C with bindings for Python 3.7+. Prior to 5.12.1, when ujson.dump() writes to a file-like object and the write operation raises an exception, the serialized JSON string object is not decremented, leaking memory. Each failed write operation leaks the full size of the serialized payload. This vulnerability is fixed in 5.12.1.

langchain-ai langsmith-sdk

LangSmith Client SDKs provide SDK's for interacting with the LangSmith platform. Prior to LangSmith SDK Python 0.8.0 and JS/TS 0.6.0, the LangSmith SDK's prompt pull methods (pull_prompt / pull_prompt_commit in Python, pullPrompt / pullPromptCommit in JS/TS) fetch and deserialize prompt manifests from the LangSmith Hub. These manifests may contain serialized LangChain objects and model configuration that affect runtime behavior. When pulling a public prompt by owner/name identifier, the manifest content is controlled by an external party, but prior versions of the SDK did not distinguish this from pulling a prompt within the caller's own organization. This vulnerability is fixed in LangSmith SDK Python 0.8.0 and JS/TS 0.6.0.

inducer relate

RELATE is a web-based courseware package. Versions prior to commit 555f0efb1c5bd7531c07cd73724d7e566a81f620 have a stored cross-site scripting vulnerability that allows any enrolled student to execute arbitrary JavaScript in an administrator's browser session, potentially leading to full admin account takeover. The `get_user()` method in `ParticipationAdmin` renders user-controlled input using `mark_safe` combined with Python's % string formatting. This bypasses Django\'s automatic HTML escaping entirely. The value returned by `get_full_name` is derived directly from the `first_name` and `last_name` fields of the User model. These fields are freely editable by any authenticated user through the profile page (`/profile/`) with no sanitization applied. When an admin views the Participation list in the Django admin panel, the unsanitized value is rendered directly into the HTML response, causing the injected script to execute in the admin's browser. Commit 555f0efb1c5bd7531c07cd73724d7e566a81f620 fixes the issue.

BentoML · Bentoml bentoml

BentoML is a Python library for building online serving systems optimized for AI apps and model inference. Prior to 1.4.39, a malicious bentofile.yaml containing a newline-injected value in envs[*].name produces unquoted RUN directives in the BentoML-generated Dockerfile. When the victim runs bentoml containerize on the imported bento, those RUN directives execute on the host during docker build. This vulnerability is fixed in 1.4.39.

BentoML · Bentoml bentoml

BentoML is a Python library for building online serving systems optimized for AI apps and model inference. Prior to 1.4.39, src/bentoml/_internal/container/frontend/dockerfile/templates/base_v2.j2 interpolates docker.base_image raw with no escaping, newline filtering, or validation. A malicious bento.yaml with a multi-line docker.base_image value smuggles arbitrary Dockerfile directives into the generated Dockerfile, and bentoml containerize then runs docker build which executes the injected RUN directives on the victim host. This vulnerability is fixed in 1.4.39.

langchain-ai langchain · Langchain langchain

LangChain is a framework for building agents and LLM-powered applications. Prior to 0.3.85 and 1.3.3, LangChain contains older runtime code paths that deserialize run inputs, run outputs, or other application-controlled payloads using overly broad object allowlists. These paths may call load() with allowed_objects="all". This does not enable arbitrary Python object deserialization, but it does allow any trusted LangChain-serializable object to be revived, which is broader than these runtime paths require. As a result, attacker-supplied LangChain serialized constructor dictionaries may cause trusted runtime paths to instantiate classes with untrusted constructor arguments. This vulnerability is fixed in 0.3.85 and 1.3.3.

huggingface/transformers · Huggingface transformers

A critical remote code execution vulnerability exists in all versions of the HuggingFace transformers library prior to version 5.3.0. The vulnerability allows an attacker to craft a malicious `config.json` file containing the `_attn_implementation_internal` field set to an attacker-controlled HuggingFace Hub repository ID. When a victim loads this model using the standard `AutoModelForCausalLM.from_pretrained()` API, the library downloads and executes arbitrary Python code from the attacker's repository with the victim's full OS privileges. This issue arises due to unfiltered deserialization of configuration attributes, insufficient sanitization of internal fields, and unsandboxed execution of downloaded kernels. The vulnerability bypasses the `trust_remote_code` security mechanism, is invisible to the victim, and exploits the standard documented usage pattern, making it particularly severe. Users are advised to upgrade to version 5.3.0 or later to mitigate this issue.

Docker Desktop · Docker docker desktop

The MLX inference backend in Docker Model Runner on macOS uses the MLX-LM library, which unconditionally imports and executes arbitrary Python files from model directories via the model_file configuration field in config.json. When a model's config.json specifies a model_file pointing to a Python file, MLX-LM uses importlib to load and execute it with no trust_remote_code gate or equivalent safety check. The MLX backend runs without sandboxing, resulting in arbitrary code execution on the Docker host as the Docker Desktop user. Any container on the Docker network can trigger this by calling the model-runner.docker.internal API to pull a malicious model from an attacker-controlled OCI registry and request inference.

Docker Desktop · Docker docker desktop

The vllm-metal inference backend in Docker Model Runner on macOS unconditionally sets trust_remote_code=True when loading model tokenizers, and runs without sandboxing. This causes transformers.AutoTokenizer.from_pretrained() to import and execute arbitrary Python files included in any model pulled from an OCI registry, resulting in arbitrary code execution on the Docker host as the Docker Desktop user when inference is triggered. Any container on the Docker network can trigger this by calling the model-runner.docker.internal API to pull a malicious model and request inference.

AWS Amazon Braket Python SDK

Insecure deserialization in the job results processing component in Amazon Braket SDK before 1.117.0 might allow a remote authenticated user with S3 write access to the job output bucket to achieve arbitrary code execution on any machine that processes job results. We recommend you upgrade to amazon-braket-sdk version 1.117.0 or later.

mlflow/mlflow · Lfprojects mlflow

In mlflow/mlflow versions prior to 3.11.0, the `get_or_create_nfs_tmp_dir()` function in `mlflow/utils/file_utils.py` creates temporary directories with world-writable permissions (0o777), and the `_create_model_downloading_tmp_dir()` function in `mlflow/pyfunc/__init__.py` creates directories with group-writable permissions (0o770). These insecure permissions allow local attackers to tamper with model artifacts, such as cloudpickle-serialized Python objects, and achieve arbitrary code execution when the tampered artifacts are deserialized via `cloudpickle.load()`. This vulnerability is particularly critical in environments with shared NFS mounts, such as Databricks, where NFS is enabled by default. The issue is a continuation of the vulnerability class addressed in CVE-2025-10279, which was only partially fixed.

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.

python-pillow Pillow · Python pillow

Pillow is a Python imaging library. From version 10.3.0 to before version 12.2.0, processing a malicious PSD file could lead to memory corruption, potentially resulting in a crash or arbitrary code execution. This issue has been patched in version 12.2.0.

befeleme pyp2spec

pyp2spec generates working Fedora RPM spec file for Python projects. Prior to version 0.14.1, pyp2spec was writing PyPI package metadata (e.g. the summary field) into the generated spec file without escaping RPM macro directives. When a packager then runs rpmbuild, those directives get evaluated, so a malicious package can execute arbitrary commands on the build machine. This issue has been patched in version 0.14.1.

geopython pygeoapi

pygeoapi is a Python server implementation of the OGC API suite of standards. From version 0.23.0 to before version 0.23.3, OGC API process execution requests can use the subscriber object to requests to internal HTTP services. This issue has been patched in version 0.23.3.

geopython pygeoapi

pygeoapi is a Python server implementation of the OGC API suite of standards. From version 0.23.0 to before version 0.23.3, a raw string path concatenation vulnerability in pygeoapi's STAC FileSystemProvider plugin can allow for requests to STAC collection based collections to expose directories without authentication. The issue manifests when pygeoapi is deployed without a proxy or web front end that would normalize URLs with .. values, along with a resource of type stac-collection defined in configuration. This issue has been patched in version 0.23.3.

gitpython-developers GitPython · Gitpython project gitpython

GitPython is a python library used to interact with Git repositories. Prior to version 3.1.49, GitConfigParser.set_value() passes values to Python's configparser without validating for newlines. GitPython's own _write() converts embedded newlines into indented continuation lines (e.g. \n becomes \n\t), but Git still accepts an indented [core] stanza as a section header — so the injected core.hooksPath becomes effective configuration. Any Git operation that invokes hooks (commit, merge, checkout) will then execute scripts from the attacker-controlled path. This issue has been patched in version 3.1.49.

gitpython-developers GitPython · Gitpython project gitpython

GitPython is a python library used to interact with Git repositories. Prior to version 3.1.48, a vulnerability in GitPython allows attackers who can supply a crafted reference path to an application using GitPython to write, overwrite, move, or delete files outside the repository’s .git directory via insufficient validation of reference paths in reference creation, rename, and delete operations. This issue has been patched in version 3.1.48.

gitpython-developers GitPython · Gitpython project gitpython

GitPython is a python library used to interact with Git repositories. From version 3.1.30 to before version 3.1.47, GitPython blocks dangerous Git options such as --upload-pack and --receive-pack by default, but the equivalent Python kwargs upload_pack and receive_pack bypass that check. If an application passes attacker-controlled kwargs into Repo.clone_from(), Remote.fetch(), Remote.pull(), or Remote.push(), this leads to arbitrary command execution even when allow_unsafe_options is left at its default value of False. This issue has been patched in version 3.1.47.

rucio · Cern rucio

### Summary A SQL injection vulnerability exists in Rucio versions 1.30.0 and later before 35.8.5, 38.5.5, 39.4.2, and 40.1.1, in `FilterEngine.create_postgres_query()`. This allows any authenticated Rucio user to execute arbitrary SQL against the PostgreSQL metadata database through the DID search endpoint (`GET /dids/<scope>/dids/search`). When the `postgres_meta` metadata plugin is configured, attacker-controlled filter keys and values are interpolated directly into raw SQL strings via Python `.format()`, then passed to `psycopg3`'s `sql.SQL()` which treats the string as trusted SQL syntax. Depending on the database privileges assigned to the service account, exploitation can expose sensitive tables, modify or delete metadata, access server-side files, or achieve code execution through PostgreSQL features such as COPY ... FROM PROGRAM. This issue affects deployments that explicitly use the postgres_meta metadata plugin. This vulnerability has been fixed in versions 35.8.5, 38.5.5, 39.4.2, and 40.1.1.

rucio · Cern rucio

A SQL injection vulnerability in `FilterEngine.create_sqla_query()` allows any authenticated Rucio user to execute arbitrary SQL against the backend database through the DID search endpoint (`GET /dids/<scope>/dids/search`). On Oracle deployments attacker-controlled filter keys and values are interpolated directly into `sqlalchemy.text()` via Python `.format()`, completely bypassing parameterization. This enables full database compromise including extraction of authentication tokens, password hashes, and all managed data identifiers. This affects versions 1.27.0 and later before 35.8.5, 38.5.5, 39.4.2, and 40.1.1. The vulnerability exists in `lib/rucio/core/did_meta_plugins/filter_engine.py` within the `create_sqla_query()` method. When the database dialect is Oracle, filter expressions for JSON metadata columns are constructed using `text()` with Python string formatting. Both `key` and `value` are attacker-controlled strings derived from HTTP query parameters. The `text()` function creates a raw SQL fragment — it does **not** escape or parameterize its contents. Any authenticated Rucio user can exploit this through the DID search API to execute arbitrary SQL against the backend database. This can expose all managed data identifiers and sensitive tables such as identities, tokens, accounts, rse_settings, and rules, and may allow modification of database contents. The issue affects Oracle deployments using the default json_meta plugin and does not affect PostgreSQL or MySQL deployments using that plugin. This vulnerability has been fixed in versions 35.8.5, 38.5.5, 39.4.2, and 40.1.1.

jupyter-server jupyter_server · Red Hat Migration Toolkit for Applications 8.2 · Red Hat OpenShift AI (RHOAI) · Jupyter jupyter server

Jupyter Server is the backend for Jupyter web applications. In versions 2.17.0 and earlier, the Origin header validation uses Python's re.match() to check incoming origins against the allow_origin_pat configuration value. Because re.match() only anchors at the start of the string and does not require a full match, a pattern intended to match only a trusted domain (e.g., trusted.example.com) will also match any origin that begins with that domain followed by additional characters (e.g., trusted.example.com.evil.com). An attacker who controls such a domain can bypass the CORS origin restriction and make cross-origin requests to the Jupyter Server API from an untrusted site. This issue has been fixed in version 2.18.0.

Frappe Technologies Frappe Framework (ERPNext) · Frappe erpnext

Frappe Framework ERPNext 13.4.0 contains a sandbox escape vulnerability in RestrictedPython that allows authenticated users with System Manager role to execute arbitrary code by exploiting frame introspection. Attackers can create a server script via the /app/server-script endpoint and access the gi_frame attribute to traverse the call stack and invoke os.popen to execute system commands.

UsamaK98 python-notebook-mcp

A flaw has been found in UsamaK98 python-notebook-mcp up to a05a232815809a7e425b5fa7be26e0d4369894c2. Impacted is the function create_notebook/read_notebook/edit_cell/add_cell of the file server.py. This manipulation causes path traversal. It is possible to initiate the attack remotely. The exploit has been published and may be used. This product is using a rolling release to provide continious delivery. Therefore, no version details for affected nor updated releases are available. The project was informed of the problem early through an issue report but has not responded yet.

n8n-io n8n · N8n n8n

n8n is an open source workflow automation platform. Prior to versions 1.123.32, 2.17.4, and 2.18.1, an authenticated user with permission to create or modify workflows containing a Python Code Node could escape the sandbox and achieve arbitrary code execution on the task runner container. This issue only affects instances where the Python Task Runner is enabled. This issue has been patched in versions 1.123.32, 2.17.4, and 2.18.1.

OpenC3 cosmos

OpenC3 COSMOS provides the functionality needed to send commands to and receive data from one or more embedded systems. Prior to version 7.0.0-rc3, the Script Runner widget allows users to execute Python and Ruby scripts directly from the openc3-COSMOS-script-runner-api container. Because all the docker containers share a network, users can execute specially crafted scripts to bypass the API permissions check and perform administrative actions, including reading and modifying data inside the Redis database, which can be used to read secrets and change COSMOS settings, as well as read and write to the buckets service, which holds configuration, log, and plugin files. These actions are normally only available from the Admin Console or with administrative privileges. Any user with permission to create and run scripts can connect to any service in the docker network. This issue has been patched in version 7.0.0-rc3.

icip-cas PPTAgent

PPTAgent is an agentic framework for reflective PowerPoint generation. Prior to commit 418491a, PPTAgent is vulnerable to arbitrary code execution via Python eval() of LLM-generated code with builtins in scope. This issue has been patched via commit 418491a.

netbox-community netbox

NetBox versions 4.3.5 through 4.5.4 contain a remote code execution vulnerability in the RenderTemplateMixin.get_environment_params() method that allows authenticated users with exporttemplate or configtemplate permissions to execute arbitrary code by specifying malicious Python callables in the environment_params field. Attackers can bypass Jinja2 SandboxedEnvironment protections by setting the finalize parameter to any importable Python callable such as subprocess.getoutput, which is invoked on every rendered expression outside the sandbox's call interception mechanism, achieving remote code execution as the NetBox service user.

OpenStack ironic-python-agent · Red Hat OpenShift Container Platform 4.19 · Red Hat OpenShift Container Platform 4.20 · Red Hat OpenShift Container Platform 4.21

An issue was discovered in OpenStack ironic-python-agent 1.0.0 through 11.5.0. Ironic Python Agent (IPA) sometimes executes grub-install from within a chroot of the deployed partition image, leading to code execution in the case of a malicious image.

Zurich Instruments LabOne Q · Zhinst labone q

The LabOne Q serialization framework uses a class-loading mechanism (import_cls) to dynamically import and instantiate Python classes during deserialization. Prior to the fix, this mechanism accepted arbitrary fully-qualified class names from the serialized data without any validation of the target class or restriction on which modules could be imported. An attacker can craft a serialized experiment file that causes the deserialization engine to import and instantiate arbitrary Python classes with attacker-controlled constructor arguments, resulting in arbitrary code execution in the context of the user running the Python process. Exploitation requires the victim to load a malicious file using LabOne Q's deserialization functions, for example a compromised experiment file shared for collaboration or support purposes.