Vulnerabilidades de la pila Python · Página 69

Wagtail: Denial of service via unbounded filter specs in the image preview

Wagtail is an open source content management system built on Django. In versions prior to 7.0.8, 7.3.3 and 7.4.2, an authenticated admin user can trigger expensive rendition processing with purposefully crafted filter specs resulting in potentially service degradation. The vulnerability is not exploitable by an ordinary site visitor without access to the Wagtail admin. This issue has been fixed in versions 7.0.8, 7.3.3, and 7.4.2.

• Actualiza a 7.0.8
• Actualiza a 7.3.3
• Actualiza a 7.4.2

Malicious code in okite (PyPI)

Part of the "Hades" wave of the Shai-Hulud supply-chain campaign. On 2026-06-08, malicious phantom releases of okite were published to PyPI using stolen credentials. The package executes a bundled JavaScript payload (via the Bun runtime) on import that harvests and exfiltrates credentials and attempts self-propagation. This entry is a summary; behavior may not be fully characterized here. See the linked references for detailed analysis and indicators of compromise.

Malicious code in nucbox (PyPI)

Part of the "Hades" wave of the Shai-Hulud supply-chain campaign. On 2026-06-08, malicious phantom releases of nucbox were published to PyPI using stolen credentials. The package executes a bundled JavaScript payload (via the Bun runtime) on import that harvests and exfiltrates credentials and attempts self-propagation. This entry is a summary; behavior may not be fully characterized here. See the linked references for detailed analysis and indicators of compromise.

Malicious code in napari-ufish (PyPI)

Part of the "Hades" wave of the Shai-Hulud supply-chain campaign. On 2026-06-08, malicious phantom releases of napari-ufish were published to PyPI using stolen credentials. The package executes a bundled JavaScript payload (via the Bun runtime) on import that harvests and exfiltrates credentials and attempts self-propagation. This entry is a summary; behavior may not be fully characterized here. See the linked references for detailed analysis and indicators of compromise.

Overly permissive File Permissions in AWS CLI

Overly permissive file permissions in AWS CLI before 1.44.78 (v1) and 2.34.29 (v2) on Unix-like systems where the umask has not been configured to restrict file permissions (the default on most systems) may allow other local users on the same host to read credentials written by certain CLI subcommands (aws codeartifact login, aws iam create-virtual-mfa-device, aws deploy register). To remediate this issue, users should upgrade to AWS CLI 1.44.78 (v1) or 2.34.29 (v2) or later.

• Actualiza a 1.44.78

Gradio < 6.16.0 Path Traversal via FileExplorer.preprocess()

Gradio before 6.16.0 contain a path traversal vulnerability in the FileExplorer component's preprocess() method that allows unauthenticated attackers to escape the configured root directory by supplying path segments containing directory traversal sequences or absolute paths. Attackers can provide crafted path segments that cause os.path.join to discard the root_dir prefix entirely, resulting in arbitrary file read or exposure of sensitive files outside the intended directory.

• Actualiza a 6.16.0

Arbitrary HDF5 File Read via Virtual Dataset Bypass in keras-team/keras

Keras versions up to and including 3.13.2 are vulnerable to an arbitrary HDF5 file read due to an incomplete fix for CVE-2026-1669. The vulnerability resides in the `H5IOStore._verify_dataset()` and `file_editor.py` methods, which fail to check the `dataset.is_virtual` property of HDF5 datasets. This allows an attacker to craft a malicious `.keras` model archive or `.h5` weights file containing a Virtual Dataset (VDS) that references external HDF5 files on the victim's filesystem. When the victim loads the model using `keras.models.load_model()` or `keras.saving.load_model()`, the external file is transparently read, leading to potential info

• Actualiza a 3.12.2, 3.14.1
• Actualiza a 3.12.3
• Actualiza a 3.15.0

Ray < 2.56.0 Unsafe Deserialization RCE via WebDataset Reader

Ray prior to 2.56.0 contains an unsafe deserialization vulnerability in the WebDataset reader that allows attackers to achieve remote code execution by supplying a malicious tar archive to the read_webdataset() function. The _default_decoder() function in webdataset_datasource.py unconditionally calls pickle.loads() on tar entries with .pkl/.pickle extensions and torch.load() with weights_only=False on .pt/.pth entries, executing arbitrary code inside Ray remote workers on every worker that processes the malicious archive.

• Actualiza a 2.56.0

picklescan - Arbitrary Code Execution via Undetected profile.Profile.run

picklescan before 0.0.29 fails to detect the built-in python profile.Profile.run function when used in pickle reduce methods, allowing attackers to execute arbitrary code. Remote attackers can craft malicious pickle files that bypass picklescan detection and achieve code execution upon deserialization.

• Actualiza a 0.0.29

picklescan - Remote Code Execution via code.InteractiveInterpreter Detection Bypass

picklescan before 0.0.29 fails to detect malicious pickle files using code.InteractiveInterpreter.runcode in reduce methods. Attackers can craft pickle payloads that bypass picklescan detection and execute arbitrary code when loaded via pickle.load().

• Actualiza a 0.0.29

picklescan - Arbitrary Code Execution via Undetected doctest.debug_script

picklescan before 0.0.30 fails to detect the doctest.debug_script function when analyzing pickle files, allowing attackers to execute arbitrary code. Remote attackers can craft malicious pickle files embedding doctest.debug_script calls that bypass picklescan detection and execute arbitrary commands upon pickle.load invocation.

• Actualiza a 0.0.30

picklescan - Arbitrary Code Execution via Undetected cProfile.run in Pickle Deserialization

picklescan before 0.0.30 fails to detect cProfile.run function calls in pickle reduce methods, allowing attackers to execute arbitrary code. Remote attackers can craft malicious pickle files with cProfile.run payloads that bypass picklescan detection and achieve code execution upon deserialization.

• Actualiza a 0.0.30

Picklescan - Arbitrary Code Execution via Unsafe Numpy Function Detection Bypass

Picklescan before 0.0.25 fails to detect unsafe global functions in the Numpy library, allowing attackers to bypass static analysis and execute arbitrary code during deserialization. Attackers can craft malicious pickle files using numpy.testing._private.utils.runstring within the reduce method to import dangerous libraries like os and execute arbitrary OS commands when the pickle file is loaded.

• Actualiza a 0.0.25

picklescan - Remote Code Execution via Undetected trace.Trace.runctx in Pickle Files

picklescan before 0.0.29 fails to detect the built-in Python trace.Trace.runctx function when used in pickle file reduce methods, allowing attackers to execute arbitrary code. Remote attackers can craft malicious pickle files with trace.Trace.runctx payloads that bypass picklescan detection and execute code upon pickle.load() invocation.

• Actualiza a 0.0.29

picklescan - Undetected Remote Code Execution via torch.utils.collect_env.run

picklescan before 0.0.28 fails to detect malicious pickle files using torch.utils.collect_env.run function in reduce methods. Attackers can embed undetected code in pickle files that executes remote commands when loaded by victims.

• Actualiza a 0.0.28

picklescan - Arbitrary Code Execution via Undetected trace.Trace.run in Pickle Files

picklescan before 0.0.29 fails to detect the built-in trace.Trace.run function when analyzing pickle files, allowing attackers to embed undetected malicious code. Remote attackers can craft malicious pickle files using trace.Trace.run in the reduce method to achieve arbitrary code execution when pickle.load processes the file.

• Actualiza a 0.0.29

pypdf: Missing stream length values ignore defined limits

pypdf is a free and open-source pure-python PDF library. Prior to 6.13.3, a maliciously crafted PDF can cause DoS. An attacker who uses this vulnerability can craft a PDF which leads to large memory usage, as MAX_DECLARED_STREAM_LENGTH is sometimes ignored. This requires parsing a content stream without a /Length value. This issue has been fixed in version 6.13.3.

• Actualiza a 6.13.3
• Actualiza a 6.19.0-1

MessagePack: Out-of-bounds read/crash on Unpacker reuse after caught error

MessagePack is the serializer implementation for Python msgpack.org. Prior to 1.2.1, there is an Out-of-bounds read/crash on Unpacker reuse after a caught error, potentially leading to a DoS attack. If the Unpacker is used repeatedly after an error occurs, the process may crash with a SEGV. This issue has been fixed in version 1.2.1.

• Actualiza a 1.1.2-3
• Actualiza a 1.2.1

Malicious code in mrbios (PyPI)

Part of the "Hades" wave of the Shai-Hulud supply-chain campaign. On 2026-06-08, malicious phantom releases of mrbios were published to PyPI using stolen credentials. The package executes a bundled JavaScript payload (via the Bun runtime) on import that harvests and exfiltrates credentials and attempts self-propagation. This entry is a summary; behavior may not be fully characterized here. See the linked references for detailed analysis and indicators of compromise.

Malicious code in magique-ai (PyPI)

Part of the "Hades" wave of the Shai-Hulud supply-chain campaign. On 2026-06-08, malicious phantom releases of magique-ai were published to PyPI using stolen credentials. The package executes a bundled JavaScript payload (via the Bun runtime) on import that harvests and exfiltrates credentials and attempts self-propagation. This entry is a summary; behavior may not be fully characterized here. See the linked references for detailed analysis and indicators of compromise.