Unsafe deserialization in OneCompression allows RCE via a crafted model file.
Fujitsu Research's OneCompression library 1.2.0 contains an unsafe deserialization vulnerability that allows attackers to execute arbitrary code by supplying a crafted model.pt checkpoint file, as QuantizedModelLoader.load_quantized_model_pt() unconditionally calls torch.load with weights_only=False, invoking Python's pickle machinery during deserialization. Attackers can embed malicious __reduce__ methods in a crafted model checkpoint to execute arbitrary Python code, including system commands, when the library loads the file from a caller-selected model directory.
Snowflake.core privesc via path traversal and parameter pollution.
Insufficient input sanitization in Snowflake Python API (`snowflake.core`) versions prior to 1.13.0 allowed confused-deputy privilege escalation through two related weaknesses: path traversal (CWE-22) via unencoded `..` identifier path segments, and HTTP parameter pollution (CWE-141) via unencoded `&`/`#`/`=` characters in query string values. An attacker with access to a downstream application built on snowflake.core could exploit the path traversal by supplying `..` as an object name, causing `snowflake.core` to issue REST requests against a parent resource or exploit the parameter pollution by injecting `&`/`#`/`=` into a free-form name field to override constraints on swap, clone, or rename operations โ all executed under the application's privileged session. Successful exploitation requires the attacker to control an identifier or object-name string in an application built on snowflake.core that passes it to `snowflake.core` under a higher-privileged Snowflake session (e.g., an EXECUTE AS OWNER stored procedure, Streamlit app, or Native App). The fix is available in Snowflake Python API version 1.13.0, which also addresses several additional security findings. Users must manually upgrade.
Calibre allows arbitrary code execution via crafted e-book metadata.
calibre is an e-book manager. Prior to 9.12.0, calibre processes attacker-controlled composite_template metadata from a malicious EPUB, OPF, PDF, or similar file through program: and a nested template() call whose formatter does not inherit allow_python_templates=False, allowing a nested python: template to reach compile_python_template and execute arbitrary Python code when the file is opened or imported. This issue is fixed in version 9.12.0.
Unauthenticated path traversal in DB-GPT file upload allows RCE.
DB-GPT v0.8.1 contains an unauthenticated path traversal vulnerability that allows remote attackers to write arbitrary files to any location on the server by injecting directory traversal sequences into the user_id HTTP header of the Python file-upload endpoint. Attackers can send a crafted multipart upload request with a traversal-poisoned user_id header to escape the intended upload directory and write attacker-controlled content to locations such as Python startup hooks, cron directories, or agent scripts, resulting in remote code execution.
A memory leak in python-socketio from incomplete binary messages can cause a DoS.
python-socketio is a Python implementation of the Socket.IO realtime client and server. The python-socketio server stores binary `EVENT` and `ACK` messages in memory while it waits to receive their binary attachments. Once all the attachments are received, these messages are then processed. Prior to version 5.16.4, an attacker can submit a binary message and intentionally omit sending one or more of its attachments to cause the message along with the partial list of received attachments to stay in memory for a long time. Version 5.16.4 takes the following measures to address this issue: Binary packets are only accepted from authenticated clients and, when a client disconnects, the server checks if there is a partial binary message being held for the client and deletes it.
python-engineio vulnerable to DoS via unchecked incoming message size.
python-engineio is a Python implementation of the Engine.IO realtime client and server. Versions prior to 4.13.2 have two specific configurations of the python-engineio server in which the size of incoming messages is not checked before the messages are loaded into memory. An attacker can take advantage of these to cause unnecessary memory allocations in the python-engineio server. The two cases are POST requests, when using ASGI with the long polling transport and WebSocket messages, when using Aiohttp with the WebSocket transport. Version 4.13.2 addresses this issue. ASGI severs now only load the body of incoming requests into memory after the client is confirmed to be known and authenticated, and the payload size is below the maximum allowed size. Requests that do not comply with these requirements are discarded. Aiohttp servers configure the maximum payload size in the underlying WebSocket layer from Aiohttp, so that large messages are discarded by Aiohttp before they are delivered to python-engineio.
python-engineio heartbeat allows DoS via excessive thread creation.
python-engineio is a Python implementation of the Engine.IO realtime client and server. Prior to version 4.13.2, an attacker can cause the creation of unnecessary background threads in the python-engineio server by exploiting the heartbeat mechanism, which launches a thread when a new connection is received, and when the client sends a PONG packet. This issue primarily affects synchronous servers. Asynchronous servers allocate background tasks instead of physical threads, which are lightweight and less likely to cause denial of service. However, the fix that was implemented was also applied to the asynchronous case. Version 4.13.2 addresses this issue as follows: The initial background thread (or async task( for heartbeat management is only launched if a client passes authentication in the `connect` handler; and the server now ensures that there is only one background heatbeat thread (or async task) per client at a given point in time. Out of sequence PONG packets are now discarded when an active heartbeat thread is already running.
Local security bypass in VS Code Python extension via untrusted code.
Inclusion of functionality from untrusted control sphere in Visual Studio Code - Python extension allows an unauthorized attacker to bypass a security feature locally.
NLTK's URL validator allows SSRF attacks via the shared address space.
A Server-Side Request Forgery (SSRF) vulnerability exists in nltk/nltk versions 3.9.4 and the current develop branch. The `nltk.pathsec.validate_network_url()` function, intended to prevent SSRF by rejecting internal network addresses, fails to reject IPs in the RFC 6598 shared address space (`100.64.0.0/10`). This occurs because Python's `ipaddress` module does not classify such addresses as `is_private` or `is_global`, and the current guard only checks `is_private` and a few explicit categories. An attacker who can influence a URL passed to NLTK's network-loading helpers can exploit this vulnerability to make a strict-mode application send requests to shared-address-space hosts, potentially exposing non-public infrastructure reachable from the application host. The impact is limited to SSRF-style confidentiality exposure, with no code execution claimed.
Unauthenticated path traversal in lollms < 3.0 allows arbitrary file read.
A path traversal vulnerability exists in parisneo/lollms version 2.1.0, specifically in the SPA catch-all route implemented in `backend/routers/ui.py`. The vulnerability arises from the improper handling of user-controlled path input, which is directly joined into a filesystem path without sanitization or containment checks. URL-encoded dot-dot sequences (`%2e%2e`) bypass Starlette's built-in path normalization and are resolved by Python's `pathlib`, allowing an unauthenticated attacker to read arbitrary files on the server. This issue has been resolved in version 3.
Introducing the "VAITP dataset": a specialized repository of Python vulnerabilities and patches, meticulously compiled for the use of the security research community. As Python's prominence grows, understanding and addressing potential security vulnerabilities become crucial. Crafted by and for the cybersecurity community, this dataset offers a valuable resource for researchers, analysts, and developers to analyze and mitigate the security risks associated with Python. Through the comprehensive exploration of vulnerabilities and corresponding patches, the VAITP dataset fosters a safer and more resilient Python ecosystem, encouraging collaborative advancements in programming security.
The supreme art of war is to subdue the enemy without fighting.
Sun Tzu – “The Art of War”
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