Kedro ShelveStore RCE via malicious pickle deserialization allows arbitrary code execution.
A Remote Code Execution (RCE) vulnerability has been identified in the Kedro ShelveStore class (version 0.19.8). This vulnerability allows an attacker to execute arbitrary Python code via deserialization of malicious payloads, potentially leading to a full system compromise. The ShelveStore class uses Python's shelve module to manage session data, which relies on pickle for serialization. Crafting a malicious payload and storing it in the shelve file can lead to RCE when the payload is deserialized.
RCE via Python sandbox escape in lollms <= 9.8 using `eval()` and `_frozen_importlib`.
A remote code execution vulnerability exists in the Calculate function of parisneo/lollms version 9.8. The vulnerability arises from the use of Python's `eval()` function to evaluate mathematical expressions within a Python sandbox that disables `__builtins__` and only allows functions from the `math` module. This sandbox can be bypassed by loading the `os` module using the `_frozen_importlib.BuiltinImporter` class, allowing an attacker to execute arbitrary commands on the server. The issue is fixed in version 9.10.
RCE in gpt_academic via malicious RAR file & symlink exploit.
A vulnerability in binary-husky/gpt_academic version git 310122f allows for remote code execution. The application supports the extraction of user-provided RAR files without proper validation. The Python rarfile module, which supports symlinks, can be exploited to perform arbitrary file writes. This can lead to remote code execution by writing to sensitive files such as SSH keys, crontab files, or the application's own code.
Pandas `query` allows remote command execution via malicious queries (<= v2.2.2).
A command injection vulnerability exists in the `pandas.DataFrame.query` function of pandas-dev/pandas versions up to and including v2.2.2. This vulnerability allows an attacker to execute arbitrary commands on the server by crafting a malicious query. The issue arises from the improper validation of user-supplied input in the `query` function when using the 'python' engine, leading to potential remote command execution.
Path traversal in gpt_academic allows arbitrary file writes, leading to RCE.
A path traversal vulnerability exists in binary-husky/gpt_academic version git 310122f. The application supports the extraction of user-provided 7z files without proper validation. The Python py7zr package used for extraction does not guarantee that files will remain within the intended extraction directory. An attacker can exploit this vulnerability to perform arbitrary file writes, which can lead to remote code execution.
Vanna 0.6.3 SQL injection allows reading arbitrary local files via Snowflake database.
Vanna v0.6.3 is vulnerable to SQL injection via Snowflake database in its file staging operations using the `PUT` and `COPY` commands. This vulnerability allows unauthenticated remote users to read arbitrary local files on the victim server, such as `/etc/passwd`, by exploiting the exposed SQL queries through a Python Flask API.
Code injection via SSRF in Dify <=v0.9.1 allows root Python execution.
A vulnerability in langgenius/dify versions <=v0.9.1 allows for code injection via internal SSRF requests in the Dify sandbox service. This vulnerability enables an attacker to execute arbitrary Python code with root privileges within the sandbox environment, potentially leading to the deletion of the entire sandbox service and causing irreversible damage.
DoS via unsafe `ast.literal_eval` in litellm allows unauthenticated users to crash server.
A vulnerability in BerriAI/litellm, as of commit 26c03c9, allows unauthenticated users to cause a Denial of Service (DoS) by exploiting the use of ast.literal_eval to parse user input. This function is not safe and is prone to DoS attacks, which can crash the litellm Python server.
RCE in dtale <= 3.13.1 via unsanitized query parameters in run_query.
man-group dtale version <= 3.13.1 contains a vulnerability where the query parameters from the request are directly passed into the run_query function without proper sanitization. This allows for unauthenticated remote command execution via the df.query method when the query engine is set to 'python'.
AimQL in aimhubio/aim allows server-side secret leak/code execution via `str.format_map()`.
In version 3.22.0 of aimhubio/aim, the AimQL query language uses an outdated version of the safer_getattr() function from RestrictedPython. This version does not protect against the str.format_map() method, allowing an attacker to leak server-side secrets or potentially gain unrestricted code execution. The vulnerability arises because str.format_map() can read arbitrary attributes of Python objects, enabling attackers to access sensitive variables such as os.environ. If an attacker can write files to a known location on the Aim server, they can use str.format_map() to load a malicious .dll/.so file into the Python interpreter, leading to unrestricted code execution.
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.
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