VAITP

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.

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.

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.

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'.

In version 3.22.0 of aimhubio/aim, the AimQL query language uses an outdated version of the safer_getattr() function from RestrictedPython.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.

A vulnerability in the SageMaker Workflow component of aws/sagemaker-python-sdk allows for the possibility of MD5 hash collisions in all versions. This can lead to workflows being inadvertently replaced due to the reuse of results from different configurations that produce the same MD5 hash. This issue can cause integrity problems within the pipeline, potentially leading to erroneous processing outcomes.

Jinja is an extensible templating engine. Prior to 3.1.6, an oversight in how the Jinja sandboxed environment interacts with the |attr filter allows an attacker that controls the content of a template to execute arbitrary Python code. To exploit the vulnerability, an attacker needs to control the content of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates. Jinja's sandbox does catch calls to str.format and ensures they don't escape the sandbox. However, it's possible to use the |attr filter to get a reference to a string's plain format method, bypassing the sandbox. After the fix, the |attr filter no longer bypasses the environment's attribute lookup. This vulnerability is fixed in 3.1.6.

Post-Quantum Secure Feldman's Verifiable Secret Sharing provides a Python implementation of Feldman's Verifiable Secret Sharing (VSS) scheme. In versions 0.7.6b0 and prior, the `secure_redundant_execution` function in feldman_vss.py attempts to mitigate fault injection attacks by executing a function multiple times and comparing results. However, several critical weaknesses exist. Python's execution environment cannot guarantee true isolation between redundant executions, the constant-time comparison implementation in Python is subject to timing variations, the randomized execution order and timing provide insufficient protection against sophisticated fault attacks, and the error handling may leak timing information about partial execution results. These limitations make the protection ineffective against targeted fault injection attacks, especially from attackers with physical access to the hardware. A successful fault injection attack could allow an attacker to bypass the redundancy check mechanisms, extract secret polynomial coefficients during share generation or verification, force the acceptance of invalid shares during verification, and/or manipulate the commitment verification process to accept fraudulent commitments. This undermines the core security guarantees of the Verifiable Secret Sharing scheme. As of time of publication, no patched versions of Post-Quantum Secure Feldman's Verifiable Secret Sharing exist, but other mitigations are available. Long-term remediation requires reimplementing the security-critical functions in a lower-level language like Rust. Short-term mitigations include deploying the software in environments with physical security controls, increasing the redundancy count (from 5 to a higher number) by modifying the source code, adding external verification of cryptographic operations when possible, considering using hardware security modules (HSMs) for key operations.

Post-Quantum Secure Feldman's Verifiable Secret Sharing provides a Python implementation of Feldman's Verifiable Secret Sharing (VSS) scheme. In versions 0.7.6b0 and prior, the `feldman_vss` library contains timing side-channel vulnerabilities in its matrix operations, specifically within the `_find_secure_pivot` function and potentially other parts of `_secure_matrix_solve`. These vulnerabilities are due to Python's execution model, which does not guarantee constant-time execution. An attacker with the ability to measure the execution time of these functions (e.g., through repeated calls with carefully crafted inputs) could potentially recover secret information used in the Verifiable Secret Sharing (VSS) scheme. The `_find_secure_pivot` function, used during Gaussian elimination in `_secure_matrix_solve`, attempts to find a non-zero pivot element. However, the conditional statement `if matrix[row][col] != 0 and row_random < min_value:` has execution time that depends on the value of `matrix[row][col]`. This timing difference can be exploited by an attacker. The `constant_time_compare` function in this file also does not provide a constant-time guarantee. The Python implementation of matrix operations in the _find_secure_pivot and _secure_matrix_solve functions cannot guarantee constant-time execution, potentially leaking information about secret polynomial coefficients. An attacker with the ability to make precise timing measurements of these operations could potentially extract secret information through statistical analysis of execution times, though practical exploitation would require significant expertise and controlled execution environments. Successful exploitation of these timing side-channels could allow an attacker to recover secret keys or other sensitive information protected by the VSS scheme. This could lead to a complete compromise of the shared secret. As of time of publication, no patched versions of Post-Quantum Secure Feldman's Verifiable Secret Sharing exist, but other mitigations are available. As acknowledged in the library's documentation, these vulnerabilities cannot be adequately addressed in pure Python. In the short term, consider using this library only in environments where timing measurements by attackers are infeasible. In the medium term, implement your own wrappers around critical operations using constant-time libraries in languages like Rust, Go, or C. In the long term, wait for the planned Rust implementation mentioned in the library documentation that will properly address these issues.

A vulnerability, that could result in Remote Code Execution (RCE), has been found in PlotAI. Lack of validation of LLM-generated output allows attacker to execute arbitrary Python code. Vendor commented out vulnerable line, further usage of the software requires uncommenting it and thus accepting the risk. The vendor does not plan to release a patch to fix this vulnerability.