Vyper's `sqrt` builtin may cause double evaluation of arguments with side-effects.
Vyper is a pythonic Smart Contract Language for the Ethereum virtual machine. In versions 0.3.10 and prior, using the `sqrt` builtin can result in double eval vulnerability when the argument has side-effects. It can be seen that the `build_IR` function of the `sqrt` builtin doesn't cache the argument to the stack. As such, it can be evaluated multiple times (instead of retrieving the value from the stack). No vulnerable production contracts were found. Additionally, double evaluation of side-effects should be easily discoverable in client tests. As such, the impact is low. As of time of publication, no fixed versions are available.
Double eval vulnerability in Vyper's `create_from_blueprint` function.
Vyper is a pythonic Smart Contract Language for the Ethereum virtual machine. In versions 0.3.10 and prior, using the `create_from_blueprint` builtin can result in a double eval vulnerability when `raw_args=True` and the `args` argument has side-effects. It can be seen that the `_build_create_IR` function of the `create_from_blueprint` builtin doesn't cache the mentioned `args` argument to the stack. As such, it can be evaluated multiple times (instead of retrieving the value from the stack). No vulnerable production contracts were found. Additionally, double evaluation of side-effects should be easily discoverable in client tests. As such, the impact is low. As of time of publication, no fixed versions exist.
Incorrect values logged in Vyper's `raw_log` with memory/storage topics.
Vyper is a pythonic Smart Contract Language for the Ethereum virtual machine. In versions 0.3.10 and prior, incorrect values can be logged when `raw_log` builtin is called with memory or storage arguments to be used as topics. A contract search was performed and no vulnerable contracts were found in production. The `build_IR` function of the `RawLog` class fails to properly unwrap the variables provided as topics. Consequently, incorrect values are logged as topics. As of time of publication, no fixed version is available.
Array index overflow in Vyper's `_abi_decode` can lead to exploits.
Vyper is a pythonic Smart Contract Language for the ethereum virtual machine. If an excessively large value is specified as the starting index for an array in `_abi_decode`, it can cause the read position to overflow. This results in the decoding of values outside the intended array bounds, potentially leading to exploitations in contracts that use arrays within `_abi_decode`. This vulnerability affects 0.3.10 and earlier versions.
Vyper is a pythonic Smart Contract Language for the ethereum virtual machine. When using the built-in `extract32(b, start)`, if the `start` index provided has for side effect to update `b`, the byte array to extract `32` bytes from, it could be that some dirty memory is read and returned by `extract32`. This vulnerability is fixed in 0.4.0.
Binding to INADDR_ANY exposes applications, risking unauthorized access.
dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications. Prior to versions 1.6.15, 1.7.15, and 1.8.1, Binding to `INADDR_ANY (0.0.0.0)` or `IN6ADDR_ANY (::)` exposes an application on all network interfaces, increasing the risk of unauthorized access. As stated in the Python docs, a special form for address is accepted instead of a host address: `''` represents `INADDR_ANY`, equivalent to `"0.0.0.0"`. On systems with IPv6, '' represents `IN6ADDR_ANY`, which is equivalent to `"::"`. A user who serves docs on an unsecured public network, may unknowingly be hosting an unsecured (http) web site for any remote user/system to access on the same network. The issue has has been mitigated in dbt-core v1.6.15, dbt-core v1.7.15, and dbt-core v1.8.1 by binding to localhost explicitly by default in `dbt docs serve`.
Memory leak in aiohttp 3.10.6-3.10.10 allows server resource exhaustion.
aiohttp is an asynchronous HTTP client/server framework for asyncio and Python. In versions starting with 3.10.6 and prior to 3.10.11, a memory leak can occur when a request produces a MatchInfoError. This was caused by adding an entry to a cache on each request, due to the building of each MatchInfoError producing a unique cache entry. An attacker may be able to exhaust the memory resources of a server by sending a substantial number (100,000s to millions) of such requests. Those who use any middlewares with aiohttp.web should upgrade to version 3.10.11 to receive a patch.
Tornado's cookie parsing algorithm can lead to excessive CPU usage.
Tornado is a Python web framework and asynchronous networking library. The algorithm used for parsing HTTP cookies in Tornado versions prior to 6.4.2 sometimes has quadratic complexity, leading to excessive CPU consumption when parsing maliciously-crafted cookie headers. This parsing occurs in the event loop thread and may block the processing of other requests. Version 6.4.2 fixes the issue.
Request smuggling vulnerability in aiohttp prior to version 3.10.11.
aiohttp is an asynchronous HTTP client/server framework for asyncio and Python. Prior to version 3.10.11, the Python parser parses newlines in chunk extensions incorrectly which can lead to request smuggling vulnerabilities under certain conditions. If a pure Python version of aiohttp is installed (i.e. without the usual C extensions) or `AIOHTTP_NO_EXTENSIONS` is enabled, then an attacker may be able to execute a request smuggling attack to bypass certain firewalls or proxy protections. Version 3.10.11 fixes the issue.
CPython vulnerability allows ReDoS via excessive regex backtracking in tarfile.
There is a MEDIUM severity vulnerability affecting CPython. Regular expressions that allowed excessive backtracking during tarfile.TarFile header parsing are vulnerable to ReDoS via specifically-crafted tar archives.
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”
:: Shaping the future through research and ingenuity ::
