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Python: Security and NumPy 1.20 Release

Submitted by Roy Schestowitz on Thursday 4th of March 2021 07:20:16 AM Filed under
Development
  • Python Package Index nukes 3,653 malicious libraries uploaded soon after security shortcoming highlighted

    The Python Package Index, also known as PyPI, has removed 3,653 malicious packages uploaded days after a security weakness in the use of private and public registries was highlighted.

    Python developers use PyPI to add software libraries written by other developers in their own projects. Other programming languages implement similar package management systems, all of which demand some level of trust. Developers are often advised to review any code they import from an external library though that advice isn't always followed.

    Package management systems like npm, PyPI, and RubyGems have all had to remove subverted packages in recent years. Malware authors have found that if they can get their code included in popular libraries or applications, they get free distribution and trust they haven't earned.

    Last month, security researcher Alex Birsan demonstrated how easy it is to take advantage of these systems through a form of typosquatting that exploited the interplay between public and private package registries.

  • A pair of Python vulnerabilities [LWN.net]

    Two separate vulnerabilities led to the fast-tracked release of Python 3.9.2 and 3.8.8 on February 19, though source-only releases of 3.7.10 and 3.6.13 came a few days earlier. The vulnerabilities may be problematic for some Python users and workloads; one could potentially lead to remote code execution. The other is, arguably, not exactly a flaw in the Python standard library—it simply also follows an older standard—but it can lead to web cache poisoning attacks.

    [...]

    [Update: As pointed out in an email from Moritz Muehlenhoff, Python 2.7 actually is affected by this bug. He notes that python2 on Debian 10 ("Buster") is affected and has been updated. Also, Fedora has a fix in progress for its python2.7 package.]

  • NumPy 1.20 has been released

    NumPy is a Python library that adds an array data type to the language, along with providing operators appropriate to working on arrays and matrices. By wrapping fast Fortran and C numerical routines, NumPy allows Python programmers to write performant code in what is normally a relatively slow language. NumPy 1.20.0 was announced on January 30, in what its developers describe as the largest release in the history of the project. That makes for a good opportunity to show a little bit about what NumPy is, how to use it, and to describe what's new in the release.

    [...]

    NumPy adds a new data type to Python: the multidimensional ndarray. This a container, like a Python list, but with some crucial differences. A NumPy array is usually homogeneous; while the elements of a list can be of various types, an ndarray will, typically, only contain a single, simple type, such as integers, strings, or floats. However, these arrays can instead contain arbitrary Python objects (i.e. descendants of object). This means that the elements will, for simple data types, all occupy the same amount of space in memory. The elements of an ndarray are laid out contiguously in memory, whereas there is no such guarantee for a list. In this way, they are similar to Fortran arrays. These properties of NumPy arrays are essential for efficiency because the location of each element can be directly calculated.

    Beyond just adding efficient arrays, NumPy also overloads arithmetic operators to act element-wise on the arrays. This allows the Python programmer to express computations concisely, operating on arrays as units, in many cases avoiding the need to use loops. This does not turn Python into a full-blown array language such as APL, but adds to it a syntax similar to that incorporated into Fortran 90 for array operations.

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