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Python Developers Survey: Python 2 clings on for certain use casesA new survey from the Python Software Foundation has f...
17/02/2025

Python Developers Survey: Python 2 clings on for certain use cases

A new survey from the Python Software Foundation has found that while Python 3 remains dominant both for work and play, usage of Python 2 crept up from the previous year.

The study, in association with JetBrains which polled more than 23,000 Python developers, found 93% of respondents had already implemented Python 3, based on responses from the end of 2022. Yet this was down on the 95% cited from the previous year.

To find a reason why, there are a few small clues based on Python version use cases. Python 2 remains more popular for less common use cases, ranging from computer graphics (24% of respondents citing Python 2 versus 11% for Python 3), to game development (13%/8%), mobile development (13%/5%) and multimedia app development (11%/5%).

In contrast, the most common use cases, data analysis (54%/29%), web development (46%/19%) and machine learning (38%/13%) showed the clearest lean towards Python 3. DevOps, sysadmin and writing automation scripts, which were categorised in one bucket in the report, saw a slight preference towards the previous iteration, with 23% for Python 2 and 36% for 3.

This lean could also be seen in assessing whether Python was a primary or secondary language for developers polled across use cases. For DevOps, sysadmin and automation, there was little to choose; 35% use Python as primary and 34% secondary. For the three primary use cases however – data analysis (53% primary, 44% secondary), web development (45%/31%) and machine learning (37%/29%) – there was greater separation.

86% of Python developers polled use other languages. JavaScript remains the most popular, cited by 37% of those polled, although it saw a downturn from 2021 (40%), as did HTML/CSS, which remained the second most popular (36% 2022, 38% 2021). SQL, cited by 34% of respondents, Bash/Shell (31%), and C/C++ (29%) were also keenly noted.

When it came to web frameworks, it was a dead heat between Flask and Django at 39% apiece. FastAPI was cited by one in four Python developers, gaining four percentage points on the previous year.v

Lightning AI becomes a PyTorch Foundation premier memberThe PyTorch Foundation, a neutral hub facilitating collaboration...
17/02/2025

Lightning AI becomes a PyTorch Foundation premier member

The PyTorch Foundation, a neutral hub facilitating collaboration within the deep learning community, has announced that Lightning AI has become a premier member.

Lightning AI is the driving force behind PyTorch Lightning, an open-source framework and platform empowering companies to construct and deploy cutting-edge AI products employing the latest generative AI models.

Luca Antiga, CTO of Lightning AI, expressed the importance of this collaboration:

“By joining the PyTorch Foundation, we are strengthening our commitment to boost the adoption of PyTorch across industries. We look forward to partnering with the Foundation to push the vision of PyTorch forward.”

PyTorch Lightning is a prominent project within the PyTorch ecosystem, enabling developers to create, train, fine-tune, and deploy AI models at scale. Its functionality has been instrumental in propelling the rapid adoption of PyTorch within both the research community and the corporate sector.

Ibrahim Haddad, Executive Director of the PyTorch Foundation, acknowledged Lightning AI’s significant contributions, saying:

“Lightning AI has been a great steward of the AI community, and notably a key contributor to PyTorch over the years. Their goal of making AI research scalable directly aligns with our mission at the foundation.”

As part of this collaboration, Lightning AI gains representation in the PyTorch Foundation Governing Board, a body responsible for setting policies, mission and vision statements, and determining the overarching scope of foundation initiatives.

Welcoming Antiga to their board, the foundation acknowledged his expertise and previous contributions to the field.

Antiga joined Lightning AI in April 2021 and has a rich background in AI research, having co-founded applied AI company Orobix and data-defined infrastructure provider Tensorwerk. His early core contributions to PyTorch and co-authoring of the book ‘Deep Learning with PyTorch’ highlight his expertise in the field.

Checkmarx uncovers persistent Python package threatOne notable aspect of the attack involved steganography, hiding a mal...
17/02/2025

Checkmarx uncovers persistent Python package threat
One notable aspect of the attack involved steganography, hiding a malicious payload within an innocuous-looking image file. This technique added an extra layer of stealth, making detection challenging. The attack demonstrated a consistent pattern, with code exhibiting similar obfuscation techniques and harmful payloads.

The Python packages, both camouflaged and direct, showcased a common blueprint. The attacker – aiming for persistence on compromised systems – employed scripts that identified users, created specific directories, placed and executed files, and covered their tracks post-execution.

The ultimate goal of the packages was clear: gain persistence, steal sensitive information, and achieve financial gains. The attacker targeted cryptocurrency assets, with a focus on stealing financial data. Stolen information was stored in separate files and exfiltrated to specific endpoints.

The attacker went beyond direct attacks, creating packages masquerading as API management tools. These packages – named “Pystob” and “Pywool” – enticed victims to deliberately download them, revealing obfuscated code and a multi-layered malicious payload.

To appear more legitimate, the attacker utilised the domain ‘api-hw.com’ for downloading payloads. However, the loss of control over the domain indicates a potential shift in strategy or a retreat.

The campaign uncovered by Checkmarx serves as a stark reminder of the persistent threats targeting the open-source ecosystem. Developers must exercise caution, vet packages, and leverage security assessment platforms to make informed decisions and reduce risks in their DevOps pipelines.

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