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Welcome to a brand new issue of PythonPro!

In today’sExpert Insight we bring you an excerpt from the recently published book, Offensive Security Using Python, which briefly discusses key practices such as input validation, secure authentication, session management, secure coding techniques, and the implementation of security headers.

News Highlights: Python 3.13.0, released yesterday, adds an interactive interpreter, free-threaded mode, JIT compiler, and iOS/Android support; and Rev's Reverb models for ASR and diarization outperform other open-source models.

Here are my top 5 picks from our learning resources today:

And, today’s Featured Study, evaluates the performance of AI models in geospatial code generation, revealing significant challenges in handling complex tasks, specific data formats, and specialised libraries.

Stay awesome!

Divya Anne Selvaraj

Editor-in-Chief

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In "Evaluation of Code LLMs on Geospatial Code Generation," Gramacki et al. introduce a benchmark to assess LLMs' ability to handle tasks involving spatial reasoning and data processing.

LLMs generate code based on natural language inputs and are effective in general programming tasks, particularly in data science. Geospatial data science is a field focused on analysing spatial data tied to locations. It relies on libraries like GeoPandas and Shapely for tasks such as geo-coding, spatial analysis, and data visualisation. However, the domain poses unique challenges for LLMs due to the need for spatial reasoning and the use of specialised tools, making evaluation in this area crucial. As geospatial applications expand in industries such as urban planning and environmental science, reliable AI assistance is becoming increasingly important.

This study is relevant for geospatial programmers and data scientists seeking to automate coding tasks. Current LLMs are not yet reliable for complex geospatial tasks, highlighting a need for models specifically trained for the domain. Developers and researchers can benefit by focusing on improving AI models to better support geospatial data science workflows.

The authors created a benchmark dataset categorising tasks by complexity, data format, and tool usage. The dataset includes 77 samples to test LLM performance on tasks like spatial reasoning and tool implementation. Evaluation metrics focused on accuracy and pass@1, with the results highlighting the models' struggles in handling geospatial problems. Libraries like GeoPandas and H3 were used to evaluate the models, while more complex tools like MovingPandas exposed the models' weaknesses.

This rigorous benchmark, publicly available for future research, sets a foundation for improving geospatial code generation in LLMs. The study’s methodology ensures it reflects real-world geospatial coding challenges, offering valuable insights for the development of more domain-specific AI tools.

You can learn more by reading the entire paper and accessing the benchmark dataset: geospatial-code-llms-dataset.

Here’s an excerpt from “Chapter 3: An Introduction to Web Security with Python” in the book, Offensive Security Using Python by Rejah Rehim and Manindar Mohan, published in September 2024.

Python has developed as a versatile widely used programming language in the field of modern software development. Its ease of use, readability, and rich library support have made it a popular choice for developing

web-based applications in a variety of industries. Python frameworks such as Django, Flask, and Pyramid have enabled developers to create dynamic and feature-rich web applications with speed and agility.

However, as Python web apps become more popular, there is a corresponding increase in the sophistication and diversity of attacks targeting these applications. Cybersecurity breaches can jeopardize valuable user data, interfere with corporate operations, and damage an organization’s brand. Python web applications become vulnerable to a variety of security vulnerabilities, including SQL injection, XSS, andcross-site request forgery(CSRF). The consequences of these vulnerabilities can be severe, demanding an effectivecybersecurity strategy.

Developers must be proactive to counteract this. By implementing security practices such as input validation, output encoding, and other secure coding guidelines early in the development lifecycle, developers can reduce the attack surface and improve the resilience of their Pythonweb applications.

Although we are only discussing Python-based applications here, these practices are universal and should be implemented in web applications built with anytechnology stack.

To protect against a wide range of cyber threats, it is critical to implement strong best practices. This section explains key security practices that developers should follow while developingweb apps.

Userinput validationis essential for preventing code injection attacks. Malicious inputs can exploit vulnerabilities and cause unwanted commands to be executed. Properdata sanitizationguarantees that user inputs are handled as data rather than executable code by eliminating or escaping special characters. Using libraries such asinput()and frameworks such as Flask’srequestobject can help validate and sanitizeincoming data.

Restricting unauthorized access requires effective authentication and authorization procedures. Password hashing, which uses algorithms such asbcryptorArgon2, adds an extra degree of security by ensuring that plaintext passwords are never saved.Two-factor authentication(2FA) adds an additional verification step to user authentication, increasing security.Role-Based Access Control(RBAC) allows developers to provide specific permissions to different user roles, guaranteeing that users only access functionality relevant totheir responsibilities.

Keeping user sessions secure is critical for avoiding session fixation and hijacking attempts. Using secure cookies with theHttpOnlyandSecurecharacteristics prohibits client-side script access and ensures that cookies are only sent over HTTPS. Session timeouts and measures such as session rotation can improve session securityeven further.

Following secure coding practices reduces a slew of possible vulnerabilities. Parameterized queries, made possible by libraries such assqlite3, protect against SQL injection by separating data from SQL commands. Output encoding, achieved with techniques such ashtml.escape(), avoids XSS threats by converting user inputs to innocuous text. Similarly, omitting functions such aseval()andexec()avoids uncontrolled code execution, lowering the likelihood of codeinjection attacks.

Security headersare a fundamental component of web application security. They are HTTP response headers that provide instructions to web browsers, instructing them on how to behave when interacting with the web application. Properly configured security headers can mitigate various web vulnerabilities, enhance privacy, and protect against commoncyber threats.

Here is an in-depth explanation of implementing security headers to enhance webapplication security:

Content-Security-Policy: default-src 'self'; script-src 'self' www.google-analytics.com;

Strict-Transport-Security: max-age=31536000; includeSubDomains; preload;

X-Content-Type-Options: nosniff

X-Frame-Options: DENY

Referrer-Policy: strict-origin-when-cross-origin

Packt library subscribers can continue reading the entire book for free. You can buy Offensive Security Using Python,here.

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