Source URL: https://dotat.at/@/2024-10-01-getentropy.html
Source: Hacker News
Title: Getentropy() vs. RAND_bytes()
Feedly Summary: Comments
AI Summary and Description: Yes
**Summary:** The text presents an in-depth examination of random number generation in programming environments, specifically focusing on the `getentropy()` function and its comparison with `RAND_bytes()` from OpenSSL. The insights shared are particularly relevant to developers, security professionals, and infrastructure experts as they highlight both the performance implications and cryptographic security considerations of random number generations critical for secure applications.
**Detailed Description:**
The blog post covers several vital topics about the necessity and functionality of secure random number generation in computing systems. Here are the major points discussed:
– **Importance of Secure Random Numbers:**
– UUID v4 and v7 demonstrate the demand for high-performance secure random numbers as they are often critical in ensuring the efficiency of database operations.
– The performance of DNS resolvers can be enhanced by randomizing source ports and query IDs to protect against forged responses.
– **Exploration of `getentropy()` Function:**
– Inspired by another blog post, the author experiments with the `getentropy()` function for generating secure random bytes in PostgreSQL without needing the pgcrypto extension.
– The function provides just enough high-quality randomness to securely key a userland Cryptographically Secure Pseudorandom Number Generator (CSPRNG).
– **Kernel and Userland Interaction:**
– High-quality randomness in operating systems can be sourced from unpredictable hardware events or hardware random number generators.
– Random number generation can be accessed at the user level through kernel interfaces like `/dev/urandom` or `getentropy()`.
– **Limitations and Issues:**
– Issues faced include the requirement of multiple system calls to retrieve random bytes, limitations listed by `GETENTROPY_MAX`, and ensuring the special files are present in constrained environments like containers and chroot jails.
– Additionally, processes forking introduces complexity, as repeated output can leak private keys.
– **Benchmarking Performance:**
– A benchmark comparison explores the performance of `getentropy()` against `RAND_bytes()` across several buffer sizes, revealing nuanced findings about their respective speeds and efficiency.
– Notably, the variance in performance for different versions of OpenSSL indicates that newer iterations have improved speed, beyond the initial implementation of `getentropy()`.
– **Conclusion and Future Exploration:**
– The analysis concludes that while `RAND_bytes()` often performs better than `getentropy()`, older versions of OpenSSL may not maintain this advantage.
– The author expresses curiosity about future evaluations with newer kernel features that could impact the performance, particularly regarding vDSO-based implementations.
**Key Insights for Security and Compliance Professionals:**
– Understanding the nuances of random number generation methods is crucial in maintaining security in cryptographic applications.
– Performance considerations in the selection of random number generators can directly influence application efficiency, making it essential for developers and infrastructure engineers to choose the optimal approach.
– Regularly updating systems and libraries to leverage improvements in cryptographic functions can enhance both security and performance.
Overall, the elaboration on `getentropy()` and its implications offers vital considerations for secure coding practices in software development, especially in the context of databases and networking protocols.