VernamVeil: A Fresh Take on Function-Based Encryption

2025-04-26 · Source: Datumbox · Field: Technology & Digital — Cybersecurity & Data Privacy, Software Development & Engineering · Depth: Novice, medium

Summary

VernamVeil is an experimental Python-based cipher that rethinks traditional static cryptographic keys by using a dynamic function, `fx(i, seed) -> bytes`, to generate keystreams. Developed as a learning tool by an ML scientist, it is not intended for production use but offers an educational platform for exploring cryptographic concepts. The cipher employs several techniques, including splitting messages into configurable chunks, padding real chunks with random bytes, inserting encrypted random delimiters, and injecting fake (decoy) chunks that are deterministically shuffled. It uses an XOR-based stream cipher with an evolving seed, where the seed is refreshed after each chunk, creating an avalanche effect and maintaining backward secrecy. VernamVeil also includes built-in BLAKE2b HMAC-based message authentication to detect tampering before decryption. The core algorithm is approximately 200 lines of Python code, with optional NumPy for vectorization and C extensions for hashing.

Key takeaway

For AI Engineers or Software Engineers exploring cryptographic principles, VernamVeil offers a practical, hands-on environment to understand dynamic key generation, obfuscation, and authenticated encryption. You should use this Python library as a learning playground to build intuition about encryption's inner workings, rather than for securing sensitive production data, given its experimental nature and the author's stated intent.

Key insights

VernamVeil explores function-based encryption, using dynamic keystream generation and layered obfuscation for educational purposes.

Principles

• Dynamic functions enhance keystream unpredictability.
• Layered obfuscation hides data structure.
• Seed evolution ensures avalanche effect and backward secrecy.

Method

VernamVeil splits messages into padded chunks, injects and shuffles fake chunks, then XORs with a dynamically generated, evolving keystream, finally appending a BLAKE2b HMAC for authentication.

In practice

• Experiment with custom `fx` functions for keystream generation.
• Adjust chunk size and decoy rate to observe obfuscation effects.
• Implement MAC-based verification to detect data tampering.

Topics

• Function-Based Encryption
• Symmetric Ciphers
• Keystream Generation
• Cryptographic Obfuscation
• Message Authentication Code

Code references

• datumbox/VernamVeil

Best for: Software Engineer, AI Engineer, Security Engineer

Related on AIssential

• DIG: Oracle-Guided Directed Input Generation for One-Day Vulnerabilities — Patch analysis with LLMs can synthesize explicit vulnerability preconditions, guiding effective PoC generation.
• Vercel breach prompts crypto projects to rotate API keys — A Vercel security breach via Context.ai compromised API keys, impacting crypto projects and raising concerns for Web3 infrastructure.
• How Spain’s ICFO Helped Build a Quantum Security Startup for the AI Era — Photonic quantum random number generators provide verifiable, high-quality entropy crucial for AI and post-quantum cryptography.
• VeriCWEty: Embedding enabled Line-Level CWE Detection in Verilog — An embedding-based framework improves RTL code vulnerability detection and localization.
• FlowGuard: Flow Matching for Identity-Independent Detection of Data-Free Model Stealing Attacks on Energy System Intrusion Detection Systems — FlowGuard detects data-free model stealing in energy IDS by identifying synthetic queries as out-of-distribution via flow matching.
• A new native IDE approach to prevent code leakage to LLMs: Obfuscating ASTs before the API call (Verantyx) — Obfuscating code with Kanji-infused structural semantics allows LLMs to reason without exposing proprietary data.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Datumbox.