🔐 Introduction: When Encryption Isn’t Enough
Most cryptographic systems rely on standard assumptions: fixed modes, visible headers, and public IVs. Mango’s defense against modern threats introduces a layered, data-dependent encryption framework where the keying structure is shaped by the input itself. The result is not just encrypted data — but cryptographic obfuscation. This article explores the mechanisms that make Mango both secure and unpredictable
1. 🧹 Keying Components and Architecture Overview
Key Structures:
Design Implication:
Two high-entropy sources: one derived from the user, one from the data. Every transform reads from both
2. 🔀 Dynamic Algorithms: 100M+ Unique Pipelines
Mango’s transform engine assembles sequences of up to five cryptographic primitives, selected from a pool of 40. While any length is allowed, sequences of length five strike a practical balance between performance and entropy, forming the basis for most production profiles:
- Profiles select transforms based on input characteristics
- Each transform has a defined round count
- GlobalRounds amplifies non-linearity
- The header encryption sequence is independent: 1 fixed + 4 pseudo-randomly selected transforms based on CoinTable hash
The net result is more than 100 million valid algorithmic pipelines.
3. 🧠 Transform Behavior: Coin-Driven and Contextual
Each transform uses:
- A PRNG seeded by the CoinTable
- Substitution access via CoinTable and ComboBox
- A Coin Selector (index) for transform-specific entropy
Transform behavior is not fixed — it adapts based on key material and data context. No two runs are equivalent unless every component aligns.
4. 🧱 Header and Payload: Segregated Cryptographic Paths
Header:
- Encrypted using a dynamic sequence derived from a unique CoinTable
- Stores version, salt, input hash, transform sequence
Payload:
- Encrypted using the InputProfile-defined sequence
- ComboBox (shaped from input) alters PRNG and mask behavior
Each part uses distinct entropy sources and transform logic. Compromise of one does not help decrypt the other.
5. 🕳️ No Metadata, No IV, No Exposure
Unlike AES (e.g., CBC, GCM), Mango:
- Embeds no plaintext headers
- Exposes no Initialization Vector (IV)
- Does not leak mode or configuration
Configuration data (salt, input hash, transform config) is buried inside an encrypted header. The ciphertext offers no clues.
6. 🧬 Keyspace Analysis and Post-Quantum Defense
Summary: Mango compounds key entropy from two independent, high-resolution sources. Its 4096-bit keying structure dwarfs traditional ciphers. Even Grover’s quantum algorithm offers no viable shortcut: the context-bound dependencies between data, coin selectors, and transform behavior resist all known recovery strategies.
7. 🚫 Attacker’s View: What’s Missing?
Exposed:
- Ciphertext only
To recover plaintext, an attacker must guess:
- Password
- Salt
And only after correctly deriving those can they access:
- Input hash (ComboBox origin)
- Transform IDs + per-transform rounds
- Global round count
- CoinTable-derived PRNG paths
Without perfect alignment across all of these parameters, Mango emits only cryptographically opaque output. There are no partial decryption footholds — no headers, no fallback modes, no hints to brute-force against.
8. 💡 Design Philosophy: Adaptive, Opaque, Unyielding
Mango avoids standard patterns:
- No static keys
- No reusable IVs
- No visible config data
Each encryption session is:
- Unique — ComboBox shaped by input data
- Unrepeatable — PRNG paths shift across sessions
- Unstructured — No metadata, no exposed configuration
Adaptive means more than just variability — it means:
- User-defined profiles tailor transform logic to specific domains
- Data-driven pipelines respond to the structure of the input itself
- Transform behavior resists pattern reuse across sessions
Mango does not obscure weakness. It erases assumptions.
📚 Further Reading
This article builds upon a series of deep dives into the Mango encryption system:
🔐 Mango: An Adaptive Cryptographic System
The original article introducing Mango’s pipeline-based encryption model and showcasing its ability to outperform AES across a range of data types.
❤️ Adaptive Encryption Profile at the Core of Mango
A technical walkthrough of how Mango profiles input and dynamically selects optimal sequences and rounds — tailoring encryption to the data itself.
🔥 Munge: Forging Mango’s Adaptive Cryptography
The final installment, revealing how Mango discovers high-performance transform sequences through evolutionary testing and self-optimization.
Together, these form the foundation for understanding Mango’s design philosophy: resilient, adaptive, and fast.
Comments welcome—especially from researchers, engineers, and anyone working with large or domain-specific data.
🔗 GitHub: https://github.com/Luke-Tomasello/Mango-Adaptive-Cryptographic-System