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by gus_massa
96 days ago
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> e.g., calculateTax() becomes [Symbol_A]() I expect AI to guess it. For example here in Argentina the equivalent of the VAT is 21%, so I expect that if the AI see round(x*1.21, 2) will be enough (I'm not sure it's the correct rounding, this is not accounting advice :) .) |
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## The Heart of Gatekeeper: "6-Axis Three-Dimensional Cross Structure (JCross 6-Axis IR)"
Gatekeeper is the "absolute gatekeeper" standing between the user's local environment and the external cloud LLM. Its core technology is the metamodel *"JCross IR (Intermediate Representation)"*, which reinterprets source code not merely as "text," but as a "spatial structure."
JCross IR decomposes source code into the following six dimensions (axes):
| Axis | Name | Role/Content | | :--- | :--- | :--- | | *X-axis* | *Control Flow* | Axis of time and sequence. if branches, loops, exceptions, etc. | | *Y-axis* | *Data Flow* | Axis of dependency and propagation. Variable assignment, argument passing, etc. | | *Z-axis* | *Type Constraints* | Boundary axis. Classes, type definitions, generics, etc. | | *W-axis* | *Memory & Lifecycle* | Lifespan axis. Scope lifespan, memory allocation/deallocation, etc. | | *V-axis* | *Scope & Hierarchy* | Inclusion axis. Modules, class nesting, etc. | | *U-axis* | *Semantics & Meaning* | * Most Important* Intent and concrete value axis. Specific variable names, function names, strings, etc. |
### Absolute Security through Physical Extraction of the "U-axis"
Simply encrypting variables uniformly prevents LLM from correctly interpreting even the logical structure of the code (loop scope and variable scope).
Therefore, Gatekeeper uses a rule-based parser to analyze the AST (Abstract Syntax Tree) and physically extracts only the "U-axis (semantics)" from this six-axis cross, isolating (masking) it in a local vault (JCrossIRVault). Only the "pure logic and structural framework" consisting of the remaining five axes (X, Y, Z, W, V) is sent to the external cloud LLM.
### Actual Transformation Image (Before / After)
Let's look at how the code is specifically hidden.
*[Before] Raw Source Code (as it is on the user's machine)* ```swift func calculateTax(price: Double) -> Double { let taxRate = 1.21 return round(price * taxRate, 2) }
[After] Masked JCross IR (as it is sent to Cloud LLM) When sent to LLM, business logic and sensitive information (U axis) are replaced with unique hash tokens, but the *topology (X/Y/Z/V/W axes)* where "function arguments are multiplied and returned as a value" is completely maintained.
func FUNC_0xA1B2(VAR_0x3C4: TYPE_DOUBLE) -> TYPE_DOUBLE { let VAR_0x9D5 = CONST_0xF1 return CALL_0xE5A(VAR_0x3C4 * VAR_0x9D5, CONST_0x02) } LLM looks at this symbolized structure puzzle, understands it at the structure level, and returns a patch. Finally, Verantyx restores the original complete code by reinjecting the U axis from the local Vault. Overall Flow: From Rule-Based Analysis to Inverse Transform This "extract" to "restore" pipeline is executed by robust rule-based processing, which does not tolerate inference errors. graph TD A[Raw Source Code] -->|1. AST Analysis| B[Rule-Based 6-Axis Parser] B -->|2. 6-Axis Decomposition| C{Gatekeeper Engine}
C -->|3. U-Axis Isolation| D[(Local Vault<br>JCrossIR Vault)] C -->|4. Extraction of Remaining 5 Axis| E[Masked IR<br>FUNC_0x...]
E -->|5. Submission| F((Cloud LLM<br>Claude / GPT-4))
F -->|6. Structural Inference| G[GraphPatch JSON<br>Structural Patch] G -->|7. Patch Application| H{Reverse Transpilation Engine<br>Reverse Transpilation}
D -->|8. U-Axis Reinjection| H H -->|9. Restoration Complete| I[Modified Source Code]