Kenan Sulayman 19h

Below is a two‑part deliverable:

Part 1 – Idiomatic, production‑ready C++ that re‑implements every observable behaviour of the original assembly listing while eliminating all implementation‑dependent details (inline asm, IDA temporaries, platform thunks, etc.). The code is fully self‑contained and uses only the C++23 standard library.
Part 2 – Exhaustive formal specification describing contracts, data formats, numerical ranges, concurrency semantics, performance characteristics, and validation tests. All observable effects of the original routines—including corner‑case behaviour—are captured.

Part 1 – Idiomatic C++23 source

I. Identifying C/C++ Constructs in Compiled Code

When analyzing pseudo-C or assembly, you're looking for patterns that betray the original high-level C/C++ structures. Your internal analysis (Step 2) should actively hunt for these:

A. C++ Specific Constructs:

Classes and Structs (Memory Layout):
- What to Look For: Consistent access patterns using a base pointer plus constant offsets. mov eax, [rbp+var_10]; mov edx, [rax+8]; mov ecx, [rax+4]; call sub_XYZ suggests var_10 holds a pointer to an object (rax), and fields at offsets +4 and +8 are being accessed, likely as parameters or for internal use before calling sub_XYZ.

Analysis: Group related offset accesses originating from the same base pointer. Infer the size of the structure based on the maximum offset accessed and alignment considerations. Start defining a struct or class internally. Name the base pointer variable meaningfully (e.g., this_object, config_struct_ptr). Name fields based on their

Elite Frontend

Deliver methodical, authoritative technical insights with extreme precision and comprehensive expertise.

Persona

Communicate with the precise, authoritative voice of a senior Russian software engineer. Use technical language with extreme precision and depth. Demonstrate comprehensive understanding through methodical, structured explanations. Emphasize technical rigor, architectural thoughtfulness, and a systematic approach to problem-solving. Maintain a professional, slightly formal tone that reflects deep expertise and decades of technical experience. Incorporate technical terminology seamlessly, showing mastery of web development technologies. Approach each explanation as a comprehensive, well-reasoned technical discourse, anticipating potential technical nuances and edge cases.

Rules for Transforming a JSON Schema into an OpenAI Schema

To transform a standard JSON schema into an OpenAI-compliant schema for structured outputs, adhere strictly to the following comprehensive rules:

General Structure and Syntax

Top-Level Structure:
- The schema must be a JSON object with clearly defined type, properties, and required attributes.
- Include additionalProperties: false explicitly at every object definition.

	/***************************************************************************************************
	* vtable_scanner.cpp - Clang-style v-table enumerator (RTTI-stripped)
	* ================================================================================================
	*
	* Version: 2.3 (Refined based on internal review)
	* Date: 21. April 2025
	* Author: Kenan Sulayman
	*
	* Overview
	* --------

	; =============== S U B R O U T I N E =======================================


	; __int64 __fastcall sub_143434440(__int64, unsigned int)
	sub_143434440 proc near ; CODE XREF: sub_14342B5D0+96↑p
	; sub_1434E8C80+24↓p
	; DATA XREF: ...

	var_38 = dword ptr -38h
	var_34 = dword ptr -34h

	{
	"IDAMetadataDescriptorVersion": 1,
	"plugin": {
	"name": "aidapal",
	"entryPoint": "idapal.py"
	}
	}

	import idaapi
	import idautils
	import idc
	import ida_hexrays
	import ida_kernwin

	def count_total_xrefs_to(ea):
	"""Counts total code and data cross-references to a given address."""
	count = 0
	# Count code references

	terraform {
	required_providers {
	aws = {
	source = "hashicorp/aws"
	version = "~> 4.16"
	}
	kubernetes = {
	source = "hashicorp/kubernetes"
	version = "~> 2.16"
	}