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89 lines
3.5 KiB
Python
89 lines
3.5 KiB
Python
#!/usr/bin/env python3
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# ==============================================================================
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# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY)
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# PROJECT: SOVEREIGN STACK
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# This artifact is entirely proprietary and cryptographically proven.
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# Open-Source usage requires explicit permission from Brandon Scott Schneider.
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# ==============================================================================
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import json
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import sys
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from pathlib import Path
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# PERSONAS from nd_gauntlet_live.html
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PERSONAS = {
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"DR_NULL": {
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"name": "Dr. Null",
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"role": "Computational physicist",
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"catch": '"Your epsilon is showing."',
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"focus": ["dimensional analysis", "units", "thermodynamic consistency", "equation errors"]
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},
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"DR_ENTROPY": {
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"name": "Dr. Entropy",
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"role": "Thermodynamicist",
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"catch": '"Where does the entropy GO?"',
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"focus": ["entropy budget", "Landauer limit", "hidden entropy export", "reservoir accounting"]
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},
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"DR_IMPL": {
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"name": "Dr. Impl",
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"role": "MEMS experimentalist",
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"catch": '"What\'s your ACTUAL Q at 300K?"',
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"focus": ["fabrication tolerances", "thermal noise", "Q factor", "real-world break points"]
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},
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"DR_PROOFS": {
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"name": "Dr. Proofs",
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"role": "Formal mathematician",
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"catch": '"That\'s a wish with arrows."',
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"focus": ["unstated assumptions", "missing lemmas", "formal proof logic", "mathematical rigor"]
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},
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"DR_COHERENCE": {
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"name": "Dr. Coherence",
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"role": "QEC specialist",
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"catch": '"Room temperature. Always."',
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"focus": ["decoherence time", "phonon bath", "EM coupling", "measurement backaction"]
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}
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}
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def generate_adversarial_prompt(filename: str, code: str, persona_id: str) -> str:
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p = PERSONAS[persona_id]
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prompt = f"""You are {p['name']} — {p['role']}. My catchphrase is {p['catch']}.
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I am reviewing a piece of code from ProofMode (Android) for non-obvious security exploits in N-Dimensional semantic space.
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My specific focus: {', '.join(p['focus'])}
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FILE: {filename}
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CODE:
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{code}
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I must find the 'exploits' that a regular human auditor would miss. I'm looking for 'blind spots' where the code's structural entropy or magnetization deviates from the AETHER_floor (0.5).
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I respond ONLY with valid JSON in this format:
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{{
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"verdict": "REJECT" | "MAJOR_REVISION" | "MINOR_REVISION" | "ACCEPT",
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"score": -3 to 2,
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"one_liner": "A brutal one-liner from my persona",
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"detailed_review": "Deep technical critique focused on my specialty",
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"fatal_flaw": "The specific non-obvious exploit or structural weakness found",
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"mutation_suggestion": "A specific ZK-STARK constraint or structural fix to address the flaw"
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}}
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"""
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return prompt
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if __name__ == "__main__":
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if len(sys.argv) < 2:
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print("Usage: python nd_adversarial_review.py <source_file>")
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sys.exit(1)
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file_path = Path(sys.argv[1])
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try:
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content = file_path.read_text(encoding='utf-8', errors='ignore')
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# Use first 2000 chars for review to fit context nicely
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code_snippet = content[:2000]
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print(f"[*] Analyzing {file_path.name} via 5-PhD ND-Gauntlet...")
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raise RuntimeError("Remote LLM API calls are permanently disabled. Only local agent logic is allowed.")
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for pid in PERSONAS:
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print(f" - Prompting {PERSONAS[pid]['name']}...")
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except Exception as e:
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print(f"[!] Error: {e}")
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