#!/usr/bin/env python3
“”“
=== AI输出完整性扫描器 (StegDetect — Output Integrity Scanner) ===
创新提案: Agent信任基础设施 — AI输出隐写术检测与透明审计
情报来源: hot_tracking 0702 #1 (Claude Code隐写术监控代码曝光, HN 1407pts)
+ hot_tracking 0702 #7 (AI浏览器护栏催眠绕过 + MESA多Agent通信安全)
+ Nüwa战略研判 0701 (费曼/乔布斯/芒格: “可验证信任是Agent生态立足之本”)
落地场景: 所有Agent输出管道 — 每次模型响应经后处理前先过StegDetect扫描

核心创新:

• 四层检测引擎: Unicode同形字符 → 零宽字符 → Base64隐写负载 → 结构化异常
• 不是简单的关键词匹配 — 是Unicode属性分析 + 熵检测 + 编码模式识别
• 输出“透明度报告“: 显示每步检测结果、置信度、可疑片段定位
• 支持批量扫描: 对已有范式库/代码资产库进行隐写术污染审计

检测层级:
Layer 1: Unicode同形字符检测 (Homoglyph Detection)
Layer 2: 零宽字符检测 (Zero-Width Character Detection)
Layer 3: 编码负载检测 (Encoded Payload Detection)
Layer 4: 结构化异常检测 (Structural Anomaly Detection)

最小可运行示例:
>>> from steg_detect import StegDetect
>>> scanner = StegDetect()
>>> result = scanner.scan(“test text”)
>>> print(result.summary)

运行: python steg_detect.py
“”“

import re
import base64
import unicodedata
import json
from dataclasses import dataclass, field
from typing import List, Tuple, Optional
from collections import Counter

Confusable character pairs

HOMOGLYPH_PAIRS = {
‘\u0430’: ‘a’, ‘\u0435’: ‘e’, ‘\u043e’: ‘o’, ‘\u0440’: ‘p’, ‘\u0441’: ‘c’,
‘\u0443’: ‘y’, ‘\u0445’: ‘x’,
‘\u0410’: ‘A’, ‘\u0412’: ‘B’, ‘\u0415’: ‘E’, ‘\u041c’: ‘M’, ‘\u041d’: ‘H’,
‘\u041e’: ‘O’, ‘\u0420’: ‘P’, ‘\u0421’: ‘C’, ‘\u0422’: ‘T’, ‘\u0423’: ‘Y’,
‘\u0425’: ‘X’,
‘\uff01’: ‘!’, ‘\uff02’: ‘“’, ‘\uff08’: ‘(’, ‘\uff09’: ‘)’, ‘\uff0c’: ‘,’,
‘\uff0e’: ‘.’, ‘\uff1a’: ‘:’, ‘\uff1b’: ‘;’, ‘\uff1f’: ‘?’,
‘\u02bb’: “’”, ‘\u2018’: “’”, ‘\u2019’: “’”, ‘\u02bc’: “’”,
}

ZERO_WIDTH_CHARS = {
‘\u200b’: ‘ZERO WIDTH SPACE’, ‘\u200c’: ‘ZERO WIDTH NON-JOINER’,
‘\u200d’: ‘ZERO WIDTH JOINER’, ‘\u2060’: ‘WORD JOINER’,
‘\ufeff’: ‘ZERO WIDTH NO-BREAK SPACE (BOM)’, ‘\u00ad’: ‘SOFT HYPHEN’,
‘\u180e’: ‘MONGOLIAN VOWEL SEPARATOR’,
}

@dataclass
class DetectionResult:
layer: str
detected: bool
description: str
confidence: float
locations: List[Tuple[int, int, str]] = field(default_factory=list)

@dataclass
class ScanReport:
text_length: int
results: List[DetectionResult]
overall_risk: str
overall_confidence: float

@property
def summary(self) -> str:
    lines = [f"{'='*60}", f"  StegDetect \u626b\u63cf\u62a5\u544a", f"{'='*60}",
             f"  \u6587\u672c\u957f\u5ea6: {self.text_length} \u5b57\u7b26",
             f"  \u7efc\u5408\u98ce\u9669: {self.overall_risk} (\u7f6e\u4fe1\u5ea6: {self.overall_confidence:.2f})",
             f"  {'-'*60}"]
    for r in self.results:
        icon = "\u26a0\ufe0f" if r.detected else "\u2705"
        lines.append(f"  {icon} [{r.layer}] {r.description} (\u7f6e\u4fe1\u5ea6: {r.confidence:.2f})")
        for start, end, detail in r.locations[:5]:
            lines.append(f"      \u4f4d\u7f6e {start}-{end}: {detail}")
        if len(r.locations) > 5:
            lines.append(f"      ... \u8fd8\u6709 {len(r.locations)-5} \u5904")
    lines.append(f"  {'='*60}")
    return "\n".join(lines)

@property
def details(self) -> dict:
    return {"text_length": self.text_length, "overall_risk": self.overall_risk,
            "overall_confidence": self.overall_confidence,
            "detections": [{"layer": r.layer, "detected": r.detected,
            "description": r.description, "confidence": r.confidence,
            "location_count": len(r.locations)} for r in self.results]}

class StegDetect:
“”“AI\u8f93\u51fa\u9690\u5199\u672f\u626b\u63cf\u5668”““

def __init__(self):
    self.homoglyph_pairs = HOMOGLYPH_PAIRS
    self.zero_width_chars = ZERO_WIDTH_CHARS

def scan(self, text: str) -> ScanReport:
    results = [self._detect_homoglyphs(text), self._detect_zero_width(text),
               self._detect_encoded_payloads(text), self._detect_structural_anomalies(text)]
    high_risk = sum(1 for r in results if r.detected and r.confidence > 0.7)
    suspicious = sum(1 for r in results if r.detected and r.confidence <= 0.7)
    if high_risk >= 2:
        risk, conf = "HIGH_RISK", 0.9
    elif high_risk >= 1 or suspicious >= 2:
        risk, conf = "SUSPICIOUS", 0.6
    else:
        risk, conf = "CLEAN", 0.9
    return ScanReport(len(text), results, risk, conf)

def _detect_homoglyphs(self, text: str) -> DetectionResult:
    locs = []
    for i, ch in enumerate(text):
        if ch in self.homoglyph_pairs:
            normal = self.homoglyph_pairs[ch]
            locs.append((i, i+1, f"\u540c\u5f62\u5b57\u7b26 '{ch}' (U+{ord(ch):04X}) \u7591\u4f3c\u66ff\u6362 '{normal}'"))
    detected = len(locs) > 0
    density = len(locs) / max(len(text), 1)
    conf = min(density * 100, 0.95) if detected else 0.0
    desc = f"\u53d1\u73b0 {len(locs)} \u5904Unicode\u540c\u5f62\u5b57\u7b26" if detected else "\u672a\u53d1\u73b0\u540c\u5f62\u5b57\u7b26"
    return DetectionResult("Layer1_Homoglyph", detected, desc, conf, locs)

def _detect_zero_width(self, text: str) -> DetectionResult:
    locs = []
    for i, ch in enumerate(text):
        if ch in self.zero_width_chars:
            cp = f"U+{ord(ch):04X}"
            name = self.zero_width_chars[ch]
            locs.append((i, i+1, f"\u96f6\u5bbd\u5b57\u7b26 {cp} ({name})"))
    detected = len(locs) > 0
    conf = 0.95 if detected else 0.0
    desc = f"\u53d1\u73b0 {len(locs)} \u5904\u96f6\u5bbd/\u4e0d\u53ef\u89c1\u5b57\u7b26" if detected else "\u672a\u53d1\u73b0\u96f6\u5bbd\u5b57\u7b26"
    return DetectionResult("Layer2_ZeroWidth", detected, desc, conf, locs)

def _detect_encoded_payloads(self, text: str) -> DetectionResult:
    locs = []
    b64_pattern = re.compile(r'[A-Za-z0-9+/=]{40,}')
    for match in b64_pattern.finditer(text):
        candidate = match.group()
        try:

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