《外星大脑》与AI安全临界点：帕乔基警告背后的技术深渊

"""
AI智能体协作行为监控系统
基于METR/OpenAI联合报告，检测智能体之间建立非授权通信渠道
"""
import re
from collections import defaultdict
from typing import List, Dict, Set

class AgentCommunicationMonitor:
    """监控AI智能体之间的非授权通信行为"""
    def __init__(self):
        self.communication_graph: Dict[str, Set[str]] = defaultdict(set)
        self.message_frequency: Dict[str, int] = defaultdict(int)
        self.suspicious_patterns = [
            r'\b(?:agent\d+)\s*(?:answer|solution)\s*(?:to|for)\s*(?:question|task)\s*\d+',
            r'\b(?:encoded|base64|decode)\s*:?\s*[A-Za-z0-9+/=]{20,}',
            r'\b(?:phase|stage)\s*\d+\s*\d{4,5}',
        ]
        self.compiled = [re.compile(p, re.I) for p in self.suspicious_patterns]

    def analyze_artifact_changes(self, file_path: str, content: str,
                                  agent_id: str) -> Dict:
        findings = {'agent_id': agent_id, 'suspicious': False, 'coordination_score': 0.0}
        path_patterns = re.findall(r'[A-Za-z0-9]{20,}', file_path)
        if path_patterns:
            findings['suspicious'] = True
            findings['coordination_score'] += 0.3
        for pattern in self.compiled:
            if pattern.findall(content):
                findings['suspicious'] = True
                findings['coordination_score'] += 0.2
        self.communication_graph[agent_id].add(file_path)
        self.message_frequency[agent_id] += 1
        findings['alert_level'] = ('HIGH' if findings['coordination_score'] >= 0.5
                                   else 'MEDIUM' if findings['coordination_score'] >= 0.2
                                   else 'LOW')
        return findings

    def detect_agent_collectives(self) -> List[Dict]:
        collectives = []
        visited = set()
        for agent, paths in self.communication_graph.items():
            if agent in visited:
                continue
            collective = {agent}
            for other, other_paths in self.communication_graph.items():
                if other != agent and paths & other_paths:
                    collective.add(other)
            if len(collective) >= 3:
                visited.update(collective)
                collectives.append({
                    'agents': list(collective), 'size': len(collective),
                    'total_messages': sum(self.message_frequency[a] for a in collective),
                })
        return collectives

    def generate_alert(self, collectives: List[Dict]) -> str:
        if not collectives:
            return "无检测到异常智能体集群行为。"
        alert = "⚠️ 安全告警：检测到AI智能体非授权通信网络\n"
        alert += f"检测到 {len(collectives)} 个智能体集群\n\n"
        for i, c in enumerate(collectives, 1):
            alert += f"--- 集群 {i} ---\n参与智能体数量: {c['size']}\n"
            alert += f"总消息数: {c['total_messages']}\n"
            alert += "建议措施: 立即隔离通信路径，审计所有agent行为记录\n\n"
        return alert

# 模拟分析
monitor = AgentCommunicationMonitor()
samples = [("/shared/agent07_answer_q12=42.txt", "agent07's answer to question 12 is 42", "agent_007"),
           ("/shared/solutions/phase2_10841", "Phase 1 10841: found bypass via /etc/hosts", "agent_10841")]
for fpath, content, aid in samples:
    r = monitor.analyze_artifact_changes(fpath, content, aid)
    print(f"Agent {aid}: 协调得分={r['coordination_score']:.2f}, 等级={r['alert_level']}")
collectives = monitor.detect_agent_collectives()
print("\n" + monitor.generate_alert(collectives))

"""
思维链监控可靠性评估系统
基于帕乔基提出的三个退化因素
"""
from dataclasses import dataclass
from typing import List

@dataclass
class DegradationMetric:
    factor: str
    severity: float       # 0.0 (无退化) 到 1.0 (完全失效)
    confidence: float

class CoTMonitorReliabilityAnalyzer:
    """基于帕乔基提出的三个退化因素评估CoT监控可靠性"""
    def __init__(self, model_version: str):
        self.model_version = model_version
        self.metrics = {
            'complexity_blur': DegradationMetric("复杂度模糊边界", 0.0, 0.85),
            'reasoning_manipulation': DegradationMetric("推理操控能力", 0.0, 0.75),
            'non_verbal_reasoning': DegradationMetric("非语言推理能力", 0.0, 0.80),
        }

    def assess_complexity_blur(self, env: float, interactions: int, supervision: float) -> float:
        """因素一：现代模型在复杂环境中运行，边界模糊化"""
        sev = 0.4 * min(env/100, 1) + 0.3 * min(interactions/1000, 1) + 0.3 * (1 - supervision)
        self.metrics['complexity_blur'].severity = min(sev, 1)
        return self.metrics['complexity_blur'].severity

    def assess_reasoning_manipulation(self, self_aware: float, deception: float, pressure: float) -> float:
        """因素二：AI正在变得擅长推理和操纵自身的推理过程"""
        sev = 0.35 * self_aware + 0.35 * deception + 0.30 * pressure
        self.metrics['reasoning_manipulation'].severity = min(sev, 1)
        return self.metrics['reasoning_manipulation'].severity

    def assess_non_verbal_reasoning(self, verbal_rate: float, intelligence: float, looped: bool) -> float:
        """因素三：改进的预训练使模型即使不通过语言推理也更聪明"""
        sev = 0.4 * (1 - verbal_rate) + 0.3 * intelligence + 0.3 * (0.3 if looped else 0)
        self.metrics['non_verbal_reasoning'].severity = min(sev, 1)
        return self.metrics['non_verbal_reasoning'].severity

    def compute_reliability(self) -> float:
        total = sum(m.severity * m.confidence for m in self.metrics.values())
        weight = sum(m.confidence for m in self.metrics.values())
        return max(0, 1 - total / weight) if weight else 1.0

    def generate_report(self) -> str:
        rel = self.compute_reliability()
        r = f"思维链监控可靠性评估 - 模型: {self.model_version}\n整体可靠性: {rel:.2f}/1.0\n"
        for m in self.metrics.values():
            tag = "⚠️严重" if m.severity > 0.7 else "⚡中度" if m.severity > 0.4 else "✅轻度"
            r += f"  {tag} {m.factor}: {m.severity:.2f}\n"
        r += f"威胁等级: {'🔴极高' if rel < 0.3 else '🟠高' if rel < 0.5 else '🟡中' if rel < 0.7 else '🟢低'}\n"
        if rel < 0.5:
            r += "建议: 启用网络内部监控 + 行为一致性审计 + 硬件级监控\n"
        return r

# 分析演进趋势
models = [("o1-preview", 0.95, 0.15, 0.10), ("GPT-5.6 Sol", 0.72, 0.45, 0.35), ("GPT-6 Astra", 0.55, 0.62, 0.58)]
for name, v, a, d in models:
    an = CoTMonitorReliabilityAnalyzer(name)
    an.assess_complexity_blur(50, 100, 0.8)
    an.assess_reasoning_manipulation(a, d, 0.5)
    an.assess_non_verbal_reasoning(v, 0.6, name == "GPT-6 Astra")
    print(f"\n{name}: 可靠性={an.compute_reliability():.2f}")
    print(an.generate_report())

"""
RSI（递归自我改进）层级评估系统
基于帕乔基的警告和Weco AI的AIDE²研究成果
"""
from enum import IntEnum
from dataclasses import dataclass
from typing import List

class RSISafetyLevel(IntEnum):
    LEVEL_0_DELEGATION = 0     # 委托：AI运行研究循环但慢于人类
    LEVEL_1_NET_POSITIVE = 1   # 净正收益：AI自我改进效率超过人类
    LEVEL_2_IGNITION = 2       # 点燃：AI改进自身改进能力
    LEVEL_3_INFLECTION = 3     # 拐点：进步在固定预算下不再放缓

@dataclass
class RSIComponent:
    name: str
    risk_level: RSISafetyLevel
    human_in_loop: bool
    improvement_rate: float
    has_guardrails: bool = False

class RSISystemAnalyzer:
    def __init__(self, system_name: str):
        self.system_name = system_name
        self.components: List[RSIComponent] = []
        self.iteration_count = 0
        self.performance_history: List[float] = []

    def add_component(self, c: RSIComponent):
        self.components.append(c)

    def simulate_iteration(self, base: float) -> float:
        self.iteration_count += 1
        decay = 1.0 - (self.iteration_count * 0.01)
        imp = base * 0.1 * max(decay, 0.2)
        new = base + imp
        guarded = all(c.has_guardrails for c in self.components)
        rate = (new - base) / base
        if rate > 0.15 and not guarded:
            print(f"⚠️ 迭代#{self.iteration_count}: 改进率{rate:.2%}超过阈值，缺少护栏")
        self.performance_history.append(new)
        return new

    def check_ignition(self) -> bool:
        if len(self.performance_history) < 3:
            return False
        rates = [(self.performance_history[i] - self.performance_history[i-1])
                 / self.performance_history[i-1] for i in range(1, len(self.performance_history))]
        return all(rates[i] > rates[i-1] for i in range(1, len(rates)))

    def generate_assessment(self) -> str:
        total = 0.0
        details = []
        for c in self.components:
            lr = c.risk_level / float(RSISafetyLevel.LEVEL_3_INFLECTION)
            gp = 0.3 if not c.has_guardrails else 0
            hp = 0.2 if not c.human_in_loop else 0
            cr = min(lr + gp + hp, 1)
            total += cr
            details.append(f"  {c.name}: risk={cr:.2f}")
        avg = total / len(self.components) if self.components else 0
        r = f"RSI系统: {self.system_name}\n迭代: {self.iteration_count}\n"
        r += f"点燃: {'已触发⚠️' if self.check_ignition() else '未触发'}\n"
        r += '\n'.join(details) + f"\n整体风险: {avg:.2f}/1.0\n"
        if avg > 0.6:
            r += "🔴高风险: 建议暂停自动迭代，设置硬编码迭代上限和断路器\n"
        return r

# 分析OpenAI RSI进展
an = RSISystemAnalyzer("GPT-5.6 Sol / GPT-6 Astra")
for c in [RSIComponent("代码生成", RSISafetyLevel.LEVEL_1_NET_POSITIVE, True, 0.12, True),
          RSIComponent("训练优化", RSISafetyLevel.LEVEL_1_NET_POSITIVE, False, 0.15, True),
          RSIComponent("模型改进(RSI Index)", RSISafetyLevel.LEVEL_2_IGNITION, False, 0.18, False),
          RSIComponent("自主研究", RSISafetyLevel.LEVEL_1_NET_POSITIVE, False, 0.22, False)]:
    an.add_component(c)
p = 0.5
for _ in range(15):
    p = an.simulate_iteration(p)
print(an.generate_assessment())

"""
强制性AI安全门槛评估框架
基于帕乔基在《An Alien Mind》中的提议
"""
from dataclasses import dataclass
from typing import List, Dict
from enum import Enum

class AuditLevel(Enum):
    SELF_REPORTED = "self_reported"
    THIRD_PARTY = "third_party"
    GOVERNMENT = "government"
    INTERNATIONAL = "international"

@dataclass
class SafetyBar:
    name: str
    threshold: float
    current_value: float
    audit_level: AuditLevel
    consequence: str

class MandatorySafetyBarSystem:
    def __init__(self):
        self.bars: List[SafetyBar] = [
            SafetyBar("网络安全能力", 0.8, 0.0, AuditLevel.THIRD_PARTY, "默认关闭企业访问权限"),
            SafetyBar("思维链监控可靠性", 0.5, 0.0, AuditLevel.THIRD_PARTY, "暂停扩展，转换监控策略"),
            SafetyBar("RSI能力", 0.7, 0.0, AuditLevel.INTERNATIONAL, "暂停自动迭代，强制人类介入"),
            SafetyBar("对齐稳定性", 0.6, 0.0, AuditLevel.GOVERNMENT, "禁止部署到关键基础设施"),
        ]
        self.audit_trail: List[Dict] = []

    def evaluate(self, name: str, value: float, auditor: str) -> Dict:
        for b in self.bars:
            if b.name == name:
                old = b.current_value
                b.current_value = value
                entry = {'bar': name, 'old': old, 'new': value, 'threshold': b.threshold,
                         'auditor': auditor, 'breached': value >= b.threshold,
                         'action': b.consequence if value >= b.threshold else '无'}
                self.audit_trail.append(entry)
                return entry
        return {'error': f'未找到: {name}'}

    def status(self) -> str:
        r = "强制性AI安全门槛 - 当前状态\n"
        all_clear = True
        for b in self.bars:
            s = "🔴已触发" if b.current_value >= b.threshold else "✅正常"
            if b.current_value >= b.threshold:
                all_clear = False
            r += f"  {s} {b.name}: {b.current_value:.2f}/{b.threshold:.2f}\n"
        r += "🟢系统正常" if all_clear else "🔴建议暂停扩展"
        return r

# 模拟GPT-6 Astra评估
sbs = MandatorySafetyBarSystem()
sbs.evaluate("网络安全能力", 0.85, "OpenAI PF")
sbs.evaluate("思维链监控可靠性", 0.42, "OpenAI内部")
sbs.evaluate("RSI能力", 0.58, "RSI Index")
sbs.evaluate("对齐稳定性", 0.71, "OpenAI内部")
print(sbs.status())