Google Gemini 3.6 Flash系列 + Flash Cyber：Agent工作负载优化与网络安全模型的技术纵深

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import List, Dict, Optional, Tuple
import math
import time

class AgentAwareMoE(nn.Module):
    """
    Gemini 3.6 Flash风格的Agent感知MoE路由层
    针对Agent多步推理场景优化专家路由策略
    """
    def __init__(self, hidden_dim: int, num_experts: int, top_k: int,
                 agent_aware: bool = True):
        super().__init__()
        self.hidden_dim = hidden_dim
        self.num_experts = num_experts
        self.top_k = top_k
        self.agent_aware = agent_aware

        # 专家网络
        self.experts = nn.ModuleList([
            nn.Sequential(
                nn.Linear(hidden_dim, hidden_dim * 4),
                nn.GELU(),
                nn.Linear(hidden_dim * 4, hidden_dim)
            ) for _ in range(num_experts)
        ])

        # 标准路由
        self.router = nn.Linear(hidden_dim, num_experts)

        # Agent感知路由（Flash 3.6新增）
        # 维护一个步骤级路由缓存，避免重复激活
        self.agent_context_proj = nn.Linear(hidden_dim, hidden_dim)

        # Token效率缓存
        self.step_cache: Dict[int, torch.Tensor] = {}
        self.cache_hits = 0
        self.cache_misses = 0

    def forward(self, x: torch.Tensor, step_id: Optional[int] = None) -> torch.Tensor:
        batch_size, seq_len, _ = x.shape
        x_flat = x.view(-1, self.hidden_dim)

        if self.agent_aware and step_id is not None:
            # 检查步骤缓存
            if step_id in self.step_cache:
                routing_weights = self.step_cache[step_id]
                self.cache_hits += 1
            else:
                routing_logits = self.router(x_flat)
                routing_weights = F.softmax(routing_logits, dim=-1)
                self.step_cache[step_id] = routing_weights
                self.cache_misses += 1

            # Top-K routing
            top_k_weights, top_k_indices = torch.topk(routing_weights, self.top_k, dim=-1)
            top_k_weights = F.softmax(top_k_weights, dim=-1)
        else:
            routing_logits = self.router(x_flat)
            routing_weights = F.softmax(routing_logits, dim=-1)
            top_k_weights, top_k_indices = torch.topk(routing_weights, self.top_k, dim=-1)
            top_k_weights = F.softmax(top_k_weights, dim=-1)

        # 专家计算
        final_output = torch.zeros_like(x_flat)
        for i in range(self.num_experts):
            mask = (top_k_indices == i).any(dim=-1)
            if mask.any():
                expert_output = self.experts[i](x_flat[mask])
                weight_mask = (top_k_indices == i).float()
                weight_sum = weight_mask.sum(dim=-1, keepdim=True)
                final_output[mask] += expert_output * weight_sum[mask]

        return final_output.view(batch_size, seq_len, -1)


class AgentTaskRouter:
    """
    Agent任务级路由优化器
    Gemini 3.6 Flash通过分析Agent任务类型动态选择推理路径
    """
    def __init__(self):
        self.task_profiles: Dict[str, Dict] = {
            "code_generation": {
                "preferred_experts": [0, 1, 3],
                "max_tokens_per_step": 2048,
                "cache_strategy": "aggressive"
            },
            "tool_calling": {
                "preferred_experts": [2, 4, 5],
                "max_tokens_per_step": 512,
                "cache_strategy": "moderate"
            },
            "reasoning": {
                "preferred_experts": [0, 2, 6],
                "max_tokens_per_step": 4096,
                "cache_strategy": "conservative"
            },
            "summarization": {
                "preferred_experts": [1, 4, 7],
                "max_tokens_per_step": 1024,
                "cache_strategy": "aggressive"
            }
        }

    def classify_task(self, prompt: str) -> str:
        """基于prompt特征分类Agent任务类型"""
        code_keywords = ["function", "def ", "class ", "import", "api", "endpoint"]
        tool_keywords = ["search", "query", "fetch", "call", "request", "tool"]
        reasoning_keywords = ["why", "how", "analyze", "compare", "explain", "reason"]

        prompt_lower = prompt.lower()

        code_score = sum(1 for kw in code_keywords if kw in prompt_lower)
        tool_score = sum(1 for kw in tool_keywords if kw in prompt_lower)
        reasoning_score = sum(1 for kw in reasoning_keywords if kw in prompt_lower)

        scores = {
            "code_generation": code_score,
            "tool_calling": tool_score,
            "reasoning": reasoning_score,
            "summarization": 0
        }

        return max(scores, key=scores.get)

    def get_forward_strategy(self, task_type: str) -> Dict:
        """获取Agent任务前向策略"""
        return self.task_profiles.get(task_type, self.task_profiles["reasoning"])


# 模拟Agent多步推理中的Token效率
def simulate_agent_workflow(model, task_router, prompts, steps_per_prompt=5):
    """
    模拟Agent工作流中的多步推理，对比Flash 3.6的缓存效率
    """
    total_tokens = 0

    for prompt in prompts:
        task_type = task_router.classify_task(prompt)
        strategy = task_router.get_forward_strategy(task_type)

        for step in range(steps_per_prompt):
            batch_size = 1
            seq_len = strategy["max_tokens_per_step"] // steps_per_prompt
            dummy_input = torch.randn(batch_size, seq_len, model.hidden_dim)

            _ = model(dummy_input, step_id=step)
            total_tokens += seq_len

    total_ops = model.cache_hits + model.cache_misses
    cache_hit_rate = model.cache_hits / total_ops if total_ops > 0 else 0

    return {
        "total_tokens": total_tokens,
        "cache_hits": model.cache_hits,
        "cache_misses": model.cache_misses,
        "cache_hit_rate": cache_hit_rate,
        "estimated_tokens_saved": model.cache_hits * 768
    }


# 执行模拟
model = AgentAwareMoE(hidden_dim=768, num_experts=8, top_k=2)
task_router = AgentTaskRouter()

prompts = [
    "Write a function to query the database and return results",
    "Search for the latest research papers on transformer architecture",
    "Explain the difference between GPT and BERT in detail",
    "Summarize the key findings from the provided documents",
    "Call the weather API and format the response"
]

results = simulate_agent_workflow(model, task_router, prompts)
print("Agent工作流模拟结果:")
print("  总Token数: {}".format(results["total_tokens"]))
print("  缓存命中: {}".format(results["cache_hits"]))
print("  缓存未命中: {}".format(results["cache_misses"]))
print("  缓存命中率: {:.2%}".format(results["cache_hit_rate"]))
print("  估计节省Token: {}".format(results["estimated_tokens_saved"]))

package main

import (
	"encoding/json"
	"fmt"
	"math"
	"sync"
	"time"
)

// AdaptiveKVCache 实现Gemini 3.6 Flash的自适应KV缓存管理
type AdaptiveKVCache struct {
	mu              sync.RWMutex
	cache           map[string]*KVCacheEntry
	maxCacheSize    int
	compressionRate float64
}

type KVCacheEntry struct {
	Key         string
	KV          [][][]float64
	AccessCount int
	LastAccess  time.Time
	Priority    float64
	TokenCount  int
}

func NewAdaptiveKVCache(maxSize int, compressionRate float64) *AdaptiveKVCache {
	return &AdaptiveKVCache{
		cache:           make(map[string]*KVCacheEntry),
		maxCacheSize:    maxSize,
		compressionRate: compressionRate,
	}
}

// CompressContext 压缩Agent上下文，保留关键信息
func (c *AdaptiveKVCache) CompressContext(tokens []int,
	importanceScores []float64) []int {
	if len(tokens) != len(importanceScores) {
		return tokens
	}

	cumulative := make([]float64, len(tokens))
	total := 0.0
	for i, score := range importanceScores {
		total += score
		cumulative[i] = total
	}

	targetLen := int(float64(len(tokens)) * (1.0 - c.compressionRate))
	if targetLen < 1 {
		targetLen = 1
	}

	compressed := make([]int, 0, targetLen)
	step := total / float64(targetLen)
	nextThreshold := step

	for i, token := range tokens {
		if cumulative[i] >= nextThreshold || cumulative[i] == cumulative[len(cumulative)-1] {
			compressed = append(compressed, token)
			nextThreshold += step
		}
	}

	return compressed
}

// AgentStepCache 实现Agent步骤级缓存
type AgentStepCache struct {
	mu        sync.RWMutex
	stepCache map[string]*StepCacheEntry
	ttl       time.Duration
}

type StepCacheEntry struct {
	TaskID     string
	StepNumber int
	Routing    []int
	KVCache    *AdaptiveKVCache
	Result     string
	CreatedAt  time.Time
}

func NewAgentStepCache(ttl time.Duration) *AgentStepCache {
	return &AgentStepCache{
		stepCache: make(map[string]*StepCacheEntry),
		ttl:       ttl,
	}
}

func (c *AgentStepCache) GetOrCreateStep(taskID string, stepNum int) *StepCacheEntry {
	c.mu.Lock()
	defer c.mu.Unlock()

	key := fmt.Sprintf("%s:%d", taskID, stepNum)
	if entry, exists := c.stepCache[key]; exists {
		if time.Since(entry.CreatedAt) < c.ttl {
			entry.AccessCount++
			return entry
		}
		delete(c.stepCache, key)
	}

	entry := &StepCacheEntry{
		TaskID:    taskID,
		StepNumber: stepNum,
		CreatedAt: time.Now(),
	}
	c.stepCache[key] = entry
	return entry
}

// AgentWorkloadOptimizer 整体Agent工作负载优化器
type AgentWorkloadOptimizer struct {
	kvCache     *AdaptiveKVCache
	stepCache   *AgentStepCache
	taskProfiles map[string]*TaskProfile
}

type TaskProfile struct {
	AvgTokensPerStep int
	CacheStrategy    string
	PreferredExperts []int
}

func NewAgentWorkloadOptimizer() *AgentWorkloadOptimizer {
	return &AgentWorkloadOptimizer{
		kvCache:   NewAdaptiveKVCache(10000, 0.3),
		stepCache: NewAgentStepCache(5 * time.Minute),
		taskProfiles: map[string]*TaskProfile{
			"code_generation": {
				AvgTokensPerStep: 2048,
				CacheStrategy:    "aggressive",
				PreferredExperts: []int{0, 1, 3},
			},
			"tool_calling": {
				AvgTokensPerStep: 512,
				CacheStrategy:    "moderate",
				PreferredExperts: []int{2, 4, 5},
			},
			"reasoning": {
				AvgTokensPerStep: 4096,
				CacheStrategy:    "conservative",
				PreferredExperts: []int{0, 2, 6},
			},
		},
	}
}

// OptimizeAgentWorkflow 优化整个Agent工作流
func (o *AgentWorkloadOptimizer) OptimizeAgentWorkflow(
	taskType string, steps []string) map[string]interface{} {

	profile, exists := o.taskProfiles[taskType]
	if !exists {
		profile = o.taskProfiles["reasoning"]
	}

	totalTokens := 0
	cacheHits := 0
	cacheMisses := 0
	startTime := time.Now()

	for i, step := range steps {
		taskID := fmt.Sprintf("task_%s_%d", taskType, i)

		entry := o.stepCache.GetOrCreateStep(taskID, i)
		if entry.Routing != nil {
			cacheHits++
			totalTokens += profile.AvgTokensPerStep / 2
			continue
		}

		cacheMisses++

		compressedTokens := profile.AvgTokensPerStep
		if profile.CacheStrategy == "aggressive" {
			importanceScores := make([]float64, compressedTokens)
			for j := range importanceScores {
				importanceScores[j] = math.Exp(-float64(j) / float64(compressedTokens)*3)
			}
			sampleTokens := make([]int, compressedTokens)
			compressed := o.kvCache.CompressContext(sampleTokens, importanceScores)
			compressedTokens = len(compressed)
		}

		totalTokens += compressedTokens
		entry.Routing = profile.PreferredExperts
	}

	elapsed := time.Since(startTime)

	return map[string]interface{}{
		"task_type":      taskType,
		"total_steps":    len(steps),
		"total_tokens":   totalTokens,
		"cache_hits":     cacheHits,
		"cache_misses":   cacheMisses,
		"hit_rate":       float64(cacheHits) / float64(cacheHits+cacheMisses) * 100,
		"elapsed_ms":     elapsed.Milliseconds(),
	}
}

func main() {
	optimizer := NewAgentWorkloadOptimizer()

	workflows := map[string][]string{
		"code_generation": {
			"分析需求", "设计架构", "编写代码", "测试验证", "优化重构",
		},
		"tool_calling": {
			"解析意图", "选择工具", "构造参数", "调用API", "格式化结果",
		},
		"reasoning": {
			"理解问题", "分解子问题", "搜索信息", "综合分析", "生成结论",
		},
	}

	for taskType, steps := range workflows {
		result := optimizer.OptimizeAgentWorkflow(taskType, steps)
		resultJSON, _ := json.MarshalIndent(result, "", "  ")
		fmt.Printf("任务类型: %s\n%s\n\n", taskType, string(resultJSON))
	}
}

class FlashCyberVulnerabilityDetector:
    """
    Gemini 3.5 Flash Cyber安全漏洞检测器的核心实现
    基于多视角代码分析 + 控制流图 + 数据流分析的混合架构
    """
    def __init__(self, model_dim: int = 768, num_heads: int = 12):
        self.model_dim = model_dim
        self.num_heads = num_heads

        # 代码表示层
        self.code_encoder = CodeEncoder(model_dim)

        # 漏洞模式识别
        self.pattern_matcher = VulnerabilityPatternMatcher()

        # 控制流分析
        self.cfg_analyzer = ControlFlowAnalyzer()

        # 数据流分析
        self.df_analyzer = DataFlowAnalyzer()

        # 安全策略生成器
        self.patch_generator = SecurityPatchGenerator()

    def analyze_code(self, source_code: str, language: str = "python") -> Dict:
        """对代码进行多维度安全分析"""
        # 1. 代码表示
        encoded = self.code_encoder.encode(source_code, language)

        # 2. 漏洞模式匹配
        pattern_results = self.pattern_matcher.scan(encoded)

        # 3. 控制流分析
        cfg_results = self.cfg_analyzer.analyze(source_code, language)

        # 4. 数据流分析
        df_results = self.df_analyzer.analyze(source_code, language)

        # 5. 综合判断
        vulnerabilities = self._aggregate_findings(
            pattern_results, cfg_results, df_results
        )

        return vulnerabilities

    def _aggregate_findings(self, pattern_results, cfg_results, df_results):
        """聚合多维度分析结果"""
        combined = {
            "vulnerabilities": [],
            "risk_score": 0.0,
            "summary": ""
        }
        return combined


class CodeEncoder:
    """代码编码器 - 将源代码转换为结构化表示"""
    def __init__(self, dim: int):
        self.dim = dim
        self.tokenizer = CodeTokenizer()

    def encode(self, code: str, language: str):
        tokens = self.tokenizer.tokenize(code, language)
        return {"token_count": len(tokens), "language": language}


class CodeTokenizer:
    """代码专用Tokenizer，支持多种编程语言"""
    def __init__(self):
        self.language_keywords = {
            "python": ["def", "class", "import", "from", "return", "if", "elif",
                      "else", "for", "while", "try", "except", "finally", "with",
                      "as", "lambda", "yield", "async", "await"],
            "go": ["func", "type", "struct", "interface", "import", "package",
                   "var", "const", "return", "if", "else", "for", "range", "switch",
                   "case", "defer", "go", "chan", "select", "map"],
            "javascript": ["function", "class", "const", "let", "var", "import",
                          "export", "return", "if", "else", "for", "while", "try",
                          "catch", "async", "await", "new", "this", "typeof"]
        }

    def tokenize(self, code: str, language: str):
        """将代码分词为语义单元"""
        keywords = self.language_keywords.get(language.lower(), [])
        tokens = []
        current = ""

        for char in code:
            if char.isalnum() or char == '_':
                current += char
            else:
                if current:
                    tokens.append(current)
                    current = ""
                if char.strip():
                    tokens.append(char)

        if current:
            tokens.append(current)

        return tokens


class VulnerabilityPatternMatcher:
    """漏洞模式匹配器 - 识别已知漏洞模式"""
    def __init__(self):
        self.patterns = self._load_vulnerability_patterns()

    def _load_vulnerability_patterns(self):
        return {
            "sql_injection": {
                "patterns": [
                    r"execute\(.*\+.*\)",
                    r"SELECT.*WHERE.*%",
                    r"raw_query\(.*f['\"]"
                ],
                "severity": "critical",
                "cwe": "CWE-89",
                "description": "SQL注入漏洞，直接拼接用户输入"
            },
            "command_injection": {
                "patterns": [
                    r"os\.system\(.*\+",
                    r"subprocess\.call\(.*\+",
                    r"exec\(.*\+.*\)"
                ],
                "severity": "critical",
                "cwe": "CWE-78",
                "description": "命令注入，未过滤用户输入"
            },
            "path_traversal": {
                "patterns": [
                    r"open\(.*\+.*input",
                    r"Path\(.*\+.*\)",
                    r"os\.path\.join\(.*input"
                ],
                "severity": "high",
                "cwe": "CWE-22",
                "description": "路径遍历，未限制文件访问范围"
            },
            "xss": {
                "patterns": [
                    r"innerHTML\s*=",
                    r"document\.write\(.*input",
                    r"dangerouslySetInnerHTML"
                ],
                "severity": "high",
                "cwe": "CWE-79",
                "description": "跨站脚本，未对输出进行转义"
            },
            "insecure_deserialization": {
                "patterns": [
                    r"pickle\.loads\(.*input",
                    r"yaml\.load\(.*input",
                ],
                "severity": "high",
                "cwe": "CWE-502",
                "description": "不安全的反序列化"
            }
        }

    def scan(self, code_encoded):
        """扫描代码中的漏洞模式"""
        findings = []
        return findings


class ControlFlowAnalyzer:
    """控制流分析器 - 构建代码的控制流图"""
    def analyze(self, code: str, language: str):
        """分析代码的控制流结构"""
        return {
            "has_unreachable_code": False,
            "has_infinite_loop": False,
            "dangerous_branches": [],
            "cfg_complexity": 0.0
        }


class DataFlowAnalyzer:
    """数据流分析器 - 追踪数据的流向"""
    def analyze(self, code: str, language: str):
        """分析代码中的数据流"""
        return {
            "taint_sources": [],
            "sensitive_sinks": [],
            "unclean_paths": [],
            "clean_paths": []
        }

    def track_taint(self, source: str, code_lines: List[str]):
        """污点追踪 - 从源头到敏感操作"""
        taint_paths = []
        for i, line in enumerate(code_lines):
            if source in line:
                taint_paths.append({
                    "line": i + 1,
                    "source": source,
                    "sink": line,
                    "is_cleaned": "sanitize" in line.lower() or "escape" in line.lower()
                })
        return taint_paths


class SecurityPatchGenerator:
    """安全补丁生成器 - 自动生成修复建议"""
    def __init__(self):
        self.patch_templates = {
            "sql_injection": self._patch_sql_injection,
            "command_injection": self._patch_command_injection,
            "path_traversal": self._patch_path_traversal,
            "xss": self._patch_xss,
            "insecure_deserialization": self._patch_deserialization
        }

    def generate_patch(self, vulnerability, original_code):
        """根据漏洞类型生成修复补丁"""
        patch_func = self.patch_templates.get(vulnerability["type"])
        if patch_func:
            return patch_func(original_code)
        return "# 无法自动生成补丁，请手动审查"

    def _patch_sql_injection(self, code):
        return """# 修复建议：使用参数化查询替代字符串拼接
# 修改前：
# cursor.execute(f"SELECT * FROM users WHERE id = {user_input}")
# 修改后：
cursor.execute("SELECT * FROM users WHERE id = %s", (user_input,))"""

    def _patch_command_injection(self, code):
        return """# 修复建议：使用shlex.quote()转义输入
# 修改前：
# os.system(f"ping {user_input}")
# 修改后：
import shlex, subprocess
subprocess.run(["ping", shlex.quote(user_input)], shell=False)"""

    def _patch_path_traversal(self, code):
        return """# 修复建议：使用os.path.realpath验证路径
# 修改前：
# open(f"/data/{user_input}", "r")
# 修改后：
import os
safe_path = os.path.realpath(os.path.join("/data", user_input))
if not safe_path.startswith("/data/"):
    raise PermissionError("路径越权")
open(safe_path, "r")"""

    def _patch_xss(self, code):
        return """# 修复建议：使用textContent替代innerHTML
# 修改前：
# element.innerHTML = user_input
# 修改后：
element.textContent = user_input"""


# 使用示例
def demonstrate_flash_cyber():
    """演示Flash Cyber的代码安全分析流程"""
    detector = FlashCyberVulnerabilityDetector()

    # 测试代码 - 包含多个漏洞的示例
    vulnerable_code = """
def get_user_data(user_id):
    # SQL注入漏洞
    query = f"SELECT * FROM users WHERE id = {user_id}"
    cursor.execute(query)

    # 命令注入漏洞
    import os
    os.system(f"ping {user_id}")

    # 路径遍历漏洞
    with open(f"/data/{user_id}/profile.txt", "r") as f:
        return f.read()
"""

    result = detector.analyze_code(vulnerable_code, "python")
    print("Flash Cyber 安全分析结果:")
    print("  检测到 {} 个漏洞".format(len(result.get("vulnerabilities", []))))
    for vuln in result.get("vulnerabilities", []):
        print("  - {}: {} (严重度: {})".format(vuln["type"], vuln["description"], vuln["severity"]))
        print("    修复建议: {}".format(vuln["patch"]))


if __name__ == "__main__":
    demonstrate_flash_cyber()