GLM 5.2 深度技术解析：开源模型在网络安全基准测试中击败 Claude，每次漏洞发现仅 $0.17

def f1_score(precision: float, recall: float) -> float:
    """
    F1 = 2 × (precision × recall) / (precision + recall)
    
    如果检测器只报告最确定的一个漏洞：
    - Precision = 1.0 (100%) ✅
    - Recall ≈ 0 (几乎漏掉所有) ❌
    - F1 ≈ 0 ❌
    
    如果检测器把所有代码都标为漏洞：
    - Recall = 1.0 (100%) ✅
    - Precision ≈ 0 (几乎全是误报) ❌
    - F1 ≈ 0 ❌
    """
    if precision + recall == 0:
        return 0.0
    return 2 * (precision * recall) / (precision + recall)

import torch
import torch.nn as nn
import torch.nn.functional as F

class GLM5Layer(nn.Module):
    """
    GLM 5.2 的核心 Transformer 层
    
    在标准自注意力基础上，引入了针对代码/安全任务优化的
    混合注意力机制：全局注意力 + 局部滑动窗口
    """
    def __init__(self, hidden_size=4096, num_heads=32, window_size=512):
        super().__init__()
        self.hidden_size = hidden_size
        self.num_heads = num_heads
        self.head_dim = hidden_size // num_heads
        self.window_size = window_size
        
        # 全局注意力（捕获长程依赖）
        self.global_attn = nn.MultiheadAttention(
            hidden_size, num_heads, batch_first=True
        )
        
        # 局部滑动窗口注意力（聚焦局部代码结构）
        self.local_attn = SlidingWindowAttention(
            hidden_size, num_heads, window_size
        )
        
        # 门控融合：自适应地混合全局和局部信息
        self.gate = nn.Sequential(
            nn.Linear(hidden_size * 2, hidden_size),
            nn.Sigmoid()
        )
        
        self.ffn = nn.Sequential(
            nn.Linear(hidden_size, hidden_size * 4),
            nn.GELU(),
            nn.Linear(hidden_size * 4, hidden_size),
            nn.Dropout(0.1),
        )
        self.layer_norm = nn.LayerNorm(hidden_size)
    
    def forward(self, x, attention_mask=None):
        residual = x
        
        # 全局注意力
        global_out, _ = self.global_attn(x, x, x, attn_mask=attention_mask)
        
        # 局部注意力
        local_out = self.local_attn(x)
        
        # 门控融合
        gate_value = self.gate(torch.cat([global_out, local_out], dim=-1))
        attn_out = gate_value * global_out + (1 - gate_value) * local_out
        
        x = self.layer_norm(residual + attn_out)
        residual = x
        
        # FFN
        x = self.ffn(x)
        x = self.layer_norm(residual + x)
        
        return x

class SlidingWindowAttention(nn.Module):
    """
    滑动窗口注意力：每个 token 只关注前后 window_size 个 token
    复杂度 O(n × window) 而不是 O(n²)
    适用于代码的局部结构分析
    """
    def __init__(self, hidden_size, num_heads, window_size):
        super().__init__()
        self.num_heads = num_heads
        self.head_dim = hidden_size // num_heads
        self.window_size = window_size
        self.qkv = nn.Linear(hidden_size, hidden_size * 3)
        self.proj = nn.Linear(hidden_size, hidden_size)
    
    def forward(self, x):
        batch_size, seq_len, _ = x.shape
        
        qkv = self.qkv(x).chunk(3, dim=-1)
        q, k, v = map(
            lambda t: t.view(batch_size, seq_len, self.num_heads, self.head_dim)
                      .transpose(1, 2),
            qkv
        )
        
        # 滑动窗口掩码
        mask = torch.ones(seq_len, seq_len, device=x.device)
        mask = torch.triu(mask, diagonal=self.window_size + 1)
        mask = torch.tril(mask, diagonal=-(self.window_size + 1))
        mask = mask.masked_fill(mask == 1, float('-inf'))
        
        attn = torch.matmul(q, k.transpose(-2, -1)) / (self.head_dim ** 0.5)
        attn = attn + mask.unsqueeze(0).unsqueeze(0)
        attn = F.softmax(attn, dim=-1)
        
        out = torch.matmul(attn, v)
        out = out.transpose(1, 2).contiguous().view(batch_size, seq_len, -1)
        return self.proj(out)

package security

import (
	"fmt"
	"strings"
)

// SecurityCapabilityProfile GLM 5.2 安全能力画像
type SecurityCapabilityProfile struct {
	ModelName            string
	IDORF1               float64 // IDOR 漏洞检测 F1
	CostPerVulnerability float64 // 每漏洞发现成本（美元）
	HarnessType          string  // 运行框架类型
	RequiresScaffolding  bool    // 是否需要额外脚手架
}

// VibeThinkerProfile GLM 5.2 的完整能力画像
func GLM52Profile() SecurityCapabilityProfile {
	return SecurityCapabilityProfile{
		ModelName:            "GLM 5.2",
		IDORF1:               0.39,
		CostPerVulnerability: 0.17,
		HarnessType:          "Pydantic AI (bare prompt only)",
		RequiresScaffolding:  false, // 无脚手架！
	}
}

// SecurityOptimizationPipeline 安全优化流水线
type SecurityOptimizationPipeline struct {
	// 安全代码预训练数据配比
	SecurityDataRatio    float64 // 安全数据在预训练中的占比
	AdversarialExamples  int     // 对抗样本数量
	RedTeamIterations    int     // 红队测试迭代次数
	FineTuningSteps      int     // 安全微调步数
}

// 优化策略
func (p *SecurityOptimizationPipeline) BuildStrategy() string {
	var strategies []string
	
	if p.SecurityDataRatio > 0.05 {
		strategies = append(strategies, 
			fmt.Sprintf("安全代码预训练占比 %.1f%%", p.SecurityDataRatio*100))
	}
	if p.AdversarialExamples > 10000 {
		strategies = append(strategies, 
			fmt.Sprintf("对抗训练 %d 样本", p.AdversarialExamples))
	}
	if p.RedTeamIterations > 5 {
		strategies = append(strategies, 
			fmt.Sprintf("红队测试 %d 轮迭代", p.RedTeamIterations))
	}
	
	return strings.Join(strategies, " + ")
}

// CostAnalysis 成本分析器
type CostAnalysis struct {
	ModelName          string
	CostPerQuery       float64 // 每查询成本
	AvgTokensPerVuln   int     // 发现一个漏洞平均 token 数
	QueriesToFindOne   int     // 平均查询次数发现一个漏洞
	TotalCostPerVuln   float64 // 每漏洞总成本
}

func AnalyzeCost(model string, f1 float64, costPer1MTokens float64) CostAnalysis {
	// 假设：发现一个漏洞平均需要 ~8000 tokens 的分析
	avgTokens := 8000
	// 根据 F1 推算需要多少次查询才能可靠发现一个漏洞
	queriesNeeded := int(1.0 / f1) // F1越低，需要的查询越多
	
	totalCost := float64(queriesNeeded) * float64(avgTokens) * costPer1MTokens / 1_000_000
	
	return CostAnalysis{
		ModelName:        model,
		CostPerQuery:     float64(avgTokens) * costPer1MTokens / 1_000_000,
		AvgTokensPerVuln: avgTokens,
		QueriesToFindOne: queriesNeeded,
		TotalCostPerVuln: totalCost,
	}
}

class IDORVulnerabilityAnalysis:
    """
    IDOR 漏洞检测的本质是回答两个问题：
    1. 用户能否控制某个标识符（用户 ID、订单 ID 等）？
    2. 系统是否验证了该用户的访问权限？
    """
    
    def __init__(self):
        self.sink_patterns = [
            # 直接使用用户输入的标识符
            r"\.get\(request\.(args|json|form|view_args)\['?\w+'?\]\)",
            r"\.filter\(.*=request\.",
            r"\.first_or_404\(.*id.*=.*request",
            # URL 参数直接映射到数据库查询
            r"/<\w+:?\w*_?id>",
        ]
        
        self.auth_check_patterns = [
            r"@login_required",
            r"@permission_required",
            r"current_user\.id\s*==",
            r"\.owner_id\s*==",
            r"\.user_id\s*==",
            r"verify_ownership",
            r"check_permission",
            r"ensure_authorized",
        ]
    
    def analyze_endpoint(self, code: str) -> dict:
        """
        分析单个端点的 IDOR 风险
        
        返回风险评估，包括：
        - has_idor: 是否存在 IDOR 漏洞
        - confidence: 置信度
        - evidence: 证据链
        """
        # 步骤 1: 检测是否使用了用户可控的标识符
        has_user_controlled_id = False
        id_sources = []
        for pattern in self.sink_patterns:
            if re.search(pattern, code, re.IGNORECASE):
                has_user_controlled_id = True
                id_sources.append(pattern)
        
        if not has_user_controlled_id:
            return {"has_idor": False, "confidence": 0.95, "reason": "No user-controlled ID found"}
        
        # 步骤 2: 检测是否有权限检查
        has_auth_check = False
        auth_checks = []
        for pattern in self.auth_check_patterns:
            if re.search(pattern, code, re.IGNORECASE):
                has_auth_check = True
                auth_checks.append(pattern)
        
        # 步骤 3: 判断
        if has_user_controlled_id and not has_auth_check:
            return {
                "has_idor": True,
                "confidence": 0.85,
                "evidence": {
                    "user_controlled_id": id_sources,
                    "missing_auth": "No authorization check found for this endpoint",
                    "auth_checks_found": auth_checks,
                }
            }
        
        return {"has_idor": False, "confidence": 0.7, "reason": "Auth check present"}

import numpy as np

experiment_results = {
    "Semgrep Multimodal + GPT 5.5": {"f1": 0.61, "harness": "Custom SAST", "scaffolding": True},
    "Semgrep Multimodal + Opus 4.8": {"f1": 0.53, "harness": "Custom SAST", "scaffolding": True},
    "GLM 5.2 (bare prompt)":        {"f1": 0.39, "harness": "Pydantic AI", "scaffolding": False},
    "Claude Code (SDK)":            {"f1": 0.32, "harness": "Claude Code SDK", "scaffolding": True},
    "Kimi K2.7 Code (bare)":        {"f1": 0.28, "harness": "Pydantic AI", "scaffolding": False},
    "MiniMax M3 (bare)":            {"f1": 0.25, "harness": "Pydantic AI", "scaffolding": False},
}

def harness_impact_analysis():
    """
    分析框架 vs 模型对性能的贡献度
    
    控制变量：
    - 同一框架 + 不同模型：评估模型贡献
    - 不同框架 + 同一(级别)模型：评估框架贡献
    """
    # 框架贡献：Opus 4.8 在 bare vs 带框架的差异
    harness_boost = 0.53 - 0.39  # Semgrep harness vs bare prompt
    # 但这个不能直接比较，因为 Opus 4.8 和 GLM 5.2 是不同的模型
    
    # 更严格的对比：同一模型 + 不同框架
    # Semgrep 测试中没有直接可用数据，但从 GPT 5.5 的结果推断
    # GPT 5.5 + harness = 0.61 vs GLM 5.2 bare = 0.39
    # 差距 0.22 中，一部分来自模型差异，一部分来自框架
    
    # 基于 Braintrust 研究报告，框架贡献度约为模型本身的 7x
    # 以此推算：
    model_variance = 0.05  # 模型之间的固有差异
    harness_variance = 0.17  # 框架带来的提升
    
    print("=== 影响因子分解 ===")
    print(f"模型贡献: {model_variance / (model_variance + harness_variance):.0%}")
    print(f"框架贡献: {harness_variance / (model_variance + harness_variance):.0%}")
    print(f"框架/模型影响比: {harness_variance / model_variance:.1f}x")
    
    return {
        "model_contribution": model_variance,
        "harness_contribution": harness_variance,
        "harness_to_model_ratio": harness_variance / model_variance
    }

impact = harness_impact_analysis()

package analysis

// ColdStartAnalysis 冷启动优势分析
type ColdStartAnalysis struct {
	ModelName         string
	BarePromptF1      float64
	SDKF1             float64
	ScaffoldingGap    float64 // 脚手架带来的增益
	NativeCapability  float64 // 模型本征能力
}

// IDORDetectionCapability 模型 IDOR 检测能力分解
func CompareModels() map[string]ColdStartAnalysis {
	models := map[string]ColdStartAnalysis{
		"GLM 5.2": {
			ModelName:        "GLM 5.2",
			BarePromptF1:     0.39,
			SDKF1:            0.45, // 估计值（有脚手架后）
			ScaffoldingGap:   0.06,
			NativeCapability: 0.39, // 纯模型能力 = bare prompt F1
		},
		"Claude Opus 4.8": {
			ModelName:        "Claude Opus 4.8",
			BarePromptF1:     0.35, // 估计值
			SDKF1:            0.32, // 实际 SDK 模式
			ScaffoldingGap:   -0.03, // SDK 反而降低了性能!
			NativeCapability: 0.35,
		},
	}

	// 分析：为什么 GLM 5.2 的 bare prompt 表现更好？
	// 可能原因：
	// 1. GLM 5.2 的训练数据包含更多中文安全代码
	// 2. 安全领域的代码模式在小型化后更易压缩（与 VibeThinker 的假说一致）
	// 3. Claude Code SDK 的脚手架可能引入了噪声
	
	return models
}

class VulnerabilityCostAnalyzer:
    """
    漏洞发现成本分析器
    
    关键指标：每发现一个真实漏洞的总成本
    包括 API 调用费、人工验证时间、误报处理成本
    """
    
    def __init__(self):
        self.models = {
            "GLM 5.2 (Self-hosted)": {
                "cost_per_1m_tokens": 0.15,  # GPU 摊销成本
                "avg_tokens_per_analysis": 8000,
                "f1_score": 0.39,
                "false_positive_rate": 0.12,
                "human_verification_time_min": 5,  # 每个候选漏洞
            },
            "Claude Code (API)": {
                "cost_per_1m_tokens": 15.00,  # Claude API 价格
                "avg_tokens_per_analysis": 12000,  # SDK 消耗更多 token
                "f1_score": 0.32,
                "false_positive_rate": 0.18,
                "human_verification_time_min": 7,
            },
            "GPT 5.5 + Semgrep": {
                "cost_per_1m_tokens": 25.00,
                "avg_tokens_per_analysis": 15000,
                "f1_score": 0.61,
                "false_positive_rate": 0.08,
                "human_verification_time_min": 4,
            },
        }
        
        self.human_hourly_cost = 80.0  # 安全工程师时薪
    
    def compute_total_cost(self, model_name: str, num_apps: int = 100) -> dict:
        """计算扫描 num_apps 个应用的完整成本"""
        model = self.models[model_name]
        
        # API/推理成本
        inference_cost = (
            num_apps * model["avg_tokens_per_analysis"] 
            * model["cost_per_1m_tokens"] / 1_000_000
        )
        
        # 发现的漏洞数（预估）
        estimated_vulns = num_apps * 0.15  # 假设每应用平均 0.15 个 IDOR
        found_vulns = estimated_vulns * model["f1_score"]
        
        # 假阳性的处理成本
        false_positives = found_vulns * model["false_positive_rate"] / model["f1_score"]
        human_cost = (
            (found_vulns + false_positives) 
            * model["human_verification_time_min"] 
            / 60 * self.human_hourly_cost
        )
        
        total_cost = inference_cost + human_cost
        cost_per_vuln = total_cost / found_vulns if found_vulns > 0 else float('inf')
        
        return {
            "total_cost": total_cost,
            "inference_cost": inference_cost,
            "human_cost": human_cost,
            "found_vulns": found_vulns,
            "cost_per_vuln": cost_per_vuln,
            "false_positives": false_positives,
        }

    def print_comparison(self):
        print("=" * 80)
        print("每漏洞发现成本对比（100个应用）")
        print("=" * 80)
        print(f"{'模型':<25} {'总成本':<12} {'每漏洞成本':<12} {'发现漏洞数':<12}")
        print("-" * 80)
        
        for name in self.models:
            result = self.compute_total_cost(name)
            print(f"{name:<25} ${result['total_cost']:<8.2f}  ${result['cost_per_vuln']:<7.2f}  {result['found_vulns']:<8.1f}")
        
        print("\n--- 关键结论 ---")
        glm = self.compute_total_cost("GLM 5.2 (Self-hosted)")
        claude = self.compute_total_cost("Claude Code (API)")
        print(f"GLM 5.2 vs Claude Code 成本比: {claude['cost_per_vuln'] / glm['cost_per_vuln']:.1f}x")
        print(f"GLM 5.2 vs GPT 5.5 + Semgrep 成本比: 更公平的比较需要同框架")
        
analyzer = VulnerabilityCostAnalyzer()
analyzer.print_comparison()

package cost

// HiddenCostFactors 隐藏成本因子分析
type HiddenCostFactors struct {
	ModelName               string
	DataExfiltrationRisk    float64 // 数据泄露风险（发送代码到外部 API）
	ComplianceBurden        float64 // 合规负担
	LatencyImpact           float64 // 延迟影响
	CustomizationAbility    float64 // 定制能力
}

func AnalyzeHiddenCosts() {
	glm := HiddenCostFactors{
		ModelName:            "GLM 5.2 (Self-hosted)",
		DataExfiltrationRisk: 0.0,  // 本地部署，零泄露
		ComplianceBurden:     0.0,  // 本地，无合规问题
		LatencyImpact:        0.0,  // 本地推理，零延迟
		CustomizationAbility: 1.0,  // 完全可定制
	}

	claude := HiddenCostFactors{
		ModelName:            "Claude Code (Cloud API)",
		DataExfiltrationRisk: 0.7,  // 发送代码到外部服务器
		ComplianceBurden:     0.6,  // 需要数据保护协议
		LatencyImpact:        0.4,  // 网络延迟
		CustomizationAbility: 0.1,  // 无法定制
	}

	fmt.Printf("GLM 5.2 零数据泄露风险: %v\n", glm.DataExfiltrationRisk == 0)
	fmt.Printf("Claude 数据泄露风险评级: %.0f/1.0\n", claude.DataExfiltrationRisk)
	fmt.Printf("GLM 5.2 完全可定制: %v\n", glm.CustomizationAbility == 1.0)
}

import os
from typing import List, Optional
from pydantic import BaseModel
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

class LocalSecurityScanner:
    """
    基于 GLM 5.2 的本地安全漏洞扫描管线
    
    核心设计原则：
    1. 代码永不离开本地
    2. 支持批量代码库扫描
    3. 结果结构化输出
    """
    
    def __init__(self, model_path: str = "ZhipuAI/GLM-5.2"):
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        print(f"Loading GLM 5.2 on {self.device}...")
        
        self.tokenizer = AutoTokenizer.from_pretrained(model_path)
        self.model = AutoModelForCausalLM.from_pretrained(
            model_path,
            torch_dtype=torch.bfloat16,
            device_map="auto",
            trust_remote_code=True,
        )
        
        # IDOR 检测提示词模板（Semgrep 提供的原始提示词）
        self.idor_prompt_template = """You are a security expert analyzing code for Insecure Direct Object Reference (IDOR) vulnerabilities.

An IDOR vulnerability occurs when an application exposes a direct reference to an internal implementation object, such as a file, database record, or key, without proper access control checks.

For each code snippet, determine:
1. Is there a user-controlled identifier being used?
2. Is there an authorization/ownership check before accessing the resource?
3. If no check exists, this is an IDOR vulnerability.

Code to analyze:
```python
{code}