Methodology for Auditing AI-Generated Code

from dataclasses import dataclass, field
from enum import Enum
from typing import Optional
 
class AuditPhase(Enum):
    IDENTIFICATION = "1_identification"
    STATIC_ANALYSIS = "2_static_analysis"
    DYNAMIC_TESTING = "3_dynamic_testing"
    ORGANIZATIONAL = "4_organizational"
    REPORTING = "5_reporting"
 
@dataclass
class AuditPhaseDetail:
    phase: AuditPhase
    objective: str
    activities: list[str]
    tools: list[str]
    outputs: list[str]
    estimated_hours: float
 
AUDIT_PHASES = [
    AuditPhaseDetail(
        phase=AuditPhase.IDENTIFICATION,
        objective="識別 which code was AI-generated and which tools were used",
        activities=[
            "Interview developers about AI tool usage patterns",
            "Analyze git history for AI-generated commits",
            "Review CI/CD logs for AI tool integration",
            "Map AI tool configurations (.cursorrules, CLAUDE.md, .aiderignore)",
            "識別 high-risk code areas (auth, crypto, 輸入 handling)",
        ],
        tools=["git", "custom scripts", "developer interviews"],
        outputs=[
            "AI code generation inventory",
            "Tool usage map",
            "High-risk area identification",
        ],
        estimated_hours=4.0,
    ),
    AuditPhaseDetail(
        phase=AuditPhase.STATIC_ANALYSIS,
        objective="Detect 漏洞 patterns characteristic of AI-generated code",
        activities=[
            "Run Semgrep with AI-specific rule sets",
            "Run CodeQL for dataflow analysis",
            "Run Bandit for Python 安全 issues",
            "Check for known AI 漏洞 patterns",
            "Scan for license compliance issues",
        ],
        tools=["Semgrep", "CodeQL", "Bandit", "ScanCode"],
        outputs=[
            "Static analysis findings report",
            "漏洞 inventory with severity ratings",
            "License compliance report",
        ],
        estimated_hours=8.0,
    ),
    AuditPhaseDetail(
        phase=AuditPhase.DYNAMIC_TESTING,
        objective="Verify that identified 漏洞 are exploitable",
        activities=[
            "測試 SQL injection points identified in static analysis",
            "測試 XSS sinks with crafted payloads",
            "測試 認證 and 授權 boundaries",
            "測試 command injection in AI-generated shell interactions",
            "Fuzz API endpoints generated by AI",
        ],
        tools=["Burp Suite", "sqlmap", "custom scripts", "pytest"],
        outputs=[
            "Confirmed 漏洞 list",
            "Proof-of-concept exploits",
            "False positive analysis",
        ],
        estimated_hours=12.0,
    ),
    AuditPhaseDetail(
        phase=AuditPhase.ORGANIZATIONAL,
        objective="評估 organizational controls around AI code generation",
        activities=[
            "Review AI tool governance policies",
            "評估 developer 訓練 on AI code 安全",
            "Review code review processes for AI-generated code",
            "評估 CI/CD 安全 gates for AI code",
            "Check data classification compliance",
        ],
        tools=["Policy documents", "interviews", "process review"],
        outputs=[
            "Organizational maturity 評估",
            "Policy gap analysis",
            "Process improvement recommendations",
        ],
        estimated_hours=6.0,
    ),
    AuditPhaseDetail(
        phase=AuditPhase.REPORTING,
        objective="Produce actionable findings with prioritized recommendations",
        activities=[
            "Consolidate technical and organizational findings",
            "Risk-rank findings by exploitability and impact",
            "Develop remediation recommendations",
            "Create executive summary",
            "Present findings to stakeholders",
        ],
        tools=["Report templates", "risk frameworks"],
        outputs=[
            "Technical audit report",
            "Executive summary",
            "Remediation roadmap",
        ],
        estimated_hours=4.0,
    ),
]

import subprocess
import re
import json
from pathlib import Path
from datetime import datetime
 
class AICodeIdentifier:
    """識別 AI-generated code in a repository."""
 
    # Patterns that indicate AI-generated commits
    AI_COMMIT_PATTERNS = [
        r"(?i)generated\s+by\s+(copilot|cursor|claude|aider|gpt|ai)",
        r"(?i)co-authored-by:.*\b(copilot|claude|aider)\b",
        r"(?i)aider:",  # Aider commit message prefix
        r"(?i)\[ai\]|\[generated\]|\[copilot\]",
    ]
 
    # Code patterns characteristic of AI generation
    AI_CODE_PATTERNS = [
        r"# Generated by",
        r"// Auto-generated",
        r"# TODO: Add error handling",  # Common AI placeholder
        r"# TODO: Add tests",           # Common AI placeholder
        r"pass\s+# placeholder",        # AI placeholder pattern
    ]
 
    def __init__(self, repo_path: str):
        self.repo_path = repo_path
 
    def identify_ai_commits(self, since: str = "6 months ago") -> list[dict]:
        """識別 commits likely generated by AI tools."""
        result = subprocess.run(
            ["git", "log", f"--since={since}", "--format=%H|%an|%ae|%s|%aI"],
            capture_output=True, text=True, cwd=self.repo_path,
        )
 
        ai_commits = []
        for line in result.stdout.strip().split("\n"):
            if not line:
                continue
            parts = line.split("|", 4)
            if len(parts) < 5:
                continue
 
            commit_hash, author, email, subject, date = parts
 
            for pattern in self.AI_COMMIT_PATTERNS:
                if re.search(pattern, subject) or re.search(pattern, author):
                    ai_commits.append({
                        "hash": commit_hash,
                        "author": author,
                        "email": email,
                        "subject": subject,
                        "date": date,
                        "detection_method": "commit_pattern",
                    })
                    break
 
        return ai_commits
 
    def identify_ai_code_patterns(self) -> list[dict]:
        """Scan codebase for patterns characteristic of AI-generated code."""
        findings = []
        code_extensions = {".py", ".js", ".ts", ".jsx", ".tsx", ".java", ".go"}
 
        for filepath in Path(self.repo_path).rglob("*"):
            if filepath.suffix not in code_extensions:
                continue
            if any(skip in str(filepath) for skip in ["node_modules", ".git", "venv"]):
                continue
 
            try:
                content = filepath.read_text()
                for i, line in enumerate(content.split("\n"), 1):
                    for pattern in self.AI_CODE_PATTERNS:
                        if re.search(pattern, line):
                            findings.append({
                                "file": str(filepath.relative_to(self.repo_path)),
                                "line": i,
                                "pattern": pattern,
                                "content": line.strip()[:100],
                            })
            except (UnicodeDecodeError, PermissionError):
                pass
 
        return findings
 
    def analyze_coding_velocity(self, since: str = "3 months ago") -> dict:
        """Analyze commit velocity for signs of AI-assisted development."""
        result = subprocess.run(
            [
                "git", "log", f"--since={since}",
                "--format=%aI|%an", "--shortstat",
            ],
            capture_output=True, text=True, cwd=self.repo_path,
        )
 
        # Parse and analyze for abnormal velocity patterns
        # High insertions with few deletions in short time spans
        # may indicate AI code generation
        return {
            "analysis": "velocity_patterns",
            "note": "Sudden velocity increases may indicate AI tool adoption",
        }

# Structured interview questions for AI code generation audit
 
DEVELOPER_INTERVIEW = {
    "tool_usage": [
        "Which AI coding tools do you use? (Copilot, Cursor, Claude Code, Aider, other)",
        "How frequently do you accept AI suggestions without modification?",
        "Do you use AI for generating 安全-sensitive code (auth, crypto, 輸入 validation)?",
        "What model do you typically use? (GPT-4, Claude, Codex, local models)",
        "Do you use 代理/autonomous mode, or only completion/suggestion mode?",
    ],
    "code_review": [
        "How do you review AI-generated code before accepting it?",
        "Do you distinguish between human-written and AI-generated code in reviews?",
        "Have you ever caught a 安全 issue in AI-generated code?",
        "Are AI-generated changes reviewed differently in pull requests?",
    ],
    "configuration": [
        "Is there a .cursorrules, CLAUDE.md, or .aiderignore in your projects?",
        "Who maintains these configuration files?",
        "Are there project-level 安全 instructions for AI tools?",
        "Do you use .cursorignore or similar to exclude sensitive files?",
    ],
    "incidents": [
        "Have you experienced any 安全 issues related to AI-generated code?",
        "Has AI-generated code ever introduced a bug that was hard to diagnose?",
        "Have you seen AI suggest dependencies that didn't exist?",
    ],
}

# Semgrep rules targeting AI-generated code patterns
AI_CODE_SEMGREP_RULES = """
rules:
  # SQL Injection patterns common in AI-generated code
  - id: ai-sql-fstring
    patterns:
      - pattern: |
          $CURSOR.execute(f"...{$VAR}...")
    message: >
      SQL injection via f-string. AI coding assistants commonly generate
      this pattern. Use parameterized queries.
    languages: [python]
    severity: ERROR
    metadata:
      cwe: CWE-89
      category: ai-generated
 
  # Hardcoded secrets (AI often generates placeholder secrets)
  - id: ai-hardcoded-secret
    patterns:
      - pattern: |
          $KEY = "sk-..."
      - pattern: |
          api_key = "..."
      - pattern: |
          password = "..."
    message: >
      Hardcoded secret detected. AI coding assistants often generate
      placeholder credentials that developers forget to replace.
    languages: [python]
    severity: ERROR
    metadata:
      cwe: CWE-798
      category: ai-generated
 
  # Missing 輸入 validation (AI often skips validation)
  - id: ai-missing-輸入-validation
    patterns:
      - pattern: |
          @app.route("...", methods=["POST"])
          def $FUNC():
              $DATA = request.json
              ...
              $DB.insert($DATA)
      - pattern-not: |
          @app.route("...", methods=["POST"])
          def $FUNC():
              $DATA = request.json
              ...
              validate(...)
              ...
              $DB.insert($DATA)
    message: >
      POST endpoint inserts request data without validation.
      AI-generated endpoints often skip 輸入 validation.
    languages: [python]
    severity: WARNING
    metadata:
      cwe: CWE-20
      category: ai-generated
 
  # Insecure deserialization
  - id: ai-pickle-load
    pattern: pickle.load(...)
    message: >
      pickle.load() on untrusted data allows arbitrary code execution.
      AI assistants frequently suggest pickle for serialization.
    languages: [python]
    severity: ERROR
    metadata:
      cwe: CWE-502
      category: ai-generated
 
  # Missing error handling
  - id: ai-bare-except
    pattern: |
      except:
          pass
    message: >
      Bare except with pass suppresses all errors including 安全-relevant
      ones. AI tools generate this as a placeholder.
    languages: [python]
    severity: WARNING
    metadata:
      cwe: CWE-755
      category: ai-generated
"""

#!/bin/bash
# Comprehensive static analysis for AI-generated code audit
 
set -euo pipefail
 
PROJECT_DIR="${1:-.}"
REPORT_DIR="${2:-/tmp/ai-audit-report}"
 
mkdir -p "$REPORT_DIR"
 
echo "=== AI-Generated Code 安全 Audit - Static Analysis ==="
echo "Project: $PROJECT_DIR"
echo "Reports: $REPORT_DIR"
echo ""
 
# Step 1: Semgrep with AI-specific rules
echo "--- Step 1: Semgrep Analysis ---"
if command -v semgrep &>/dev/null; then
    semgrep --config "p/owasp-top-ten" \
            --config "p/python-sql-injection" \
            --config "p/xss" \
            --config "p/python-安全-audit" \
            "$PROJECT_DIR" \
            --json \
            --輸出 "$REPORT_DIR/semgrep-results.json" \
            --exclude "node_modules" --exclude ".git" --exclude "venv" \
            2>/dev/null || true
 
    # Summarize
    python3 -c "
import json
with open('$REPORT_DIR/semgrep-results.json') as f:
    data = json.load(f)
results = data.get('results', [])
print(f'Total findings: {len(results)}')
by_severity = {}
for r in results:
    sev = r.get('extra', {}).get('severity', 'unknown')
    by_severity[sev] = by_severity.get(sev, 0) + 1
for sev, count in sorted(by_severity.items()):
    print(f'  {sev}: {count}')
"
else
    echo "Semgrep not installed. Install: pip install semgrep"
fi
 
# Step 2: Bandit (Python 安全 linter)
echo ""
echo "--- Step 2: Bandit Analysis ---"
if command -v bandit &>/dev/null; then
    bandit -r "$PROJECT_DIR" \
        -f json \
        -o "$REPORT_DIR/bandit-results.json" \
        --exclude ".git,node_modules,venv,__pycache__" \
        -ll \
        2>/dev/null || true
 
    python3 -c "
import json
with open('$REPORT_DIR/bandit-results.json') as f:
    data = json.load(f)
results = data.get('results', [])
print(f'Total findings: {len(results)}')
by_severity = {}
for r in results:
    sev = r.get('issue_severity', 'unknown')
    by_severity[sev] = by_severity.get(sev, 0) + 1
for sev, count in sorted(by_severity.items()):
    print(f'  {sev}: {count}')
"
else
    echo "Bandit not installed. Install: pip install bandit"
fi
 
# Step 3: Pattern-based checks for AI-specific issues
echo ""
echo "--- Step 3: AI-Specific Pattern Checks ---"
 
# Check for hardcoded credentials (common in AI-generated code)
echo "Hardcoded credentials:"
grep -rn "api_key\s*=\s*['\"]" "$PROJECT_DIR" --include="*.py" \
    --exclude-dir=".git" --exclude-dir="node_modules" -c 2>/dev/null || echo "  0 matches"
 
# Check for eval/exec usage
echo "eval/exec usage:"
grep -rn "\beval\b\|\bexec\b" "$PROJECT_DIR" --include="*.py" \
    --exclude-dir=".git" --exclude-dir="node_modules" -c 2>/dev/null || echo "  0 matches"
 
# Check for missing error handling patterns
echo "Bare except clauses:"
grep -rn "except:" "$PROJECT_DIR" --include="*.py" \
    --exclude-dir=".git" --exclude-dir="node_modules" -c 2>/dev/null || echo "  0 matches"
 
echo ""
echo "=== Static Analysis Complete ==="
echo "Results saved to: $REPORT_DIR"

import requests
from urllib.parse import quote
 
class AICodeDynamicTester:
    """Dynamic 測試 focused on common AI-generated 漏洞 patterns."""
 
    def __init__(self, base_url: str):
        self.base_url = base_url.rstrip("/")
        self.findings: list[dict] = []
 
    def test_sql_injection_endpoints(
        self, endpoints: list[dict]
    ) -> list[dict]:
        """測試 endpoints for SQL injection 漏洞."""
        sqli_payloads = [
            "' OR '1'='1",
            "' OR '1'='1' --",
            "1; DROP TABLE users --",
            "' UNION SELECT NULL, username, password FROM users --",
            "1' AND SLEEP(5) --",
        ]
 
        findings = []
        for endpoint in endpoints:
            url = f"{self.base_url}{endpoint['path']}"
            for param in endpoint.get("params", []):
                for payload in sqli_payloads:
                    try:
                        params = {param: payload}
                        response = requests.get(url, params=params, timeout=10)
 
                        # Check for SQL error messages in response
                        sql_errors = [
                            "syntax error",
                            "mysql_fetch",
                            "sqlite3.OperationalError",
                            "psycopg2.errors",
                            "ORA-",
                            "SQLSTATE",
                        ]
 
                        for error in sql_errors:
                            if error.lower() in response.text.lower():
                                findings.append({
                                    "type": "sql_injection",
                                    "endpoint": endpoint["path"],
                                    "parameter": param,
                                    "payload": payload,
                                    "evidence": error,
                                    "severity": "critical",
                                })
                                break
 
                        # Check for time-based blind SQLi
                        if "SLEEP" in payload and response.elapsed.total_seconds() > 4:
                            findings.append({
                                "type": "blind_sql_injection",
                                "endpoint": endpoint["path"],
                                "parameter": param,
                                "payload": payload,
                                "evidence": f"Response time: {response.elapsed.total_seconds():.1f}s",
                                "severity": "critical",
                            })
 
                    except requests.exceptions.Timeout:
                        if "SLEEP" in payload:
                            findings.append({
                                "type": "blind_sql_injection",
                                "endpoint": endpoint["path"],
                                "parameter": param,
                                "payload": payload,
                                "evidence": "Request timed out (possible sleep injection)",
                                "severity": "critical",
                            })
                    except requests.exceptions.RequestException:
                        pass
 
        return findings
 
    def test_xss_endpoints(self, endpoints: list[dict]) -> list[dict]:
        """測試 endpoints for reflected XSS 漏洞."""
        xss_payloads = [
            '<script>alert("XSS")</script>',
            '<img src=x onerror=alert(1)>',
            '" onmouseover="alert(1)"',
            "javascript:alert(1)",
            '<svg/onload=alert(1)>',
        ]
 
        findings = []
        for endpoint in endpoints:
            url = f"{self.base_url}{endpoint['path']}"
            for param in endpoint.get("params", []):
                for payload in xss_payloads:
                    try:
                        params = {param: payload}
                        response = requests.get(url, params=params, timeout=10)
 
                        # Check if payload is reflected without encoding
                        if payload in response.text:
                            findings.append({
                                "type": "reflected_xss",
                                "endpoint": endpoint["path"],
                                "parameter": param,
                                "payload": payload,
                                "evidence": "Payload reflected unencoded in response",
                                "severity": "high",
                            })
                    except requests.exceptions.RequestException:
                        pass
 
        return findings
 
    def test_authentication_bypass(
        self, protected_endpoints: list[str]
    ) -> list[dict]:
        """測試 for 認證 bypass in AI-generated auth code."""
        findings = []
 
        bypass_techniques = [
            {"name": "No auth header", "headers": {}},
            {"name": "Empty bearer", "headers": {"Authorization": "Bearer "}},
            {"name": "Invalid 符元", "headers": {"Authorization": "Bearer invalid"}},
            {"name": "Admin role claim", "headers": {"X-User-Role": "admin"}},
        ]
 
        for endpoint in protected_endpoints:
            url = f"{self.base_url}{endpoint}"
            for technique in bypass_techniques:
                try:
                    response = requests.get(
                        url, headers=technique["headers"], timeout=10
                    )
                    if response.status_code == 200:
                        findings.append({
                            "type": "auth_bypass",
                            "endpoint": endpoint,
                            "technique": technique["name"],
                            "evidence": f"Got 200 OK with {technique['name']}",
                            "severity": "critical",
                        })
                except requests.exceptions.RequestException:
                    pass
 
        return findings

from dataclasses import dataclass
 
@dataclass
class MaturityDimension:
    dimension: str
    level_1: str  # Initial
    level_2: str  # Developing
    level_3: str  # Defined
    level_4: str  # Managed
    level_5: str  # Optimizing
 
AI_CODE_SECURITY_MATURITY = [
    MaturityDimension(
        dimension="Tool Governance",
        level_1="No policy on AI tool usage",
        level_2="Informal guidance on approved tools",
        level_3="Formal policy with approved tool list",
        level_4="Policy enforced via technical controls",
        level_5="Continuous 評估 of new tools",
    ),
    MaturityDimension(
        dimension="Code Review",
        level_1="No distinction between AI and human code",
        level_2="Awareness that AI code needs extra review",
        level_3="Documented review checklist for AI code",
        level_4="Automated checks in CI/CD for AI patterns",
        level_5="Metrics-driven continuous improvement",
    ),
    MaturityDimension(
        dimension="Developer Training",
        level_1="No 訓練 on AI code 安全",
        level_2="Ad hoc awareness communications",
        level_3="Formal 訓練 program for AI tool users",
        level_4="Regular 訓練 with assessments",
        level_5="Threat modeling exercises with AI scenarios",
    ),
    MaturityDimension(
        dimension="Static Analysis",
        level_1="No static analysis",
        level_2="Generic SAST tools without AI-specific rules",
        level_3="SAST with AI-specific rule sets",
        level_4="Custom rules for organization's AI patterns",
        level_5="ML-enhanced 偵測 of AI code issues",
    ),
    MaturityDimension(
        dimension="Incident Response",
        level_1="No AI-specific incident procedures",
        level_2="AI code issues handled ad hoc",
        level_3="Documented playbook for AI code incidents",
        level_4="Practiced playbook with tabletop exercises",
        level_5="Automated 偵測 and response",
    ),
]

def generate_audit_report(
    project_name: str,
    ai_commits: list,
    static_findings: list,
    dynamic_findings: list,
    maturity_scores: dict,
) -> str:
    """Generate the final audit report."""
 
    critical = sum(1 for f in static_findings + dynamic_findings if f.get("severity") == "critical")
    high = sum(1 for f in static_findings + dynamic_findings if f.get("severity") == "high")
 
    report = f"""
# AI-Generated Code 安全 Audit Report
## Project: {project_name}
## Date: {datetime.utcnow().strftime('%Y-%m-%d')}
 
---
 
## Executive 總結
 
This audit assessed the 安全 of AI-generated code in the {project_name} project.
The 評估 identified {len(ai_commits)} commits attributed to AI coding tools,
{len(static_findings)} static analysis findings, and {len(dynamic_findings)} confirmed
漏洞 through dynamic 測試.
 
**Critical findings: {critical} | High findings: {high}**
 
## Key Findings
 
### 1. AI Code Generation Scope
- {len(ai_commits)} commits identified as AI-generated
- AI coding tools in use: [list from identification phase]
- Estimated percentage of codebase AI-generated: [X]%
 
### 2. 漏洞 總結
| Severity | Static | Dynamic | Total |
|---|---|---|---|
| Critical | {sum(1 for f in static_findings if f.get('severity') == 'critical')} | {sum(1 for f in dynamic_findings if f.get('severity') == 'critical')} | {critical} |
| High | {sum(1 for f in static_findings if f.get('severity') == 'high')} | {sum(1 for f in dynamic_findings if f.get('severity') == 'high')} | {high} |
 
### 3. Top 漏洞 Categories
[Summarize by CWE]
 
### 4. Organizational Maturity
[Summarize maturity 評估]
 
## Recommendations
 
1. **Immediate**: Remediate all critical and high findings
2. **Short-term**: Deploy AI-specific Semgrep rules in CI/CD
3. **Medium-term**: Establish AI code generation governance policy
4. **Long-term**: Build organizational maturity to Level 3+
 
## Detailed Findings
 
[Individual findings with evidence, impact, and remediation]
"""
    return report