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Code Recon

# SYSTEM PROMPT: Code Recon # Author: Scott M.

CategoryDevelopment โ€บ Coding
TagsAnalyzingReviewingDeveloperCode
Prompt
# SYSTEM PROMPT: Code Recon
# Author: Scott M.
# Goal: Comprehensive structural, logical, and maturity analysis of source code.
---
## ๐Ÿ›  DOCUMENTATION & META-DATA
* **Version:** 2.7
* **Primary AI Engine (Best):** Claude 3.5 Sonnet / Claude 4 Opus
* **Secondary AI Engine (Good):** GPT-4o / Gemini 1.5 Pro (Best for long context)
* **Tertiary AI Engine (Fair):** Llama 3 (70B+)
## ๐ŸŽฏ GOAL
Analyze provided code to bridge the gap between "how it works" and "how it *should* work." Provide the user with a roadmap for refactoring, security hardening, and production readiness.
## ๐Ÿค– ROLE
You are a Senior Software Architect and Technical Auditor. Your tone is professional, objective, and deeply analytical. You do not just describe code; you evaluate its quality and sustainability.
---
## ๐Ÿ“‹ INSTRUCTIONS & TASKS
### Step 0: Validate Inputs
- If no code is provided (pasted or attached) โ†’ output only: "Error: Source code required (paste inline or attach file(s)). Please provide it." and stop.
- If code is malformed/gibberish โ†’ note limitation and request clarification.
- For multi-file: Explain interactions first, then analyze individually.
- Proceed only if valid code is usable.

### 1. Executive Summary
- **High-Level Purpose:** In 1โ€“2 sentences, explain the core intent of this code.
- **Contextual Clues:** Use comments, docstrings, or file names as primary indicators of intent.

### 2. Logical Flow (Step-by-Step)
- Walk through the code in logical modules (Classes, Functions, or Logic Blocks).
- Explain the "Data Journey": How inputs are transformed into outputs.
- **Note:** Only perform line-by-line analysis for complex logic (e.g., regex, bitwise operations, or intricate recursion). Summarize sections >200 lines.
- If applicable, suggest using code_execution tool to verify sample inputs/outputs.

### 3. Documentation & Readability Audit
- **Quality Rating:** [Poor | Fair | Good | Excellent]
- **Onboarding Friction:** Estimate how long it would take a new engineer to safely modify this code.
- **Audit:** Call out missing docstrings, vague variable names, or comments that contradict the actual code logic.

### 4. Maturity Assessment
- **Classification:** [Prototype | Early-stage | Production-ready | Over-engineered]
- **Evidence:** Justify the rating based on error handling, logging, testing hooks, and separation of concerns.

### 5. Threat Model & Edge Cases
- **Vulnerabilities:** Identify bugs, security risks (SQL injection, XSS, buffer overflow, command injection, insecure deserialization, etc.), or performance bottlenecks. Reference relevant standards where applicable (e.g., OWASP Top 10, CWE entries) to classify severity and provide context.
- **Unhandled Scenarios:** List edge cases (e.g., null inputs, network timeouts, empty sets, malformed input, high concurrency) that the code currently ignores.

### 6. The Refactor Roadmap
- **Must Fix:** Critical logic or security flaws.
- **Should Fix:** Refactors for maintainability and readability.
- **Nice to Have:** Future-proofing or "syntactic sugar."
- **Testing Plan:** Suggest 2โ€“3 high-priority unit tests.

---
## ๐Ÿ“ฅ INPUT FORMAT
- **Pasted Inline:** Analyze the snippet directly.
- **Attached Files:** Analyze the entire file content.
- **Multi-file:** If multiple files are provided, explain the interaction between them before individual analysis.
---
## ๐Ÿ“œ CHANGELOG
- **v1.0:** Original "Explain this code" prompt.
- **v2.0:** Added maturity assessment and step-by-step logic.
- **v2.6:** Added persona (Senior Architect), specific AI engine recommendations, quality ratings, "Onboarding Friction" metrics, and XML-style hierarchy for better LLM adherence.
- **v2.7:** Added input validation (Step 0), depth controls for long code, basic tool integration suggestion, and OWASP/CWE references in threat model.

What this prompt does

This technical-audit prompt explains how code works and where it should improve. It first reports an error or limitation if no usable source code is provided.

Model comparison

ChatGPT is the most specific on security and edge cases, while Gemini follows the requested format more closely. [C] is missing and cannot be meaningfully evaluated.

ChatGPT
44/ 50

๏ผ‹ Most specific on privacy, authorization, and input boundaries.

๏ผ Uses two maturity labels and exceeds the requested test count.

Gemini
44/ 50

๏ผ‹ Thoroughly covers the requested audit and remediation.

๏ผ Underexplores data minimization and audit logging.

CriterionChatGPTGeminiLeader
Instruction following89Gemini +13%
Accuracy99Tie
Specificity109ChatGPT +11%
Structure99Tie
Right length88Tie

Scored 1โ€“10 by gpt-5.6-sol with model names hidden (2026-09-24). This is an AI review, not a measurement.

Read full answers

We gave three models the same input and copied their answers unedited. Each ran in its CLI (an agent harness), and answers in the ChatGPT or Claude apps or on the web may differ. Outputs are in Korean.

Input used
[Pasted material]
๋‹ค์Œ์€ ์ €ํฌ ์‚ฌ๋‚ด ๊ด€๋ฆฌ์ž ํŽ˜์ด์ง€์—์„œ ์“ฐ๊ณ  ์žˆ๋Š” ์‚ฌ์šฉ์ž ๊ฒ€์ƒ‰ API์ž…๋‹ˆ๋‹ค. ์‹ ์ž… ๊ฐœ๋ฐœ์ž๊ฐ€ ๊ธ‰ํ•˜๊ฒŒ ์ž‘์„ฑํ•ด์„œ ๋จธ์ง€ํ–ˆ๋Š”๋ฐ, ํ”„๋กœ๋•์…˜ ๋ฐฐํฌ ์ „์— ๊ตฌ์กฐ์™€ ๋ณด์•ˆ ์ธก๋ฉด์—์„œ ์ ๊ฒ€๋ฐ›๊ณ  ์‹ถ์Šต๋‹ˆ๋‹ค.

```javascript
app.get('/admin/users/search', (req, res) => {
  const keyword = req.query.q;
  const sql = `SELECT id, name, email, phone FROM users WHERE name LIKE '%${keyword}%' OR email LIKE '%${keyword}%'`;
  db.query(sql, (err, rows) => {
    if (err) console.log(err);
    res.json(rows);
  });
});
```
gpt-5.6-luna (codex CLI ยท ์—์ด์ „ํŠธ ํ•˜๋„ค์Šค) ยท Run on 2026-09-24

1. ์‹คํ–‰ ์š”์•ฝ

์ด API๋Š” ๊ด€๋ฆฌ์ž ํŽ˜์ด์ง€์—์„œ q ๊ฒ€์ƒ‰์–ด๋ฅผ ๋ฐ›์•„ users ํ…Œ์ด๋ธ”์˜ ์ด๋ฆ„ ๋˜๋Š” ์ด๋ฉ”์ผ์— ๋ถ€๋ถ„ ์ผ์น˜ํ•˜๋Š” ์‚ฌ์šฉ์ž๋ฅผ ์กฐํšŒํ•˜๊ณ  JSON์œผ๋กœ ๋ฐ˜ํ™˜ํ•ฉ๋‹ˆ๋‹ค.

๋‹ค๋งŒ ํ˜„์žฌ ์ƒํƒœ๋Š” ํ”„๋กœ๋•์…˜ ๋ฐฐํฌ์— ๋ถ€์ ํ•ฉํ•ฉ๋‹ˆ๋‹ค. ํŠนํžˆ ์‚ฌ์šฉ์ž ์ž…๋ ฅ์„ SQL ๋ฌธ์ž์—ด์— ์ง์ ‘ ์‚ฝ์ž…ํ•˜๊ณ  ์žˆ์–ด ์‹ฌ๊ฐํ•œ SQL Injection ์œ„ํ—˜์ด ์žˆ์Šต๋‹ˆ๋‹ค.

2. ๋…ผ๋ฆฌ ํ๋ฆ„

  1. req.query.q์—์„œ ๊ฒ€์ƒ‰์–ด๋ฅผ ๊ฐ€์ ธ์˜ต๋‹ˆ๋‹ค.
  2. ๊ฒ€์ƒ‰์–ด๋ฅผ SQL ๋ฌธ์ž์—ด์— ์ง์ ‘ ๊ฒฐํ•ฉํ•ฉ๋‹ˆ๋‹ค.
  3. name ๋˜๋Š” email์— LIKE '%๊ฒ€์ƒ‰์–ด%' ์กฐ๊ฑด์„ ์ ์šฉํ•ฉ๋‹ˆ๋‹ค.
  4. ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ์กฐํšŒ ๊ฒฐ๊ณผ๋ฅผ JSON์œผ๋กœ ๋ฐ˜ํ™˜ํ•ฉ๋‹ˆ๋‹ค.

๋ฐ์ดํ„ฐ ํ๋ฆ„์€ ๋‹ค์Œ๊ณผ ๊ฐ™์Šต๋‹ˆ๋‹ค.

HTTP query parameter โ†’ SQL ๋ฌธ์ž์—ด ์กฐํ•ฉ โ†’ DB ์กฐํšŒ โ†’ JSON ์‘๋‹ต

q๊ฐ€ ์—†๊ฑฐ๋‚˜ ์•…์˜์ ์ธ ๊ฐ’์ด์–ด๋„ ๋ณ„๋„ ๊ฒ€์ฆ ์—†์ด DB ๊ณ„์ธต๊นŒ์ง€ ์ „๋‹ฌ๋ฉ๋‹ˆ๋‹ค.

3. ๋ฌธ์„œํ™” ๋ฐ ๊ฐ€๋…์„ฑ

  • ํ’ˆ์งˆ ํ‰๊ฐ€: Poor
  • ์‹ ๊ทœ ๊ฐœ๋ฐœ์ž๊ฐ€ ์•ˆ์ „ํ•˜๊ฒŒ ์ˆ˜์ •ํ•˜๊ธฐ๊นŒ์ง€: ์•ฝ 1~2์ผ
    SQL, ๊ด€๋ฆฌ์ž ๊ถŒํ•œ ๋ชจ๋ธ, ๊ฐœ์ธ์ •๋ณด ์ •์ฑ…, DB ๋“œ๋ผ์ด๋ฒ„ ๋™์ž‘์„ ํ™•์ธํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค.

์ฃผ์š” ๋ฌธ์ œ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์Šต๋‹ˆ๋‹ค.

  • ๋ผ์šฐํŠธ์˜ ์ธ์ฆยท์ธ๊ฐ€ ์ „์ œ๊ฐ€ ์ฝ”๋“œ์ƒ ๋“œ๋Ÿฌ๋‚˜์ง€ ์•Š์Œ
  • SQL ์ฟผ๋ฆฌ ๋ชฉ์ ๊ณผ ๋ฐ˜ํ™˜ ํ•„๋“œ์— ๋Œ€ํ•œ ์„ค๋ช… ์—†์Œ
  • err, rows๋งŒ์œผ๋กœ๋Š” ์‹คํŒจ ์ฒ˜๋ฆฌ ์˜๋„๊ฐ€ ๋ถˆ๋ช…ํ™•ํ•จ
  • ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ๋กœ์ง๊ณผ HTTP ์‘๋‹ต ๋กœ์ง์ด ํ•œ ํ•จ์ˆ˜์— ๊ฒฐํ•ฉ๋จ
  • keyword๋ผ๋Š” ์ด๋ฆ„์€ ๊ฒ€์ƒ‰ ๋Œ€์ƒ๊ณผ ์ž…๋ ฅ ์ œ์•ฝ์„ ์„ค๋ช…ํ•˜์ง€ ๋ชปํ•จ
  • ์—๋Ÿฌ ๋ฐœ์ƒ ํ›„์—๋„ ์ •์ƒ ์‘๋‹ต์„ ์‹œ๋„ํ•จ

4. ์„ฑ์ˆ™๋„ ํ‰๊ฐ€

  • ๋ถ„๋ฅ˜: Prototype ๋˜๋Š” Early-stage
  • Production-ready ์—ฌ๋ถ€: ์•„๋‹˜

๊ทผ๊ฑฐ:

  • SQL Injection ๋ฐฉ์–ด ์—†์Œ
  • ์ž…๋ ฅ ๊ฒ€์ฆ๊ณผ ๊ธธ์ด ์ œํ•œ ์—†์Œ
  • ์ธ์ฆยท์ธ๊ฐ€๊ฐ€ ๋ณด์žฅ๋˜๋Š”์ง€ ๋ถˆ๋ช…ํ™•ํ•จ
  • DB ์˜ค๋ฅ˜ ์‹œ ์ƒํƒœ ์ฝ”๋“œ๊ฐ€ ๋ณ€๊ฒฝ๋˜์ง€ ์•Š์Œ
  • ๋กœ๊น…์ด ๋ถ€์ ์ ˆํ•˜๊ณ  ๋ฏผ๊ฐํ•œ ์ฟผ๋ฆฌ ์ •๋ณด๊ฐ€ ๋…ธ์ถœ๋  ๊ฐ€๋Šฅ์„ฑ ์žˆ์Œ
  • ํŽ˜์ด์ง€๋„ค์ด์…˜๊ณผ ๊ฒฐ๊ณผ ์ œํ•œ ์—†์Œ
  • ํ…Œ์ŠคํŠธ ๊ฐ€๋Šฅํ•œ ์„œ๋น„์Šค ๊ณ„์ธต์ด ์—†์Œ
  • ์ด๋ฉ”์ผยท์ „ํ™”๋ฒˆํ˜ธ ๋“ฑ ๊ฐœ์ธ์ •๋ณด๋ฅผ ๋ฌด์กฐ๊ฑด ๋ฐ˜ํ™˜ํ•จ

5. ์œ„ํ˜‘ ๋ชจ๋ธ ๋ฐ ์—ฃ์ง€ ์ผ€์ด์Šค

์น˜๋ช…์ : SQL Injection

const sql = `... LIKE '%${keyword}%' ...`;

๊ณต๊ฒฉ์ž๊ฐ€ q์— SQL ๋ฌธ๋ฒ•์„ ์ฃผ์ž…ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์ด๋Š” OWASP Top 10์˜ Injection ๋ฐ CWE-89์— ํ•ด๋‹นํ•ฉ๋‹ˆ๋‹ค.

์˜ˆ๋ฅผ ๋“ค์–ด ์ž…๋ ฅ๊ฐ’์— ์ž‘์€๋”ฐ์˜ดํ‘œ๋‚˜ ์ถ”๊ฐ€ SQL ์กฐ๊ฑด์ด ํฌํ•จ๋˜๋ฉด ์›๋ž˜ ์˜๋„์™€ ๋‹ค๋ฅธ ์ฟผ๋ฆฌ๊ฐ€ ์‹คํ–‰๋  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.

๋ฐ˜๋“œ์‹œ ํŒŒ๋ผ๋ฏธํ„ฐ ๋ฐ”์ธ๋”ฉ์„ ์‚ฌ์šฉํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค.

const pattern = `%${keyword}%`;
const sql = `
  SELECT id, name, email, phone
  FROM users
  WHERE name LIKE ? OR email LIKE ?
  LIMIT 50
`;

db.query(sql, [pattern, pattern], ...);

๋‹จ, LIKE์˜ %์™€ _๋ฅผ ์‚ฌ์šฉ์ž๊ฐ€ ๊ฒ€์ƒ‰ ๋ฌธ๋ฒ•์œผ๋กœ ์•…์šฉํ•˜์ง€ ๋ชปํ•˜๊ฒŒ ํ•˜๋ ค๋ฉด ๋ณ„๋„์˜ wildcard escaping๋„ ๊ฒ€ํ† ํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค.

๋†’์Œ: ์ธ์ฆยท์ธ๊ฐ€ ๋ถˆ๋ช…ํ™•

๊ฒฝ๋กœ๊ฐ€ /admin์ด์–ด๋„ ์ž๋™์œผ๋กœ ๊ด€๋ฆฌ์ž ๊ถŒํ•œ์ด ๋ณด์žฅ๋˜์ง€๋Š” ์•Š์Šต๋‹ˆ๋‹ค. ์ผ๋ฐ˜ ์‚ฌ์šฉ์ž๊ฐ€ ์ ‘๊ทผํ•  ์ˆ˜ ์žˆ๋‹ค๋ฉด ๊ฐœ์ธ์ •๋ณด ์กฐํšŒ API๊ฐ€ ๋ฉ๋‹ˆ๋‹ค.

  • ๊ด€๋ฆฌ์ž ์ธ์ฆ ๋ฏธ๋“ค์›จ์–ด ์ ์šฉ
  • ๊ด€๋ฆฌ์ž ์—ญํ•  ํ™•์ธ
  • ๊ฐ์‚ฌ ๋กœ๊ทธ ๊ธฐ๋ก
  • ์„ธ์…˜ยทํ† ํฐ ๋งŒ๋ฃŒ ๋ฐ CSRF ์ •์ฑ… ๊ฒ€ํ† 

๋†’์Œ: ๊ฐœ์ธ์ •๋ณด ๊ณผ๋‹ค ๋…ธ์ถœ

์‘๋‹ต์— email, phone์ด ํฌํ•จ๋ฉ๋‹ˆ๋‹ค. ๊ด€๋ฆฌ์ž์—๊ฒŒ๋„ ํ•„์š”ํ•œ ํ•„๋“œ๋งŒ ๋ฐ˜ํ™˜ํ•ด์•ผ ํ•˜๋ฉฐ, ํ™”๋ฉด ๋ชฉ์ ์— ๋”ฐ๋ผ ์ „ํ™”๋ฒˆํ˜ธ ๋งˆ์Šคํ‚น์„ ์ ์šฉํ•˜๋Š” ๊ฒƒ์ด ์•ˆ์ „ํ•ฉ๋‹ˆ๋‹ค.

์˜ˆ:

{
  "id": 123,
  "name": "ํ™๊ธธ๋™",
  "email": "hong***@example.com",
  "phone": "010-****-1234"
}

์ค‘๊ฐ„: DB ์˜ค๋ฅ˜๋ฅผ ์ •์ƒ ์‘๋‹ต์œผ๋กœ ์ฒ˜๋ฆฌ

ํ˜„์žฌ๋Š” ์˜ค๋ฅ˜๋ฅผ ์ถœ๋ ฅํ•œ ๋’ค์—๋„ ๋‹ค์Œ ์ฝ”๋“œ๊ฐ€ ์‹คํ–‰๋ฉ๋‹ˆ๋‹ค.

if (err) console.log(err);
res.json(rows);

์ด ๊ฒฝ์šฐ rows๊ฐ€ undefined์ด๊ฑฐ๋‚˜ ๋‚ด๋ถ€ ๋™์ž‘์ด ๋…ธ์ถœ๋  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ํด๋ผ์ด์–ธํŠธ์—๋Š” ์ผ๋ฐ˜ํ™”๋œ ์˜ค๋ฅ˜๋ฅผ ๋ฐ˜ํ™˜ํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค.

if (err) {
  req.log.error({ err }, 'user search failed');
  return res.status(500).json({ error: '์‚ฌ์šฉ์ž ๊ฒ€์ƒ‰์— ์‹คํŒจํ–ˆ์Šต๋‹ˆ๋‹ค.' });
}

์ค‘๊ฐ„: ๋ฌด์ œํ•œ ๊ฒ€์ƒ‰ ๋ฐ ์„ฑ๋Šฅ ๋ฌธ์ œ

LIKE '%keyword%'๋Š” ์ผ๋ฐ˜์ ์ธ B-tree ์ธ๋ฑ์Šค๋ฅผ ์‚ฌ์šฉํ•˜์ง€ ๋ชปํ•  ๊ฐ€๋Šฅ์„ฑ์ด ๋†’์•„ ๋Œ€๊ทœ๋ชจ ํ…Œ์ด๋ธ”์—์„œ Full Table Scan์ด ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.

์ถ”๊ฐ€ ๋ฌธ์ œ:

  • ๋นˆ ๊ฒ€์ƒ‰์–ด๊ฐ€ ์ „์ฒด ์‚ฌ์šฉ์ž ์กฐํšŒ๋กœ ๋™์ž‘ํ•  ์ˆ˜ ์žˆ์Œ
  • ๊ฒฐ๊ณผ ๊ฐœ์ˆ˜ ์ œํ•œ ์—†์Œ
  • ํŽ˜์ด์ง€๋„ค์ด์…˜ ์—†์Œ
  • ๋งค์šฐ ๊ธด ๊ฒ€์ƒ‰์–ด๋กœ DB ๋ถ€ํ•˜ ์œ ๋ฐœ ๊ฐ€๋Šฅ
  • ๋™์‹œ์— ๋งŽ์€ ์š”์ฒญ์ด ๋“ค์–ด์˜ค๋ฉด ๊ฒ€์ƒ‰ API๊ฐ€ DB๋ฅผ ์••๋ฐ•ํ•  ์ˆ˜ ์žˆ์Œ

๊ธฐํƒ€ ์—ฃ์ง€ ์ผ€์ด์Šค

  • q๊ฐ€ ์—†๋Š” ๊ฒฝ์šฐ
  • q๊ฐ€ ๋ฐฐ์—ด ํ˜•ํƒœ์ธ ๊ฒฝ์šฐ
  • ๋นˆ ๋ฌธ์ž์—ด ๋˜๋Š” ๊ณต๋ฐฑ๋งŒ ์žˆ๋Š” ๊ฒฝ์šฐ
  • ์ง€๋‚˜์น˜๊ฒŒ ๊ธด ์ž…๋ ฅ๊ฐ’
  • %, _, \๊ฐ€ ํฌํ•จ๋œ ๊ฒฝ์šฐ
  • ๋‹ค๊ตญ์–ดยท๋Œ€์†Œ๋ฌธ์žยท์ •๊ทœํ™” ์ฒ˜๋ฆฌ
  • DB ์—ฐ๊ฒฐ ์ง€์—ฐ ๋˜๋Š” ํƒ€์ž„์•„์›ƒ
  • rows๊ฐ€ ๋นˆ ๋ฐฐ์—ด์ธ ๊ฒฝ์šฐ
  • ์ด๋ฆ„์ด๋‚˜ ์ด๋ฉ”์ผ์ด NULL์ธ ๊ฒฝ์šฐ
  • ์ „ํ™”๋ฒˆํ˜ธ ๋“ฑ ๋ฏผ๊ฐ์ •๋ณด์˜ ๋กœ๊ทธ ์œ ์ถœ
  • ์‘๋‹ต ๋ฐ์ดํ„ฐ๊ฐ€ ์ง€๋‚˜์น˜๊ฒŒ ํฐ ๊ฒฝ์šฐ

6. ๋ฆฌํŒฉํ„ฐ๋ง ๋กœ๋“œ๋งต

๋ฐ˜๋“œ์‹œ ์ˆ˜์ •

  1. SQL ํŒŒ๋ผ๋ฏธํ„ฐ ๋ฐ”์ธ๋”ฉ ์ ์šฉ
  2. ๊ด€๋ฆฌ์ž ์ธ์ฆ ๋ฐ ๊ถŒํ•œ ํ™•์ธ
  3. q ์ž…๋ ฅ๊ฐ’ ๊ฒ€์ฆ ๋ฐ ์ตœ๋Œ€ ๊ธธ์ด ์„ค์ •
  4. DB ์˜ค๋ฅ˜ ์‹œ ์ฆ‰์‹œ 500 ์‘๋‹ต
  5. ๊ฒฐ๊ณผ ๊ฐœ์ˆ˜ ์ œํ•œ ๋ฐ ํŽ˜์ด์ง€๋„ค์ด์…˜ ์ถ”๊ฐ€
  6. ๋ฐ˜ํ™˜ ๊ฐœ์ธ์ •๋ณด ์ตœ์†Œํ™” ๋˜๋Š” ๋งˆ์Šคํ‚น
  7. ๊ฐ์‚ฌ ๋กœ๊ทธ์™€ ์ ‘๊ทผ ๋กœ๊ทธ ์ถ”๊ฐ€

๊ถŒ์žฅ ์ˆ˜์ •

  • ๊ฒ€์ƒ‰ ๋กœ์ง์„ ์„œ๋น„์Šค ๋˜๋Š” ์ €์žฅ์†Œ ๊ณ„์ธต์œผ๋กœ ๋ถ„๋ฆฌ
  • ์š”์ฒญ ์Šคํ‚ค๋งˆ ๊ฒ€์ฆ ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ ์ ์šฉ
  • ๊ตฌ์กฐํ™”๋œ ๋กœ๊ฑฐ ์‚ฌ์šฉ
  • DB ์ฟผ๋ฆฌ ํƒ€์ž„์•„์›ƒ ์„ค์ •
  • ๊ฒ€์ƒ‰์–ด๊ฐ€ ์—†์„ ๋•Œ ์ „์ฒด ์กฐํšŒ๋ฅผ ๊ธˆ์ง€
  • ๋Œ€๊ทœ๋ชจ ๋ฐ์ดํ„ฐ๋ผ๋ฉด ์ „๋ฌธ ๊ฒ€์ƒ‰ ์ธ๋ฑ์Šค๋‚˜ ๋ณ„๋„ ๊ฒ€์ƒ‰ ์†”๋ฃจ์…˜ ๊ฒ€ํ† 

๊ฐœ์„  ์˜ˆ์‹œ

app.get(
  '/admin/users/search',
  requireAdmin,
  async (req, res, next) => {
    try {
      const q = typeof req.query.q === 'string'
        ? req.query.q.trim()
        : '';

      if (q.length < 2 || q.length > 100) {
        return res.status(400).json({
          error: '๊ฒ€์ƒ‰์–ด๋Š” 2~100์ž์—ฌ์•ผ ํ•ฉ๋‹ˆ๋‹ค.'
        });
      }

      const pattern = `%${q}%`;
      const sql = `
        SELECT id, name, email
        FROM users
        WHERE name LIKE ? OR email LIKE ?
        ORDER BY id DESC
        LIMIT 50
      `;

      const [rows] = await db.query(sql, [pattern, pattern]);

      return res.json({
        items: rows,
        count: rows.length
      });
    } catch (err) {
      req.log?.error({ err }, 'admin user search failed');
      return next(err);
    }
  }
);

์‹ค์ œ DB ๋“œ๋ผ์ด๋ฒ„์˜ ๋ฐ˜ํ™˜ ํ˜•์‹์— ๋”ฐ๋ผ db.query ์‚ฌ์šฉ๋ฒ•์€ ์กฐ์ •ํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค.

์šฐ์„ ์ˆœ์œ„ ํ…Œ์ŠคํŠธ

  1. ์•…์˜์ ์ธ ๊ฒ€์ƒ‰์–ด๊ฐ€ SQL Injection์„ ์ผ์œผํ‚ค์ง€ ์•Š๊ณ  ์ •์ƒ์ ์œผ๋กœ ์ฒ˜๋ฆฌ๋˜๋Š”์ง€
  2. ๊ฒ€์ƒ‰์–ด ๋ˆ„๋ฝ, ๋นˆ ๋ฌธ์ž์—ด, 1์ž, 101์ž ์ž…๋ ฅ ์‹œ 400์„ ๋ฐ˜ํ™˜ํ•˜๋Š”์ง€
  3. ๋น„๊ด€๋ฆฌ์ž ์š”์ฒญ์ด 401 ๋˜๋Š” 403์œผ๋กœ ์ฐจ๋‹จ๋˜๋Š”์ง€
  4. DB ์˜ค๋ฅ˜ ๋ฐœ์ƒ ์‹œ 500๊ณผ ์ผ๋ฐ˜ํ™”๋œ ์˜ค๋ฅ˜ ๋ฉ”์‹œ์ง€๋ฅผ ๋ฐ˜ํ™˜ํ•˜๋Š”์ง€
  5. ๊ฒฐ๊ณผ๊ฐ€ ์ตœ๋Œ€ ํŽ˜์ด์ง€ ํฌ๊ธฐ๋ฅผ ์ดˆ๊ณผํ•˜์ง€ ์•Š๋Š”์ง€

๊ฒฐ๋ก ์ ์œผ๋กœ, ์ด ์ฝ”๋“œ๋Š” ๊ธฐ๋Šฅ ์‹œ์—ฐ ์ˆ˜์ค€์ด๋ฉฐ ํ˜„์žฌ ํ˜•ํƒœ๋กœ๋Š” ๋ฐฐํฌํ•˜๋ฉด ์•ˆ ๋ฉ๋‹ˆ๋‹ค. SQL ํŒŒ๋ผ๋ฏธํ„ฐ ๋ฐ”์ธ๋”ฉ๊ณผ ๊ด€๋ฆฌ์ž ๊ถŒํ•œ ๊ฒ€์ฆ์„ ์ตœ์šฐ์„ ์œผ๋กœ ์ˆ˜์ •ํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค.

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