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Uncontrolled Resource Consumption

🛡️ 8 件のルールが検出します

Uncontrolled Resource Consumption

The product does not properly control the allocation and maintenance of a limited resource, thereby enabling an actor to influence the amount of resources consumed, eventually leading to the exhaustion of available resources.

Limited resources include memory, file system storage, database connection pool entries, and CPU. If an attacker can trigger the allocation of these limited resources, but the number or size of the resources is not controlled, then the attacker could cause a denial of service.

普及度
頻繁に悪用される
影響度
ミディアム
レビュー推奨
予防
文書化済み
8 件の修正例
2 予防
2 予防

この脆弱性の修正方法

8 件の Shoulder 検出ルールに基づく Resource Exhaustion の予防策。

LLM Denial of Service MEDIUM

Set MaxTokens limits, validate input length, and configure timeouts for LLM API calls

+13 -3 go
  func handler(w http.ResponseWriter, r *http.Request) {
      var req ChatRequest
      json.NewDecoder(r.Body).Decode(&req)
-     resp, _ := client.CreateChatCompletion(ctx, openai.ChatCompletionRequest{
-         Model:    "gpt-4",
-         Messages: []openai.ChatCompletionMessage{{Content: req.Message}},
+ 
+     message := req.Message
+     if len(message) > 2000 {
+         message = message[:2000]
+     }
+ 
+     ctx, cancel := context.WithTimeout(r.Context(), 30*time.Second)
+     defer cancel()
+ 
+     resp, _ := client.CreateChatCompletion(ctx, openai.ChatCompletionRequest{
+         Model:     "gpt-4",
+         Messages:  []openai.ChatCompletionMessage{{Content: message}},
+         MaxTokens: 500,
      })
      json.NewEncoder(w).Encode(resp)
  }
  
Missing Request Size Limits MEDIUM

Use http.MaxBytesReader to limit request body size before reading

+6 -1 go
  func handler(w http.ResponseWriter, r *http.Request) {
-     body, _ := io.ReadAll(r.Body)
+     r.Body = http.MaxBytesReader(w, r.Body, 10*1024*1024)
+     body, err := io.ReadAll(r.Body)
+     if err != nil {
+         http.Error(w, "Request too large", 413)
+         return
+     }
      process(body)
  }
  
Denial of Service via Resource Exhaustion MEDIUM

Limit goroutines with semaphore, set HTTP timeouts, and validate allocation sizes

+5 -2 go
  func process(items []string) {
-     for _, item := range items {
-         go func(i string) {
+     sem := make(chan struct{}, 100)
+     for _, item := range items {
+         sem <- struct{}{}
+         go func(i string) {
+             defer func() { <-sem }()
              expensiveOperation(i)
          }(item)
      }
  }
  
LLM Denial of Service MEDIUM

Set max_tokens limits and validate input length before LLM API calls

+5 -3 javascript
- const response = await openai.chat.completions.create({
-   model: 'gpt-4',
-   messages: [{ role: 'user', content: req.body.message }]
+ const message = req.body.message.substring(0, 2000);
+ const response = await openai.chat.completions.create({
+   model: 'gpt-4',
+   messages: [{ role: 'user', content: message }],
+   max_tokens: 500
  });
  
Denial of Service via Unbounded Child Processes MEDIUM

Configure timeout and maxBuffer for child process execution to prevent resource exhaustion

+4 -1 javascript
- const { stdout } = await execPromise(`ping -c 4 ${domain}`);
+ const { stdout } = await execPromise(`ping -c 4 ${domain}`, {
+   timeout: 5000,
+   maxBuffer: 1024 * 100
+ });
  
Missing Resource Limits MEDIUM

Define CPU and memory resource limits to prevent resource exhaustion and denial of service

+7 -2 yaml
  apiVersion: v1
  kind: Pod
  spec:
    containers:
    - name: app
      image: nginx:1.25
-     ports:
-       - containerPort: 80
+     resources:
+       requests:
+         memory: "128Mi"
+         cpu: "250m"
+       limits:
+         memory: "256Mi"
+         cpu: "500m"
  
LLM Denial of Service MEDIUM

Set max_tokens limits, validate input length, and configure timeouts for LLM API calls

+11 -5 python
- @app.route('/chat', methods=['POST'])
- def chat():
-     response = openai.chat.completions.create(
-         model='gpt-4',
-         messages=[{'role': 'user', 'content': request.json['message']}]
+ MAX_INPUT_LENGTH = 2000
+ MAX_OUTPUT_TOKENS = 500
+ 
+ @app.route('/chat', methods=['POST'])
+ def chat():
+     message = request.json['message'][:MAX_INPUT_LENGTH]
+     response = openai.chat.completions.create(
+         model='gpt-4',
+         messages=[{'role': 'user', 'content': message}],
+         max_tokens=MAX_OUTPUT_TOKENS,
+         timeout=30
      )
      return jsonify(response.choices[0].message.content)
  
Resource Exhaustion / Denial of Service MEDIUM

Set size limits on file reads, bound loop iterations, and add timeouts

+8 -5 python
- from flask import request
- 
- @app.route('/upload', methods=['POST'])
- def upload():
-     content = request.files['file'].read()
+ from flask import Flask, request
+ 
+ app = Flask(__name__)
+ app.config['MAX_CONTENT_LENGTH'] = 10 * 1024 * 1024  # 10 MB
+ 
+ @app.route('/upload', methods=['POST'])
+ def upload():
+     content = request.files['file'].read(10 * 1024 * 1024)
      return process(content)
  
3 検出
3 検出

コードの脆弱性を見つける

Shoulderを使用してコードのUncontrolled Resource Consumptionパターンをスキャンしましょう。 8 ルール.

ターミナル
# Scan with Shoulder CLI
npx @shoulderdev/cli trust --cwe=400

# Or scan entire project
npx @shoulderdev/cli trust .

検出ルール (8)

4 警告サイン
4 警告サイン

コードレビューで注目すべき点

これらのパターンはUncontrolled Resource Consumptionの潜在的な脆弱性を示しています。コードレビューとセキュリティ監査中に探してください。

🟡
LLM API call lacks resource limits go-llm-denial-of-service
🟡
AI/LLM API calls lacking token limits or input validation that could enable denial of service go-llm-denial-of-service
🟡
Unbounded resource usage can lead to DoS go-resource-exhaustion
🟡
AI/LLM API calls that lack token limits, potentially enabling denial of service attacks javascript-llm-denial-of-service
🟡
child process execution (exec, spawn) without proper resource limits javascript-unbounded-exec-dos
🟡
Container is missing resource limits. kubernetes-missing-resource-limits
🟡
containers missing resource limits kubernetes-missing-resource-limits
🟡
operations that can cause resource exhaustion: unbounded loops on user input, reading entire large f python-resource-exhaustion
🔍

コードベースをスキャン: Uncontrolled Resource Consumption

Shoulder CLI はコードベース全体から脆弱なパターンを見つけます。