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Uncontrolled Resource Consumption

🛡️ 8 条规则检测到此问题

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.

普遍性
频繁被利用
影响
建议审查
预防
已记录
8 个修复示例
2 预防
2 预防

如何修复此漏洞

基于 8 条 Shoulder 检测规则的 Resource Exhaustion 预防策略。

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 检测
3 检测

查找代码中的漏洞

使用Shoulder扫描代码中的Uncontrolled Resource Consumption模式。 8 规则.

终端
# Scan with Shoulder CLI
npx @shoulderdev/cli trust --cwe=400

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

检测规则 (8)

4 警告信号
4 警告信号

代码审查中需要关注的内容

这些模式表明潜在的Uncontrolled Resource Consumption漏洞。在代码审查和安全审计中注意查找。

🟡
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
🔍

扫描你的代码库: Uncontrolled Resource Consumption

Shoulder CLI 在整个代码库中找到易受攻击的模式。