Uncontrolled Resource Consumption (CWE-400)

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.

Prévalence

Élevée

Fréquemment exploitée

Impact

Moyen

Revue recommandée

Prévention

Documentée

8 exemples de correctifs

2 Prévention

Comment corriger cette vulnérabilité

Stratégies de prévention pour Resource Exhaustion basées sur 8 règles de détection Shoulder.

🐹 Go Tout voir Go détails →

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)
      }
  }

🟨 JavaScript Tout voir JavaScript détails →

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
+ });

☸️ Kubernetes Tout voir Kubernetes détails →

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"

🐍 Python Tout voir Python détails →

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 Détection

Trouvez les vulnérabilités dans votre code

Utilisez Shoulder pour scanner votre code à la recherche de patterns Uncontrolled Resource Consumption. 8 règles.

terminal

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

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

Règles de Détection (8)

🐹 Go 3 rules

LLM Denial of Service MEDIUM

Detects AI/LLM API calls lacking token limits or input validation that could enable denial of service.

Missing Request Size Limits MEDIUM

Request body read without size limit using ioutil.ReadAll or io.ReadAll.

Denial of Service via Resource Exhaustion MEDIUM

Unbounded goroutines, missing timeouts, or unchecked allocations from user input.

🟨 Javascript 2 rules

LLM Denial of Service MEDIUM

Detects AI/LLM API calls that lack token limits, potentially enabling denial of service attacks. OWASP LLM04 - Model Denial of Service. DoS attacks against LLMs can: - Exhaust API quotas through unbounded token generation - Cause excessive costs via high token usage - Degrade service availability This rule detects: - Missing max_tokens limits on completions - Missing input length validation - Unbounded streaming responses NOTE: Rate limiting is covered separately by the Express rate-limiting

Denial of Service via Unbounded Child Processes MEDIUM

Detects child process execution (exec, spawn) without proper resource limits. Without timeout or maxBuffer configuration, these processes can: - Hang indefinitely, consuming server resources - Flood memory with unbounded output - Enable DoS attacks through resource exhaustion This is especially critical when the command can be influenced by user input or interacts with external resources (network requests, git operations, etc.).

🔷 Typescript 2 rules

LLM Denial of Service MEDIUM

Denial of Service via Unbounded Child Processes MEDIUM

🐍 Python 2 rules

LLM Denial of Service MEDIUM

Detects AI/LLM API calls that lack token limits, potentially enabling denial of service attacks. OWASP LLM04 - Model Denial of Service. DoS attacks against LLMs can: - Exhaust API quotas through unbounded token generation - Cause excessive costs via high token usage - Degrade service availability This rule detects: - Missing max_tokens limits on completions - Missing input length validation NOTE: Rate limiting is covered separately by framework-specific rate-limiting rules.

Resource Exhaustion / Denial of Service MEDIUM

Detects operations that can cause resource exhaustion: unbounded loops on user input, reading entire large files into memory, recursive operations without depth limits, or missing timeouts. These can lead to memory exhaustion or CPU starvation (DoS).

📄 Yaml 1 rules

Missing Resource Limits MEDIUM

Detects containers missing resource limits.

4 Signes d'Alerte

Ce qu'il faut surveiller lors des revues de code

Ces patterns indiquent des vulnérabilités potentielles Uncontrolled Resource Consumption. Recherchez-les lors des revues de code et des audits de sécurité.

🟡

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

🔍

Scannez votre base de code pour Uncontrolled Resource Consumption

Shoulder CLI trouve les motifs vulnérables dans toute votre base de code.

npx shoulder trust Parcourir toutes les règles