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.
이 취약점을 수정하는 방법
8개의 Shoulder 탐지 규칙을 기반으로 한 Resource Exhaustion 예방 전략.
Set MaxTokens limits, validate input length, and configure timeouts for LLM API calls
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) }
Use http.MaxBytesReader to limit request body size before reading
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) }
Limit goroutines with semaphore, set HTTP timeouts, and validate allocation sizes
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) } }
Set max_tokens limits and validate input length before LLM API calls
- 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 });
Configure timeout and maxBuffer for child process execution to prevent resource exhaustion
- const { stdout } = await execPromise(`ping -c 4 ${domain}`); + const { stdout } = await execPromise(`ping -c 4 ${domain}`, { + timeout: 5000, + maxBuffer: 1024 * 100 + });
Define CPU and memory resource limits to prevent resource exhaustion and denial of service
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"
Set max_tokens limits, validate input length, and configure timeouts for LLM API calls
- @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)
Set size limits on file reads, bound loop iterations, and add timeouts
- 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)
코드에서 취약점 찾기
Shoulder를 사용하여 코드에서 Uncontrolled Resource Consumption 패턴을 스캔하세요. 8 규칙.
# Scan with Shoulder CLI npx @shoulderdev/cli trust --cwe=400 # Or scan entire project npx @shoulderdev/cli trust .
탐지 규칙 (8)
코드 리뷰에서 주의할 점
이 패턴은 잠재적인 Uncontrolled Resource Consumption 취약점을 나타냅니다. 코드 리뷰와 보안 감사 중에 찾아보세요.
코드베이스를 스캔하세요: Uncontrolled Resource Consumption
Shoulder CLI는 전체 코드베이스에서 취약한 패턴을 찾아냅니다.