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.
如何修复此漏洞
基于 8 条 Shoulder 检测规则的 Resource Exhaustion 预防策略。
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)
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.
# Scan with Shoulder CLI npx @shoulderdev/cli trust --cwe=400 # Or scan entire project npx @shoulderdev/cli trust .
检测规则 (8)
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。