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Improper Access Control

🛡️ 4 rules detect this

Improper Access Control

The product does not restrict or incorrectly restricts access to a resource from an unauthorized actor.

Access control involves determining which subjects can access which objects. When access control is implemented incorrectly, it can lead to unauthorized access to sensitive data or functionality.

Prevalence
High
Frequently exploited
Impact
High
3 high-severity rules
Prevention
Documented
4 fix examples
2 Prevention
2 Prevention

How to fix this vulnerability

Prevention strategies for Improper Access Control based on 4 Shoulder detection rules.

LLM Insecure Plugin Design HIGH

Validate tool inputs against strict schemas and use an allowlist for permitted tools

+18 -2 go
- func handleToolCall(toolCall ToolCall) (interface{}, error) {
-     return tools[toolCall.Name](toolCall.Arguments)
+ var toolRegistry = map[string]ToolConfig{
+     "search":  {Handler: searchHandler, Validator: validateSearch, Permission: "read"},
+     "weather": {Handler: weatherHandler, Validator: validateWeather, Permission: "read"},
+ }
+ 
+ func handleToolCall(userPerms map[string]bool, toolCall ToolCall) (interface{}, error) {
+     config, ok := toolRegistry[toolCall.Name]
+     if !ok {
+         return nil, fmt.Errorf("unknown tool: %s", toolCall.Name)
+     }
+     if !userPerms[config.Permission] {
+         return nil, fmt.Errorf("permission denied for tool: %s", toolCall.Name)
+     }
+     args, err := config.Validator(toolCall.Arguments)
+     if err != nil {
+         return nil, fmt.Errorf("invalid arguments: %w", err)
+     }
+     return config.Handler(args)
  }
  
LLM Insecure Plugin Design HIGH

Validate tool inputs against schemas and use allowlists for permitted tools

+10 -3 javascript
- for (const toolCall of response.tool_calls) {
-   const fn = tools[toolCall.function.name];
-   const result = await fn(JSON.parse(toolCall.function.arguments));
+ const allowedTools = new Set(['search', 'calculate', 'getWeather']);
+ 
+ for (const toolCall of response.tool_calls) {
+   if (!allowedTools.has(toolCall.function.name)) {
+     throw new Error('Unknown tool');
+   }
+   const validate = ajv.compile(toolSchemas[toolCall.function.name]);
+   const args = JSON.parse(toolCall.function.arguments);
+   if (!validate(args)) throw new Error('Invalid arguments');
+   await tools[toolCall.function.name](args);
  }
  
Missing Network Policy MEDIUM

Define NetworkPolicy resources to restrict pod-to-pod traffic and enforce network segmentation

+17 -11 yaml
- apiVersion: apps/v1
- kind: Deployment
- metadata:
-   name: web
- spec:
-   replicas: 3
-   template:
-     spec:
-       containers:
-       - name: web
-         image: nginx:1.25
+ apiVersion: networking.k8s.io/v1
+ kind: NetworkPolicy
+ metadata:
+   name: web-policy
+ spec:
+   podSelector:
+     matchLabels:
+       app: web
+   policyTypes:
+   - Ingress
+   ingress:
+   - from:
+     - podSelector:
+         matchLabels:
+           role: frontend
+     ports:
+     - port: 80
  
LLM Insecure Plugin Design HIGH

Use Pydantic for tool input validation and maintain a strict allowlist for permitted tools

+14 -4 python
- def handle_tool_call(tool_call):
-     name = tool_call.function.name
-     args = json.loads(tool_call.function.arguments)
-     return tools[name](args)
+ from pydantic import BaseModel, Field
+ 
+ class SearchArgs(BaseModel):
+     query: str = Field(max_length=100, pattern=r'^[a-zA-Z0-9\s]+$')
+ 
+ ALLOWED_TOOLS = {'search_products': SearchArgs, 'get_weather': WeatherArgs}
+ 
+ def handle_tool_call(tool_call):
+     name = tool_call.function.name
+     if name not in ALLOWED_TOOLS:
+         raise ValueError(f'Unknown tool: {name}')
+     schema = ALLOWED_TOOLS[name]
+     args = schema.parse_raw(tool_call.function.arguments)
+     return handlers[name](args)
  
3 Detection
3 Detection

Find vulnerabilities in your code

Use Shoulder to scan your codebase for Improper Access Control patterns. 4 rules.

terminal
# Scan with Shoulder CLI
npx @shoulderdev/cli trust --cwe=284

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

Detection Rules (4)

4 Warning Signs
4 Warning Signs

What to watch for in code reviews

These patterns indicate potential Improper Access Control vulnerabilities. Look for these during code reviews and security audits.

🟠
Insecure plugin implementation: ... go-llm-insecure-plugin
🟠
insecure plugin/function calling implementations in AI/LLM systems without proper validation go-llm-insecure-plugin
🟡
Workload has no NetworkPolicy for network segmentation kubernetes-missing-network-policy
🟡
Kubernetes deployments without associated NetworkPolicy resources kubernetes-missing-network-policy
🔍

Scan your codebase for Improper Access Control

Shoulder CLI finds vulnerable patterns across your entire codebase.