When auditing serverless applications before production deployment
When investigating potential data exposure through function environment variables or logs
When assessing the blast radius of a compromised serverless function execution role
When compliance reviews require documentation of serverless security controls
When building secure-by-default templates for serverless deployments
Do not use for container or VM security assessments (use container scanning tools), for API security testing (use DAST tools on the API Gateway layer), or for real-time serverless threat detection (use AWS Lambda Extensions with security agents).
Prerequisites
AWS CLI, Azure CLI, and gcloud CLI configured with appropriate permissions
Access to read function configurations, policies, and execution roles
Prowler or Checkov for automated serverless security scanning
SAM CLI or Serverless Framework for local function analysis
CloudTrail, Azure Monitor, or Cloud Audit Logs enabled for function invocation monitoring
Workflow
Step 1: Enumerate All Serverless Functions and Configurations
List all functions across cloud providers with their runtime, memory, timeout, and network settings.
# AWS Lambda: List all functions with key security attributes
aws lambda list-functions \
--query 'Functions[*].[FunctionName,Runtime,MemorySize,Timeout,Role,VpcConfig.VpcId,Layers[*].Arn]' \
--output table
# Check for functions using deprecated runtimes
aws lambda list-functions \
--query 'Functions[?Runtime==`python3.7` || Runtime==`nodejs14.x` || Runtime==`dotnetcore3.1`].[FunctionName,Runtime]' \
--output table
# Azure Functions: List all function apps
az functionapp list \
--query "[].{Name:name, Runtime:siteConfig.linuxFxVersion, ResourceGroup:resourceGroup, HttpsOnly:httpsOnly}" \
-o table
# GCP Cloud Functions: List all functions
gcloud functions list \
--format="table(name, runtime, status, httpsTrigger.url, serviceAccountEmail, vpcConnector)"
Step 2: Audit Execution Role Permissions
Review IAM roles attached to functions for overly permissive policies.
"
import json, sys
doc = json.load(sys.stdin)
for stmt in doc.get('Statement', []):
actions = stmt.get('Action', [])
if isinstance(actions, str): actions = [actions]
resources = stmt.get('Resource', [])
if isinstance(resources, str): resources = [resources]
if '*' in actions or any(a.endswith(':*') for a in actions):
print(f' WARNING: {stmt[\"Effect\"]} {actions} on {resources}')
"
done
done
Step 3: Check Environment Variables for Secrets
Scan function environment variables for hardcoded credentials, API keys, and database connection strings.
# AWS Lambda: Extract environment variablesfor func in $(aws lambda list-functions --query 'Functions[*].FunctionName' --output text); do
envvars=$(aws lambda get-function-configuration --function-name "$func" \
--query 'Environment.Variables' --output json 2>/dev/null)
if [ "$envvars" != "null" ] && [ -n "$envvars" ]; thenecho"=== $func ==="echo"$envvars" | python3 -c "
import json, sys, re
vars = json.load(sys.stdin)
sensitive_patterns = [
r'(?i)(password|secret|key|token|credential|api.?key)',
r'(?i)(aws.?access|aws.?secret)',
r'(?i)(database.?url|connection.?string|db.?pass)',
r'AKIA[0-9A-Z]{16}'
]
for key, value in vars.items():
for pattern in sensitive_patterns:
if re.search(pattern, key) or re.search(pattern, str(value)):
masked = value[:4] + '****' + value[-4:] if len(value) > 8 else '****'
print(f' SENSITIVE: {key} = {masked}')
break
"fidone# Azure Functions: Check app settingsfor app in $(az functionapp list --query "[].name" -o tsv); do
rg=$(az functionapp show --name "$app" --query "resourceGroup" -o tsv)
echo"=== $app ==="
az functionapp config appsettings list \
--name "$app" --resource-group "$rg" \
--query "[?contains(name,'KEY') || contains(name,'SECRET') || contains(name,'PASSWORD')].{Name:name}" \
-o table 2>/dev/null
done
Step 4: Review Function Triggers and Access Controls
Verify that function triggers have appropriate authentication and authorization.
# AWS: Check for unauthenticated Lambda function URLs
aws lambda list-function-url-configs \
--function-name FUNCTION_NAME \
--query 'FunctionUrlConfigs[*].[FunctionUrl,AuthType,Cors]' --output table
# Check for resource-based policies allowing public invocationfor func in $(aws lambda list-functions --query 'Functions[*].FunctionName' --output text); do
policy=$(aws lambda get-policy --function-name "$func" --query 'Policy' --output text 2>/dev/null)
if [ -n "$policy" ]; thenecho"$policy" | python3 -c "
import json, sys
doc = json.loads(sys.stdin.read())
for stmt in doc.get('Statement', []):
principal = stmt.get('Principal', {})
if principal == '*' or principal == {'AWS': '*'}:
print(f'WARNING: $func has public invoke policy: {stmt.get(\"Sid\", \"unnamed\")}')" 2>/dev/null
fidone# GCP: Check for unauthenticated Cloud Functions
gcloud functions list --format=json | python3 -c "
import json, sys
functions = json.load(sys.stdin)
for func in functions:
name = func.get('name', '').split('/')[-1]
trigger = func.get('httpsTrigger', {})
if trigger and func.get('ingressSettings') == 'ALLOW_ALL':
print(f'WARNING: {name} allows all ingress traffic')
"
Step 5: Analyze Function Code for Security Vulnerabilities
Review function code for common serverless security issues.
# Download Lambda function code for review
aws lambda get-function --function-name FUNCTION_NAME \
--query 'Code.Location' --output text | xargs curl -o function.zip
unzip function.zip -d function-code/
# Scan with Bandit (Python) or ESLint security plugin (Node.js)# Python functions
pip install bandit
bandit -r function-code/ -f json -o bandit-results.json
# Node.js functions
npm install -g eslint @microsoft/eslint-plugin-sdl
eslint --ext .js function-code/
# Check for common serverless vulnerabilities:# 1. SQL injection in database queries# 2. Command injection via os.system or subprocess# 3. Insecure deserialization# 4. Event data injection (untrusted event parameters)# 5. Excessive function permissions
grep -rn "os.system\|subprocess\|eval(\|exec(" function-code/ || echo"No obvious injection patterns"
grep -rn "pickle.loads\|yaml.load\b" function-code/ || echo"No deserialization risks"
Step 6: Run Automated Serverless Security Scanning
Execute Checkov and Prowler for automated compliance checks on serverless resources.
IAM role assumed by a serverless function during execution that defines what AWS/cloud resources the function can access
Event Injection
Serverless-specific attack where untrusted data in the event trigger payload is used unsafely in function logic
Function URL
Direct HTTP(S) endpoint for invoking Lambda functions without API Gateway, which may be configured without authentication
Cold Start
Initial function execution that includes container provisioning, during which security agents and extensions must initialize
Resource-Based Policy
Policy attached to the function itself that defines who can invoke it, separate from the execution role
Secrets Manager Integration
Pattern of retrieving sensitive configuration from a secrets management service rather than storing in environment variables
Tools & Systems
AWS Lambda: Primary serverless compute platform with execution roles, layers, and resource policies
Checkov: Static analysis tool for infrastructure-as-code with serverless-specific security policies
Prowler: Cloud security tool with Lambda-specific checks for permissions, public access, and runtime versions
Bandit: Python static analysis tool for detecting security issues in function source code
OWASP Serverless Top 10: Security risk framework specific to serverless architectures
Common Scenarios
Scenario: Lambda Function with Admin Role Leaking Secrets via Environment Variables
Context: A security review discovers a Lambda function with AdministratorAccess execution role and database credentials stored in plaintext environment variables visible in CloudWatch logs.
Approach:
Enumerate the function's execution role and discover AdministratorAccess managed policy
Check environment variables and find DB_PASSWORD, API_KEY, and STRIPE_SECRET_KEY in plaintext
Review CloudWatch logs and find credentials printed in debug log statements
Create a scoped IAM policy granting only the specific DynamoDB and S3 actions needed
Migrate secrets to AWS Secrets Manager and update function to retrieve at runtime
Remove debug logging that outputs sensitive data
Rotate all exposed credentials and enable Lambda function encryption with KMS
Pitfalls: Changing a function's execution role can break it if the new role is too restrictive. Test in a staging environment first. Environment variable changes trigger a new function version, so ensure aliases and triggers are updated. Secrets Manager calls add latency; cache secrets within the execution context to avoid per-invocation lookups.
Output Format
Serverless Function Security Review
=======================================
Account: 123456789012
Functions Reviewed: 34
Review Date: 2026-02-23
CRITICAL FINDINGS:
[SRVL-001] Overly Permissive Execution Role
Function: payment-processor
Role: AdministratorAccess (full AWS access)
Required Permissions: DynamoDB:PutItem, S3:GetObject (2 actions)
Remediation: Create scoped policy with only required permissions
[SRVL-002] Secrets in Environment Variables
Function: payment-processor
Variables: DB_PASSWORD, STRIPE_SECRET_KEY, API_KEY
Risk: Visible in console, API, and CloudWatch logs
Remediation: Migrate to Secrets Manager, remove from env vars
SUMMARY:
Functions with admin roles: 3 / 34
Functions with secrets in env vars: 8 / 34
Functions with deprecated runtimes: 5 / 34
Functions with public access: 2 / 34
Functions without VPC: 28 / 34
Functions with wildcard permissions: 12 / 34
