June 2026: Cloud AI Security Challenge

Find and document vulnerabilities in a cloud-deployed AI service covering API security, model serving infrastructure, authentication, and data handling.

June 2026: Cloud AI Security Challenge

Audit a cloud-deployed AI service for security vulnerabilities. This challenge moves beyond prompt-level attacks to examine the infrastructure, APIs, authentication, and data handling of an AI application deployed on cloud infrastructure.

Challenge Overview

Scenario

"AISaaS" is a fictional AI-as-a-Service platform that provides hosted language model endpoints to enterprise customers. The platform offers:

• A REST API for text generation, summarization, and classification
• A web-based playground for interactive model testing
• A dashboard for managing API keys, usage, and billing
• Multi-tenant model serving with per-customer configuration
• A file upload endpoint for document processing

You have been given a standard customer account. Your goal is to find vulnerabilities in the platform's security posture across multiple categories.

Target Environment

API Endpoints

Architecture

Client → API Gateway (rate limiting, auth) → Load Balancer → Model Serving Pods (GPU)
                                                           → File Processing Workers
                                                           → Config Service → Database

The platform runs on Kubernetes with:

• NGINX-based API gateway
• Redis for session management and caching
• PostgreSQL for user data, configurations, and billing
• S3-compatible object storage for uploaded files
• GPU nodes running vLLM for model serving

Objectives

Category 1: API Security (25 points)

1.1 Authentication Bypass (10 points)

Find a way to access API endpoints without valid authentication, or escalate from your API key to capabilities your account should not have.

• Look at how API keys are validated, how sessions are managed, and whether there are any unauthenticated endpoints that expose sensitive data.
• Test for common API vulnerabilities: broken object-level authorization (BOLA), broken function-level authorization, mass assignment.

1.2 Rate Limit Bypass (8 points)

Your account has a rate limit of 100 requests per minute. Find a way to exceed this limit.

• Consider how the rate limiter identifies clients. Is it based on API key, IP address, or something else? Can you manipulate the identifier?
• Look for endpoints that are not rate-limited or headers that influence rate limiting behavior.

1.3 Input Validation Flaws (7 points)

Find input validation vulnerabilities in the API. This could include:

• Server-side request forgery (SSRF) through the file upload or URL processing endpoints
• Path traversal through file handling
• Injection through model configuration parameters
• Oversized input handling

Category 2: Multi-Tenant Isolation (25 points)

2.1 Cross-Tenant Data Access (15 points)

Access data belonging to another customer of the platform. This could be:

• Another customer's generated outputs (cached responses)
• Another customer's model configuration
• Another customer's uploaded documents
• Another customer's usage data or API keys

2.2 Configuration Leakage (10 points)

Extract system-level configuration that should not be accessible to customers:

• Model serving parameters (GPU allocation, batch size, model version)
• Infrastructure details (internal hostnames, service endpoints, environment variables)
• Other customer account metadata

Category 3: Model Serving Security (25 points)

3.1 Model Endpoint Abuse (10 points)

Find ways to abuse the model serving infrastructure beyond normal usage:

• Prompt injection that causes the model to leak its serving configuration
• Requests that cause disproportionate resource consumption (denial-of-service)
• Inputs that trigger error messages revealing internal information

3.2 Inference Data Leakage (8 points)

Determine whether the model serving infrastructure leaks information across requests:

• Does the model's KV cache retain information from previous requests (by other users)?
• Do error messages or timing differences reveal information about the model or other users' inputs?
• Is there any cross-contamination between tenants sharing the same GPU nodes?

3.3 File Processing Vulnerabilities (7 points)

Exploit the file upload and processing pipeline:

• Upload files that trigger vulnerable parsing libraries
• Attempt path traversal through filenames or archive contents
• Test for XML external entity (XXE) injection through document formats
• Upload files that cause excessive resource consumption

Category 4: Data Handling (25 points)

4.1 Logging and Monitoring Gaps (10 points)

Identify gaps in the platform's security logging:

• Are failed authentication attempts logged?
• Are access control violations detected and logged?
• Can you perform actions that should be monitored but are not?
• Can you tamper with or evade audit logs?

4.2 Data Retention Issues (8 points)

Investigate how the platform handles data lifecycle:

• Are deleted files actually removed from storage?
• Do cached responses persist after the user's session ends?
• Is uploaded data properly isolated during processing?
• Can you access data that should have been purged?

4.3 Secrets Exposure (7 points)

Find exposed secrets in the platform:

• API keys or tokens in client-side code, error messages, or HTTP headers
• Database credentials or internal service tokens
• Debug endpoints or development artifacts left in production
• Information disclosure through HTTP response headers or error pages

Tools and Approach

Recommended Tools

• Burp Suite or OWASP ZAP for intercepting and manipulating HTTP requests
• curl or httpie for API testing
• nmap or similar for service enumeration (only against the challenge target)
• jq for parsing JSON responses
• Python requests library for scripting automated tests

Methodology

Follow a structured approach:

1. Reconnaissance. Map the API surface, identify all endpoints, and understand the authentication model.
2. Authentication testing. Test for authentication bypasses, session management issues, and key handling flaws.
3. Authorization testing. Test for horizontal and vertical privilege escalation at each endpoint.
4. Input validation. Fuzz inputs for injection, overflow, and traversal vulnerabilities.
5. Business logic. Test for logic flaws in billing, usage tracking, and configuration management.
6. Infrastructure. Look for misconfigurations, exposed services, and information disclosure.

Scoring

Bonus Points (up to 10)

• Comprehensive vulnerability report following industry-standard format (CVSS scoring, clear remediation guidance)
• Discovery of vulnerabilities not anticipated by the challenge designers
• Demonstration of attack chains combining multiple vulnerabilities

Submission Format

Submit a structured vulnerability report:

# Cloud AI Security Assessment - June 2026
 
## Executive Summary
[Brief overview of findings and risk assessment]
 
## Findings
 
### Finding 1: [Title]
- **Severity:** Critical/High/Medium/Low
- **Category:** [API Security/Multi-Tenant/Model Serving/Data Handling]
- **CVSS Score:** [if applicable]
 
#### Description
[What the vulnerability is]
 
#### Proof of Concept
[Step-by-step reproduction with exact requests/responses]
 
#### Impact
[What an attacker could achieve]
 
#### Remediation
[Specific fix recommendations]

Key Concepts

Cloud AI vs. Traditional Cloud Security

Cloud AI services share many vulnerabilities with traditional web applications, but they also have unique attack surfaces:

• Model serving infrastructure introduces GPU-specific concerns, including shared memory, batch processing across tenants, and KV cache management.
• Inference APIs must handle adversarial inputs that target both the application layer and the model layer simultaneously.
• Data pipelines for training and fine-tuning create additional surfaces for data poisoning and exfiltration.
• Cost models based on token usage create economic denial-of-service opportunities.

The AI Security Stack

This challenge covers the full stack of an AI deployment:

Approach Tips

Start with Passive Reconnaissance

Before sending any exploit payloads, map the attack surface thoroughly:

• Enumerate all API endpoints and their parameters using the documentation and by probing for undocumented endpoints
• Examine HTTP response headers for technology fingerprints (server software, framework versions, security headers)
• Check for client-side source code, JavaScript files, or API documentation that reveals internal structure
• Look for error messages that disclose stack traces, file paths, or configuration details

Passive reconnaissance costs nothing and reveals attack surface you would otherwise miss.

Organize by Attack Surface, Not Technique

It is tempting to go through a checklist of techniques (SQL injection, XSS, SSRF) and try each one. A more effective approach is to organize by attack surface:

1. Authentication surface. How is identity established? Test all authentication mechanisms.
2. Authorization surface. How are permissions enforced? Test every endpoint for horizontal and vertical privilege escalation.
3. Input processing surface. What inputs does the system accept? Test each input for injection, overflow, and format confusion.
4. Data boundary surface. Where does data cross trust boundaries? Test for leakage at each boundary.
5. Model interaction surface. Where does user input reach the model? Test for prompt injection and model-specific attacks.

This approach ensures comprehensive coverage because it maps to how the system is structured, not to how your toolkit is organized.

Document Everything

Cloud security assessments produce large volumes of evidence. Without organized documentation, you will:

• Forget which endpoints you tested and what the results were
• Waste time re-testing things you already checked
• Produce a report that is disorganized and hard to follow

Use a structured note-taking system from the start. For each endpoint you test, record: the request, the response, your interpretation, and whether it warrants further investigation.

Think Like an Operator

Many cloud AI security issues are not traditional vulnerabilities but operational misconfigurations:

• Default credentials that were never changed
• Debug endpoints left enabled in production
• Overly permissive IAM roles granted during development and never tightened
• Logging gaps where security-relevant events are not captured
• Secrets in environment variables instead of a secrets manager

These issues are easy to find with systematic checking but easy to miss if you are only looking for traditional exploits.

Further Reading

• Cloud AI Security -- foundational concepts for this challenge
• Infrastructure & Supply Chain -- infrastructure security fundamentals
• LLMOps Security -- operational security for AI systems
• July 2026 Challenge -- the next challenge