Large Language Model Security Testing¶
The LLM Security Challenge¶
The integration of Large Language Models (LLMs) into production applications introduces a fundamentally new class of security vulnerabilities that traditional application security testing cannot adequately address. As organizations deploy AI-powered features through API endpoints—enabling chatbots, content generation, code assistance, and autonomous agents—the attack surface expands beyond conventional injection and authorization vulnerabilities to include prompt manipulation, model extraction, and AI-specific denial of service attacks.
Why LLM Security Requires Specialized Testing:
LLM-integrated applications are vulnerable through their API layers, where user inputs are processed, transformed into prompts, sent to language models, and responses are returned to clients. These APIs present unique security challenges:
- Prompt Injection Attacks: Malicious inputs can manipulate the model's behavior, bypassing safety guardrails and eliciting unintended or harmful outputs
- Data Exfiltration Through Inference: Carefully crafted queries can extract training data, proprietary information, or sensitive context embedded in system prompts
- Indirect Vulnerabilities: LLM outputs may contain executable code, SQL queries, or shell commands that, when processed without proper validation, lead to traditional injection vulnerabilities
- Resource Exhaustion: Token limits and computational costs create new denial-of-service vectors specific to generative AI systems
- Plugin and Tool Exploitation: LLMs with function-calling capabilities can be manipulated to execute unauthorized actions through connected tools and APIs
Escape's LLM Security Testing Methodology¶
Escape's approach to LLM security testing is grounded in API-level vulnerability detection. Whether LLM functionality is exposed through REST endpoints, GraphQL schemas, or WebSocket connections, the security testing targets the API layer where user inputs are accepted and LLM outputs are delivered.
Automated LLM-Specific Attack Simulation:
Escape employs specialized attack payloads and behavioral analysis techniques designed for generative AI contexts. Unlike traditional fuzzing or injection testing, LLM security validation requires semantic understanding of model behavior, prompt structure, and context manipulation. The testing engine systematically probes for:
- Prompt Boundary Violations: Attempts to escape instruction contexts and inject malicious directives
- Output Validation Failures: Detection of unsafe content generation (code injection vectors, credential disclosure, PII leakage)
- Model Behavior Manipulation: Validation that security guardrails cannot be bypassed through role-playing, context confusion, or encoding attacks
- Resource Abuse Patterns: Identification of inputs that trigger excessive token consumption or computational costs
OWASP LLM Top 10 Alignment:
Escape's LLM security test suite provides comprehensive coverage of the OWASP Top 10 for Large Language Model Applications, the industry-recognized standard for AI application security. This alignment ensures that testing addresses the most critical and prevalent vulnerability classes identified by the security community.
Comprehensive Test Coverage¶
Escape provides 12 specialized security tests addressing all dynamically testable categories within the OWASP LLM Top 10. The test suite distinguishes between vulnerabilities that can be detected through automated API testing (active tests) and those requiring code review, architectural analysis, or supply chain auditing (advisory tests).
Active Testing Coverage¶
The following tests are automatically executed during API and WebApp DAST scans when LLM endpoints are detected:
| Vulnerability Class | REST API Support | GraphQL Support | Default Severity | OWASP LLM Top 10 Mapping |
|---|---|---|---|---|
| LLM Prompt Injection | LLM01:2023 | |||
| LLM Insecure Output Handling | LLM02:2023 | |||
| LLM Model Denial of Service | LLM04:2023 | |||
| LLM Sensitive Information Disclosure | LLM06:2023 | |||
| LLM Insecure Plugin Design | LLM07:2023 | |||
| LLM Excessive Agency | LLM08:2023 | |||
| LLM Jailbreak | LLM01:2023 | |||
| LLM Endpoint Detection | N/A (Discovery) |
Advisory Coverage¶
The following vulnerability classes are included in Escape's knowledge base with detailed remediation guidance. While these cannot be validated through automated API testing alone, Escape provides detection recommendations and architectural best practices:
| Vulnerability Class | Why Manual Review Is Required | OWASP LLM Top 10 Mapping |
|---|---|---|
| LLM Training Data Poisoning | Requires analysis of model training pipelines, data sourcing, and fine-tuning processes (not observable via API behavior) | LLM03:2023 |
| LLM Supply Chain Vulnerabilities | Requires dependency analysis, model provenance verification, and third-party component auditing (architectural security concern) | LLM05:2023 |
| LLM Overreliance | Requires assessment of business process design and human oversight mechanisms (application logic validation, not API vulnerability) | LLM09:2023 |
| LLM Model Theft | Requires monitoring of API access patterns, rate limiting configuration, and architectural controls (policy enforcement analysis) | LLM10:2023 |
Automated LLM Endpoint Discovery¶
Escape automatically identifies API endpoints that integrate with Large Language Models by analyzing response patterns, semantic characteristics, and behavioral signatures. This discovery process enables targeted LLM security testing without requiring manual endpoint classification.
Detection Methodology:
The LLM Endpoint Detection test analyzes API responses for characteristics indicative of generative AI integration, including:
- Response length and variability patterns consistent with natural language generation
- Semantic coherence analysis suggesting AI-generated content
- Token-based response structures and metadata
- Latency patterns consistent with LLM inference
- API schema and parameter naming conventions common to AI integrations
Once identified, these endpoints are subjected to the full suite of LLM-specific security tests, ensuring comprehensive coverage of AI-powered functionality within the application.