LLM AST ensures that your AI behaves consistently, predictably, and safely even under adversarial conditions.
LLM AST - Secure LLMs
Securing the reasoning engine that powers your AILarge Language Models are the brain of modern AI applications, generating, reasoning, and making decisions but they can also hallucinate, leak data, or be manipulated through prompt injection. Traditional testing cannot detect these cognitive threats, which is why Entersoft’s LLM AST (Large Language Model Application Security Testing) is designed to test the model’s behavior itself, not just the surrounding code, ensuring your AI’s reasoning remains secure, aligned, and trustworthy.What Is LLM AST?
LLM AST is a core module under Entersoft’s AIAST (AI Application Security Testing) umbrella.
LLM AST focuses on the model layer — evaluating its prompts, outputs, decision boundaries, and moderation logic.
It identifies where your model may
- Misinterpret malicious prompts
- Generate unsafe or non-compliant output
- Leak sensitive training data
- Fall prey to prompt injections or overreliance attacks
Why LLM Security Testing Matters
As AI scales, LLMs bridge business data and users, but without testing, risks increase.
Prompt Injection
Hidden instructions override intended model behavior.
Sensitive Data Leakage
Memorized data or confidential context resurfacing.
Hallucinations
Fabricated or inaccurate responses that mislead decisions.
Overreliance & Model Bias
Models generating unsafe or unauthorized actions.
Insecure Plugin & Agent Integration
AI agents triggering harmful operations.
Supply Chain & Dependency Risks
Vulnerable libraries or third-party models introducing hidden threats.
LLM AST identifies, measures, and mitigates risks ahead of user impact or compliance issues.
LLM AST Methodalogy
LLM AST Methodology secures your large language models by testing prompts, architecture, and integrations. It identifies risks, prevents data leakage, and ensures AI reliability and governance.
Architecture & Prompt Review
Understand model configurations, prompts, and integrations by reviewing system prompts, guardrails, plugins, and API usage.
Threat Modeling (OWASP LLM Top 10)
Identify high-risk model interactions and data exposures by mapping model functions to LLM01–LLM10 risks.
Prompt Injection & Jailbreak Testing
Evaluate model resilience against instruction override using red team injection campaigns and prompt sanitization tests.
Hallucination & Reliability Assessment
Quantify hallucination rates under adversarial input by stress-testing factual accuracy with poisoned or incomplete data.
Data Leakage Validation
Detect inadvertent exposure of training or session data through membership inference and context retention testing.
Governance & Policy Validation
Check compliance with Responsible AI standards by reviewing moderation, logging, and traceability controls
OWASP LLM Top 10 Alignment
Entersoft’s LLM AST directly maps to the OWASP Top 10 for LLM Applications the global benchmark for AI security testing.
- LLM01 - Prompt Injection: Comprehensive injection simulation & prompt hardening.
- LLM02 - Insecure Output Handling: Validation of output rendering & sanitization pipelines.
- LLM06 – Sensitive Info Disclosure Leakage & inference testing for PII & secrets.
- LLM08 – Excessive Agency: Testing of plugin & agent boundaries.
- LLM09 – Overreliance: Evaluating AI hallucinations and decision safety.
- LLM10 – Model Theft: Query pattern analysis and API isolation review.
WHY CHOOSE ENTERSOFT LLM AST
Key Security Layers We Test
Prompt Layer
- System prompt inspection and sanitization.
- Injection and jailbreak simulation.
- Validation of guardrails and dynamic filters.
Output Layer
- Factuality and toxicity analysis.
- Content policy enforcement testing.
- Evaluation of hallucination defense mechanisms.
Model API Layer
- Input/output logging and access control review.
- Rate-limiting, cost exhaustion, and misuse simulation.
- Audit of tokenization, batching, and model isolation.
Governance & Explainability
- Explainability check for regulatory reporting.
- Compliance mapping with ISO/IEC 42001 and NIST AI RMF.
- Review of audit logs, retention, and moderation evidence.
Entersoft Delivering Excellence
Across Industries
Deliverables
- AI Threat Model & Data Flow Diagram
- RAG AST Findings Report (severity, description, remediation)
- Proof-of-Concept Evidence & Logs
- Risk Register & 30-Day Fix Plan
- OWASP LLM Top 10 Mapping Sheet
- Attestation Pack for Compliance & Client Sharing
Industries We Serve
- Financial Services: AI chatbots trained on customer data.
- Healthcare: Clinical knowledge assistants.
- Government & Public Sector: Citizen service bots and policy assistants.
- Technology & Cybersecurity: SOC automation and AI threat intelligence tools.
- EdTech & Research: RAG-based content retrieval platforms.
LLM Security Testing in the Enterprise Workflow
LLM AST fits naturally into the AI SDLC or MLOps pipeline:
Design Stage Threat model your LLM integrations before deployment.
Pre-Release Stage Conduct controlled adversarial prompt testing.
Post-Deployment Enable continuous monitoring for jailbreaks or prompt leaks.
Periodic Retesting Validate that retraining or model updates maintain resilience.
