AI & LLM Application Security Testing
Offensive security assessments for GenAI applications, autonomous AI agents, and RAG vector store integrations. We test for prompt injection, sensitive context leakage, tool-calling abuse, and vector database isolation flaws.
AI SaaS startups, LLM agent builders, and RAG database platforms.
Direct/indirect prompt injection, vector tenant isolation, and tool abuse.
Adversarial PoCs, guardrail fixes, executive summary & 30-day verified retest.
Who We Help
GenAI SaaS Products
Startups integrating OpenAI, Anthropic, or open-source LLMs into customer-facing multi-tenant products.
Autonomous AI Agents
Systems with tool-execution privileges (e.g., executing SQL queries, sending emails, or triggering API webhooks).
RAG & Vector Search Platforms
Knowledge retrieval systems using Pinecone, Qdrant, Weaviate, or pgvector storing sensitive enterprise documents.
Enterprise Copilots & Chatbots
Internal enterprise assistants accessing HR, finance, and confidential internal intellectual property.
What We Test in AI & LLM Applications
Direct & Indirect Prompt Injection
Testing system prompt bypasses, instruction override attacks, and indirect injection embedded within external web/document inputs.
Sensitive Information Disclosure
Evaluating LLM training data extraction, system prompt leakage, and proprietary backend prompt architecture disclosure.
RAG Vector Database Tenant Isolation
Auditing vector search query filters (e.g. pgvector, Pinecone) to ensure Tenant A cannot retrieve documents belonging to Tenant B.
Excessive Agency & Tool Abuse
Fuzzing LLM tool parameters to verify if adversarial prompts can trigger unauthorized database deletions, file writes, or API calls.
Model Denial of Service (DoS)
Testing context window overflow, resource-intensive query loops, and recursive token consumption triggers.
Supply Chain & Dependency Vulnerabilities
Reviewing vulnerable LangChain, LlamaIndex, model checkpoints, and third-party plugin integrations.
Insecure Output Handling & SSRF/XSS
Evaluating whether raw LLM output fed into downstream browsers or backend interpreters causes XSS or remote execution.
Guardrails & Filter Robustness
Testing jailbreaks and adversarial Unicode/base64 encoding to bypass NeMo Guardrails, Llama Guard, and moderation layers.
What You Receive from TrustLayerLabs
AI Security Vulnerability Report
Complete technical findings breakdown with CVSS v3.1 scoring, LLM attack vectors, and risk classifications.
Adversarial Prompt PoC Payloads
Reproducible prompt injection strings, jailbreak templates, and automated harness scripts.
Deterministic Guardrail Remediation
Practical defense patterns including vector metadata filtering, input sanitation, and output validation code.
Executive Summary for Stakeholders
A clear risk briefing designed for founders, CTOs, enterprise procurement reviewers, and investors.
Developer Debrief Call
Direct consultation with our lead offensive practitioner to walk your engineering team through fix strategies.
30-Day Retest & Verification Letter
Re-evaluation of patched guardrails and issuance of a formal Retest Verification Letter.
Deploying AI models via REST or GraphQL microservices? Explore our API Security Testing services.
API Security →Hosting AI pipelines on AWS, GCP, or Azure? Explore our Cloud Security Assessment audits.
Cloud Security →AI Security Testing FAQs
Q:Why is traditional penetration testing insufficient for AI & LLM applications?
Traditional security tools look for network ports, web headers, and SQL syntax, but they cannot evaluate non-deterministic natural language inputs, prompt injection vectors, tool-execution agency, or semantic vector store tenant isolation. AI security requires human-led adversarial testing against the OWASP Top 10 for LLMs.
Q:What is Indirect Prompt Injection and why is it dangerous?
Indirect prompt injection occurs when an LLM processes untrusted external data (such as a webpage, customer support ticket, or uploaded PDF) that contains adversarial instructions. The LLM treats this external content as instructions, potentially exfiltrating sensitive context or executing unauthorized backend API actions.
Q:How do you test multi-tenant isolation in RAG systems?
We examine how vector embeddings and queries are generated, stored, and filtered. We craft adversarial similarity search queries to verify that tenant ID metadata filtering is enforced deterministically before LLM context ingestion, preventing cross-tenant document leakage.
Q:How long does an AI application security audit take?
A typical AI application security assessment takes 5 to 7 business days depending on model integration depth, tool-calling complexity, and vector store scale.
Ready to Scope Your AI Security Audit?
Book a 30-minute scoping call with our lead security architects under mutual NDA to review your AI application architecture and prompt safety requirements.
Initiate Your Security Assessment
Request a scope review or connect directly with our security practitioners.
Direct Channels
Connect with us for scoping advice, security assessment enquiries, or to execute a mutual NDA. We aim to respond within one business day.
📍 Operations: Distributed Team • Remote-First Security Assessments
Scope of Delivery: Serving SaaS, FinTech & AI teams across India and globally
30-Minute Confidential Security Review
Speak directly with an offensive security architect. Choose your time slot with real-time calendar synchronization.