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:What is AI application security testing and what does it cover?
AI application security testing is an offensive security assessment tailored for systems integrating Large Language Models (LLMs), autonomous agents, and Retrieval-Augmented Generation (RAG) architectures. It covers direct and indirect prompt injection, vector store multi-tenant isolation, excessive agency in tool-calling workflows, sensitive context disclosure, and insecure output handling across your application stack.
Q:How should permissions and authorization be enforced in RAG applications?
Authorization in RAG applications must be enforced deterministically at the data retrieval layer before context reaches the LLM. Vector queries must include mandatory metadata filters matching the authenticated user's tenant and role permissions (e.g., filter: { tenantId: user.tenantId, acl: { $in: user.groups } }). Attempting to enforce permissions via prompt instructions alone is ineffective because system prompts can be overridden by adversarial user inputs.
Q:What is Indirect Prompt Injection and why is it dangerous?
Indirect prompt injection occurs when an AI application ingests untrusted third-party data—such as parsed PDFs, scraped web content, or incoming emails—that contains hidden adversarial instructions. When the model processes this data, it may follow the attacker's embedded instructions rather than the developer's system prompt, potentially triggering unauthorized tool calls or leaking private workspace documents.
Q:How do you evaluate AI agent tool permissions and excessive agency?
We audit the boundary between the model's natural language reasoning and your backend tools (APIs, SQL databases, email services). We test whether malicious prompt manipulation can coerce the agent into calling unintended functions, escalating parameter values (e.g., modifying SQL WHERE clauses), or executing destructive write operations without human-in-the-loop confirmation.
Q:Does every AI application have these vulnerabilities?
No. Security risk is determined by architecture and privilege boundaries. An isolated, read-only documentation chatbot has a fundamentally different risk profile than an autonomous agent with database write access or a multi-tenant RAG platform querying confidential corporate archives. Our assessment scopes testing to your specific architectural boundaries.
Q:How long does an AI application security audit take?
A standard AI application security assessment requires 5 to 7 business days, depending on model integration depth, tool-calling complexity, and vector store architecture. Urgent findings are shared in a preliminary debrief within 48 to 72 hours.
Q:Is vulnerability retesting included after our engineering team implements guardrails?
Yes. Every assessment includes 30 days of complimentary retesting. Once your developers implement deterministic filtering, prompt defenses, or tool permission boundaries, we re-test the identified attack vectors and issue an updated report and Retest Verification Letter.
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 offensive security practitioners. Mutual NDA executed upfront.
Connect with us for scoping advice, security assessment enquiries, or to execute a mutual NDA before technical discussions.
📍 Distributed Team: Remote-First Security Assessments
Jurisdiction: Govt. of India Registered MSME · UDYAM-AP-21-0044317
30-Minute Confidential Security Review
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