There's a security conversation happening in boardrooms and security teams right now that wasn't happening three years ago: our AI systems can be attacked, and we don't really know how to test them.

It's a legitimate concern. Organizations are embedding large language models into customer-facing products, internal tools, HR systems, legal research platforms, code generation pipelines, and customer support automation. They're building AI agents that can browse the internet, write and execute code, send emails, query databases, and take actions in external systems. They're using retrieval-augmented generation (RAG) to connect LLMs to their proprietary data. And they're doing all of this at a pace that has significantly outrun their security testing practices.

The problem is that AI systems — particularly LLMs and AI agents — fail in ways that traditional software doesn't. You can't just run a vulnerability scanner against them. They don't have CVEs for prompt injection. There's no patch for a model that leaks sensitive training data when asked the right question. And the OWASP LLM Top 10 — the industry's emerging standard for AI security risks — describes attack vectors that most penetration testers have never tested for.

InTechsters provides specialized AI security testing — AI VAPT — for organizations building, deploying, or integrating AI and LLM systems. Our assessments combine manual adversarial testing by experienced security professionals with AI-specific testing frameworks to evaluate your systems against real-world attack scenarios — not theoretical ones.

What We Test

LLM Application Security Testing

Internal Vulnerability Assessment — Scanning internal networks, servers, workstations, and devices to identify weaknesses accessible from within your organization — simulating what an insider or compromised employee device could see.

Risk-Ranked Reporting (CVE / CVSS) — Every finding mapped to CVE identifiers with CVSS severity scoring, exploitability context, and business impact assessment for prioritized remediation.

Prompt Injection (Direct & Indirect) — The #1 vulnerability in the OWASP LLM Top 10. Testing for direct prompt injection — where an attacker manipulates the LLM's behavior through malicious user input — and indirect prompt injection — where malicious instructions are embedded in external content the LLM processes (web pages, documents, database records). We test for instruction override, system prompt bypass, jailbreaking, role-playing exploits, and multi-turn injection chains.

System Prompt Leakage — Testing whether your system prompt — which may contain confidential instructions, business logic, proprietary workflows, or sensitive configuration — can be extracted through careful questioning and adversarial prompting.

Sensitive Information Disclosure — Evaluating whether the model leaks training data, PII, confidential business information, internal architecture details, or other sensitive information through direct queries, memorization attacks, and inference techniques.

Insecure Output Handling — Testing how your application handles LLM outputs — whether generated content is passed unsanitized to downstream systems, executed as code, rendered as HTML (XSS via LLM), or used in database queries (SQL injection via LLM output).

Excessive Agency — For AI systems with tool use, function calling, or agentic capabilities — evaluating whether the model can be manipulated into taking unauthorized actions, exceeding its intended permissions, or operating outside its defined boundaries.

Model Deniel of Service — Testing for unbounded consumption vulnerabilities — whether an attacker can craft inputs that cause the model to consume disproportionate compute resources, leading to degraded performance or service unavailability.

Vector & Embedding Weakness — For RAG-based systems — evaluating the security of your vector database, embedding pipelines, and retrieval mechanisms. Testing for poisoning attacks where malicious content injected into the knowledge base influences model outputs, and authorization gaps where the model retrieves content the user shouldn't have access to.

Supply Chain Vulnerabilities — Assessment of your AI supply chain — the models, datasets, fine-tuning pipelines, plugins, and third-party integrations that your AI system depends on — for tampering, integrity failures, and backdoor risks.

Misinformation & Hallucination Risk Assessment — Evaluation of your LLM application's exposure to confident misinformation — where the model generates plausible but factually incorrect outputs — and the controls you have in place to detect and mitigate this risk in high-stakes contexts.

AI Agent & Agentic System Testing

AI agents represent a fundamentally different risk profile from standalone LLM chatbots. When a model can plan, decide, and execute actions across external tools and systems, the blast radius of a successful attack expands dramatically.

Agentic OWASP Top 10 Assessment  — Testing against OWASP's dedicated Top 10 for Agentic AI Systems (released late 2025) — covering malicious tool integration, privilege escalation through agent chains, orchestration manipulation, and unsafe autonomous action execution.

Tool & Function Call Security — Evaluating the security of tools and APIs exposed to AI agents — whether an agent can be manipulated into calling tools with unauthorized parameters, bypassing input validation, or escalating privileges through tool chaining.

Multi-Agent Trust Boundary Testing — For systems where multiple AI agents communicate and delegate tasks — testing trust boundaries between agents, authorization enforcement, and the risk of a compromised agent influencing the behavior of downstream agents.

Autonomous Action Risk Assessment — Evaluating the guardrails on autonomous agent actions — whether the agent can be induced to send emails, make purchases, modify files, execute code, or take other real-world actions outside its intended scope.

Memory & Context Manipulation  — Testing AI agent memory systems — short-term context windows, long-term memory stores, and external memory databases — for injection, manipulation, and unauthorized access.

Machine Learning Model Security Testing

Adversarial Input Testing  — Crafting inputs specifically designed to cause ML models to misclassify, produce incorrect outputs, or behave in unexpected ways — particularly relevant for computer vision, fraud detection, malware classification, and other ML-driven decision systems.

Model Extraction Attacks — Testing whether an attacker can reconstruct a functional replica of your proprietary ML model through systematic querying of your API — extracting intellectual property and enabling offline adversarial attack development.

Data Poisoning Assessment — Evaluating the integrity of your training data pipelines and model update processes for risks of poisoning — where maliciously crafted training data influences model behavior in ways that serve an attacker's objectives.

Model Inversion Attacks — Testing whether an attacker can reconstruct sensitive training data from model outputs — particularly critical for models trained on personal health data, financial records, or other sensitive information.

Backdoor & Trojan Detection  — Assessment of pre-trained models and fine-tuned models for hidden backdoors — where the model behaves normally under standard conditions but produces attacker-controlled outputs when a specific trigger is present.

Membership Inference Testing — Evaluating whether an attacker can determine whether specific data was used to train your model — with privacy implications for models trained on sensitive personal data.

AI-Integrated Application Security

API Security for AI Endpoints  — Standard API security testing applied specifically to AI inference endpoints — authentication, authorization, rate limiting, input validation, output filtering, and API key exposure.

AI Feature Security Review — Security assessment of AI-powered features within your existing applications — chatbots, recommendation engines, content moderation systems, fraud detection, automated decision-making — evaluated in the context of your overall application security posture.

RAG Pipeline Security Assessment — End-to-end security review of your retrieval-augmented generation architecture — document ingestion, chunking, embedding, vector storage, retrieval logic, and output generation — for injection risks, authorization gaps, and data leakage.

AI Development Pipeline Security (MLSecOps) — Security assessment of your ML development pipeline — model training environments, experiment tracking, model registries, deployment pipelines, and inference infrastructure — for supply chain risks and unauthorized access.

Our Methodology

Scoping & Threat Modeling — We define the assessment scope, understand your AI system architecture, identify threat actors and attack scenarios relevant to your use case, and develop an adversarial testing plan.

Reconnaissance & Architecture Review — Review of AI system design, model selection, integration patterns, data flows, access controls, and existing security controls.

Adversarial Testing — Manual adversarial testing by experienced AI security professionals, augmented by specialized AI security testing tools — testing across all applicable OWASP LLM Top 10 and Agentic Top 10 categories.

ML-Specific Attack Simulation — Where applicable, executing adversarial input attacks, model extraction attempts, and other ML-specific attack techniques against your deployed models.

Findings Analysis & Risk Rating — Every finding is documented with clear description, attack scenario, evidence, business impact, and risk rating — prioritized by exploitability and real-world consequence.

Reporting — Executive summary for leadership, technical report for engineering and security teams, remediation guidance, and mapping to OWASP LLM Top 10, NIST AI RMF, and applicable regulatory frameworks.

Remediation Support & Retesting — Guidance on remediating identified vulnerabilities, with optional retesting to validate that fixes are effective.

Frameworks & Standards We Test Against

OWASP LLM Top 10 (2025) — The industry standard for LLM application security risks

OWASP Agentic AI Top 10 (2025) — Purpose-built risk framework for autonomous AI agent systems

NIST AI Risk Management Framework (AI RMF) — Comprehensive AI risk framework from NIST

MITRE ATLAS — Adversarial threat landscape for AI systems

ISO/IEC 42001 — AI management system standard

EU AI Act — Regulatory requirements for high-risk AI systems

Frequently Asked Questions

We use an off-the-shelf LLM from OpenAI or Anthropic. Do we still need AI VAPT?

Yes. The model provider secures the model infrastructure — but your application, how you integrate the model, what tools you expose to it, how you handle its outputs, and what data you allow it to access are entirely your responsibility. Most AI security risks live in the application layer, not in the model itself.

How is AI VAPT different from traditional application penetration testing?

Traditional VAPT tests for vulnerabilities like SQL injection, XSS, authentication bypass, and misconfigurations. AI VAPT tests for a fundamentally different class of vulnerabilities — prompt injection, model manipulation, data extraction through adversarial prompting, excessive agency, and AI-specific supply chain risks — that require specialized knowledge and techniques.

We're still in development. Is it too early to test?

It's actually the best time. Security testing during development is dramatically cheaper and faster than remediation after deployment. We offer pre-deployment AI security reviews that integrate into your development lifecycle.

Do you test AI systems built on models we host ourselves?

Yes. Whether your AI system uses a hosted API (OpenAI, Anthropic, Google, AWS Bedrock, Azure OpenAI) or a self-hosted model (Llama, Mistral, Falcon, or custom fine-tuned), we test the full stack.

Test Your AI Systems Before Attackers Do → Contact InTechsters