AI & LLM Penetration Testing Services

Your AI accepts instructions from anyone. We make sure attackers can’t exploit that.

Request a Quote
Schedule a 30 Minute Walkthrough

Your AI Has Been Live Longer Than It’s Been Tested

Your AI application accepts natural language as input. So does every attacker who targets it. Raxis AI penetration testing uncovers the vulnerabilities that traditional application security testing was never designed to find.

2025 PENETRATION TESTING THREAT DATA

SOURCES: Gartner, VERIZON DBIR 2025, IBM COST OF A DATA BREACH 2025

Enterprises with deployed GenAI in production by 202680%
Average U.S. data breach cost$10.22M
Year-over-year rise in vulnerability exploitation34%

Prompt injection is the #1 vulnerability in the OWASP Top 10 for LLM Applications 2025 — and the hardest to detect with automated tools alone. Source: OWASP Top 10 for LLM Applications, 2025 Edition

Your AI Is an Attack Surface

hacker on laptop icon

Your AI Accepts Attacker-Controlled Input

Every prompt is an attack vector. Unlike form fields and API parameters, natural language inputs can manipulate model behavior, override system instructions, and exfiltrate data — all without triggering your WAF or SIEM.

unknown person icon

Traditional Pentests Miss the Model Layer

Standard web application and API penetration tests evaluate authentication, injection, and access controls. They don’t test how your LLM responds to adversarial prompts, whether your RAG pipeline leaks sensitive documents, or if your AI agent can be tricked into unauthorized actions.
checkbox icon with pencil

AI Compliance Requirements Are Already Here

Regulators and enterprise buyers are catching up fast. The OWASP Top 10 for LLM Applications, MITRE ATLAS, NIST AI RMF, and the EU AI Act are establishing baseline security expectations for AI systems. If your pentest report can’t address AI-specific risks, it’s already incomplete.

What We Test

Prompt Injection & Jailbreak Testing

Prompt injection is the #1 LLM vulnerability and the hardest to detect with automated scans. Raxis engineers craft adversarial prompts targeting direct injection, indirect injection through documents and web content the LLM processes, and multimodal injection across text, images, and file uploads. We test whether your guardrails hold under sustained, creative attack pressure.

System Prompt Extraction & Leakage

Your system prompt contains operational logic, behavioral rules, and often sensitive configuration details. Raxis tests whether attackers can extract system instructions, API keys, internal URLs, or business logic from your prompts, exposing the blueprint of your AI application.

Sensitive Data Disclosure

LLMs can leak training data, user conversations, PII, and proprietary information through carefully crafted prompts. Raxis tests for data extraction across the entire pipeline, from the model itself to your RAG knowledge base, conversation history, and connected data sources.

RAG Pipeline & Vector Database Security

If your application uses Retrieval-Augmented Generation, the retrieval layer is a high-value target. Raxis tests your vector databases, embedding pipelines, and retrieval logic for poisoned embeddings, similarity manipulation, unauthorized document access, and injection attacks that ride in on retrieved content.

AI Agent & Tool Use Exploitation

Agentic AI systems that call APIs, execute code, query databases, or take autonomous actions expand your attack surface dramatically. Raxis tests whether attackers can manipulate your AI agent into privilege escalation through tool calls, data exfiltration via connected integrations, or cascading unauthorized operations.

Model Abuse & Denial of Service

Not every attack targets your data. Raxis tests for unbounded consumption attacks, recursive prompt loops, and resource exhaustion techniques that can inflate your API costs, degrade model performance, or take your AI application offline.

Output Handling & Downstream Injection

What your LLM generates matters as much as what it ingests. Raxis tests whether model outputs can trigger XSS, SQL injection, SSRF, or command injection in downstream systems that consume AI-generated content without proper sanitization.

Supply Chain & Model Integrity

Third-party models, plugins, fine-tuning datasets, and inference infrastructure introduce risks your development team may not have scoped. Raxis evaluates your AI supply chain for compromised model weights, backdoored plugins, insecure hosting, and training data poisoning vectors.

Why Raxis for AI & LLM Penetration Testing

AI security testing isn’t a checkbox added to a web app pentest. It requires engineers who understand how language models reason, how retrieval systems work, and how agentic architectures fail.

Request A Quote Schedule Call
AI decision graphic

Human-Led, Adversarial-First Methodology

Automated LLM scanning tools test for known prompt templates. Raxis engineers think like attackers, chaining prompt injection with tool exploitation and finding novel attack paths no scanner has a signature for.

Full-Stack AI Assessment

Raxis assesses the complete AI attack surface: model behavior, system prompts, RAG pipelines, vector databases, agent tool calls, API integrations, and the application layer that wraps it all together. Vulnerabilities in AI systems rarely exist in one layer.

Framework-Aligned, Audit-Ready Reporting

Our reports align to OWASP Top 10 for LLM Applications and MITRE ATLAS. Reports include proof-of-concept demonstrations, full attack chains, business-calibrated risk ratings, and remediation guidance your engineering team can act on immediately.

Request A Quote Schedule Call

Who Needs AI & LLM Penetration Testing

If You’re Deploying AI, You’re Deploying Risk

Request A Quote Schedule Call

Organizations Shipping AI-Powered Products

If your product includes a chatbot, copilot, recommendation engine, or any feature powered by a language model, your customers and their data are exposed to AI-specific attack vectors. Raxis testing validates your defenses before your users discover the gaps.

Enterprises Deploying Internal AI Tools

Internal AI assistants, document Q&A systems, and AI-powered workflows process sensitive corporate data — HR records, financial documents, legal contracts, customer PII. Raxis tests whether those tools can be tricked into disclosing data they were designed to protect.

Companies Facing AI Compliance Requirements

The OWASP Top 10 for LLMs, NIST AI RMF, EU AI Act, and emerging industry-specific AI regulations are establishing minimum security testing standards for AI applications. Enterprise buyers are increasingly requiring AI security assessments as part of vendor risk reviews. Raxis delivers the testing and documentation you need to meet these expectations.

Our AI & LLM Penetration Testing Methodology

Raxis AI penetration testing follows a structured methodology aligned with the OWASP Top 10 for LLM Applications and the MITRE ATLAS framework — adapted and extended based on our own offensive research.

01

Architecture Review & Threat Modeling

We map your complete AI architecture — models, prompts, retrieval systems, agent capabilities, tool integrations, data flows, and trust boundaries. This scoping phase identifies which attack surfaces exist and where the highest-risk targets are.

02

System Prompt & Configuration Analysis

Raxis engineers analyze your system prompts, guardrails, content filters, and access controls to identify weaknesses before active testing begins. We evaluate whether your defensive layers are resilient in theory — then break them in practice.

03

Adversarial Prompt Testing

The core of every AI engagement. We execute hundreds of targeted prompt injection attacks — direct, indirect, and multimodal — testing your model’s resilience to jailbreaks, instruction override, role manipulation, and context window exploitation. This isn’t template-based scanning. It’s manual, creative, and persistent.

04

Data Extraction & Disclosure Testing

We attempt to extract sensitive data through the model — training data, system prompts, RAG documents, user conversation history, and any connected data sources. Every successful extraction is documented with a full proof-of-concept.

05

RAG & Retrieval Pipeline Assessment

For applications using Retrieval-Augmented Generation, we test the retrieval logic, vector database access controls, embedding integrity, and content injection pathways. We verify whether attackers can poison, manipulate, or bypass your knowledge base.

06

Agent & Tool Exploitation

If your AI application has agentic capabilities — API calls, code execution, database queries, email sending — we test whether an attacker can manipulate the agent into unauthorized actions, privilege escalation, or data exfiltration through tool abuse.

07

Output Validation & Downstream Impact

We trace AI-generated outputs through your entire application stack, testing whether model responses can trigger injection attacks, bypass security controls, or corrupt data in downstream systems.

08

Reporting & Remediation

Every finding includes a proof-of-concept attack, a full attack chain narrative, risk rating, and specific remediation steps. Raxis delivers reports your engineering team can act on and your compliance team can present to auditors and stakeholders.

Compliance

AI Security Frameworks & Compliance

Raxis AI penetration testing supports compliance with the standards and frameworks governing AI application security.

Contact Us Schedule Call

OWASP Top 10 for LLM Applications

Full coverage across all 10 risk categories — prompt injection, sensitive information disclosure, supply chain, data poisoning, improper output handling, excessive agency, system prompt leakage, vector & embedding weaknesses, misinformation, and unbounded consumption

MITRE ATLAS

Adversarial testing mapped to ATLAS tactics, techniques, and procedures for ML systems

NIST AI Risk Management Framework

Testing aligned with NIST AI RMF governance, mapping, measurement, and management functions

EU AI Act

Security assessment supporting conformity requirements for high-risk AI systems

SOC 2 / ISO 27001

AI-specific findings documented for inclusion in broader compliance reporting

PCI DSS 4.0

Testing AI systems that process, store, or transmit cardholder data

HIPAA

Validating AI applications handling protected health information

Frequently Asked Questions About AI & LLM Penetration Testing

AI penetration testing is a specialized security assessment that evaluates AI-powered applications — particularly those built on large language models — for vulnerabilities that traditional web application and network pentests don’t cover. This includes prompt injection, data leakage, model manipulation, agent exploitation, RAG pipeline attacks, and supply chain risks unique to AI systems.

A standard application pentest evaluates authentication, authorization, input validation, and business logic. AI penetration testing adds an entirely new attack surface: the model layer. Raxis tests how your LLM responds to adversarial prompts, whether your RAG system leaks data, if your AI agent can be manipulated into unauthorized actions, and whether model outputs can trigger downstream vulnerabilities — none of which a traditional pentest methodology addresses.

Prompt injection is an attack where adversarial input manipulates an LLM into ignoring its instructions, revealing sensitive data, or performing unintended actions. Direct prompt injection targets user-facing input fields. Indirect prompt injection hides malicious instructions in documents, web pages, or other content the LLM processes. It’s the #1 vulnerability in the OWASP Top 10 for LLM Applications because it exploits a fundamental challenge in how language models process input.

Raxis AI penetration testing aligns with the OWASP Top 10 for LLM Applications and the MITRE ATLAS framework for adversarial threat modeling of ML systems. Our methodology also supports compliance with the NIST AI Risk Management Framework, EU AI Act requirements, and industry-specific standards including SOC 2, ISO 27001, PCI DSS, and HIPAA where AI systems are in scope.

Yes. RAG-based architectures introduce retrieval, embedding, and knowledge base attack surfaces that most security teams haven’t tested before. Raxis evaluates your vector database access controls, embedding pipeline integrity, retrieval logic, and content injection vectors — ensuring that your AI application can’t be manipulated through the data it retrieves.

Agentic AI systems are a primary focus of our testing methodology. We assess whether attackers can manipulate your agent into calling unauthorized APIs, escalating privileges through tool use, exfiltrating data through connected integrations, or executing actions the system was never designed to perform. As AI agents gain more autonomy, the blast radius of a successful attack grows with them.

Yes. Raxis tests AI applications regardless of whether they’re built on commercial APIs (OpenAI, Anthropic, Google, Mistral), open-source models, or custom fine-tuned models. Our testing methodology focuses on the application layer, prompt design, retrieval systems, and integration architecture — not just the underlying model.

At minimum, after every significant change to your model, prompts, RAG knowledge base, agent capabilities, or connected integrations. AI applications evolve faster than traditional software, and each change can introduce new vulnerabilities. For organizations with rapidly iterating AI products, Raxis Attack (PTaaS) provides continuous testing that keeps pace with your development cycle.

Can’t find an Answer?

Name(Required)
Please let us know what's on your mind. Have a question for us? Ask away.
Popped Culture Newsletter
Would you like to opt in and receive our Popped Culture Newsletter? Typically about once a month, we send out an email with news on the latest in the cybersecurity industry, as well as insights on penetration testing trends.

Our security experts will contact you within 1 business day