AI & LLM Penetration Testing Services
Your AI accepts instructions from anyone. We make sure attackers can't exploit that.
Your AI Is an Attack Surface
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.
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.
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.
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.
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.
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.
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
FAQ: AI & LLM Penetration Testing
What is AI 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.
How is AI penetration testing different from a standard application pentest?
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.
What is prompt injection?
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.
What frameworks does Raxis use for AI security testing?
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.
Do you test RAG applications and vector databases?
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.
What about AI agents that call APIs or execute code?
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.
Can you test custom or fine-tuned models?
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.
How often should we test our AI applications?
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.