Penetration Testing of AI Integrations & LLM
Modern AI and Large Language Model (LLM) applications bring automation - and new risks. AI integrations penetration testing is essential to verify resilience against manipulation, prompt injection, jailbreaks, data leakage, and authorization bypass.
We apply the OWASP Top 10 for LLM and proven approaches for an LLM integrations penetration test. We cover model behavior, AI agents and RAG, APIs, and the integration environment including identity, access logic, and security guardrails.
Our AI application penetration testing covers:
AI/LLM Model Vulnerabilities
We detect weaknesses such as prompt injection, model inversion, data leakage, unauthorized access to training data, or missing output guardrails.
API Integration Security
We analyze access and communication security across AI APIs, including authentication, authorization, encryption, and protection against malicious requests.
Adversarial Scenarios
We test models against adversarial AI techniques such as evasion, model stealing, data poisoning, and prompt leakage.
Why AI pentesting matters for your organization
- Uncover risks before production launches
- Protect decision-making algorithms from manipulation
- Safeguard input/output channels against abuse
- Strengthen AI API and backend architecture security
- Align with OWASP AI Top 10 and best practices
How our AI testing process works
- Review model architecture, integrations, and access patterns
- Test APIs, inputs, and handling of unexpected scenarios
- Simulate real-world attacks in a sandboxed environment
- Deliver a technical report and present remediation guidance
Scope of AI & LLM Integrations Penetration Testing
What we test
- LLM integrations (OpenAI, Azure OpenAI, Anthropic, Mistral, local models)
- RAG pipelines: extraction, indexing, retrieval, response
- AI agents, tools and plug-ins (tool use, function calling)
- APIs and webhooks; authentication & authorization (OAuth2/OIDC, API keys)
- Prompts, system instructions, and guardrails
- Monitoring, audit logs, and security policies
Typical threats
- Prompt injection & jailbreak scenarios; prompt leakage
- Model/knowledge extraction; sensitive data exfiltration
- Data poisoning and supply-chain risks in RAG
- Authorization bypass via tools/agents
- Over-reliance/faith and hallucinations with security impact
- Denial of wallet/DoS via inefficient prompts/calls
Our AI Integrations Pentesting Methodology
Preparation & Risk Modeling
Workshops, threat models aligned with OWASP Top 10 for LLM, mapping assets and data flows.
Testing & Validation
Targeted attacks on prompts, agents and APIs, abuse cases, negative testing, and guardrail verification.
Report & Recommendations
Prioritized findings with reproduction steps, impact, remediation guidance, retest, and consulting.
What You Receive in an AI/LLM Pentest
- Executive summary and risk score
- Technical report with PoC evidence and reproduction steps
- Recommendations for prompts, RAG, agents, APIs and infrastructure
- Retest after remediation with confirmation of removed risks
- Recommended policies and guardrails (policy, filtering, moderation)
- Security checklists and CI/CD controls for AI changes
- Consulting for secure deployment and monitoring
Modern AI Systems Demand Modern Defense
If your product or service integrates AI, it’s critical to validate security against advanced abuse techniques. HAXORIS delivers professional penetration testing of AI & LLM solutions as part of a comprehensive cybersecurity assessment.
Why Choose HAXORIS?
Experience
Our experts have long-standing experience in offensive security, red teaming, and penetration testing.
Transparency
Every step is transparent and clear so you know what to expect. We keep you updated throughout to achieve the best results.
Collaboration
We work closely with your team to achieve the best outcomes and provide all necessary information and deliverables.
Professionalism
Our work is performed to the highest professional standards, following ethics and security principles.
THEY TRUST US
FAQ
Security testing of LLMs, agents and RAG integrations to verify resilience against prompt injection, jailbreaks, data leakage and tool abuse.
Prompt injection, jailbreaks, prompt leakage, data exfiltration, authorization bypass via agents, data poisoning in RAG, and DoS/denial-of-wallet.
OpenAI & Azure OpenAI, Anthropic, Google Vertex AI, Mistral, Llama and local models; LangChain/LlamaIndex, RAG pipelines and vector databases.
A technical report with proof and recommendations, an executive summary, and a retest after you implement fixes to confirm critical risks are removed.