LLM penetration testing
LLM penetration testing attacks an application built on a large language model to find what an attacker could make it do. HYDN tests for prompt injection, jailbreaks, system prompt and data leakage, unsafe output handling, excessive agency in tools and plugins, and abuse of the surrounding application, using the HYDN AI Attack Engine alongside manual testing, mapped to the OWASP Top 10 for LLM Applications.
What we test
- Prompt injection. Direct injection through user input, and indirect injection through documents, emails, web pages and tool results the model reads. See prompt injection.
- Jailbreaks and guardrail bypass. Whether content and policy controls hold up against multi-turn, role-play, encoding and multilingual attacks.
- Data leakage. System prompts, other users’ data, retrieval sources and secrets exposed through the model’s answers.
- Output handling. Model output that reaches a browser, database, shell or API without validation, leading to XSS, injection or unintended actions.
- Excessive agency. Tools, plugins and actions the model can trigger, and whether it can be steered into using them against the user or the business.
- Retrieval and embeddings. Poisoned or over-permissive knowledge bases that change answers or leak data.
- Resource abuse. Inputs that drive up cost or degrade service.
How an engagement runs
HYDN scopes the work with you, proposes an approach, executes the testing, reports back, and guides remediation through to re-test. Testing combines the HYDN AI Attack Engine, which supports adversarial threat generation, multi-turn red teaming, guardrail bypass, fuzzing, multilingual threat generation, poisoning and exfiltration, with manual work by our security team.
Mapped to OWASP
Findings are mapped to the OWASP Top 10 for LLM Applications, which gives your engineers and your auditors a shared reference.
Frequently asked questions
How is LLM testing different from a normal web app test?
The model is a new kind of input handler. It follows instructions from any text it reads, so an attacker can try to steer it through user input, documents, web pages or tool results. Testing has to cover those paths as well as the usual web flaws around the model.
Do you use automated tools?
The HYDN AI Attack Engine generates adversarial inputs at scale, including multi-turn, multilingual and guardrail bypass attempts. Human testers decide what matters, chain findings together and confirm impact. We still use human security experts for all client engagements.
What do we receive?
Full human and machine readable reports with threat and risk indicators, remediation recommendations, and re-testing once fixes are in place.
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