Developers of LLM-based products
Find exploitable flaws before release, and give customers credible evidence of security.
/ LLM PENETRATION TESTING
LLM Penetration Testing
Applications built on large language models open up a new attack surface. Our penetration test follows the OWASP LLM Top 10 and blends conventional application security with AI-specific techniques to show what an attacker can really achieve.
[ 01 ] / WHAT IT IS
What is an LLM penetration test, and what does it cover?
In short
An LLM penetration test is a security assessment of applications built on large language models, run against standard categories (the OWASP Top 10 for LLM Applications). It tests AI-specific attacks (such as prompt injection, insecure output handling and excessive agency) together with conventional web and API attacks.
An LLM-based application is more than a model. It includes the user interface, the API, prompt templates, data sources (such as the vector database behind a RAG system), external tools and plugins, and the code that processes the model's output. An LLM penetration test examines that whole chain through an attacker's eyes, looking for conventional and AI-specific weaknesses together.
The test is guided by the OWASP Top 10 for LLM Applications. It covers prompt injection (steering the model with hidden instructions), sensitive information disclosure, supply chain risks, data and model poisoning, improper output handling (where model output gets executed as code), excessive agency, system prompt leakage, vector and embedding weaknesses, misinformation and unbounded resource consumption.
It is the narrower, security-focused counterpart of AI Red Teaming: it follows standard categories, builds on the methodology of conventional penetration testing, and gives developers findings they can reproduce and fix directly. Together the two give the fullest picture.
Methodologies and standards
- OWASP Top 10 for LLM Applications
- OWASP ASVS
- OWASP API Security Top 10
- MITRE ATLAS
- NIST AI RMF
- ISO/IEC 27001
[ 02 ] / WHO IT'S FOR
Who benefits from an LLM penetration test?
Anyone integrating a large language model into a product or an internal process where security matters.
Companies using chatbots, coding or internal assistants
Assistants connected to internal knowledge bases, email or developer tools can carry a serious data-leakage risk.
Teams building RAG systems
Externally sourced content entering the knowledge base can hide instructions. The test shows whether your system handles that.
Builders of agents and automation
Manipulating an over-privileged agent can cause direct business damage. The test exposes those paths.
Providers in regulated sectors
Financial, healthcare and public-sector organizations may be expected to document security testing of their AI systems.
Buyers of third-party AI services
Testing a supplier's AI product before purchase and after integration is well worth the effort.
[ 03 ] / WHAT'S INCLUDED
What an LLM penetration test covers
We tailor the scope to your application's architecture. The following areas are typical.
01
Prompt injection testing
Direct and indirect injection (hidden in documents, web pages and emails), including attacks disguised by encoding and multi-step techniques.
02
Sensitive information and system prompt leakage
We check whether the model discloses its instructions, internal documents, other users' data or information derived from training data.
03
Improper output handling
We verify that the model's output is processed safely (XSS, SQL injection, code execution, SSRF through the output).
04
Agents, plugins and tool calls
We examine the permissions of connected tools, validation of calls and whether user confirmations can be bypassed (excessive agency).
05
RAG and vector databases
We test data and access separation, poisoning of the knowledge base, embeddings and unauthorized data access through queries.
06
Conventional web and API security
Authentication, authorization, rate limits and the APIs around the application, tested to OWASP ASVS and API Top 10.
07
Resource abuse and cost risk
We test for unbounded consumption, denial of service and token-driven cost abuse.
08
Supply chain and model management
We review the provenance and integrity of the models, datasets, plugins and dependencies in use.
[ 04 ] / HOW IT WORKS
How an LLM penetration test runs
The process follows classic application security testing, extended with AI-specific techniques.
Learning the architecture
We map the model, prompt templates, data sources, integrations and data flows, and define the scope.
Threat modeling
We establish who might attack, what they could reach, and which OWASP LLM Top 10 categories matter most.
Testing
Manual and automated tests, combined with conventional application security methods, probe the system.
Exploitation and impact
We prove the real impact of flaws we find, such as data leakage or an unauthorized action, in a controlled way.
Reporting
We document with reproducible steps, evidence, risk ratings and specific remediation advice.
Retest
After fixes, we confirm the issues are gone and that the new defenses did not introduce new weaknesses.
[ 05 ] / WHAT IT SOLVES
What an LLM penetration test solves, and why it pays off
In LLM applications, conventional flaws (such as broken access control) and new AI-specific flaws appear together. A manipulated model's output can be the way in to something bigger: data theft, an unauthorized action or a route to internal systems. The test examines that chain end to end.
The report is immediately usable by developers: specific flaws, reproducible steps and remediation advice, so problems can be fixed cheaply before release.
- finds LLM-specific and conventional vulnerabilities together
- helps prevent leakage of confidential data
- reduces the permission risk of agents and integrations
- evidence for customers, auditors and regulators
- verifiable coverage against the OWASP LLM Top 10
What you receive
- executive summary
- OWASP LLM Top 10 coverage map
- exploitable vulnerabilities with evidence
- risk ratings by impact and likelihood
- specific remediation advice for developers
- retest report
[ 06 ] / WHY QYNTAR
Why have Qyntar run your LLM penetration test?
Because LLM application security sits where AI-specific knowledge meets conventional application security.
Application security roots
We build on web and API testing experience, so we find the classic weaknesses alongside the AI ones.
Current methodology
Testing is based on the OWASP LLM Top 10 and MITRE ATLAS, and we keep up with new attack techniques.
Developer-friendly reporting
Findings are reproducible and recommendations are specific, so your developers can fix quickly.
Broad coverage
AI Red Teaming, source code review and cloud security assessment together give a complete picture of your AI system.
[ 07 ] / FAQ
Frequently asked questions: LLM Penetration Testing
What is an LLM penetration test?
A security assessment of applications built on large language models, run against standard categories (OWASP Top 10 for LLM Applications) and conventional application security methods, with controlled exploitation.
How does an LLM penetration test differ from AI Red Teaming?
The LLM penetration test concentrates on technical security, along standard categories. AI Red Teaming is broader, creative and objective-driven, and also covers misuse, harmful output and reliability risks.
What is the OWASP Top 10 for LLM Applications?
An OWASP project listing the ten most critical risks for applications built on large language models, among them prompt injection, sensitive information disclosure, supply chain risk, data poisoning and excessive agency. The list is updated regularly.
Do you test our own model or a third-party API?
We test the application and its integrations, whether the model is your own or comes from a provider. For third-party models we check the provider's terms of use beforehand and seek permission where needed.
Do you need source code or privileged access?
A black-box test is possible, but knowing the prompt templates, architecture and configuration allows a more precise and efficient assessment. We settle the level of cooperation when agreeing the scope.
Could the test endanger production or data?
We run the test in a controlled way, preferably in a test or staging environment. In production we work under agreed rules and stop conditions, and we do not perform destructive actions.
When should we retest?
After significant changes to the model, prompts or data sources, when a new capability (a new tool or plugin) is added, and periodically, because new attack techniques keep appearing.
[ 08 ] / RELATED SERVICES
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[ + ] / CONTACT
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