LLM penetration testing
Accelerate AI deployment without compromising security
Strengthen the resilience of your AI applications by uncovering model, prompt, and integration risks before they reach production.
Contact us Contact us Contact usYour AI systems deserve more than surface-level testing
Our team combines adversarial prompt engineering, automated red-teaming, and expert manual logic validation to assess risk across your models, agents, and integration layers. The result: clear, validated findings that strengthen resilience without slowing innovation.
Industry-standard processes for complete LLM confidence
Each engagement aligns with the OWASP Top 10 for Large Language Model Applications, ensuring your testing reflects the latest standards in generative AI security.
Planning and preparation
We define your AI asset landscape, including foundational models (e.g., GPT, Gemini, Claude), system prompts, plugin and tool access, authentication layers, and acceptable use policies.
Discovery and enumeration
We map conversational flows, API integrations, agent workflows, and vector database connections to understand how your system ingests, processes, and retrieves contextual data.
Penetration attempt and exploitation
Both automated and manual penetration testing are performed to determine weakness in the application. Response is reviewed and critical functions are mapped to find different paths to escalation. Any critical findings are immediately presented to customers to reduce risk of attacks occurring against critical findings.
Exploitation and validation
Using automated red-teaming tools and expert manual jailbreaking, we test for vulnerabilities such as prompt injection, sensitive data and PII extraction, insecure output handling, and model denial of service.
Reporting and remediation guidance
Receive a comprehensive report featuring validated prompt-based exploits, prioritized severity ratings, and prescriptive guidance to strengthen system instructions, guardrails, and API layers.
Insights that speed up innovation
- AI and LLM penetration testing validates chat interfaces, APIs, and model integrations across the generative lifecycle
- Identify prompt injection, jailbreak, and guardrail evasion risks before public exposure
- Assess RAG pipelines and vector databases for unauthorized retrieval and data poisoning
- Evaluate agentic workflows to ensure tool and plugin access stays within intended controls
- Strengthen trust in customer-facing AI systems without degrading performance
- Support compliance, governance, and responsible AI adoption with validated security assurance
Findings for forward motion
Every engagement concludes with transparent, validated results.
- Immediate notification of critical findings
- Executive presentation of initial findings
- Final and executive summary
- Detailed findings and remediation
- Optional retesting of initial findings
- A final report with updated findings
Certifications our testers hold
OSWE
OSCP
OSED
OSCE
OSEP
CHFI
CISSP
OSWA
COMPTIA
CPENT
BSCP
OSWE
OSCP
OSED
OSCE
OSEP
CHFI
CISSP
OSWA
COMPTIA
CPENT
BSCP
OSWE
OSCP
OSED
OSCE
OSEP
CHFI
CISSP
OSWA
COMPTIA
CPENT
BSCP
Have a question?
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What is LLM Penetration Testing?
LLM penetration testing evaluates the security of generative AI systems, including chatbots, LLMs, and RAG architectures. Unlike traditional testing, it focuses on model behavior, prompt logic, guardrails, and integration layers. Using adversarial techniques, we identify vulnerabilities such as prompt injection, data leakage, and unsafe output handling. The goal is to validate resilience while enabling confident AI adoption.
How does an LLM Penetration Test differ from a standard Web Application Penetration Test?
Traditional web application testing focuses on vulnerabilities such as SQL injection, cross-site scripting (XSS), and server misconfigurations. LLM penetration testing evaluates the logic and behavior of the model itself. We use adversarial tactics to manipulate prompts, bypass guardrails, and attempt sensitive data extraction. The emphasis shifts from infrastructure alone to how intelligence is applied and controlled.
What types of attacks does the LLM penetration test cover?
Our methodology aligns with the OWASP Top 10 for Large Language Model Applications. We test for prompt injection, insecure output handling, sensitive data leakage, model misuse, and denial-of-service scenarios. We also evaluate API keys, vector databases, and integration layers to ensure your broader AI ecosystem is secure.
Why choose a Rhymetec LLM Penetration Test?
Rhymetec combines adversarial prompt engineering, automated red-teaming, and expert manual validation aligned to the OWASP Top 10 for LLMs. Our approach evaluates both model behavior and the surrounding integration ecosystem. You receive executive-ready reporting, prioritized findings, and prescriptive remediation guidance. The result is structured security assurance that supports AI growth.
Do I need an LLM Penetration Test if I'm using a third-party model like Claude, OpenAI, or Gemini?
Yes. Even if the underlying model is secure, your implementation layer can introduce risk. System prompts, integrations, plugins, and data handling workflows create potential exposure points. We assess how your deployment responds to malicious inputs and validate that guardrails function as intended, ensuring your application does not become the weak entry point.
What compliance standards does this help satisfy?
Our LLM penetration testing supports emerging regulatory and governance frameworks, including the EU AI Act, the NIST AI Risk Management Framework (AI RMF), and ISO/IEC 42001. We deliver structured reporting that helps demonstrate validated safety testing and responsible AI deployment. This strengthens both regulatory readiness and executive confidence.
What is LLM Penetration Testing?
LLM penetration testing evaluates the security of generative AI systems, including chatbots, LLMs, and RAG architectures. Unlike traditional testing, it focuses on model behavior, prompt logic, guardrails, and integration layers. Using adversarial techniques, we identify vulnerabilities such as prompt injection, data leakage, and unsafe output handling. The goal is to validate resilience while enabling confident AI adoption.
Why choose a Rhymetec LLM Penetration Test?
Rhymetec combines adversarial prompt engineering, automated red-teaming, and expert manual validation aligned to the OWASP Top 10 for LLMs. Our approach evaluates both model behavior and the surrounding integration ecosystem. You receive executive-ready reporting, prioritized findings, and prescriptive remediation guidance. The result is structured security assurance that supports AI growth.
How does an LLM Penetration Test differ from a standard Web Application Penetration Test?
Traditional web application testing focuses on vulnerabilities such as SQL injection, cross-site scripting (XSS), and server misconfigurations. LLM penetration testing evaluates the logic and behavior of the model itself. We use adversarial tactics to manipulate prompts, bypass guardrails, and attempt sensitive data extraction. The emphasis shifts from infrastructure alone to how intelligence is applied and controlled.
Do I need an LLM Penetration Test if I'm using a third-party model like Claude, OpenAI, or Gemini?
Yes. Even if the underlying model is secure, your implementation layer can introduce risk. System prompts, integrations, plugins, and data handling workflows create potential exposure points. We assess how your deployment responds to malicious inputs and validate that guardrails function as intended, ensuring your application does not become the weak entry point.
What types of attacks does the LLM penetration test cover?
Our methodology aligns with the OWASP Top 10 for Large Language Model Applications. We test for prompt injection, insecure output handling, sensitive data leakage, model misuse, and denial-of-service scenarios. We also evaluate API keys, vector databases, and integration layers to ensure your broader AI ecosystem is secure.
What compliance standards does this help satisfy?
Our LLM penetration testing supports emerging regulatory and governance frameworks, including the EU AI Act, the NIST AI Risk Management Framework (AI RMF), and ISO/IEC 42001. We deliver structured reporting that helps demonstrate validated safety testing and responsible AI deployment. This strengthens both regulatory readiness and executive confidence.
Security with benefits
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