Find Your LLM's Weaknesses Before Attackers Do
LLMs introduce a fundamentally new class of vulnerabilities - ones that traditional pentest tools weren't built to find. Our AI-native penetration testing methodology is designed specifically for language models, covering prompt attacks, model extraction, jailbreaks, data leakage, and more.
Why LLM Security Testing Is Non-Negotiable
LLMs deployed without security testing have vulnerabilities that traditional security tools cannot find
Find Vulnerabilities Before Attackers Do
LLM vulnerabilities are actively exploited in the wild. Prompt injection attacks, system prompt leakage, and jailbreaks have been demonstrated against major commercial AI products. Our testing identifies your specific weaknesses before they become incidents.
Meet Enterprise Security Requirements
Enterprise customers and regulated industries require security validation before deploying AI-powered products. Our pentest report - mapped to OWASP LLM Top 10 - provides the evidence you need for security reviews, procurement questionnaires, and compliance audits.
Protect Your Users and Your Reputation
A jailbroken or manipulated LLM in your product can generate harmful content, reveal sensitive data, or be used to attack your users. Proactive security testing protects both your users and your brand before a public incident forces you to act.
What's Included
Full-spectrum LLM security testing mapped to the OWASP LLM Top 10
Prompt Injection & Jailbreak Testing
We attempt to override your LLM's system prompt, safety instructions, and intended behaviour using a comprehensive library of prompt injection techniques - including direct injection, role-playing attacks, token smuggling, and adversarial suffixes mapped against your specific model and deployment configuration.
System Prompt Extraction Attempts
Your system prompt often contains proprietary instructions, persona definitions, and sensitive configuration. We test whether it can be extracted through direct questioning, indirect inference, or context manipulation - and help you protect it accordingly.
Data Leakage & Training Data Inference
LLMs can inadvertently reveal sensitive information from their training data or fine-tuning datasets. We probe for personally identifiable information, internal documents, API keys, and confidential business data that the model may reproduce under adversarial questioning.
Model Inversion & Extraction Attacks
Through systematic querying, an attacker may be able to reconstruct elements of a fine-tuned model or replicate its behaviour - undermining your IP investment. We test the feasibility of model extraction against your deployment and recommend mitigations.
Indirect Prompt Injection via External Content
When LLMs process web pages, documents, emails, or database content, that external data becomes part of the model's context. Attackers can embed malicious instructions in those sources to hijack the model's behaviour. We test every external data source your LLM processes.
RAG Pipeline Attack Scenarios
Retrieval-Augmented Generation pipelines introduce additional attack surfaces. We test for data poisoning, retrieval manipulation, cross-context contamination, and document-based injection attacks against your RAG implementation.
OWASP LLM Top 10 Compliance Reporting
Our findings are mapped to the OWASP LLM Top 10 framework, providing a structured, industry-standard view of your LLM security posture. Reports include severity ratings, CVSS scoring where applicable, and a prioritised remediation list ready for your dev team.
Our Testing Methodology
A structured, adversarial approach designed from the ground up for large language models
Reconnaissance & Scoping
We map your entire LLM deployment - system prompt, model version, API configuration, connected data sources, and downstream actions the model can trigger. This intelligence shapes a targeted testing plan focused on your highest-risk attack surface.
Adversarial Testing
We execute a systematic battery of attacks against your LLM - prompt injection, jailbreaking, data extraction, indirect manipulation, and more - using both automated tooling and manual expert techniques developed specifically for language models.
Report & Remediate
We deliver a detailed pentest report mapped to OWASP LLM Top 10, with severity ratings, reproduction steps, and precise remediation guidance. We include a re-test of all critical and high findings after your team applies fixes.
Why Choose IronProbe for LLM Pentesting?
Designed for companies deploying LLM-powered products - chatbots, copilots, customer-facing AI - who need to validate security before or after launch.
AI-Native Methodology
Our pentesting methodology was built specifically for LLMs, not adapted from web app playbooks. We use techniques developed against real LLM deployments, not theoretical attack models.
OWASP LLM Top 10 Reporting
Every finding is mapped to the OWASP LLM Top 10, giving you a standardised, enterprise-ready view of your LLM security posture that is recognised across the industry.
Fast Turnaround, Actionable Results
Most LLM pentests are delivered within 5-10 business days. Findings are written for developers - clear reproduction steps, root cause analysis, and code-level remediation guidance.
Frequently Asked Questions
Common questions about LLM penetration testing
The OWASP LLM Top 10 is an industry-standard framework that identifies the ten most critical security risks for LLM applications, including prompt injection, insecure output handling, training data poisoning, model denial of service, and supply chain vulnerabilities. Mapping your pentest findings to this framework provides a standardised, widely-recognised view of your security posture that is meaningful to security teams, enterprise buyers, and regulators.
Yes. Our testing methodology is model-aware: different models have different susceptibility profiles, different jailbreak techniques, and different deployment configurations. We tailor our approach based on the specific model you're using - whether that's a commercial API (GPT, Claude, Gemini, Llama via API), a self-hosted open-source model (Llama, Mistral, Falcon), or a fine-tuned custom model.
Ideally, we engage at two points: during development (to catch architectural issues before they're baked in) and before launch or major feature releases (as a final gate). Post-launch testing is also valuable - particularly if you've updated your system prompt, connected new data sources, or upgraded to a new model version. LLM security is not a one-time exercise because the threat landscape evolves as rapidly as the technology.
Traditional web app pentesting looks for vulnerabilities in code: injection flaws, broken auth, misconfigured systems. LLM pentesting is fundamentally different - the 'vulnerability surface' includes the model's training, its system prompt, its reasoning patterns, and the data it processes at runtime. You can't scan an LLM with a web app scanner. The techniques, tooling, and expertise required are entirely different, which is why specialist AI security testing is necessary.
Get a Detailed, Actionable LLM Pentest Report
Mapped to OWASP LLM Top 10. Written for your developers. Verified after fixes. Book a free scoping call to get started.
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