LLM penetration testing
Test how well your generative AI stands up to attacks of every kind (prompt injection, jailbreaks and more).
What is an LLM pentest?
A penetration test on an LLM (Large Language Model) evaluates how well a language model resists attacks aimed at extracting your sensitive data, generating responses that would hurt your brand, or even compromising your infrastructure when the model can do function calling.
Objectives
Our audits identify the attack vectors specific to LLMs, such as:
Prompt injection (jailbreak)
Malicious content generation
Leakage of sensitive information (PII)
Logical escalation through conversation
Bypass of safeguards and roles
The HELX approach
To secure LLM-based systems, we run penetration tests tailored to their architecture. Drawing on the latest security research on well-known models (OpenAI, Anthropic, Google DeepMind and others), we put yours through advanced attack techniques: adversarial suffixes, role-based attacks, encoding and encryption tricks, indirect jailbreaks. Whether your solution relies on ChatGPT, Claude, LLaMA or an open source model, we help you identify its vulnerabilities and put concrete defenses in place (guardrails, RLHF, contextual filtering). Protect your users and your brand against the new risks that come with AI.
Audit types
An LLM penetration test can be run with different levels of access to the system. During our conversations, we choose together the scenario best suited to your security stakes.
Black box
Simulates an external attacker with no access to the system instructions or the source code. Relies solely on the public interface (chatbot, API and so on).
Gray box
Testing with partial access: a logged-in user, API documentation, a sample prompt or conversation history. Lets us explore more realistic scenarios.
White box
Full access to the system: system prompt, filtering rules, logs, even integration code. Allows a more exhaustive assessment of the attack surface and the protections.
Methodology
Our security audits of LLM applications follow a dedicated offensive approach covering the full exposure of the model. We assess its resilience against text manipulation, indirect attacks and the extraction of sensitive data through language.
- 01
Information gathering
Identification of the components exposing an LLM: chatbot, API, text generation engines. Analysis of the business goals of the model and its role in the application architecture.
- 02
Interaction mapping
Inventory of the user-model interaction types (free-form prompts, structured queries, conversational context, assigned role) and identification of the potential injection points.
- 03
Vulnerability research
Deployment of known bypass techniques: prompt injection, adversarial suffixes, logical jailbreaks, multilingual attacks, context or persona hijacking.
- 04
Exploitation
Controlled execution of offensive scenarios to test the generation of forbidden content, filter bypasses or the leakage of sensitive information (PII, system prompts).
- 05
Post-exploitation
Use of the responses or access obtained to simulate more complex attacks (role switching, chained attack simulation, exfiltration through successive dialogues).
- 06
Risk assessment
Analysis of how much control the model retains, how stable the protections are, and the real risks to the user or the target system. Concrete defensive recommendations.
A bit of technical detail
Our LLM audit methodology relies on proven security standards (OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF). In particular, we look for the following vulnerabilities:
- Prompt injection (direct or indirect)
- Restriction bypass
- Leakage of sensitive information
- Evasion through encoding / obfuscation
- Role manipulation (persona injection)
- Multilingual or contextual exploitation
- Bypassing restrictions through formats (JSON, Markdown and others)
- Business logic vulnerabilities
- Logical escalation through interaction
- Triggering of unanticipated critical behaviors
Our other penetration tests
Frequently asked questions
How much does an LLM penetration test cost?
A fixed price of €4,250 excl. VAT, covering five days of expertise, with a 15% discount for small businesses, SMBs and non-profits. The scope is defined together during scoping: chatbot, RAG, agents, integrations.
We use OpenAI/Azure: is the model not already secure?
The model may be; your integration rarely is. Prompt injection, data exfiltration through your document base (RAG), bypass of your guardrails, abuse of the tools connected to the agent: the flaws come from your assembly, not from the model itself.
Which frameworks do you rely on?
The OWASP Top 10 for LLM and our internal offensive R&D, with scenarios tailored to your use case (sensitive data leakage, unauthorized actions, context poisoning).
Can the test corrupt our knowledge base?
No: poisoning attempts are run in test spaces or are tagged and cleaned up afterwards, and no real data ever leaves your environment.
Tell us about your project.
Let us talk through your needs and expectations and build the right service for you.