LLM and AI system security: audit and penetration testing
Your agents and chatbots tested the way an attacker would test them.
A chatbot or AI agent opens a new entry point into your information system. It reads text coming from outside, has access to internal data and sometimes to tools capable of acting. A malicious instruction hidden in a message, a document or a web page can be enough to hijack it.
These risks are not covered by a conventional penetration test. Valtieri tests your AI systems the way an attacker would: direct and indirect prompt injection, bypassing of guardrails, extraction of confidential information, misuse of connected tools. The approach is based on the OWASP Top 10 for LLM Applications, the reference framework for risks specific to applications built on large language models.
The engagement lasts 2 to 4 weeks. It concludes with a report that management can read and a remediation plan directly usable by your technical teams.
What you get
- Prompt injection and guardrail bypass tests, on scenarios adapted to your context.
- A search for data leaks: system prompts, internal documents, personal data.
- A review of access rights, connected tools and logging.
- A report ranking each flaw by severity, with proof of exploitation.
- A prioritised remediation plan and the guardrails to deploy.
How it runs
Scoping
We define the scope tested, the accounts provided, the rules of engagement and the actions prohibited during testing. Everything is validated in writing.
Architecture review
We map what the system reads, what it accesses and what it can trigger. This is where the most serious attack paths lie.
Offensive testing
Prompt injection, bypassing, data extraction, misuse of connected tools: each scenario is documented and reproducible.
Reporting and remediation
Results are presented to management and technical teams, with a prioritised remediation plan and retesting of fixed flaws if you wish.
Frequently asked
What is prompt injection?
It is a malicious instruction slipped into the text the AI reads, through a user, a document, an email or a web page. It can push the system to ignore its rules, disclose information or misuse its tools.
Could testing disrupt production?
Rules of engagement are set before testing. Destructive scenarios are run in a test environment or with dedicated accounts, never on real data without written agreement.
Do you also test AI tools bought from a vendor?
Yes, within the limits the vendor's contract allows. We mainly test your configuration: connected data, granted rights, integrations. That is often where the flaws are.
Further reading
In the same practice area : AI and automation
- AI shipped to production
A prototype does not survive production. We ship what does.
- Managed AI agents
Your agents in production, monitored and under control.
- Process automation
Repetitive work handed back to the machines.
- AI governance
Know which AI tools run in your organisation, and under which rules.
- AI training and adoption
Teams that use AI well, and safely.
- AI assistant for your customers
An assistant that answers accurately, and knows when to hand over.
Let us talk about your context.
Thirty minutes to qualify the need. The written scope follows within fourteen working days.
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