AI Red Teaming in Georgia: What It Actually Tests

Most security testing in Georgia still means a network scan or a web app pentest. That covers infrastructure and code — but it says nothing about the LLM-powered feature your team shipped last quarter, or the AI agent that now has access to your internal tools. Almost no one locally is testing that layer at all.

That's the gap AI red teaming closes.

It's not the same as a pentest

A conventional penetration test targets systems that behave predictably — a server either has a patch or it doesn't, an endpoint either validates input or it doesn't. A language model doesn't behave that predictably. Its "vulnerabilities" live in how it interprets instructions, what it trusts as input, and how much autonomy it's been given to act on your behalf. Testing that requires a different mindset and a different set of techniques than scanning ports or fuzzing forms.

What we actually test

An AI red team engagement probes the model and everything wired to it:

• Prompt injection & jailbreaks — getting the model to ignore its own instructions.
• Indirect injection — instructions hidden in a document, a webpage, or a tool result the model reads, rather than typed directly by a user.
• RAG poisoning & retrieval hijacking — corrupting or manipulating the data a retrieval-augmented system pulls from.
• Multi-agent attack chains — one compromised agent influencing others it talks to.
• MCP & tool exploitation — abusing the tools and permissions an agent has been granted.
• Data exfiltration — getting the model to leak something it shouldn't, through its own output.

Each of these maps to a category in the OWASP Top 10 for LLM Applications, which is quickly becoming the reference framework for this kind of work — the same way the original OWASP Top 10 became the reference for web application security.

Why this matters specifically here

More Georgian companies — in fintech, in customer support, in product teams generally — are shipping AI features and AI agents into production. Most of that is happening without anyone adversarially testing what the model can be tricked into doing, or what an agent with too much tool access could be abused to reach. The risk isn't hypothetical; it's just untested.

A pentest will tell you if your login page is secure. It won't tell you whether your support chatbot can be talked into revealing another customer's data, or whether an internal AI agent can be manipulated into calling a tool it was never meant to use unsupervised.

What an engagement looks like

Same rigor as any offensive security engagement: scope agreed in writing, real adversarial attempts against the model and its surrounding systems, findings mapped to the OWASP LLM Top 10 with clear severity ratings, and a retest once fixes are in place. No scanner output dressed up as a report — every finding comes with proof.

If you're building or have already shipped an LLM product, an AI agent, or anything RAG-based, and no one has adversarially tested it yet, that's the gap worth closing next.

See the full AI Red Teaming service →