This Is How
Your LLM Gets Hacked.
Three stories. Three integration patterns. Three ways a well-intentioned AI feature becomes an attacker's entry point into your product.
The Chatbot That Became a Weapon
Attack Patterns
You add an AI chatbot to your website to handle customer support. It goes live on a Monday. By Friday, users are happy. The team celebrates. No one tested what happens when someone tries to break it.
One week later: a single crafted message is enough. The chatbot forgets it works for you. It leaks your internal system prompt, reveals pricing logic your competitors would pay for, and starts handing out discounts and refunds to anyone who asks the right way.
Your brand voice is now the attacker's weapon. Customers receive fraudulent responses that look like they came from you. Support tickets get escalated or closed by the attacker. You find out when a customer complains — a week too late.
The RAG System That Became a Data Tap
Attack Patterns
You connect an AI assistant to your internal knowledge base — contracts, HR policies, financial docs, client data. It lives in Slack. Your team uses it daily. It saves hours. Nobody asked who else could talk to it.
An attacker finds the API. With the right questions, the model hands over documents it was never supposed to surface — salaries, client contracts, M&A plans. No authentication required. The AI is helpful by design. That's the vulnerability.
Then it gets worse: the attacker plants false instructions inside documents your AI trusts. Now your own team is acting on attacker-authored guidance — silently, confidently, with no idea anything is wrong.
The Agent That Turned Against You
Attack Patterns
You deploy an AI agent to automate work — it reads emails, calls APIs, executes code, manages files. It has access to everything it needs to do its job. Your team loves it. It runs 24/7 with no supervision.
The attacker doesn't need to hack your systems. They just need to put instructions somewhere your agent will read them — a PDF, a webpage, a support ticket. The agent can't tell the difference between your orders and theirs. So it obeys.
Your agent is now working for the attacker — on your infrastructure, with your credentials. It exfiltrates data, modifies records, forwards emails. Quietly. Automatically. The attacker never logged in. There's no alert. You may never know it happened.
What we test for
The 7 LLM Attack Surfaces We Cover
Prompt Injection
Direct and indirect injection attacks that override model instructions or hijack context.
Data Exfiltration
Forcing the model to surface sensitive data through crafted inputs and output manipulation.
RAG Poisoning
Corrupting the knowledge base that the model retrieves from, making it return attacker-controlled responses.
Agent & Plugin Hijacking
Compromising tool-using agents to execute unauthorized actions through the model's own capabilities.
Jailbreaking & Guardrail Bypass
Circumventing content filters and safety layers to make the model generate disallowed outputs.
Insecure Tool Use
Abusing function calls and tool integrations to trigger unintended side effects with real-world consequences.
Training Data Poisoning
Analyzing fine-tuned and RLHF-adjusted models for backdoors and biased behavior introduced at training time.
+ New Vectors
The LLM attack surface evolves every sprint. We track emerging techniques continuously.
Why this keeps happening
LLMs are built to be helpful. That's exactly the problem.
Every LLM is optimized to follow instructions and satisfy requests. There is no built-in concept of "authorized instruction." The model cannot distinguish your system prompt from an attacker's injected payload. Security has to be designed, tested, and validated explicitly — and almost no one does this before shipping.
The companies we work with are not careless. They move fast. Their engineering teams are strong. But LLM security is a different discipline entirely — it requires adversarial thinking that most teams have never been trained for.
Hybrid LLM Security Service
We combine AI-powered automated scanning with 10+ years of hands-on offensive LLM experience — purpose-built for growth-stage companies that ship AI features without a dedicated security team.
What's Included Every Cycle
Each monthly engagement covers your full LLM attack surface — from the model interface down to the data layer, agents, and integrations.
- Full OWASP LLM Top 10 + LLMSVS assessment
- Prompt injection & jailbreak campaign testing
- RAG boundary testing & knowledge base poisoning
- AI agent attack surface mapping & hijacking tests
- Insecure tool use & function call abuse testing
- Guardrail bypass & model behavior analysis
- Training data poisoning review (fine-tuned models)
- Full findings report with severity-ranked remediations
Investment
Annual retainer · Monthly billing · Fixed fee
Exact price depends on model count, integration complexity, and agent scope — scoped on the initial 15-min call.
How We Test
Your LLM Systems
Every engagement follows four phases. We think like attackers, not auditors — every finding comes with a reproduction path and a fix.
Attack Surface Mapping
We document every model endpoint, integration, agent tool, data pipeline, and trust boundary before a single test runs. No assumptions.
Active Red-Teaming
Prompt injection campaigns, jailbreak sequences, RAG boundary probes, agent hijacking simulations, and tool call abuse. We run real attacks.
Technical Report
Every finding includes: exact attack path, proof-of-concept payload, CVSS-based severity score, and step-by-step remediation. Engineer-ready.
Debrief & Retest
Live debrief call with your engineering team. One free retest cycle for critical findings. Confirm fixes before the next sprint ships.
From first conversation to full security report in 30 days.
Here's exactly what happens after you book the call.
Free 15-Min Call
We ask you one question. You talk. We listen and confirm whether we can help concretely.
Written Proposal
Within 48 hours you receive a written proposal — service scope, deliverables, and a fixed price.
NDA + 50% to Start
We sign an NDA. You pay 50% upfront. We start the following Monday. Weekly sync throughout.
Report in <30 Days
Full password-protected report + debrief call. You pay the remaining 50%. Done.
Starting $3,500/month
20 hours of dedicated LLM security work per month, just for you. Annual commitment with monthly billing. Cancel anytime after the first year.
Exact price depends on model count, integration complexity, and agentic surface area — scoped on the initial call.
NDA + Contract Signed
Everything is confidential from the first technical discussion.
50% on Signed Contract
Work begins the next Monday so both sides can prepare.
50% on Report Delivery
Full findings + remediations in less than 30 days. Pay when it lands.
The only question we'll ask on our call
"If we could do anything to your AI —
what would scare you the most?"
That's it. That's the entire call. Your answer tells us everything we need to know about where your real exposure is — and whether we can help.
15 minutes. No pitch deck. No obligation. If it's a fit, we'll tell you exactly what we'd test and what it costs. If it's not, we'll tell you that too.
Book Your Free 15-Min Call
What Happens When
You Actually Test Your LLM
Karl T.
Managing Partner, Fintech Company
"We are so glad that we allowed the Pentestiverse team to show us how vulnerable we were on production and we never knew it. It is not theoretical risk believe me, our whole reputation was at stake. Thanks Pentestiverse Team for saving the day!"
Katriin L.
CTO, AI Startup
"Our internal RAG assistant had access to contracts, HR data, and financial projections. We never considered it an attack surface. The Pentestiverse team found a way to extract documents across user boundaries with a single query. That would have been a GDPR nightmare. Thanks!"