Secure your AI before someone else tests it for you
Your chatbots, copilots, and AI agents are now part of your attack surface. Pentest Testing Corp delivers AI penetration testing built on the OWASP LLM Top 10, uncovering the prompt injection, data leakage, and agent abuse flaws that traditional scanners never see.
Most of the companies we work with aren’t building AI from the ground up. They’re SaaS teams who’ve added a chatbot, wired in an LLM API, or built a copilot on top of their existing product. The integration layer between your AI and your systems is where most vulnerabilities live, and that’s where we focus.
Engagements start from $9,500
Final pricing depends on system type, integration depth, exposed LLM APIs and agent tools, and whether adversarial red team testing is included. Fixed-price, no hourly billing, no scope-creep surprises.
Service overview
What AI penetration testing actually is
AI penetration testing is the practice of attacking your own AI systems on purpose, under controlled conditions, so real attackers can’t do it first. It goes well beyond checking whether an API responds correctly. We probe how your models reason, what they can be tricked into revealing, and what actions an attacker can force them to take.
Most security tools were built for a web stack: HTTP requests, SQL queries, broken access control. None of that catches a malicious instruction hidden inside a support ticket that your AI assistant reads and obeys. A web application firewall doesn’t understand intent. A vulnerability scanner can’t tell the difference between a user asking a question and an attacker rewriting your system prompt.
Why businesses need this now
AI moved from experiment to production faster than security caught up. Companies are wiring language models into customer support, internal copilots, document search, and autonomous agents that touch real systems and real data. Every one of those integrations is a new way in.
The risk is concrete. An attacker can manipulate a customer-facing chatbot into leaking another customer’s records. A hidden instruction in an uploaded PDF can hijack an AI agent that has email or database access. A poorly scoped copilot can be coaxed into revealing the confidential prompt and business logic behind it. If you’ve already had a standard web or API pentest this year, it’s worth checking whether it actually covered this: see AI penetration testing vs traditional pentest for a breakdown of what typically gets missed.
Auditors have noticed. The OWASP LLM Top 10 is increasingly referenced when validating SOC 2, HIPAA, and similar controls inside AI-enabled systems. An AI security assessment is becoming a baseline expectation, not a nice-to-have. We help you get ahead of it.
What we test
We test the full range of AI systems that businesses are putting into production. If it makes decisions, answers questions, or acts on your behalf, it’s in scope.
Conversational chatbots and assistants
Customer-facing and internal chatbots, including their guardrails, content filters, and the boundary between user input and system instructions.
LLM APIs and integrations
The endpoints, authentication, rate limiting, and data handling behind your model calls. This is where LLM security testing overlaps with classic API security, and where a lot of damage hides.
RAG applications
Retrieval-augmented generation systems that pull from vector databases and knowledge bases. We look at retrieval poisoning, cross-tenant data exposure, and what happens when untrusted content enters the pipeline.
AI agents and autonomous workflows
Multi-step agents with access to tools, files, and external systems. The more an agent can do, the more an attacker can make it do.
Copilots and embedded assistants
Coding copilots, productivity assistants, and AI features bolted onto existing products, including how they inherit permissions from the host application.
Vision and multimodal models
Image and document processing pipelines, where instructions can be hidden in pixels, metadata, or file structure rather than plain text.
Our methodology
Our AI red team assessment follows a structured process mapped to the OWASP LLM Top 10 (2025), the most widely referenced framework for large language model security. We don’t run a single tool and hand you a PDF. We work the system the way a determined attacker would, then document everything so your team can act on it.
PHASE 01
Scoping and threat modeling
We start by understanding what your AI does, what it can access, and who interacts with it. Together we define the attack surface, the assets at risk, and the rules of engagement so testing stays safe and focused.
PHASE 02
Reconnaissance and surface mapping
We map every entry point: prompts, APIs, file uploads, retrieval sources, connected tools, and agent capabilities. Understanding the full reach of a system is half the work, especially with agents that can chain actions together.
PHASE 03
Exploitation and adversarial testing
This is the core of the engagement. We attempt prompt injection, system prompt extraction, data exfiltration, jailbreaks, retrieval poisoning, and agent manipulation. Where a vulnerability exists, we prove it with a working proof of concept rather than a theoretical flag.
PHASE 04
Impact analysis
A finding only matters if you understand what it costs you. We trace each vulnerability to its real business impact: data exposed, actions an attacker could trigger, compliance obligations affected, and how far the blast radius extends.
PHASE 05
Reporting and delivery
You receive a clear, prioritized report with reproduction steps, evidence, risk ratings, and specific remediation guidance. We walk your team through it so nothing gets lost in translation.
PHASE 06
Retesting and validation
After you’ve fixed the issues, we verify the fixes actually hold. A finding isn’t closed until we’ve confirmed it.
Vulnerabilities we find
These are the categories we hunt for in every AI security assessment, aligned with the OWASP LLM Top 10. Each one represents a real way attackers compromise AI systems in production.
Prompt injection (LLM01)
The top-ranked risk for a reason. Attackers craft input that the model reads as a new instruction instead of data, overriding its intended behavior. We test both direct injection through user prompts and the subtler indirect variety.
Indirect prompt injection
Hidden instructions planted in content the AI later processes: a document, a web page, an email, a calendar invite. The user never sees it, but the model obeys it. This is one of the most dangerous and overlooked flaws in agentic systems.
Sensitive information disclosure (LLM02)
We test whether your AI can be coaxed into revealing PII, secrets, internal data, or another user’s information through its outputs or traces.
System prompt leakage (LLM07)
The instructions, business logic, and guardrails behind your AI are valuable to an attacker. We test whether they can be extracted, then used to bypass your protections.
Insecure LLM API and output handling (LLM05)
When downstream systems trust model output without validation, you get injection, code execution, and broken access control wearing a new hat. We test where AI output flows into the rest of your stack.
Excessive agency and agent abuse (LLM06)
Agents often hold more capability, autonomy, or permission than they safely need. We test whether an attacker can push an over-privileged agent into sending messages, modifying data, or invoking tools it shouldn’t.
Vector and embedding weaknesses (LLM08)
For RAG systems, we test for poisoned vector stores, cross-tenant data leakage, and manipulated retrieval that quietly corrupts everything the model generates.
Data and model poisoning (LLM04)
Where applicable, we assess the integrity of training, fine-tuning, and retrieval data that shapes how your model behaves.
Unbounded consumption (LLM10)
Uncontrolled resource use that an attacker can weaponize into denial of service or runaway cost. We test the limits, or the lack of them.
Deliverables
When the engagement wraps, you don’t get a vague summary. You get everything your team needs to fix what we found and prove it to anyone who asks.
- Executive summary written for decision-makers, covering overall risk posture and business impact in plain language
- Detailed technical report with every finding, severity rating, affected system, and clear reproduction steps
- Proof-of-concept evidence demonstrating each exploitable vulnerability so there’s no ambiguity about whether it’s real
- Risk-rated findings prioritized so your team knows what to fix first
- Specific remediation guidance mapped to each issue, written to be actionable rather than generic
- OWASP LLM Top 10 mapping showing exactly where you stand against the industry-standard framework
- Remediation walkthrough with your team to answer questions and align on fixes
- Free retest to validate that your fixes actually closed the gaps
Pricing and scoping
We don’t hide pricing behind a sales call, and we don’t pretend a complex AI system fits a fixed price tag. Both extremes waste your time. Here’s how it actually works.
Every AI penetration testing engagement is fixed-price and scoped to your architecture. You know the number before any work starts, and it doesn’t move unless the scope does. Engagements start from $9,500+, and the final figure depends on a few concrete factors:
- The type and number of AI systems in scope (a single chatbot is very different from a multi-agent platform)
- How deeply the AI is integrated with your APIs, databases, and internal tools
- Whether your system uses RAG, vector databases, or fine-tuned proprietary models
- Whether the engagement includes full adversarial red team testing and training-data review
To keep scoping simple, most clients land in one of three tiers. We’ll confirm exact pricing in your quote.
Starter – LLM baseline evaluation
From $9,500
Built for applications that use third-party model APIs (OpenAI, Anthropic, and similar) with limited backend integration. This is the right entry point for teams shipping their first AI feature.
- Prompt injection
- Output manipulation
- System prompt leakage
- Data exposure
Professional – Integrations and agentic abuse
$15,000 to $35,000
For AI applications with active plugins, internal tools, and RAG pipelines. This is where most production AI systems belong.
- Permission boundaries
- Agent abuse
- Indirect injection through retrieved content
- Complex API access controls
Enterprise – Adversarial and full pipeline review
$35,000 to $75,000
A comprehensive assessment for proprietary ML models and complex agentic systems.
- Deep training-data exposure review
- Advanced adversarial input testing
- Full penetration testing of the surrounding cloud infrastructure
Not sure where you fit? That’s what the scoping call is for. We’ll recommend the right tier and send a fixed-price proposal within one business day, no sales pressure required.
Why choose Pentest Testing Corp
We attack AI like attackers do, not like a checklist.
Anyone can run an automated scan. We do hands-on, manual adversarial testing led by certified penetration testers who understand how these systems break in the real world.
We’re built on the current standard.
Our methodology tracks the OWASP LLM Top 10 (2025), not last year’s assumptions. AI security moves fast, and so do we.
We speak business, not just code.
Our reports work for your engineers and your board. Technical depth where it’s needed, clear impact where decisions get made.
We prove every finding.
No theoretical risks padding a report. If we flag it, we can demonstrate it.
We don’t disappear after delivery.
Remediation support and a validation retest are part of the engagement, because a report you can’t act on isn’t worth much.
Confidentiality is non-negotiable.
Your systems, data, and findings stay private. We treat your trust as the asset it is.
AI testing and your compliance program
Auditors are catching up to where your technology already is. SOC 2 trust service criteria around logical access (CC6.1, CC6.6) and change management (CC8.1) increasingly apply to AI systems that touch customer data or make decisions on your behalf. Enterprise customers completing vendor security questionnaires are asking directly about AI security testing, and “we haven’t done it yet” is becoming a harder answer to give.
NIST’s AI Risk Management Framework provides the governance layer, and the OWASP LLM Top 10 is the testing standard auditors and procurement teams are starting to reference by name. We structure our AI penetration testing findings so they map directly to the controls your auditor is already asking about, whether that’s SOC 2, ISO 27001, or a customer’s vendor security review. You get a report your security team can act on and your auditor can file.
Industries we serve
AI is showing up everywhere, and so are we. We deliver AI penetration testing across:
- SaaS and technology companies embedding AI into their products
- Financial services and fintech, where AI touches sensitive transactions and personal data
- Healthcare and health tech, where AI assistants handle protected information
- E-commerce and retail, where customer-facing chatbots are now standard
- Legal and professional services deploying AI for document analysis and research
- Startups and scale-ups shipping AI features fast and needing security to keep pace
- AI-native companies and developer platforms building LLM-powered products, agents, or APIs that serve other businesses
- Enterprises integrating copilots and autonomous agents into core operations
If you’re putting AI in front of customers or wiring it into your systems, we can help you do it securely.
Engagement process
We’ve made working with us straightforward, from the first message to the final retest.
STEP 01
First contact
You reach out through our site or book a call. Tell us what you’ve built and what’s keeping you up at night.
STEP 02
Scoping call
We discuss your AI systems, goals, timeline, and constraints, then define what’s in scope and agree on rules of engagement.
STEP 03
Proposal and agreement
You receive a clear proposal covering scope, approach, timeline, and pricing. Once it’s signed, we schedule the work.
STEP 04
Assessment
Our team runs the full AI red team assessment against your systems, following the methodology above and keeping you informed of anything critical in real time.
STEP 05
Reporting
We deliver your detailed report with prioritized findings, evidence, and remediation guidance, then walk your team through it.
STEP 06
Remediation support
Your team fixes the issues with our guidance close at hand for any questions that come up.
STEP 07
Retest and sign-off
We validate your fixes and confirm the vulnerabilities are closed, so you can move forward with confidence.
Frequently asked questions about our AI penetration testing
How much does AI penetration testing cost?
Engagements start from $9,500+, with the final fixed price set by the scope: how many AI systems are in play, how deeply they’re integrated, whether RAG or proprietary models are involved, and whether you want full adversarial red team testing. We give you the exact number up front in a fixed-price proposal, so there’s no hourly billing and no surprises mid-engagement. A quick scoping call is the fastest way to get an accurate figure.
How is AI penetration testing different from a regular penetration test?
A traditional pentest targets your network, applications, and infrastructure: the HTTP layer, authentication, access control, and so on. AI penetration testing targets the model’s reasoning, the data it retrieves, the tools it can invoke, and the agentic logic built around it. A classic SQL injection control won’t stop a prompt injection, which is exactly why this needs its own discipline. Many clients run both for complete coverage.
We use a third-party model like GPT or Claude. Isn’t security the provider’s job?
The model provider secures the model itself. You’re responsible for everything you’ve built around it: your prompts, your retrieval sources, your agent permissions, your API connections, and the systems your AI can reach. That’s the integration layer, and it’s where most real-world AI vulnerabilities live. A provider’s rate limiting doesn’t stop prompt injection through a support ticket your AI reads. Their content policy doesn’t prevent an attacker from extracting your system prompt. Our testing focuses on exactly that layer, what you control and what you’re responsible for securing.
Will testing disrupt our production systems?
No. We define safe rules of engagement during scoping, and where production testing carries risk, we work in staging or coordinate carefully with your team. Avoiding disruption is part of how we plan every engagement.
How long does an AI security assessment take?
It depends on scope and complexity. A focused test of a single chatbot moves quickly. A full assessment of a multi-agent system with tool access and RAG pipelines takes longer. We give you a clear timeline in the proposal so there are no surprises.
What do we actually walk away with?
A prioritized report with proof-of-concept evidence for every finding, business-level impact analysis, specific remediation guidance, a mapping against the OWASP LLM Top 10, and a free retest once you’ve applied the fixes. You’ll know exactly what’s wrong, why it matters, and how to fix it.
Don’t wait for an attacker to find the gap
Your AI is already in production, already taking input, already connected to systems that matter. The question isn’t whether it can be tested. It’s whether you test it first, or someone else does.
Pentest Testing Corp delivers AI penetration testing that finds the flaws traditional security misses, proves them, and shows you how to fix them. Let’s secure what you’ve built before it becomes a headline.