88% of organizations now regularly use artificial intelligence (AI) in at least one business function. While adoption of AI technologies has accelerated rapidly, security measures often lag. The rush to roll out AI has, in many cases, overshadowed essential testing and safety protocols. This is particularly a worry when AI and Large Language Models (LLMs) become deeply embedded within organizational workflows and systems in a way that most software isn’t.

These systems frequently interact with sensitive data, have broad access to internal tools and knowledge bases, and generate outputs employees rely on to make decisions. Once deployed, they quickly become a central repository of organizational data through routine employee input, increasing their value as a target for attackers.

A recent hacking exercise against McKinsey & Company’s internal AI platform, Lilli, exposed how quickly these environments can be compromised. Researchers at CodeWall used an AI agent to access large volumes of sensitive data, including proprietary research and system-level prompts, in just two hours. In a real-world scenario with threat actors, the stakes are much higher.

These risks are too significant to address with conventional security tools alone. According to IBM’s 2025 Cost of a Data Breach Report, 63% of organizations had no AI governance policy at all, and 97% of those that suffered an AI-related security incident had no proper access controls in place. Securing AI systems requires a different kind of thinking, and a different kind of test.

What is AI penetration testing?

AI penetration testing assesses AI and LLM systems for security weaknesses. Like traditional penetration testing, the goal is to identify vulnerabilities before an attacker does. The difference is in what’s being tested and how those systems behave.

With AI, the focus shifts away from misconfigurations or unpatched software. Instead, testing looks at how a model responds to inputs, what data it can access, and whether it can be manipulated into acting outside its intended boundaries.

This distinction is crucial, and it’s why a typical web application penetration test won’t fully cover AI-specific risks. Tests built for web applications are likely to miss vectors like prompt injection attacks and Retrieval-Augmented Generation (RAG) pipeline poisoning.

That said, web app penetration testing isn’t being replaced. Each test addresses specific areas and requires its own methodology and expertise. Used together, they provide broader coverage and help ensure the entire application stack is resilient to modern threats.

It’s also worth distinguishing between AI systems and LLMs, as they require different testing approaches:

• AI refers to the end-to-end solution: This includes models, data, integrations, and automated workflows. Security concerns arise from system behavior, where attackers exploit or manipulate how the solution interacts with data and external systems.
• LLMs are the core decision-making component: The LLM is responsible for generating language-based outputs, so testing looks at those responses, including accuracy, data exposure and policy adherence.

The key vulnerabilities in AI and LLM systems

Rather than focusing solely on code or infrastructure, attackers can target how an AI system interprets and responds to input. This introduces a new set of potential exploits, as outlined in the OWASP Top 10 for LLM Applications 2025. Below are three areas security teams should prioritize when assessing AI systems.

1. Prompt injection

Prompt injection attacks involve crafting inputs that manipulate an LLM into behaving in unintended or unsafe ways. An attacker might override system instructions, extract hidden prompts or trick the model into performing otherwise restricted actions.

The challenge is that LLMs are designed to be helpful and adaptive. That flexibility makes it difficult to fully separate trusted instructions from untrusted input, especially in systems that combine user prompts with system prompts, plugins, or external data sources.

2. Sensitive information disclosure

LLMs can inadvertently expose sensitive data, even when they’re not explicitly designed to store it. This typically appears in three ways:

• Personal identifiable information disclosed during interactions with the LLM.
• Poorly configured model outputs revealing training data or proprietary algorithms.
• Sensitive business data inadvertently included in responses.

For organizations using LLMs with proprietary or regulated data, this becomes a serious concern. A single poorly handled query could result in confidential information being surfaced to an unauthorized user.

3. Data and model poisoning

Data poisoning targets the integrity of the model itself. By introducing malicious or manipulated data into training or fine-tuning pipelines, attackers can influence how the model behaves. For instance, embedding harmful information into training datasets could lead to biased outputs.

The vulnerability can remain hidden, as a poisoned model may appear to function normally while producing incorrect outputs in specific scenarios. That’s why organizations need robust validation processes.

What to expect from an AI penetration test

AI penetration tests follow clear guidelines, rules and phases to ensure ethical hackers conduct an accurate and thorough test. At Outpost24, our penetration tests are human-led, with certified experts using AI-specific adversarial techniques, not automated scanners. Our process follows five steps:

1. System discovery and mapping

As with any engagement, the first step is scoping to build a clear picture of the attack surface. With AI, that involves enumerating its components, interfaces, data flows, tool integrations, and trust boundaries.

2. Vulnerability assessment and adversarial testing

Testers first actively try to manipulate the model’s behavior. They test a range of vulnerabilities, including both direct and indirect prompt injection, jailbreaking, output manipulation, and RAG poisoning attempts. The goal is to see how the model behaves when pushed outside of its expected use. Can guardrails be bypassed? Can the model be influenced to act against its intended purpose?

3. AI role and access context testing

AI systems don’t rely on access controls in the same way as standard applications. Roles and permissions are often defined implicitly through prompts and surrounding context. This stage focuses on whether those boundaries hold up under pressure. Testers assess issues such as system prompt exposure, prompt-based privilege escalation, and unintended data access across contexts, looking for ways an attacker could move beyond the limits the system is meant to enforce.

4. Supporting interface testing

Even the most secure model can be undermined by weak surrounding infrastructure. Testers focus on API security and authentication controls, as well as rate limiting (which protects the AI system from overload), and the web interfaces or chat frontends used to interact with the model. This ensures that the broader application stack isn’t introducing avoidable risk around the AI component.

5. Analysis and reporting

In the final step, testers analyze the data. The report maps results against the OWASP LLM Top 10, and findings are prioritized based on their potential business impact, so teams can focus on what matters most. Each issue is supported with clear, practical remediation guidance to help address vulnerabilities efficiently.

Does your organization need an AI penetration test?

As organizations continue to develop AI capabilities, testing plays a key role in maintaining a strong security posture and protecting critical assets. Outpost24’s AI Penetration Testing service is designed to help secure those investments through:

• CREST-certified testing, delivered by specialists with experience across both AI and LLM systems.
• Comprehensive coverage of the AI attack surface: the model layer, prompt layer, RAG pipelines, agent workflows, and supporting APIs.
• Clear, audit-ready reporting aligned to the latest industry standards, with findings prioritized by business impact.

If you’re deploying AI or LLM systems, understanding how they behave under attack is critical. Contact us to learn how Outpost24’s AI penetration testing can help identify and reduce these risks.