BY JASON MATLOCK:

Senior Threat Labs Consultant at Avertium


For many years, companies have understood the attack surface within their infrastructure. Everything from servers to networks and web applications, even the people, were considered in-scope for security testing. This format and mindset are embedded in the way security policies are created.

But now, there is a new “shiny thing” that has become part of the equation, and it cannot be ignored.

Enter…Artificial Intelligence.

 

Ai in production: security left behind

AI is being deployed into production environments - in most instances, before any security policies are even created. It’s the same mindset that caused web developers to push out a production web or mobile application in order to stay ahead of the competition. Unfortunately, security was pushed to the end of the line as an afterthought, sometimes leaving them wide open to attacks.

When deploying AI-centric systems, enterprises have to keep in mind that these are more than just a chatbot on a website, or a search feature. AI systems run code. They make decisions by interpreting user input and generating responses. And yes, these systems can be influenced. The attacker doesn’t necessarily have to find an exploit like SQL injection. They simply need to find a way to manipulate the AI system and hope it will reveal the information they’re after.

 

the security risks of ai-powered web components

AI-powered web tools introduce new, high impact risks that can directly trigger breaches, fraud, and compliance failures if not explicitly secured.

Any web embedded AI component that accepts user input, generates output, or connects to backend systems - whether it’s a chatbot, search box, recommender, or agent - counts as an AI-powered web tool and should be treated as part of the site’s attack surface.

These AI-powered web tools, when not properly secured, introduce serious risks. Attackers can manipulate them to leak sensitive data, bypass business rules, or perform unauthorized actions through techniques like prompt injection and input manipulation. These tools may inadvertently expose PII, internal documents, API keys, or system instructions, generate confidently incorrect or harmful guidance that users trust, be abused for fraud, social engineering, or misinformation, or be leveraged as a pathway to backend systems and APIs the AI is allowed to access.

When these tools lack strong guardrails, monitoring, and access controls, they can also create compliance and legal exposure, damage customer trust, and amplify the impact of traditional web vulnerabilities by turning natural language into a powerful new attack surface.

 

the limits of traditional pen testing in ai systems

Identifying where vulnerabilities exist in these tools is where traditional pen testing falls short, and where an AI-focused pen test needs to be considered. In implementing these systems, AI often sits in front of an enterprise’s internal systems and document repositories, as well as APIs.

Attackers will interact with AI using natural language, but with carefully crafted inputs to try to bypass safeguards, extract sensitive information, and cause unintended actions. This is better known as “prompt injection”.

Vulnerability scanners will not find these issues, and a system that has been patched with the latest security updates can still be manipulated into doing something it was never intended to do. What if a malicious user visits your website and manipulates the AI-driven search box into retrieving internal documents, or worse, customers’ private data?

This is why an AI-focused penetration test should absolutely be included as part of an enterprise security assessment.

 

the case for ai penetration testing

A penetration test on an AI-powered web app differs from a traditional pen test because it treats the AI model itself as part of the attack surface, not just the surrounding infrastructure. Where traditional pen tests focus on breaking systems, AI pen tests focus on breaking behavior, decision making, and trust boundaries that emerge when language, data, and automation are fused.

An AI pen test can evaluate risk for:

  • Information disclosure
  • Prompt manipulation
  • Data source exposure
  • Business logic flaws

AI penetration testing does not replace traditional testing such as a web application assessment. Instead, it should be used in concert to enhance the security testing many organizations already perform.

Consider AI penetration testing when you:

  • Launch a new chatbot on your website
  • Connect AI to internal documents
  • Integrate AI with your APIs

AI is becoming a common component of modern platforms, and companies must stop thinking of it as just a “feature”, and more as an integral component that, without proper controls and validation, could expose your company to data and reputational risk.

The time to adopt AI application pen testing is now. Don’t wait until you’re a story on the news.

 

how avertium can help: AI Application pen testing

 Secure what your AI app can be tricked into doing before attackers try. Avertium delivers expert-led AI application penetration testing with realistic abuse cases and actionable reporting to reduce risk and support compliance. We test your AI applications end-to-end, including the LLM interface, RAG/data connectors, agents interactions, APIs, authentication and authorization, and the surrounding cloud/app stack.  

Take your next step toward adaptive and scalable security and compliance. Learn more about our AI Application Penetration Testing and our other Penetration Testing services today.

 

 

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