Penetration testing services

Penetration Testing Service for AI Systems

AI systems introduce new security risks that traditional penetration testing can often miss. Our AI model penetration testing services help companies secure AI-based products. Using automated tools and hands-on techniques, we simulate how real attackers could penetrate your AI systems. The goal of our LLM penetration testing services is to help you detect and fix AI vulnerabilities before they lead to data leaks, system failures, or misuse of your models.

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We Identify Threats Specific to AI-Powered Products

AI model can be attacked unnoticed

Attackers can subtly manipulate input data, like tweaking an image or a sentence, to fool your AI into giving the wrong output. These adversarial attacks don’t trigger alerts and can be scaled to target your entire model, which makes them hard to detect and prevent. Such LLM vulnerabilities illustrate why pentesting service for LLMs is crucial.

Data can be poisoned at the source

When attackers interfere with training data, especially from public or automated sources, they can inject harmful samples that change how the model behaves. You may not notice until your AI makes biased or dangerous decisions in production. Testing artificial intelligence for penetration reveals emerging threats that many businesses overlook.

APIs could be a backdoor

Unprotected APIs can be exploited to extract training data, reverse-engineer your model, or even crash your service. Without proper authentication and input validation, these entry points expose your AI system. This is a common application security challenge for modern AI and LLM applications.

Old systems can compromise an AI model

When AI is integrated into legacy infrastructure, outdated protocols, unpatched libraries, and weak encryption can all open doors for attackers. These overlooked vulnerabilities often become hidden pathways to your AI stack.

Sensitive data can leak through a model

Even if personal data isn’t directly exposed, attackers can probe your model’s outputs to recreate sensitive data like biometrics or financial attributes. This puts you at risk of compliance violations and data breaches. And it is another reason why a penetration testing service of AI systems is highly needed.

Loose access control can lead to a system takeover

If access permissions across your AI environment aren’t tightly managed, attackers (or even internal users) can escalate privileges or gain unauthorized access. Such unauthorized access risks are amplified in medium and large enterprises where multiple AI technologies are integrated.

Shadow AI can introduce hidden risks

Teams often deploy AI models outside standard security processes. These “shadow AI” projects may run on unsecured cloud platforms without audits, encryption, or monitoring. All these create silent entry points for malicious actors and potential cyber attacks.

Third-party models can introduce hidden threats

Pre-trained LLM models from open-source or external vendors can come with backdoors or outdated code. If left unaudited, they may introduce critical vulnerabilities into your system when they go live.

Need more information on AI penetration tests?

Contact us to discuss all benefits of our penetration testing service for AI systems. Find out how it'll help you mitigate new threats.

Our Services Test the Protection of AI-Operated Products

Adversarial attack simulation

We simulate real-world adversarial attacks where small, calculated changes are made to the inputs of your AI system. This helps detect weaknesses in how your model handles deceptive data and tests its ability to maintain accurate performance when confronted with manipulated inputs.

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Data poisoning assessment

Our security experts examine your AI’s training process to identify potential security vulnerabilities where attackers could inject harmful content. We analyze how your model handles skewed or corrupted training data and assess how susceptible it is to becoming biased or misled by malicious inputs.

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Model evasion testing

Our testing involves trying to bypass your model's decision-making processes using sophisticated input manipulations. This helps identify potential flaws in the model's security where attackers could avoid detection or manipulation by the system. Simply put, it helps spot deep understanding gaps attackers may exploit. It is especially relevant in environments like fraud detection or malware identification.

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API and integration security testing

We test the security of your model’s APIs and how it integrates with other systems. This includes checking for weak authentication, unencrypted communications, and potential data leaks through APIs. Our security engineers perform API penetration testing. They simulate attacks like unauthorized access, DoS attempts, and data extraction to check how secure your system is against external evolving threats. Our team also practices remote code execution and other application pentesting scenarios.

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System prompt leakage testing

We test for vulnerabilities in system prompts used by your AI model that could inadvertently expose sensitive data or instructions, such as API keys, roles, or user permissions. These risks can facilitate unauthorized access or control and allow attackers to bypass security measures. Our team helps identify and mitigate such exposures to ensure the integrity of your AI system. This type of testing ensures no contextual awareness or sensitive prompts leak to malicious actors.

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Compliance and security best practices review

We assess your AI systems to ensure they comply with relevant security standards and follow best practices. This includes evaluating data security measures, audit trails, and whether the model meets the necessary legal and security requirements.

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Model inversion and data leakage testing

We test the resilience of your models against attacks aimed to reverse-engineer sensitive data. Our security team uses model inversion techniques to expose any sensitive information that could be retrieved from the outputs of your AI system. Then, we assess how well it protects against unintentional data leakage.

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Third-party model and library risk assessment

Many AI systems rely on third-party LLM models or libraries. We audit these external components for hidden security flaws, outdated code, or insecure dependencies. We ensure that they don't introduce unknown risks or backdoors into your system when integrated into your workflow.

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We Help Customers Improve Their Product Cybersecurity

Conducting a pentest for a Danish software development company

See how we helped Coach Solutions improve the security of their web application

Case study

“TechMagic has great collaboration and teamwork. Also a good proactive approach to the task.Everything went as planned and on time.”

Theis Kvist Kristensen

CTO COACH SOLUTIONS

Pentesting Process Is Efficient and Transparent for All Stakeholders

Step 1

Scoping and AI threat modeling

At the very beginning, we define the scope of the pentest and identify the specific AI systems to be tested. In this phase, we perform a threat modeling exercise to map potential attack vectors and determine the security goals. This ensures that the test is adapted to your unique AI systems and prioritizes areas that pose the highest risk.

Step 2

Asset and model inventory analysis

Next, we conduct a detailed review of all AI-related assets and models within your organization. This includes a catalog of all machine learning models, datasets, APIs, and integrations that interact with the AI system. Comprehension of the full inventory helps us focus on the most critical areas that require protection and secure handling.

Step 3

Vulnerability mapping using OWASP AI Security Guide

We use the OWASP AI Security Guide as a framework for identifying and mapping vulnerabilities within your AI systems. This comprehensive guide helps us assess security risks specific to machine learning models, training data, APIs, and deployment environments. We rely on the guide to ensure that we follow best practices and industry standards in security testing for AI-specific threats.

Step 4

Adversarial and evasion attack simulation

We simulate adversarial attacks on your models to test how they respond to manipulated inputs specifically designed to trick or confuse them. Thus, we evaluate the model’s ability to detect and withstand input changes that could bypass security filters or cause incorrect outputs. We also test the model’s resilience to evasion tactics that may be used by attackers to avoid detection.

Step 5

Data pipeline and training data integrity checks

At this stage, we review your data pipelines and training processes to check for weaknesses that could lead to training data poisoning or manipulation. This includes verifying data sources, checking for biases, and ensuring that data integrity is maintained throughout the AI lifecycle. We assess if attackers could inject malicious data into the pipeline to influence model behavior.

Step 6

API and system integration testing

Here, we test the security of all APIs and integrations that interact with the AI system. This includes checking for vulnerabilities such as poor authentication, unprotected endpoints, or misconfigured settings that could expose the model to unauthorized access or data leakage. We also assess how external systems that integrate with your AI might introduce additional security risks.

Step 7

Security misconfiguration and access control review

At this point, we evaluate the security configurations of your AI environment, including user access controls, permissions, and system settings. This review ensures that the AI models and related systems are not exposed to unnecessary risks due to misconfigurations. We check for improper access controls, such as excessive privileges or weak authentication methods, that could allow unauthorized users to gain access to sensitive components.

Step 8

Reporting and remediation guidance

After completing the security and penetration testing, we provide a detailed report outlining the detected vulnerabilities, the potential impact of each issue, and prioritized remediation steps. We offer actionable guidance on how to fix the problems and secure your AI systems.

Our Team Maintains Your Confidence in Cybersecurity

Ihor Sasovets

Lead Security Engineer

Ihor is a certified security specialist with experience in penetration testing, security testing automation, cloud and mobile security. OWASP API Security Top 10 (2019) contributor. OWASP member since 2018.

Roman Kolodiy

Director of Cloud & Cybersecurity

Roman is an AWS Expert at TechMagic. Helps teams to improve system reliability, optimise testing efforts, speed up release cycles & build confidence in product quality.

Victoria Shutenko

Security Engineer

Victoria is a certified security specialist with a background in penetration testing, security testing automation, AWS cloud. Eager for enhancing software security posture and AWS solutions

Our Expertise Is Backed by 20+ Certifications

Your Business Benefits From Our AI Pen Testing Services

Strengthened resilience against adversarial attacks

We identify vulnerabilities in AI models that could be exploited through adversarial attacks, such as manipulated inputs that trick the model. Testing AI applications for these attack scenarios ensures your models can resist attempts to alter their behavior and improve their reliability.

Early AI-specific vulnerability detection

AI systems have unique risks, such as data poisoning, model inversion, and adversarial attacks, which traditional security testing methods miss. Our approach detects these AI-specific vulnerabilities early. This allows for quick remediation before they cause significant harm or system failures.

Improved trust and reliability of AI outcomes

Testing your AI models for security vulnerabilities increases the trustworthiness of their outputs. Ensuring that your AI makes accurate, unbiased, and tamper-resistant decisions boosts confidence from users and stakeholders. This leads to more consistent, dependable performance.

Enhanced protection of sensitive data

We help secure data processed by your AI systems, especially in case of sensitive information. Testing for data leakage and unauthorized access protects personal or confidential data and prevents data privacy violations or security breaches.

Increased security of APIs and integrations

AI-driven products rely on APIs and system integrations. We test the security of these components to ensure they are not vulnerable to attacks such as unauthorized access or data manipulation. Thus, we protect your AI models and the systems they interact with.

Practical reports for faster remediation

Our penetration testing services of AI integration provide detailed reports with clear steps for fixing detected vulnerabilities. This allows for rapid remediation and improvement. Additionally, we offer strategic advice on how to strengthen your AI systems against other potential threats.

We Use Tools Proven Over 10 Years of Work

OWASP ZAP

Burp Suite

Arachni

SonarQube

Semgrep

Snyk.io

Nmap

Wappalyzer

Kali Linux

Parrot Security

Choose TechMagic as Your Trusted AI Pen Testing Provider

Broad expertise in penetration testing for LLMs

Security experts at TechMagic have extensive cybersecurity experience and in-depth knowledge of large language models. That's why we skillfully identify vulnerabilities unique to AI, such as model inversion or adversarial and prompt injection attacks. We implement advanced tools in our comprehensive pentests to address the specific security risks your AI-powered products face.

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