AI Integration Security Testing.
Our AI integration security testing employs specialized techniques to identify vulnerabilities where AI systems connect, revealing complex flaws that could lead to unauthorized access, or workflow disruptions.
Breached Labs.
AI Integration Experts.
We specialize in AI Infrastructure & Integration security testing, helping organizations stay ahead of evolving threats targeting machine learning systems. Our experts combine advanced tools with manual analysis to uncover vulnerabilities such as model inversion, data poisoning, and insecure model deployment.
We simulate real-world attacks with meticulous detail, providing actionable insights that empower you to significantly strengthen your security defenses. Trust us to safeguard your digital assets with unmatched precision and care.
We simulate real-world attacks, providing actionable insights that empower you to strengthen your security defenses with precision and care.
Our team holds industry-leading certifications, demonstrating our commitment to excellence in cybersecurity.
Our Team's Certifications
Our team possesses top-tier, industry-recognized certifications, showcasing our dedication to delivering cybersecurity excellence.
Benefits of AI Infrastructure & Integration Security Testing
Discover how professional AI Infrastructure & Integration security testing safeguards your models, data, and users from emerging threats and adversarial attacks.
Identify Vulnerabilities
Uncover exploitable weaknesses in your AI models, data pipelines, APIs, and the underlying infrastructure before malicious actors can exploit them.
Ensure Compliance
Meet AI-specific regulatory requirements (e.g., EU AI Act) and industry standards like ISO 27001, SOC 2, HIPAA, PCI-DSS, reducing legal risks and building stakeholder trust in your AI systems.
Third-Party Verification
Experts provide unbiased AI security assessments with detailed reports on integration, infrastructure, and model vulnerabilities, supporting compliance and stakeholder confidence.
Prevent Data Breaches
Fix vulnerabilities in data handling and model access controls before hackers can exploit them, protecting sensitive training data, model parameters, and user information, potentially saving millions.
Secure AI Development Lifecycle
Integrate security best practices into your AI development lifecycle, identifying common pitfalls in model training, deployment, and API integration to build inherently secure AI systems.
Protect Business Value
Safeguard your reputation, customer trust in AI outputs, and competitive advantage derived from proprietary AI models and data.
Risk Visibility
Gain detailed insights into the unique security risks associated with your AI infrastructure and integrations for informed risk management and strategic decision-making.
Why AI Infrastructure & Integration Security Testing?
Security testing plays a crucial role in securing AI systems by identifying and addressing vulnerabilities in models, data pipelines, and infrastructure before they can be exploited by adversaries.
Prevent AI Model Theft and Sensitive Data Exposure
Security testing uncovers vulnerabilities that could lead to model extraction, data poisoning, or exposure of sensitive training/inference data (including PII), avoiding significant financial penalties, reputational damage, and loss of intellectual property.
Maintain User Trust and System Reliability
By proactively fixing security gaps in AI integrations and infrastructure, security testing prevents incidents that could erode user trust in AI predictions, damage your reputation, and cause users to abandon your platform.
Minimize Downtime and Operational Disruption
Security exploits can cripple AI models, and systems, leading to lost sales and operational disruptions. Security testing helps keep your business online and revenue flowing by thwarting potential cyber attacks.
Protect AI Intellectual Property
A breach targeting your AI systems can expose proprietary models, algorithms, or sensitive datasets, handing competitors an advantage. Security testing safeguards your AI intellectual property and market standing.
Schedule a
Consultation
Ready to determine the most effective strategy for your business needs? Schedule your complimentary, no-obligation assessment call with one of our experts today using the link below.
During our call, we'll begin outlining a comprehensive plan designed to safeguard your business against the cyber threats relevant to your operations.
Book a callOur AI integration security testing covers a wide range of attack vectors, ensuring comprehensive protection against common and sophisticated threats.
Adversarial Attacks
Manipulating model inputs to cause incorrect predictions.
Data Poisoning
Injecting malicious data into training sets to corrupt model behavior.
Model Inversion
Reconstructing sensitive training data from model outputs.
Membership Inference
Determining whether specific data was used in training.
Model Theft
Extracting a model’s functionality through repeated queries.
Input Manipulation
Exploiting weak input validation to bypass model logic.
Overfitting Exploitation
Leveraging overfitted models to infer private information.
Insecure Model Deployment
Deploying models without proper access controls or monitoring.
Lack of Explainability
Making it hard to detect or understand malicious model behavior.
Security Testing Methodology
Our thorough method for detecting and resolving security weaknesses.
Pre-Engagement
The initial assessment, planning, and establishing rules of engagement phase involves understanding the target system, defining the scope and objectives, and setting clear testing boundaries.
It includes obtaining authorization, planning timelines and methods, and agreeing on communication protocols. This ensures a structured, ethical, and effective penetration testing process.
- Define the scope and objectives of the AI/ML security test, including target models, and threat goals.
- Establish testing boundaries and limitations, such as specific datasets, APIs, or models to include/exclude.
- Document communication protocols between the security testing team and stakeholders, including report formats and points of contact.
- Obtain formal authorization from the AI system owner or organization to conduct the security assessment.
Reconnaissance
The reconnaissance phase involves gathering intelligence about the target system by collecting publicly available data and mapping its digital presence. It includes identifying key components, technologies, and potential entry points through passive and active techniques.
This foundational step informs subsequent testing by revealing vulnerabilities and attack surfaces without direct interaction.
- Passive information gathering from public sources, such as papers, or documentation related to the AI system.
- OSINT techniques to map the AI/ML system's digital footprint, including model endpoints, or leaked credentials.
- Domain and subdomain enumeration to identify all AI service entry points and related infrastructure.
- Technology stack identification, including frameworks, libraries, pipelines, and platforms supporting the model.
Scanning
The scanning phase focuses on actively probing the target system to identify live components, open ports, and running services.
It involves using automated tools and manual techniques to detect potential vulnerabilities and misconfigurations.
This step builds a detailed picture of the system's attack surface for further exploitation and analysis.
- Network mapping and topology discovery of AI/ML infrastructure, including gateways and compute environments.
- Port scanning and service enumeration to identify model APIs, storage layers, or orchestration systems used.
- Operating system fingerprinting of servers hosting AI models to understand underlying platform exposure.
- Initial vulnerability scanning of the AI pipeline for common flaws like poisoning, leakage, or misconfigurations.
Vulnerability Assessment
The vulnerability assessment phase entails analyzing identified weaknesses in the target system to determine their severity and potential impact.
It involves detailed scanning, manual validation, and risk prioritization to distinguish exploitable flaws from false positives.
This step provides a clear understanding of security gaps and their real-world implications.
- Detailed vulnerability scanning and analysis of the AI system, targeting flaws like model theft or data exposure.
- Manual verification of identified vulnerabilities, such as testing for inversion, poisoning, or insecure endpoints.
- False positive elimination to ensure reported AI/ML vulnerabilities are accurate and fully reproducible.
- Risk assessment and prioritization of vulnerabilities based on their severity and impact on the AI system.
Exploitation
The exploitation phase involves safely leveraging confirmed vulnerabilities to demonstrate their real-world risks and consequences.
It includes controlled attacks to gain unauthorized access, escalate privileges, or extract data, while assessing system resilience.
This step validates threats and highlights the need for remediation without causing harm.
- Controlled exploitation of confirmed vulnerabilities, such as model extraction or bypassing access controls in the AI system.
- Privilege escalation attempts within the AI system, targeting model roles or admin access via token manipulation.
- Lateral movement within the AI/ML environment, such as accessing related pipelines or training data services.
- Data access verification to confirm exposure of sensitive information like training records or model outputs.
Reporting
The reporting phase focuses on documenting findings, prioritizing vulnerabilities, and providing actionable remediation steps.
It includes creating detailed technical reports and concise summaries for stakeholders, often with visuals to clarify attack paths.
This step ensures clear communication of risks and solutions to improve security.
- Detailed technical documentation of findings, including specific AI/ML vulnerabilities like inversion or exposed APIs.
- Risk-based prioritization of vulnerabilities based on their exploitability and impact on the AI system's integrity.
- Actionable remediation recommendations, such as data sanitization or secure access control for the AI components.
- Executive summary for management, highlighting critical AI/ML security risks and business-level implications.
Testing Approaches
Understanding the difference between testing methodologies to choose the right approach for your security needs.
Black Box Testing
Testing from an external perspective with no prior knowledge, information or access to the target system's internal workings or source code.
Black box testing simulates a real-world attack scenario where the tester has no insider information, mimicking how actual attackers would approach your systems.
The tester has access only to public-facing components and must discover vulnerabilities through external reconnaissance and targeted probing.
This approach reveals what an attacker could discover and exploit without internal access or knowledge, providing an authentic assessment of your external security posture.
White Box Testing
Testing with complete internal knowledge of the system, including source code, architecture, and design documentation.
White box testing provides testers with full access to internal system details, allowing for thorough examination of code, architecture, and configuration.
This approach enables identification of vulnerabilities that might not be discovered through external testing alone, such as logical flaws, backdoors, or implementation errors.
With complete knowledge of the system, testers can target specific components and functions known to be security-critical, providing comprehensive coverage.
Our Process
Follow these essential steps to safeguard your AI integration from malicious hackers.
Contact us
Contact our team, and we'll attentively address your concerns while tailoring solutions to your specific security requirements. Whether you choose a phone call, email, or live chat, we're eager to kickstart your path to a better-protected AI integration.
Pre-Assessment Form
We provide you with an easy-to-complete pre-assessment form to gather relevant details. This allows us to gain insight into your app's structure, existing security protocols, and particular areas of concern.
Proposal Meeting
Once we've analyzed the results of the preliminary evaluation questionnaire and developed our recommended plan, we'll go over the security strategy with you and address any questions during virtual or in-person meetings.
Agreement
We send you a detailed proposal outlining our findings, recommendations, and the cost of the project. Once you approve the proposal, we proceed with the engagement.
Pre-requisite Collection
We collect all the necessary information and documents required for the assessment. This includes the application's source code, documentation, and any other relevant materials.
Breached Labs strengthened our overall security posture with their thorough penetration testing approach. Their expertise in identifying and addressing vulnerabilities was invaluable to our organization.
Chief Information Officer
Fortune 500 Tech Company
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