Artifical Intelligence Penetration Testing.
Our AI and LLM penetration testing rigorously examines your models to prevent unauthorized access, tampering, and manipulation, ensuring the integrity and security of your intelligent systems.
Breached Labs.
Penetration Testing Experts.
We specialize in AI and LLM 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 and LLM Penetration Testing
Discover how professional AI/LLM penetration testing safeguards your models, data, and users from emerging threats and adversarial attacks.
Identify AI and LLM Vulnerabilities
Uncover exploitable weaknesses in your AI models, data pipelines, and supporting infrastructure, such as data poisoning, model evasion, or inference attacks, before malicious actors exploit them.
Ensure AI and LLM Compliance & Trust
Meet AI-specific regulatory requirements and ethical guidelines (e.g., AI Act, NIST AI RMF). Reduce legal risks and build trust with users and stakeholders by demonstrating responsible AI practices.
Independent Security Assessment
Receive unbiased expert assessments of your AI system's security posture, with detailed reports outlining risks like prompt injection or model inversion, supporting compliance and stakeholder confidence.
Prevent Data & Model Breaches
Fix vulnerabilities that could expose sensitive training data, user inputs, or proprietary model architectures, saving millions in potential breach costs and protecting intellectual property.
Enhance Secure AI Development
Educate developers and data scientists on secure AI and LLM practices by identifying common pitfalls like insecure API endpoints or inadequate input validation specific to AI systems.
Protect AI Investment & Reputation
Safeguard your AI investments, reputation, customer trust, and competitive advantage from threats like model theft or manipulation.
AI-Specific Risk Visibility
Gain detailed insights into your AI and LLM system's unique security posture, including model vulnerabilities and data security risks, for better risk management and decision-making.
Why AI and LLM Penetration Testing?
Penetration 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.
Secure Sensitive Data & Prevent PII Exposure
Penetration testing uncovers vulnerabilities that could expose sensitive training data or user PII during model interaction, avoiding costly breaches, regulatory fines, and reputational damage.
Maintain User Trust & Model Integrity
Proactively fixing security gaps prevents incidents like model manipulation or biased outputs that erode user trust. Ensure your AI systems perform reliably and fairly, retaining customer confidence.
Ensure AI System Availability & Reliability
Security exploits targeting AI and LLM models (e.g., denial-of-service via resource exhaustion) can cripple operations. Penetration testing helps keep your AI services online and reliable by thwarting potential attacks.
Protect Competitive Edge and Market Position
A breach can hand competitors an advantage by exposing trade secrets or driving clients elsewhere. Penetration testing safeguards your intellectual property and market standing, keeping you ahead in the game.
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 and LLM security pentesting 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.
Penetration 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 and LLM system 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 and LLM system.
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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