Professional AI Application Penetration Testing Services
At Trilight Security, we provide comprehensive AI penetration testing services for organizations across the USA and the EU, including a strong focus on Germany. As AI systems — from customer-facing LLM chatbots to fully autonomous agents — become core components of business infrastructure, their security cannot be left to chance. Our team conducts adversarial assessments of your AI systems, exposing vulnerabilities such as prompt injection, jailbreaks, data extraction risks, training data poisoning, and excessive agency in agentic pipelines. Get in touch with Trilight Security to discover how our AI pentesting services can help you build, deploy, and maintain AI systems your customers and regulators can trust.
Our Offering
Black Box Pentesting
We provide black box AI penetration testing, where we have no prior access to your model architecture, system prompts, training data, or internal configurations. Our testers interact with the AI system solely through its exposed interfaces, simulating the perspective of an external attacker or a malicious end user.
Grey Box Pentesting
We conduct grey box AI pentests, where we have partial knowledge of the target — such as the model provider, architecture, or high-level system prompt design — but without full access to internal configs or fine-tuning details. This approach reflects the realistic threat posed by a determined user or a compromised partner.
White Box Pentesting
We conduct white box AI pentests, where we have complete access to system prompts, model configs, RAG pipeline design, tool integrations, and infrastructure. This approach enables the deepest examination of your AI system and is best suited for complex security reviews prior to launch or compliance audits.
Why AI Penetration Testing?
AI systems introduce an entirely new category of attack surface that traditional penetration testing does not address. A single successful prompt injection can cause an LLM to leak confidential data, bypass access controls, or execute unintended actions through connected tools. Agentic AI systems — capable of browsing the web, executing code, querying databases, and calling external APIs — dramatically amplify these risks, turning model-level vulnerabilities into real infrastructure compromise. By conducting AI-specific penetration testing, we help you identify these weaknesses before they are exploited, protect sensitive data processed by your models, demonstrate due diligence to regulators, and meet the growing compliance requirements of the EU AI Act, NIST AI RMF, and ISO/IEC 42001.
What We Test
Our AI penetration testing assessments provide systematic coverage of all ten OWASP Top 10 for LLM Applications (2025) vulnerability categories, supplemented by agentic AI-specific testing aligned to the OWASP Top 10 for Agentic AI Applications:
- LLM01:2025 — Prompt Injection: We test whether an attacker can craft malicious input — directly in the user prompt or hidden within external data sources processed by the model — to override the system prompt, bypass safety controls, or cause the model to perform unintended actions on behalf of the attacker.
- LLM02:2025 — Sensitive Information Disclosure: We assess whether the model can be induced to reveal confidential data from its training set, system prompt, context window, or connected data sources — including PII, API keys, internal business logic, and proprietary information.
- LLM03:2025 — Supply Chain Vulnerabilities: We evaluate the security of third-party models, datasets, fine-tuning pipelines, plugins, and pre-trained components integrated into your AI system — identifying risks introduced through the AI supply chain that bypass the security of your own implementation.
- LLM04:2025 — Data and Model Poisoning: We assess whether your training data, fine-tuning pipeline, or retrieval data sources could be manipulated by an attacker to introduce backdoors, biases, or behaviors that compromise model integrity at inference time.
- LLM05:2025 — Improper Output Handling: We test whether the model’s outputs are passed to downstream components — such as web browsers, code interpreters, databases, or APIs — without sufficient validation, enabling attacks such as Cross-Site Scripting (XSS), SQL injection, and Server-Side Request Forgery (SSRF) via model-generated content.
- LLM06:2025 — Excessive Agency: We assess whether the AI system has been granted permissions, tool access, or autonomous decision-making capabilities beyond what its function requires — and whether a compromised or manipulated model could abuse those capabilities to take unauthorized actions with real-world consequences.
- LLM07:2025 — System Prompt Leakage: We attempt to extract the contents of the system prompt through direct and indirect techniques — including jailbreaks, role-play scenarios, and multi-turn manipulation — assessing whether confidential instructions, business logic, or security controls embedded in the prompt can be recovered by an attacker.
- LLM08:2025 — Vector and Embedding Weaknesses: We evaluate the security of your vector database and embedding pipeline — including susceptibility to embedding inversion attacks, cross-tenant data leakage through semantic similarity, and indirect prompt injection via poisoned documents retrieved by the RAG pipeline.
- LLM09:2025 — Misinformation: We assess whether the model can be induced to generate confidently stated but factually incorrect outputs that could mislead users, damage your brand, or create legal and compliance exposure — including hallucination exploitation and adversarial factual manipulation.
- LLM10:2025 — Unbounded Consumption: We test whether the model or its surrounding infrastructure is vulnerable to resource exhaustion attacks — including denial-of-service via excessive token generation, compute cost amplification through adversarial prompts, and API quota abuse through automated request flooding.
Penetration Testing Process
We use a combination of manual adversarial techniques and automated tooling to simulate real-world attacks against AI systems and the infrastructure supporting them. Typically, AI penetration testing engagements include the following stages:
- Reconnaissance & Scope Definition: We collect essential information about your AI system — including the model provider, deployment architecture, exposed interfaces, tool integrations, RAG pipelines, and data sources — to build an accurate attack surface map and prioritize testing vectors.
- Automated Vulnerability Scanning: Using purpose-built AI security tools, we conduct broad automated scans across the model's input/output layer, probing for known vulnerability classes from the OWASP Top 10 for LLM Applications, including prompt injection, insecure output handling, sensitive information disclosure, and excessive agency.
- Manual Adversarial Testing: Our experts manually craft and execute adversarial prompts, multi-turn jailbreak sequences, indirect injection attacks, and role-play scenarios designed to bypass guardrails and expose model behavior that automated tools routinely miss. This phase focuses on business-logic attacks, context manipulation, and system prompt extraction attempts.
- Agentic & Tool Integration Testing: For AI systems with tool access, autonomous decision-making capabilities, or multi-agent architectures, we assess the blast radius of a successful model compromise — testing whether an attacker can leverage the AI agent to execute unintended actions, escalate privileges within connected systems, or move laterally across the infrastructure.
- RAG & Data Pipeline Assessment: We evaluate the security of your retrieval-augmented generation pipeline, testing for indirect prompt injection via poisoned data sources, unauthorized data retrieval, and information leakage through model outputs.
- Infrastructure Layer Testing: We assess the underlying infrastructure supporting your AI deployment — including APIs, model-serving endpoints, authentication mechanisms, and logging configurations — combining traditional penetration testing techniques with AI-specific attack vectors.
- Reporting: Our reports are thorough and developer-friendly, providing clear technical and business insights into every identified vulnerability. Each report includes:
-
- A detailed attack narrative describing how each vulnerability could be exploited, helping your team understand the full scope of risk.
- Specific, actionable remediation recommendations prioritized by severity and exploitability.
- Compliance mapping to OWASP Top 10 for LLM Applications, OWASP Agentic AI Top 10, NIST AI RMF, EU AI Act, and ISO/IEC 42001 as applicable.
Our Benefits
Top Certifications
Our experts combine deep offensive security expertise with specialist AI security knowledge (OSCE, OSCP, OSEP, and others), alongside hands-on experience of adversarial assessments of production LLM and agentic AI.
Top Methodologies
OWASP Top 10 for LLM Applications (2025), OWASP Agentic AI Top 10, OWASP AI Testing Guide, NIST AI RMF (Generative AI Profile), MITRE ATLAS, and ISO/IEC 42001. Also, we use our experience gained during specific AI-related tasks
Rich Deliverables
We provide comprehensive AI pentest reports detailing discovered vulnerabilities, adversarial attack narratives, remediation advice, risk prioritization, compliance mapping, and other content needed by customer.
Cost Efficiency
One of our core advantages is access to top-tier cybersecurity and AI security talent with extensive hands-on experience in demanding enterprise environments — delivered at competitive, transparent pricing.
AI Penetration Testing Methodologies
Our AI penetration testing services are grounded in the most current and relevant frameworks for AI security assessment. We follow the OWASP Top 10 for LLM Applications (2025) as the primary vulnerability taxonomy for LLM-powered systems, and the OWASP Top 10 for Agentic AI Applications for autonomous agent deployments. For adversarial machine learning threats — including model evasion, data poisoning, and model inversion — we apply the MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) framework. Governance and compliance-driven engagements are aligned with the NIST AI Risk Management Framework (AI RMF), including its Generative AI Profile (NIST AI 600-1), as well as ISO/IEC 42001 and the EU AI Act requirements for high-risk AI systems. The OWASP AI Testing Guide provides the overarching testing methodology for end-to-end AI system assessments.
Tools
Our experts tailor their toolset based on the testing type — whether black box, grey box, or white box — and the specific architecture of the target AI system. We utilize a range of purpose-built AI security tools, including Garak (NVIDIA’s LLM vulnerability scanner), PyRIT (Microsoft’s AI Red Teaming toolkit), Promptfoo, and FuzzyAI for automated adversarial scanning and multi-turn attack simulation. For manual testing and custom attack scripting, our team leverages Burp Suite for API-layer assessments, custom prompt injection libraries, and bespoke attack chains tailored to the target model’s behavior. Infrastructure-layer testing draws on our full network and web penetration testing toolset, including Nmap, Metasploit, Nessus, and Wireshark, ensuring that vulnerabilities in the systems surrounding your AI deployment are not overlooked.
Our Certifications
Deliverables
- Executive Summary: A high-level overview of the assessment results and overall risk exposure, written for management and non-technical stakeholders.
- Test Plan: A document outlining the scope, objectives, methodology, and timeline of the engagement.
- Detailed Technical Report: A comprehensive report documenting all findings, including vulnerability descriptions aligned to OWASP LLM / Agentic Top 10, proof-of-concept attack demonstrations, risk ratings, and step-by-step remediation guidance.
- Vulnerability Assessment: A full inventory of all vulnerabilities identified during the engagement, prioritized by exploitability, potential business impact, and compliance relevance.
- Attack Narratives: Detailed walkthroughs of the key adversarial scenarios executed during testing, illustrating how a real attacker could chain vulnerabilities to achieve meaningful impact.
- Evidence: Prompt logs, screenshots, API response captures, and other supporting artefacts for all identified findings.
- Compliance Mapping: A structured mapping of findings to relevant frameworks including the OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF, EU AI Act, and ISO/IEC 42001.
- Action Plan: A structured remediation roadmap with recommendations, suggested timelines, and responsible parties for each identified issue.
A presentation or briefing for relevant stakeholders — including a summary of findings, risk exposure, and recommended next steps — can be prepared upon request. After a follow-up retest to confirm that all identified vulnerabilities have been remediated, we issue a Pentest Certificate, which can be used for compliance audits, regulatory submissions, and customer due diligence communications.
Read more here:
Penetration Testing Methodologies
Penetration Test Report Sample
Penetration testing is a must for any business using digital services. We use different comprehensive tools, methodologies, and models for pentesting. DOWNLOAD our penetration test report sample and learn more.
FAQ
AI penetration testing is a security assessment in which our experts simulate real-world adversarial attacks against your AI systems — including LLM-powered applications, AI agents, RAG pipelines, and model APIs — to identify exploitable vulnerabilities before malicious actors do.
Traditional penetration testing focuses on software, network, and infrastructure vulnerabilities. AI penetration testing addresses an entirely different attack surface: the behavior of machine learning models themselves. This includes attacks such as prompt injection, jailbreaking, system prompt extraction, training data extraction, model evasion, and the exploitation of autonomous agent capabilities — none of which exist in conventional software systems.
We test a wide range of AI deployments, including LLM-powered chatbots and virtual assistants, retrieval-augmented generation (RAG) systems, AI coding assistants, autonomous and agentic AI systems with tool access, multi-agent pipelines, AI-powered APIs and decision-making systems, and custom fine-tuned models across major providers including OpenAI, Anthropic, Google, and open-source frameworks.
The most frequently identified vulnerabilities include direct and indirect prompt injection, system prompt extraction, jailbreaking and safety bypass, sensitive data leakage through model outputs, insecure tool integrations in agentic systems, excessive agency and insufficient human oversight, training data exposure, and insecure API configurations surrounding the model deployment.
Prompt injection is the AI equivalent of SQL injection. An attacker crafts malicious input — either directly in the user prompt or hidden within external data sources processed by the model — that causes the AI to ignore its instructions and perform unintended actions, disclose sensitive information, or be weaponized against its own users.
Agentic AI refers to systems where an LLM can autonomously plan and execute multi-step tasks using external tools — such as browsing the web, executing code, reading and writing files, or calling APIs. When a model can act as well as respond, the consequences of a successful attack expand dramatically: a single prompt injection can escalate from extracting text to executing unauthorized actions across connected infrastructure.
Our AI penetration testing follows the OWASP Top 10 for LLM Applications (2025), the OWASP Top 10 for Agentic AI Applications, the OWASP AI Testing Guide, MITRE ATLAS, and the NIST AI RMF (including the Generative AI Profile). For compliance-driven engagements, we align findings to the EU AI Act, ISO/IEC 42001, GDPR, and other applicable regulatory requirements.
The EU AI Act — with GPAI obligations in effect from August 2025 and high-risk AI system obligations phasing in through 2026 — imposes explicit security, transparency, and risk management requirements on AI providers and deployers operating in the EU. Penetration testing is a key mechanism for demonstrating compliance, identifying systemic risks in general-purpose AI models, and generating the documented evidence regulators require.
Duration depends on the complexity of the AI system, the number of interfaces and tool integrations in scope, and the depth of testing required. A typical engagement ranges from one to three weeks. We provide a scoping estimate prior to any engagement.
Testing is conducted in a controlled manner agreed upon upfront. We strongly recommend conducting assessments against a staging or pre-production environment wherever possible to eliminate any risk of disruption to live users. Where production testing is required, we work within defined parameters to minimize impact.
Yes. Testing AI systems earlier in the development lifecycle is strongly recommended — vulnerabilities in system prompt design, tool integration architecture, or RAG pipeline configuration are far less costly to remediate before deployment. We support assessments at any development stage, from early prototype to pre-launch.
We deliver a detailed report that includes every identified vulnerability aligned to the OWASP LLM Top 10, its risk rating, proof-of-concept attack demonstrations, a description of potential business impact, compliance mapping, and clear, actionable remediation guidance. Reports are structured to be useful for both technical teams and executive stakeholders.
We recommend testing at least annually, and additionally after significant model updates, changes to system prompts or tool integrations, expansion of agent capabilities, or in response to newly published attack techniques. The AI threat landscape is evolving rapidly, and assessments that were current six months ago may not reflect today’s attack surface.
Yes. Our team is available to consult on remediation efforts, clarify findings, and provide guidance on architectural changes, prompt hardening, guardrail design, and infrastructure controls to ensure that identified gaps are effectively closed.
Pricing depends on the complexity of the AI system, the scope of testing, the number of interfaces and tool integrations, and the depth of the assessment required. Contact us for a detailed quote tailored to your environment and objectives.
Our Recognition
