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AI and LLM Penetration Testing

Secure your artificial intelligence (AI) and large language model (LLM) systems by identifying exploitable vulnerabilities in models, prompts, data pipelines, and integrations before attackers can leverage them.

AI & LLM Security Assurance

OnDefend AI and LLM penetration testing evaluates model behavior, data exposure, and application integrations to identify real-world security weaknesses, including prompt injection, model manipulation, data inference, and unsafe integrations that help secure AI deployments, strengthen governance, and support regulatory readiness before issues lead to business harm.

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AI and LLM Environments Tested for Real-World Risk

LLM-Enabled Applications

Prompts, system instructions, embeddings, and API-level logic are tested for injection, manipulation, privilege bypass, and unsafe behavior.

LLM-Enabled Agents

LLMs direct agent actions, including prompt handling, decision logic, execution safeguards, and controls, are tested to prevent manipulation or unintended outcomes.

Custom and Fine-Tuned Models

Custom and fine-tuned models are assessed for performance drift, unsafe outputs, leakage, harmful reasoning, and manipulation vulnerabilities.

Model Integration Layers

Orchestrators, agents, plugins, vector databases, and third-party APIs are evaluated for insecure data flows and exploitable logic paths.

Standard Pen Testing Only Finds 1/3 of Your Risk.

Our team of OnDefenders identifies vulnerabilities, design weaknesses, and control failures across AI and LLM deployments to reduce risk exposure and strengthen your overall AI security and governance posture. This includes:

Prompt Injection and Model Manipulation

Prompt Injection and Model Manipulation

Inputs crafted to override system instructions, bypass safeguards, manipulate outputs, or influence downstream actions within AI-driven workflows

Insecure Prompt and System Design

Insecure Prompt and System Design

Hard-coded secrets, unsafe system prompts, or poorly constrained instructions that expose sensitive logic, data, or functionality

Training Data and Knowledge Exposure

Training Data and Knowledge Exposure

Unintended leakage of proprietary, sensitive, or regulated data through model outputs, embeddings, or inference behavior

Weak Model Access Controls

Weak Model Access Controls

Overly permissive access to models, APIs, or inference endpoints that enables unauthorized use, abuse, or privilege escalation

Unsafe Tool and API Integrations

Unsafe Tool and API Integrations

LLM-connected tools, plugins, or APIs with weak authorization, validation, or guardrails that enable data access, command execution, or service abuse

Output Poisoning and Trust Exploitation

Output Poisoning and Trust Exploitation

Manipulated or misleading outputs that can influence business decisions, automate

Lack of Isolation Between Models and Systems

Lack of Isolation Between Models and Systems

Flat or tightly coupled AI architectures that allow lateral movement between models, data stores, and enterprise applications

Model Supply-Chain Risks

Model Supply-Chain Risks

Use of unverified models, libraries, embeddings, or third-party services that introduce malicious behavior or hidden backdoors

Continuous Security Inspector Reveals the Rest

Standard AI and LLM security testing and emerging PTaaS style assessments miss how real adversaries iteratively manipulate prompts, chain integrations, abuse data flows, or adapt tactics as models and environments change. OnDefend Continuous Security Inspector (CSI) goes beyond point-in-time testing revealing the remaining two thirds of system risk.

Emergent AI Abuse Chains

Emergent AI Abuse Chains

Multi-stage attack scenarios where low-risk issues combine to produce high-impact outcomes through AI-driven workflows

Model and Dependency Supply-Chain Compromise

Model and Dependency Supply-Chain Compromise

Malicious or poisoned models, embeddings, libraries, or third-party integrations introduced through trusted pipelines

Persistent AI Manipulation Techniques

Persistent AI Manipulation Techniques

Long-lived prompt, context, or data poisoning methods that subtly influence model behavior over time

Covert AI-Assisted Command and Control

Covert AI-Assisted Command and Control

Use of AI systems to generate obfuscated instructions, evade detection, or blend malicious activity into legitimate usage patterns

Conditional and Contextual Exploitation Paths

Conditional and Contextual Exploitation Paths

Attacks that trigger only under specific prompts, user roles, data contexts, or environmental conditions

Cross-System Trust Abuse via AI

Cross-System Trust Abuse via AI

Hidden attack paths that exploit implicit trust between AI systems, APIs, users, and enterprise platforms

Stealthy Data Exfiltration via Model Outputs

Stealthy Data Exfiltration via Model Outputs

Sensitive data leakage through controlled prompting, summarization, or inference behaviors that evade traditional monitoring

Abuse of AI Control and Orchestration Layers

Abuse of Device Management and Control Planes

Misuse of AI management, orchestration, or automation features to enable lateral movement, persistence, or unauthorized actions

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Giving You The Competitive Advantage

Let OnDefend give you a decisive advantage over adversaries by combining elite offensive operators, deep cloud expertise, and intelligence-driven security validation.

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Our team partners with yours to gain a deep understanding of your environment and objectives so you receive clear communication, expert guidance, and actionable insight that ensures outcomes align with your security and business goals.

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Explore our comprehensive resource collection to enhance your organization’s security posture and stay ahead of potential threats.

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AI/LLM Testing FAQs

What is AI or LLM penetration testing?

AI/LLM pen testing is a security assessment that identifies exploitable weaknesses in AI models, prompts, data pipelines, APIs, and integrations.

Why do LLMs require specialized testing?

LLMs require specialized testing because they introduce unique risks such as prompt injection, data leakage, model manipulation, and unsafe emergent behavior that traditional scanners cannot detect.

What systems can be tested?

OnDefend tests custom models, fine-tuned models, LLM applications, vector stores, plugins, and AI-integrated workflows.

How is AI/LLM testing different from traditional penetration testing?

Traditional penetration testing evaluates code and infrastructure. AI/LLM testing validates model behavior, data risks, prompt logic, and adversarial manipulation paths.

How often should AI/LLM testing be performed?

Most organizations test before deployment and after major updates or model retraining.

Secure Your AI Systems.

Understand your real exposure with guidance from security experts.

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Purple Teaming Services

Cybersecurity Risk Assessments

Compliance Readiness

Virtual CISO

AI and LLM Penetration Testing

Secure your artificial intelligence (AI) and large language model (LLM) systems by identifying exploitable vulnerabilities in models, prompts, data pipelines, and integrations before attackers can leverage them.

AI & LLM Security Assurance

AI and LLM Environments Tested for Real-World Risk

LLM-Enabled Applications

LLM-Enabled Agents

Custom and Fine-Tuned Models

Model Integration Layers

Standard Pen Testing Only Finds 1/3 of Your Risk.

Continuous Security Inspector Reveals the Rest

Giving You The Competitive Advantage

Elite Offensive Operators

Intelligence-Driven AI Risk Focus

End-to-End AI Exposure Visibility

Real-World Model Abuse Validation

Sensitive Data Protection Assurance

Business-Ready Risk Clarity

Our Team Partners with Yours