AI and LLM Pentesting Checklist: Preparing for an AI Pentesting

Security teams already know how to test web apps, APIs, and mobile platforms. What they often underestimate is how different things become when the target is a large language model. An LLM isn’t a fixed ruleset but a probabilistic system, capable of producing unexpected outputs, leaking hidden context, or following adversarial instructions in ways that traditional applications never would. That unpredictability demands a testing approach of its own.

Over the past two years, the industry has seen repeated examples of prompt injection, data exfiltration through generated responses, and misuse of model-integrated tools.They are practical attack vectors that appear as soon as LLMs are deployed in real products and workflows. At Iterasec, we see these cases regularly in pentesting engagements and treat them as baseline risks that every AI deployment needs to address. Simply applying a generic pentest methodology won’t expose them.

That is where a structured checklist becomes useful. It provides a systematic way to prepare for an AI pentest, highlighting gaps before testing starts, and ensuring that model-specific risks, such as prompt handling, data exposure, and behavior drift, are covered. In this article, we’ll walk through an AI and LLM pentesting checklist that combines preparation steps with actionable testing areas, giving security teams a concrete framework for validating AI systems under real-world threat conditions.

Understanding LLM Threat Landscape

Large language models expand the attack surface in ways that are still unfamiliar to many security teams. Instead of testing predictable inputs and outputs, pentesters face a system that can reinterpret context, pull instructions from unexpected places, and reveal hidden data without any obvious trigger. Shifting prompts, hidden model logic, and complex integrations make it hard to tell where normal behavior ends and exploitable behavior begins.

Common Attack Vectors

• Prompt Injection Beyond the Obvious: Most security teams can spot a direct injection. The harder cases are indirect: malicious instructions embedded in training data, hidden in knowledge bases, or smuggled through retrieval pipelines. These attacks surface only when the model processes content in context, making them difficult to replicate without a structured LLM pentest checklist.
• Context and Memory Exploitation: Modern deployments often give models memory, session state, or conversation history. Attackers can pivot across interactions, chaining small leaks until the model discloses system prompts or sensitive data. 
• Integration-Induced Vulnerabilities: LLMs rarely operate in isolation. They call APIs, execute functions, or interact with business logic. Attackers exploit this “excessive agency” by making the model trigger actions outside its intended scope. Pentesters need a checklist for AI penetration testing that treats these connections as primary targets, not afterthoughts.
• Adversarial Output: Toxicity and bias are well-known, but output attacks now also include subtle manipulations, such as poisoning downstream analytics, altering financial reports, or steering decision-support systems. An AI pentesting checklist should cover output misuse as deliberately as input validation.

What ties these vectors together is how quickly one can cascade into another. A prompt injection that leaks data, or an unsafe integration that magnifies a minor flaw, makes clear that the real work begins before testing starts — with scope, environment, and compliance preparation.

AI Pentesting Checklist: Pre-Pentest Preparation

Testing LLMs without preparation usually leads to noise, false findings, or even compliance problems. The first phase of any LLM penetration testing checklist is about setting boundaries, creating a safe test environment, and confirming what can legally be touched.

Define Scope & Objectives

The scope here is broader than in a typical penetration test. It must include:

• Which model versions are in scope — base, fine-tuned, or both.
• Which integrations they can reach: APIs, plugins, or retrieval sources.
• Which risks are priority: data leakage, prompt manipulation, unsafe actions, or output misuse.

Clear objectives keep the LLM pentest checklist aligned with real threats instead of generic testing.

Set Up the Test Environment

Running blind on production is a recipe for side effects. A controlled environment is safer and more reliable:

• Sandboxed or replicated models for testing.
• Datasets that mimic sensitive inputs without exposing them.
• Logging and monitoring are tuned to capture prompt handling and model responses.

It ensures the test uncovers genuine weaknesses, not artifacts of setup.

Legal and Compliance Checks

LLM testing often crosses into sensitive territory — private data, proprietary prompts, or regulated industries. Before testing begins, confirm:

• What data can be queried.
• What logs or outputs can be retained.
• Which rules apply under GDPR, HIPAA, or local frameworks.

Building these checks into the checklist for AI penetration testing avoids surprises mid-engagement and ensures that findings can withstand audits.

Ultimate AI & LLM Pentesting Checklist

With the scope defined and the environment ready, the next step is structured testing. That is where the LLM penetration testing checklist turns from planning into action. Each area targets a specific class of weakness unique to large language models.

Threat Modeling

Threat modeling provides the foundation for an effective test.

• Mapping inputs such as user prompts, retrieved documents, and fine-tuning datasets defines where influence may enter the system.
• Outputs — from generated responses to API calls and triggered actions — show where impact can spread.
• Trust boundaries highlight what the model can legitimately access versus what must remain isolated.

These relationships determine where real attackers are likely to focus and guide the direction of the LLM pentest checklist.

Input and Prompt Validation

Prompt injection is one of the most prominent risks, but its forms vary widely.

• Direct manipulation appears in user queries.
• Indirect manipulation is hidden in documents or retrieval sources.
• Multi-turn prompts escalate gradually, often bypassing filters that seem effective in a single exchange.

A checklist for LLM penetration testing includes test coverage for each of these scenarios to verify whether protections remain consistent across contexts.

Data Handling & Privacy

Data handling weaknesses can expose sensitive information or alter model behavior, potentially compromising its accuracy.

• Training fragments or system prompts may leak when the model is pressed with crafted queries.
• Personal or regulated data may surface under specific conditions, creating compliance risks.
• Poisoning attempts in fine-tuning or retrieval pipelines can alter how the model interprets future queries.

The AI pentesting checklist accounts for both leakage and manipulation, since either can undermine trust in the system.

Access Control

Access control often decides whether a localized weakness can escalate into something broader.

• Model endpoints may suffer from weak authentication or poor token management.
• Role confusion between users and integrations can open escalation paths.
• Plugins and downstream tools can extend the attack surface far beyond the model itself.

For this reason, access is always treated as a primary category in a thorough LLM pentest checklist.

Model Behavior Testing

Even with safety layers in place, models often respond unpredictably under pressure.

• Jailbreak attempts aim to override system rules entirely.
• Adversarial queries test how the model handles toxic or harmful requests.
• Noise injection and conflicting instructions explore whether behavior remains consistent when the context becomes complex.

The checklist for AI penetration testing treats these behavioral stresses as core tests rather than side cases.

Logging & Monitoring

Defense without detection offers little long-term protection.

• Effective logging captures inputs, outputs, and anomalies without exposing more data than necessary.
• Monitoring distinguishes unusual but harmless use from clear signs of exploitation.
• Alerting thresholds are calibrated against realistic abuse patterns rather than ideal conditions.

A practical LLM penetration testing checklist verifies that attacks can be seen as well as stopped.

Incident Response

The ability to respond is as critical as the ability to detect.

• Escalation paths define who is notified when suspicious activity is spotted.
• Playbooks tailored for AI-specific incidents accelerate containment and remediation.
• Effective communication channels between engineering and compliance ensure that issues are addressed across teams rather than left unresolved.

Including incident response in the checklist for AI penetration testing confirms that findings can be acted upon quickly and effectively.

Benefits of Using LLM Penetration Testing Checklist

Testing LLMs without structure often leads to uneven results — some risks are covered in depth, while others are missed entirely. A defined LLM pentest checklist eliminates that problem by setting a consistent baseline.

Key benefits include:

• Coverage across unique risks. Prompt injection, data leakage, unsafe integrations, and output manipulation are consistently addressed, rather than sporadically.
• Evidence for compliance and audits. A clear checklist for AI penetration testing shows regulators and stakeholders that testing went beyond generic methods.
• Consistency over time. As models evolve or new integrations are added, the same LLM penetration testing checklist can be reapplied to verify whether changes introduce new weaknesses.
• Shared understanding. Developers, security teams, and business stakeholders see the same structured process, reducing misalignment on what was tested and why.

In practice, the checklist becomes a baseline. It doesn’t replace expert judgment or creativity, but it ensures the fundamentals are never skipped — even under time pressure.

Conclusion

Large language models bring new capabilities, and they also introduce attack surfaces that don’t behave like anything security teams have tested before. Prompt injection, context leaks, unsafe integrations, and adversarial output are not isolated problems — they interact and cascade. Generic pentest playbooks are insufficient to reveal these patterns.

A structured LLM penetration testing checklist sets a clear baseline. Preparation steps define scope and legality, while the core checklist ensures that every class of risk is tested consistently. The result is not just a list of vulnerabilities, but a reliable picture of how an AI system performs under realistic adversarial pressure.

For organizations embedding LLMs into products and workflows, this approach is no longer optional. The checklist for AI penetration testing provides the framework; expertise and creativity give the depth. If you’re looking for guidance, Iterasec is here to help. Contact our team to secure your AI and LLM deployments.