Services

AI/LLM Penetration Testing

LLMs unlock new business value, but also new attack surfaces: prompt injection, data leakage, insecure plugins, and poisoned training data. Left untested, these risks can compromise sensitive data, integrity, and trust. As a leading Penetration Testing company, Packetlabs simulates adversary tactics against your AI-powered applications, validating vulnerabilities unique to generative AI systems and guiding remediation. You gain evidence-based confidence that your AI systems are safe, trustworthy, and resilient, before attackers or auditors expose the gaps.

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Your three-step path to verified AI/LLM security:

1. Demonstrate Real Impact: We showcase how vulnerabilities can be exploited, translating technical risks into meaningful business impacts that executives and developers both understand. 2. Deliver Complete Reporting: Every finding is validated, reproducible, and mapped to risk severity and context, giving development teams clarity and stakeholders confidence. 3. Deliver Strategic Guidance: Receive clear, tailored recommendations to assist your development and security teams. From remediation steps to best practices, ensuring you're equipped to strengthen defenses and stay ahead of evolving threats.

Actionable insights that harden your application against real threats.

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Your three-step path to verified AI/LLM security:

Actionable insights that harden your application against real threats.

Service Highlights

Prompt Injection. Trust Manipulated.

We simulate adversary attempts to coerce your LLM into unsafe or unintended actions, like leaking sensitive data, bypassing controls, or executing harmful logic. Why it matters: Sensitive logic buried in compiled code often bypasses security reviews. Exposing it early ensures attackers can’t exploit business-critical functionality hiding in plain sight.

Our Uncompromising Standards.

Beyond Automated Testing

While automated scanners can uncover simple surface-level findings, Packetlabs’ expert-led manual-first penetration tests probe the logic, business workflows, and chained exploits that scanners routinely overlook. Leveraging manual exploitation techniques, threat-intel-driven scenarios, and creative lateral thinking, our team exposes high-impact vulnerabilities competitors miss and translates them into clear, fix-ready guidance.

CREST-Accredited Expertise

You, your leadership, and your team can’t afford guesswork; but need trust and proof that the people testing your defenses meet the highest standards. That’s why Packetlabs earned CREST-accreditation, cybersecurity’s gold standard, awarded only after rigorous, hands‑on exams and ongoing audits by the Council of Registered Security Testers.

Board-Ready Remediation Guidance

When findings are packaged for both strategy and execution from the outset, security gaps can be addressed more efficiently: executives can allocate budget, assign resources, and prioritize remediation initiatives in days rather than weeks while engineers can start fixing issues immediately, guided by specific instructions tied to the exact vulnerabilities discovered. This results in reduced time-to-fix for high-risk vulnerabilities and faster closure of attack windows.

Defence In-Depth

Packetlabs has assisted security leaders worldwide in defending against breaches. Testing like an adversary, our experts go beyond the initial target, pivoting through every in-scope system to stress-test your detection layers so you can see exactly how your “defense in depth” holds up. The result: not a single client has ever been compromised by a vulnerability we missed, providing you with board-ready proof that your organization is well-defended.

Why Invest in AI/LLM Penetration Testing?

Correct Overreliance on Automation

As artificial intelligence becomes embedded in business operations, the trustworthiness of AI-generated outputs has become a new frontier in cybersecurity and risk management. Attackers are learning to exploit that trust to manipulate models, prompts, or integrations to trigger misleading or harmful results.

Organizations must identify and test scenarios where blind trust in AI output could cause real-world harm, such as decision-making errors, manipulated information in chatbots and virtual assistants, and workflow automation failures.

Ward Against Supply Chain Vulnerabilities

Modern LLM deployments rarely run in isolation: they sit at the center of an ecosystem: plugins, microservices, ingestion pipelines, feature stores, third-party APIs, CI/CD pipelines, and orchestration tooling.

Each connection is a potential weak link. Effective AI/LLM testing must therefore treat the model and its ecosystem as the attack surface.

Protect Against Training Data Poisoning

Data poisoning is one of the most subtle yet devastating attack vectors in the AI lifecycle. When attackers manage to insert, modify, or influence the data used to train or fine-tune a model, they can stealthily alter the model’s behavior, bias its responses, or create backdoors that are difficult to detect.

As more organizations adopt continuous learning, fine-tuning pipelines, and automated data ingestion, verifying the integrity of training data has become a critical part of AI/LLM penetration testing.

Identify Real-World Attack Vectors

Modern AI systems often produce text or structured output that is consumed by web apps, dashboards, email clients, or automated workflows. When an LLM’s output is rendered or acted on without sufficient validation, that output can become an attack vector, effectively turning the model into a content generator for web exploits.

During AI/LLM security testing, our team seeks “response-to-exploit” paths and validates whether a model can be leveraged as a stepping stone for common web attacks.

Resources

Pentest Sourcing Guide

Download our Pentest Sourcing Guide to learn everything you need to know to successfully plan, scope, and execute your penetration testing projects.

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