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AI & LLM Penetration Testing Services

AI & LLM Penetration Testing Services

You're deploying AI fast. But are you securing it just as quickly? Packetlabs tests AI systems the way real adversaries do, probing LLM integrations, prompt injection paths, data leakage risks, model abuse, and API exposures so your innovation doesn't become your next breach.

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Test AI Systems Like an Attacker Would

AI risk isn't theoretical. Prompt injection, data poisoning, insecure model APIs, and privilege escalation through LLM integrations are already being exploited. Our human-led testing evaluates your AI stack including APIs, plugins, data sources, and access controls so you understand real exploit paths, not just policy gaps.

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What We Test in AI & LLM Environments

AI systems expand your attack surface. We test where adversaries are already looking.

Prompt Injection & Jailbreaks

We simulate malicious prompt crafting to bypass guardrails and extract sensitive data or override system constraints.

Learn about jailbreaking

Data Leakage & Exposure

We test for unintended disclosure of internal data, training artifacts, secrets, and cross-tenant leakage.

Read about the OWASP Top LLM risks

Model & API Security

We evaluate authentication, rate limiting, token handling, and access controls protecting AI endpoints.

Learn more about AI/LLM trends

Identity & Access Abuse

We assess how AI integrates with IAM systems and whether privilege escalation is possible through LLM workflows.

Read more about access abuse

Insecure Plugin & Integration Risk

We test third-party plugins, automation hooks, and external connectors that expand AI attack paths.

Read more about plugin risks

Business Logic Abuse

We simulate how attackers chain AI flaws with application or infrastructure weaknesses to reach sensitive systems.

Learn about attack chaining

AI/LLM Penetration Testing FAQs

What is AI/LLM Penetration Testing?

AI/LLM Penetration Testing evaluates how attackers could manipulate or abuse AI-powered systems such as chatbots, RAG pipelines, and AI agents. It focuses on risks unique to language models, including prompt injection, data leakage, and unauthorized tool execution.

AI / LLM Security Testing vs. Application Penetration Testing

AI / LLM Security TestingApplication Penetration Testing

Primary Focus

Security risks unique to AI systems, large language models, and AI-driven applications

Security of traditional web applications and backend systems

Scope

Prompt injection, model manipulation, data leakage, output abuse, AI integrations

Authentication flows, input validation, business logic, APIs, and server-side logic

Attack Surface

User prompts, training data exposure, embeddings, plugins, third-party model integrations

Web forms, session handling, APIs, client-side and server-side components

Common Vulnerabilities

Prompt injection, data exfiltration through model output, insecure model APIs, model jailbreaks

SQL injection, XSS, CSRF, broken access controls, logic flaws

Testing Approach

Simulates malicious prompt crafting, model manipulation, and abuse of AI outputs

Simulates real-world attackers exploiting application vulnerabilities

Data Exposure Risk

Sensitive training data leakage, unintended data disclosure via responses

Database exposure, session hijacking, unauthorized data access

Business Logic Abuse

Manipulating AI outputs to bypass controls or generate harmful results

Exploiting flawed workflows or authorization checks

Human-in-the-Loop Risk

Tests how users can socially engineer AI systems into unsafe responses

Typically does not evaluate AI behavioral manipulation

Impact if Compromised

Brand damage, misinformation, regulatory risk, data exposure, unsafe automated decisions

Data breach, account takeover, service disruption

Ideal For

Organizations deploying AI chatbots, copilots, AI-powered SaaS features, or LLM integrations

Organizations operating traditional web applications and SaaS platforms

AI & LLM Penetration Testing: Key Outcomes

Reduced AI Security Risk

Identify and eliminate prompt injection, jailbreaks, and instruction bypass techniques before attackers can exploit them in production environments.

Protected Sensitive Data

Prevent exposure of PII, credentials, proprietary documents, and cross-tenant data through rigorous testing of RAG pipelines and model behavior.

Controlled Agent & Tool Access

Validate authorization boundaries and ensure AI agents cannot execute unauthorized actions, escalate privileges, or misuse integrated tools.

Confident AI Deployment

Receive clear proof-of-impact findings and actionable remediation guidance so you can launch and scale AI systems securely.

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