AI & LLM Penetration Test

An AI & LLM Penetration Test uncovers prompt injection, jailbreaks, data leakage, and unsafe agent behaviour in your AI-powered applications

An AI & LLM Penetration Test is designed to identify vulnerabilities across your AI-powered application, from the model prompt and retrieval pipeline through to the tools and systems it can act on

What you'll get:
  • A comprehensive assessment of your LLM application against the OWASP Top 10 for LLM Applications
  • Prompt injection, jailbreak, and guardrail-bypass testing across direct and indirect attack paths
  • Review of RAG pipelines, vector stores, tool/plugin integrations, and agent permissions
  • A detailed report with proof-of-concept exploits, business impact, and remediation steps
  • Remediation and patch validation testing to confirm vulnerability fixes

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What is AI & LLM Penetration Testing?

AI and LLM penetration testing is a specialized security assessment of applications built on artificial intelligence and large language models — chatbots, copilots, document-summarization tools, customer-support assistants, and autonomous agents that can take actions on a user's behalf. As organizations rush to embed models from providers like OpenAI, Anthropic, and Google into their products, they expose an entirely new class of vulnerabilities that traditional web application testing was never designed to find.

Unlike conventional software, an LLM application is driven by natural language, which means the data and the instructions share the same channel. An attacker who can influence any text the model reads — a chat message, an uploaded document, a web page retrieved by the application, or an email in a connected inbox — can attempt to override the system's intended behaviour. This is the root of prompt injection, and it cascades into data leakage, unauthorized actions, and abuse of any tool or system the model is wired into.

DarkPoint Security's AI and LLM penetration tests evaluate the full attack surface of your AI deployment, from the system prompt and retrieval pipeline through to tool integrations and downstream systems, and deliver clear, prioritized remediation guidance so you can ship AI features without shipping AI risk.

AI and LLM security testing

Why Your Organization Needs AI & LLM Penetration Testing

AI features are being shipped faster than security teams can review them, and the failure modes are unfamiliar even to experienced engineers. Because LLM applications often touch sensitive data and are increasingly granted the ability to take real actions, an unassessed deployment can quietly become one of the most exposed systems you operate.

  • Prompt Injection and Jailbreaks — Attackers craft inputs that override your system prompt, bypass safety guardrails, or smuggle hidden instructions through documents, web pages, and other content the model ingests. Successful injection can turn your helpful assistant into a tool for the attacker
  • Sensitive Data Disclosure — Models can leak system prompts, API keys embedded in instructions, other users' data held in context, or proprietary information pulled into a retrieval pipeline. Testing validates that confidential data cannot be coaxed out of the application
  • Excessive Agency — When an LLM is connected to tools, plugins, databases, or agents that can send email, execute code, or modify records, an injected instruction can trigger destructive or unauthorized actions. We assess whether permissions, confirmations, and blast-radius controls are sufficient
  • Insecure Output Handling — Model output is frequently rendered in browsers, passed to shells, or used in database queries without sanitization, reintroducing classic vulnerabilities such as cross-site scripting, SSRF, and injection — now driven by attacker-controlled model responses
  • Regulatory and Trust Pressure — Customers, auditors, and frameworks aligned to SOC 2, ISO 27001, and emerging AI governance expectations increasingly require evidence that AI features have been independently security tested before launch

Our AI & LLM Testing Methodology

Our AI and LLM penetration tests follow a rigorous methodology grounded in the recognized standards for this emerging discipline:

  • OWASP Top 10 for LLM Applications (2025) — Provides the core vulnerability taxonomy for LLM systems, covering prompt injection, sensitive information disclosure, supply chain risks, data and model poisoning, improper output handling, excessive agency, system prompt leakage, vector and embedding weaknesses, misinformation, and unbounded consumption
  • MITRE ATLAS — The adversarial threat landscape for AI systems, used to model real-world attacker tactics and techniques against machine learning and LLM deployments
  • NIST AI Risk Management Framework — Frames the assessment in terms of governance, trustworthiness, and measurable risk so findings map cleanly to your broader risk program
  • PTES and NIST SP 800-115 — Govern the conventional penetration testing process for the application, APIs, and infrastructure surrounding the model

The assessment begins with threat modeling and surface mapping to understand the model in use, the system prompt, the retrieval and tool integrations, and what the application is permitted to do. We then perform adversarial input testing — direct and indirect prompt injection, jailbreaks, encoding and obfuscation bypasses, and context manipulation — followed by assessment of output handling, agent permissions, and the connected attack surface. Finally, we conduct exploitation and validation to demonstrate concrete business impact, such as data exfiltration or unauthorized actions, and document the full attack chain with reproducible proof of concept.

Testing Coverage

Our AI and LLM penetration tests cover a comprehensive range of attack vectors across the model, the application, and the systems it connects to:

  • Direct and indirect prompt injection
  • Jailbreaks and safety guardrail bypasses
  • System prompt and instruction leakage
  • Sensitive data and training-data disclosure
  • Retrieval-augmented generation (RAG) abuse
  • Vector store and embedding weaknesses
  • Insecure output handling (XSS, SSRF, injection)
  • Excessive agency and unsafe tool/plugin use
  • Agent permission and blast-radius review
  • Unbounded consumption and denial-of-wallet
  • Supply chain and model/plugin integrity
  • Authentication, authorization, and tenant isolation
  • API security of the surrounding application
  • Hosting and inference endpoint configuration

Industries We Serve

DarkPoint Security delivers AI and LLM penetration testing to organizations building artificial intelligence into customer-facing and internal products. We work with technology and SaaS companies embedding copilots, assistants, and agentic features into their platforms, where a security review is increasingly a prerequisite for enterprise sales and SOC 2 attestation. We support financial services institutions deploying AI for customer support, document processing, and decisioning under OSFI expectations for technology and cyber risk. Our team works with healthcare organizations applying LLMs to clinical documentation and patient communication while remaining accountable to PIPEDA and provincial health privacy law, and with government and public sector bodies piloting AI assistants that must meet strict data residency and confidentiality requirements.

Why Choose DarkPoint Security

  • Genuine AI Security Depth — Our testing is built on the OWASP LLM Top 10, MITRE ATLAS, and the NIST AI RMF, not a checklist bolted onto a generic web test. We understand how prompt injection, RAG, and agent tooling actually break
  • Full-Stack Assessment — We test the model behaviour and the application, APIs, authentication, and infrastructure around it, because AI risk and conventional application risk compound each other
  • Manual-First Approach — Adversarial prompt engineering and exploit development are inherently creative work. We go far beyond automated scanners to find the injection paths and agent abuses that tools miss
  • Canadian Data Residency — As a Toronto-based firm, all testing data, prompts, and reports remain within Canadian jurisdiction, addressing data sovereignty and confidentiality requirements
  • Remediation Validation — Every engagement includes follow-up retesting to confirm that identified vulnerabilities have been properly remediated without introducing new weaknesses

Frequently Asked Questions

The terms overlap, but they emphasize different goals. LLM penetration testing is a structured, scope-bounded assessment of a specific application, mapped against frameworks such as the OWASP Top 10 for LLM Applications, and produces a report of concrete vulnerabilities with remediation guidance. AI red teaming is typically broader and more adversarial, simulating a determined attacker against the full system to probe for unexpected or emergent failures, including model behaviour, guardrail bypasses, and abuse scenarios. We deliver both, and many engagements combine a systematic penetration test with a red-team phase against the deployed application.

We focus on the application and how it uses the model, because that is where almost all real-world risk lives. This includes the system prompt, the retrieval-augmented generation (RAG) pipeline, vector stores, tool and plugin integrations, agent permissions, and how model output is handled by downstream systems. We do not retrain or attack the weights of a third-party foundation model such as those from OpenAI, Anthropic, or Google, but we do assess how your use of that model can be abused through prompt injection, jailbreaks, data leakage, and excessive agency. If you host or fine-tune your own model, we additionally assess the hosting, fine-tuning data handling, and inference endpoints.

Our methodology is built on the OWASP Top 10 for Large Language Model Applications (2025), MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems), and the NIST AI Risk Management Framework, layered on top of our established penetration testing process derived from PTES and NIST SP 800-115. This combination lets us cover both AI-specific failure modes such as prompt injection and excessive agency, and the conventional application and infrastructure weaknesses that surround every AI deployment.

A typical AI or LLM penetration test takes 1 to 3 weeks depending on the complexity of the system. A single chatbot with a fixed system prompt sits at the shorter end, while an agentic application with retrieval-augmented generation, multiple tool integrations, and the ability to take actions on a user's behalf requires more time to map the attack surface and validate impact. We confirm a precise timeline during scoping.

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