Security for AI | Hack The Box

New technology. Familiar security blind spots.

AI is finding its way into applications, internal workflows and critical business systems. Each new integration adds another place for attackers to look.

The challenge is knowing whether your security team is equipped to find what they will.

LLMs can be manipulated.

An attacker-controlled prompt or retrieved document can change a model's behaviour, disclose information or trigger unintended actions

AI agents have access worth exploiting.

Connected tools, APIs and data sources can turn a manipulated agent into a route through your systems

Traditional testing doesn't tell the whole story.

Application security experience helps, but it doesn't automatically translate into understanding model behaviour or AI-specific attack chains

AI adoption is moving ahead of technical expertise.

Security teams are being asked to assess systems they haven't had the chance to attack themselves

The risks aren't hypothetical. The question is whether your team knows how to uncover them.

From the first LLM exploit to advanced offensive AI security.

AI Red Teamer Path

Learn to attack the AI stack

Developed with Google, the 12-module AI Red Teamer Path gives practitioners a structured introduction to offensive AI security.

Explore how machine learning systems and LLM applications can be attacked, from adversarial techniques to vulnerabilities in the applications built around models.

Explore the AI Red Teamer Path

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AI Security Labs and Challenges

Get inside vulnerable AI applications

Put theory to work in environments designed to be attacked.

Manipulate model inputs, investigate insecure retrieval pipelines and look for ways to exploit AI agents, tool integrations and application logic.

Practitioners can experiment, make mistakes and understand why an exploit works without putting production systems at risk.

Explore AI security labs

HTB Certified Offensive AI Expert (HTB COAE)

Put advanced expertise to the test

The HTB COAE gives experienced practitioners the opportunity to demonstrate advanced offensive AI security expertise through a hands-on examination.

Rather than recalling definitions, candidates must apply their technical knowledge to solve practical security challenges.

Explore the certification

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HTB Role Paths and Practical Labs

Strengthen the security skills AI still depends on

AI applications don't exist in isolation. They depend on APIs, infrastructure, identities and software that attackers can also target.

HTB's wider technical training helps practitioners strengthen the core penetration testing and application security skills that remain essential when assessing AI systems.

Explore skills development

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What skills does it develop?

Your program focuses on protecting AI-powered applications, models and supporting systems. For technical security teams, that means understanding how these systems can be attacked and developing the skills to identify and exploit their weaknesses before malicious actors do.

Which vulnerabilities can practitioners explore?

HTB's offensive AI security content covers areas including prompt injection, LLM exploitation, adversarial machine learning, insecure RAG applications and weaknesses in AI agents and connected tools.

Is this suitable for existing penetration testers?

Yes. Penetration testers, red teamers and application security practitioners already have much of the foundation needed to investigate AI systems. HTB helps extend that expertise into AI-specific technologies, behaviours and attack techniques.

Do practitioners need a background in machine learning?

Advanced machine learning experience isn't necessary to begin exploring offensive AI security. The AI Red Teamer Path introduces relevant concepts before progressing into more demanding technical material. Existing security knowledge remains valuable.

How is this different from AI for Security?

Security for AI is about attacking and defending AI systems themselves. AI for Security addresses a different challenge, helping security practitioners work effectively with AI tools and agents as part of their existing jobs.

Where should an organisation begin?

Start with the AI systems already entering your environment and the people expected to assess them. HTB can help identify relevant learning content, practical exercises and technical assessments for those responsibilities.