What AI Hacking Skills Can I Test on Lakera?

Updated

play.lakera.ai is a hands-on playground for testing AI hacking skills against large language models (LLMs). Based on the site's own description, it lets you "test your AI hacking skills and experience the limitations of large language models firsthand." In practice, that means the skills you exercise are the ones used to probe and break LLM behavior — most notably prompt injection and jailbreaking — rather than traditional network or software exploitation. It suits anyone curious about AI security: developers, security researchers, students, and CTF-style learners who want to see how models fail in real time.

The core skills you can test

The platform is built around manipulating a model through its input, so the relevant skill set centers on language-level attacks:

  • Prompt injection — crafting input that overrides or redirects the model's original instructions, so it does something the designer didn't intend.
  • Jailbreaking — using role-play, hypotheticals, or layered instructions to get a model to bypass its own safety or content rules.
  • Instruction hierarchy manipulation — exploiting how a model weighs system, developer, and user instructions when they conflict.
  • Goal hijacking — steering a model away from its assigned task toward an attacker-chosen objective.
  • Information extraction — trying to get a model to reveal hidden instructions, context, or data it was told to keep back.

These are the practical, transferable skills in LLM security. They don't require binary exploitation or memory-corruption knowledge; the "hacking" happens in natural language.

How the skills are tested against an LLM

The general mechanism is the same across these challenges: you send text to a model, and the model either holds its guardrails or breaks. What you're testing is the boundary between the two.

A typical loop looks like this:

  1. Read the objective. Each challenge gives you a target — for example, get the model to say a specific phrase, ignore a rule, or reveal something it was instructed to protect.
  2. Craft an input. You write a prompt designed to override the model's instructions or reframe the task.
  3. Send it and observe. The model responds. You check whether it complied, refused, or partially leaked.
  4. Iterate. If it refused, you adjust wording, framing, or structure and try again.

The expected result of a successful attempt is a model response that violates its intended behavior — which is exactly the "limitation" the site says you'll experience firsthand.

A concrete example of the pattern

Suppose a challenge tells the model never to output a certain word. A direct request fails. You might then try reframing: ask the model to complete a sentence, translate a phrase, or role-play a character who doesn't know the rule. If the model produces the forbidden output, you've demonstrated a jailbreak. The skill being tested is your ability to find the framing that slips past the guardrail — not any code-level exploit.

Difficulty and scope

Because these are language-based challenges, difficulty scales with how robustly the target model resists manipulation:

  • Easier challenges reward obvious overrides — a plain "ignore previous instructions" may work.
  • Harder challenges require multi-step reasoning, indirect phrasing, encoding tricks, or exploiting conflicts between instruction layers.

The scope is deliberately narrow and focused: it's about LLM behavior and safety boundaries, not about attacking infrastructure, networks, or applications around the model. If you're looking for classic pentesting practice, this isn't that — it's the AI-specific subset.

Who this is for

The platform fits people who want practical, low-setup exposure to AI security concepts:

  • Security learners who want to understand why LLMs are hard to secure.
  • Developers building on LLMs who need to see how prompt injection and jailbreaks actually play out.
  • CTF and puzzle enthusiasts who enjoy iterative, adversarial problem-solving.
  • Researchers and students exploring AI safety and red-teaming basics.

If your interest is in AI security specifically — how models can be manipulated through their inputs — this is a direct way to build that intuition. If you need production-grade red-teaming tooling or coverage of non-LLM attack surfaces, treat this as a starting point rather than a complete toolkit.