This blogpost follows up on my previous blogpost (in French).

TL;DR Here are the changes coming to the NorthSec CTF for 2027. Read on for an explanation and the details.

1. Teams and individuals will need to register for one of two scoreboards: the regular CTF or the AI CTF. Team size remains unchanged at a maximum of eight people per team.
2. The CTF will be the same for both scoreboards.
3. In the regular CTF, the use of generative AI tools will be allowed, but it will be forbidden to give a LLM any context specific to the CTF.
4. In the AI CTF, any LLM, harness, and AI tool will be allowed without limitation (other than our existing rules, notably the prohibition on brute-forcing the infrastructure).
5. The main CTF (the one used for challenge design and pushed forward for branding/visibility) will be the regular CTF.
6. Participants in the regular CTF will be able to switch to the AI CTF, but not the other way around.

Details and explanations

What does it mean to give a LLM context about the CTF?

This rule was designed to be easy for participants to understand, in order to minimize accidental cheating.

Broadly, this includes pasting or transcribing any information (such as text, an image, or a file) found on the forum, on the *.nsec/*.ctf domains, or physically on-site.

It is deliberately written to allow using AI as a teacher, or to write/modify scripts. We consider that these two uses don’t provide an unfair advantage and don’t distort the CTF’s spirit of problem-solving and cybersecurity knowledge acquisition.

Here’s an example of what will be allowed and forbidden in the regular CTF:

A web challenge has a login page with an endpoint to reset a password. The participant is given the backend’s FastAPI code, and the function that generates the secret reset token uses random.randrange().

It is allowed to ask an AI tool these questions:

• ✅ What is FastAPI?
• ✅ What are the possible vulnerabilities on a login or password reset page?
• ✅ Is random.randrange() secure for generating a secret token?
• ✅ Generate a Python script that takes a URL as an argument and POSTs to this endpoint while noting the timestamp, in order to brute-force the possible reset tokens. The backend generates its tokens with random.randrange(10000, 99999). The script then tries every possible token against a second URL passed as an argument until it receives an HTTP 200 status code.

It is forbidden to ask an AI tool:

• ❌ Find the vulnerability here: http://challenge.ctf
• ❌ Is this code secure?

    <the password reset or secret token generation function>
  

• ❌ Analyze this file: <the challenge's source code>
• ❌ Give me hints to solve this challenge without giving me the answer: http://challenge.ctf
• ❌ Generate a script that brute-forces the password reset tokens on http://challenge.ctf.

Participants are going to cheat in the regular CTF, right?

If NorthSec CTF were an online CTF, without a doubt. We’re betting that the community respects the event enough to adhere to this rule, which is particularly hard to enforce.

We agreed that, to maximize adherence, any rule that restricts tools must be reasonable, easy to understand, and unambiguous. Also, an alternative option should be available for those who don’t want to abide by it.

With the ban on giving context to AI, and the option to opt out of that rule by choosing the AI CTF instead, we believe we’re offering a satisfying solution for everyone. That said, we’re aware that some won’t necessarily follow the rules, and we’ve planned some measures accordingly.

We will tighten the rules on flag hoarding (waiting before submitting flags) and monitor teams at the top of the scoreboard more actively. In most cases, this won’t change anything about participants’ experience. In some cases, we might ask for an explanation of how a flag was found. In the case where proven and concealed cheating is found, the offending team will be banned from the CTF with immediate effect. In the case of self-reported accidental cheating, the options will be cancellation of points or a transfer to the AI CTF.

Despite this, we believe the best way to enforce this rule will be through community buy-in. We’re counting on every participant to not tolerate cheating among their teammates. Only with a strong enough anti-cheating culture among participants will we be able to maintain this type of CTF.

For this reason, we will be very vocal and insistent about integrity in the regular CTF. We will communicate clearly and frequently that giving context to AI is cheating and won’t be tolerated. The exact forms of this communication will be decided later.

Why will the challenges be identical for both experiences?

Creating a second set of challenges for the AI CTF (AI-first challenges, designed to only be solvable through the use of an AI agent) is an interesting option that has been proposed by several people in the community and discussed internally.

The big problem we face is the dilution of effort. Without additional challenge designers, we would end up with two CTFs, each with fewer challenges than the community is used to.

My call to recruit challenge designers for AI-first challenges during the closing ceremony also didn’t bear fruit.

Finally, the physical tracks are extremely appreciated by the community. We want all teams to have access to the full set of available physical tracks, regardless of their choice regarding AI.

Why have a “main” CTF, and why choose the regular CTF?

When people talk about the NorthSec CTF, they generally mention the quality of the challenges, the sense of community, and the festive yet serious competitive atmosphere.

All of these strengths depend on elements that are harder to guarantee with the AI CTF.

Challenge quality: since AI tools have become extremely good at solving CTF challenges, you generally can’t make a challenge that’s equally good if it’s designed to be solved by both a human AND an AI. You generally have to choose one.

A good example from this year is Monsatan Defacing, where you had to perform a supply-chain-style attack via a GitLab instance. This is a fairly rare type of challenge in CTFs. It requires learning how GitLab, CI/CD, and software dependency management work, among other things. It’s a very relevant exercise, and the challenge was designed to guide a human through learning these technologies. For an AI? Pointless. The track got solved very quickly, two hours after the CTF began.

Could Monsatan Defacing have been designed to give AI users a hard time? Absolutely. We could have had an instance with hundreds of repositories and pipelines and hidden the vulnerability in an environment much closer to a “normal” one. That would have been a good challenge for a human equipped with an AI agent, but you have to choose between the two, and most of our challenge designers aren’t interested in AI-first challenges.

Sense of community: Very common feedback from participants and challenge designers was the drop in interactions in 2026. Fewer memes, fewer write-ups, fewer exchanges with challenge designers or between participants. Is AI to blame for all of this? Probably not 100%, but it seems clear that it’s less tempting to interact with others when you have to focus on reviewing the output of three AI agents.

Atmosphere and competition: According to community feedback, the scoreboard matters even to people who aren’t there to win. That means competitive integrity matters too, because without it, the scoreboard doesn’t mean anything. A significant shortcoming of the AI CTF is the pay-to-win aspect (teams that pay more will have an advantage). Without central control over AI inference, this aspect is impossible to mitigate. So, unlike the regular scoreboard, which fairly accurately represents CTF skills, the AI scoreboard won’t necessarily be an accurate indicator of skill at building and operating AI systems in a CTF context.

In short, to preserve what makes NorthSec CTF such a beloved event, we believe most players should end up in the regular CTF, which is why we’ll be pushing it forward and why our challenges will be designed to be solved by humans.

Why not offer a single shared LLM inference endpoint for the AI CTF?

This was a popular suggestion to level the playing field and reduce the pay-to-win aspect. That said, we designed the AI CTF as the option for teams who don’t want to play with the regular CTF’s restrictions.

Some players may have access to Frontier models unavailable to the general public, some may want to solve the CTF exclusively with open-source models running on their MacBook Pro, and others may want to try the new AI module on their tool that doesn’t yet allow changing the AI inference endpoint.

A single, central inference endpoint for this CTF would therefore add a major restriction for players who are specifically trying to avoid the restrictions, on top of adding a monitoring burden to ensure competitive integrity.

Registration

It will be possible to change your choice of CTF experience until registration closes, a few days before the CTF. People who register individually or as an incomplete team will be placed on a team in a way that respects their choice.

Conclusion

These changes are the result of a lot of discussions with the community, including participants and CTF organizers.

That said, it’s very clear that community buy-in for this way of doing things will be crucial to preserving the competitive aspect of the CTF. If the community tolerates cheating, whether by praising people who find innovative ways to break the CTF’s competitive integrity, or by saying nothing to a teammate who asks Claude a forbidden question, we won’t be able to keep two scoreboards, and only the AI scoreboard will remain.

On the other hand, if as a community we value competitive integrity and mutual respect in the competition, NorthSec’s CTF can remain an excellent blend of fair competition and a hands-on experience where we learn how the technologies around us work, in order to better protect them.

We’re preparing an awesome CTF for you this year. You can already buy your tickets at the early bird price. See you next May!