What makes you so sure that closed-source companies won't run those same AI scanners on their own code?
It's closed to the public, it's not closed to them!
Having worked in quite a few agency/consultancy situations, it is far more productive to smash your head against a wall till bleeding, than to get a client to pay for security. The regular answer: "This is table stakes, we pay you for this." Combined with: "Why has velocity gone down, we don't pay you for that security or documentation crap."
There are unexploited security holes in enterprise software you can drive a boring machine through. There is a well paid "security" (aka employee surveillance) company using python2.7 (no, not patched) on each and every machine their software runs on. At some of the biggest companies in this world. They just don't care for updating this, because, why should they. There is no incentive. None.
Running AI scanners internally costs money, dev time, and management buy in to actually fix the mountain of tech debt the scanners uncover. As you said there is no incentive for that
But for bad actors the cost of pointing an LLM at an exposed endpoint or reverse engineered binary has dropped to near zero. The attackers tooling just got exponentially cheaper and faster, while the enterprise defenders budget remained at zero.
There should be a way to donate your unused tokens on every cycle to open source like rounding up at the chekout!
I've seen multiple proprietary places now including a routine AI scan of their code because it's so cheap and they may as well use-up unused tokens at the end of the week.
I mean, it's literally zero because they already paid for CC for every developer. You can't get cheaper than that.
1. shallow
2. hollow
3. flat
...
Not claiming that it's a slam dunk for open source, but the inverse does not seem correct either.
Why "minus D, E and F"? After all, once you have the harness set up, there's no additional work to add in new models, right?
Not from the automated repo scanners, but bug bounty programs can generate a lot of reports in my experience. AI tools are becoming a problem there, too, because amateurs are drawn to the bounties and will submit anything the AI hallucinates.
Closed source companies can (and should!) also run their own security audits rather than passively waiting for volunteers to spend their tokens on it.
That still exists in the OSS world too, having your code out there is no panacea. I think we'll see a real swarm of security issues across the board, but I would expect the OSS world to fare better (perhaps after a painful period).
There is no guarantee that open means that they will be discovered.
So just like a pre-AI or worse?
This really just seems like Strix marketing. Which is totally fair, but let's be reasonable here, any open-source business stands to lose way more by continuing to be open-source vs. relying on the benevolence of people scanning their code for them.
Actually the opposite is obvious - the comment you replied too talked about an abundance of good Samaritan reports - it's strange to speculate on some nebulous "gain" when responding to facts about more then enough reports concerning open source code.
> In this new closed-source world (for Cal.com), there's nothing stopping them from running their own internal security agent audits
That's one good Samaritan for a closed source app vs many for an open source one. Open source wins again.
> any open-source business stands to lose way more
That doesn't make any sense - why would it lose more when it has many more good Samaritans working for it for free?
You seem to forget that the number of vulnerabilities in a certain app is finite, an open source app will reach a secure status much faster than a closed source one, in addition to also gaining from shorter time to market.
In fact, open source will soon be much better and more capable due to new and developing technological and organizational advancements which are next to impossible to happen under a closed source regime.
But at that point, "fighting fire with fire" is still a good point. Assuming tokens are available, we could just dump the entire code base, changesets and all, our dependent configuration on the code base, company-internal domain knowledge and previous upgrade failures into a folder and tell the AI to figure out upgrade risks. Bonus points if you have decent integration tests or test setups to all of that through.
It won't be perfect, but combine that with a good tiered rollout and increasing velocity of rollouts are entirely possible.
It's kinda funny to me -- a lot of the agentic hype seems to be rewarding good practices - cooperation, documentation, unit testing, integration testing, local test setups hugely.
If the cost of security audit becomes marginal, it would seem reasonable to expect projects to publish results of such audits frequently.
There’s probably a quite hefty backlog of medium- and low-severity issues in existing projects for maintainers to suffer through first though.
This is what worries me about companies sleeping on using AI to at a bare minimum run code audits and evaluate their security routinely. I suspect as models get better we're going to see companies being hacked at a level never seen before.
Right now we've seen a few different maintainers for open source packages get hacked, who knows how many companies have someone infiltrating their internal systems with the help of AI because nobody wants to do the due dilligence of having a company do security audits on their systems.
but with cal.com i dont think this is about security lol
open source will always be an advantage just you need to decide wether it aligns with you business needs
I analyze crash dumps for a Windows application. I haven't had much luck using Claude, OpenAI, or Google models when working with WinDbg. None of the models are very good at assembly and don't seem to be able to remember the details of different calling conventions or even how some of the registers are typically used. They are all pretty good at helping me navigate WinDbg though.
How so? AI won't have access to the source code. In some cases AI may have access to deployed binaries (if your business deploys binaries) but I am not aware that it has the same capabilities against compiled code than source code.
But in a SAAS world, all AI has access to is your API. It might be still be up to no good but surely you will be several orders of magnitude less exposed than with access to source code.