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Security work is not productive.

Imagine that the whole city has to install iron doors and window grilles, because wooden doors and glass windows are not safe enough anymore.

That's a lot of work, but is it productive in the end? Probably not.

With AI it's the same. World was fine with lax security approach before. Today it is not fine. Tomorrow we might need to prove every code line before shipping, will that be more productive? Hell, no.

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> Imagine that the whole city has to install iron doors and window grilles, because wooden doors and glass windows are not safe enough anymore.

> That's a lot of work, but is it productive in the end? Probably not.

Your imaginary scenario defines the solution to be pointless. If that same city was subjected to weekly riots smashing windows & doors, and people regularly died, stopping "productive" work to install things that keep people alive and productive? That's productive.

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It's a terminology issue. Economists use the term productivity in a certain way and they even use the term "broken window fallacy" to describe this debate.

https://en.wikipedia.org/wiki/Parable_of_the_broken_window

Basically, if you accept this line of reasoning, then someone going around breaking windows and someone else going around repairing them can be seen as good because it creates jobs. But that would be nonsensical and create poverty. That's why economists focus on wealth creation not job creation.

Fixing security bugs is only needed because people exploit them. It creates work but it doesn't create wealth.

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> to illustrate why destruction, and the money spent to recover from destruction, is not actually a net benefit to society

it is distinct from question whether it is productive to defend from evil people

evil people + protecting from them is obviously net negative!

but it is not changing that of evil people are present then reasonable level of protection from them is needed, and setting it up is productive

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Fallacy applies only to incidental breaking of glass like child smashing window paying football.

Protecting yourself from bad actors also doesn't create wealth but it protects wealth you already have.

You know cybercrime and APT groups are not bored kids in their parents basement, there are groups that are running automated tooling to find vulnerable companies and if possible take all their wealth.

Just look at Binance of course, but if you are small company there are also people targeting those especially with AI assistance nowadays.

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There is a sense in which productive is being used here that is slightly subtle. Security doesn't really add anything to net output. It is, in other words, a cost. That doesn't mean it isn't valuable.

One way to think about it is that production allows you to pay for stuff. Not necessarily directly but by way of trading. It is the output you use to buy things like security.

Consider an apple farm. What does the apple farm produce to pay its bills? Apples. They could also dedicate resources to pears, &c, and that would add to their production -- to the goods they produce, the goods with which they (indirectly) purchase tractors, fence posts, &c. They could also dedicate resources to security -- more fences, &c -- and this may even be prudent. Maybe thieves are getting taller. This doesn't really add to production, though; at best, it maintains production at the status quo ante (assuming the fence is effective).

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Just like securing a system is a trade-off, attacking one is too. Risk vs reward and time vs reward. Your personal blog doesn't need to be fort Knox, just patch it properly and you should be fine.

The software is better in the end but not all of it will be productive.

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  If that same city was subjected to weekly riots
Okay. What's causing the riots?

  That's productive.
No that's spinning your wheels because an outside agitator is trying to make a quick buck.
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> If that same city was subjected to weekly riots smashing windows & doors, and people regularly died..

The point it, this was not happening before. LLMs made cheap cyber attack possible. But it didn't make the implementation of fixes correspondingly cheap. So now everyone needs to spend extra money/effort to install grills to cover where LLMs fall short.

For postgres, this absolutely makes sense though as long as people are willing to shell out the profits from LLM use to support postgres development. That is one way this could workout for everyone..

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This why we pay cybersecurity folks, developers and business people are often living in a fantasy land where people don’t steal bikes because it is illegal to do so.

Then they have a pikachu face when someone smashes their car window to steal something worth $50 they left on front seat because “who would do that?”.

What is even better thief will most likely pawn this for $10 but for him it is effortless $10.

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And now imagine the introduction of iron has led to crowbars that make smashing doors a lot easier. Now it is quite unreasonable to account the reduction in smashing doors to iron, when the previous improvement was also due to iron.
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The physical world comparison is interesting. In the real world we have a small amount of security, but most things could be broken in to. Instead of fortifying everything, we just imprison the people breaking stuff.

Obviously this doesn't work on the internet since most hacking comes from overseas out of our control. Which makes me wonder if there is a possible future where we just block packets coming from countries that don't lock up the hackers.

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> Obviously this doesn't work on the internet

Not obvious to me. I am generally a fan of Cyberspace manifesto and the free Internet, I think it's great people can reach outside their ingroup and talk to strangers and ostensible enemies.

But, that doesn't mean we should give up physical security, the guarantee that the connecting party comes from a certain physical location (state, company). I think allowing connections from anywhere by default is fundamentally flawed security architecture, for certain things.

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I don’t think North Korean hackers are attacking things from their North Korean IPs. They just buy a residential proxy. How would you prevent that?
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I think the physical world comparisons should keep in mind the kind of data a lot of these systems (eg postgres) handle. It's more like securing a bank vault than locking a home's door.
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People usually don't install steel doors because most burglary is prevented in other ways. Insecure software will be exploited this way or another, if the vulnerabilities are real and the threat model is valid.
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> Insecure software will be exploited this way or another, if the vulnerabilities are real and the threat model is valid.

The algorithm is: if the threat model is valid, there are vulnerabilities and you either have some software everybody knows about or a gang of hackers specifically targeting you, you should patch.

The part of the industry that profits from security theater will want you to patch no matter what, even if your business goes down in flames due to downtimes and new bugs introduced by patched components. They won't remind you to assess the threat model first.

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Explain that to the auditors… on whose lap does the decision fall?
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By this line of thought building nice and usable UIs is not productive too, because users can learn cli tools (they did, DOS) and it costs less to develop them. And all the work in TLS/HTTPS has been wasted because HTTP is enough. And ssh vs telnet.

It becomes productive only once your business stops for days because somebody brought it to a halt by breaking into the servers and deleting them.

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Flawed analogy! These bugs are bugs regardless of the label. Security bugs are a subset of "regular" bugs.

You have a perverse view of product ivity

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In the rest of the world making sure your product works like it is supposed to is considered table stakes. In software we somehow consider checking the parts of it we can think of 'good enough' and the rest unproductive
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> In the rest of the world making sure your product works like it is supposed to is considered table stakes.

I don't think that's true.

In medicine, science often doesn't know how specific drug really is working. They just do a lot of tests and if these tests are positive enough, they ship it. I think it's good analogy. Biology is very complicated and it's out of our grasp to truly understand how various parts of it interact with each other on so many levels. But if something helps, it's stupid not to use it just because we don't fully understand it. Sometimes it bites. Sometimes it bites 30 years later. But we still follow that approach.

Software is similar. The complexity of the systems we build is enormous, when we're taking in account all the moving parts. I just read that someone emits radiowaves toggling memory bit and ex-filtrating information. How can you account for that? Probably you can't. But we still need software for good things.

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Not many products are held to the same standard as software. An equivalent would be something like recalling a car model because the breaks stop working when an attacker cuts the brake line.
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it actually is productive, because steel bar retailers and metal door installers will make a ton of cash and then 'trickle' that money into the economy.
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It's not productive because that productivity could've gone into something else if this wasn't necessary.
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When I had a brilliant stop on defense, a few savvy fans would appreciate it. When I scored goals, everyone knew.

Some people will much appreciate the holes in Postgres that are being plugged. Many more will be bored with patch releases until new features rollout.

A software affecianado appreciates patches, and might do well to teach laypeople the benefits of a good defense.

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Security doesn’t produce anything. So no, it’s not productive by definition.
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How many of the security issues found were created by LLMs in the first place, though?
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This wave is largely bugs found in pre LLM code. I'm sure there will be a smaller wave of bugs from llm code from the sonnet 3.5 - opus 4.7 era, and peeople will always write sloppy code/use dumber models under time or cost pressure, but since Opus 4.8/GPT-5.5 xhigh LLM code stopped being intrinsically worse than human code. At this point if you're shipping llm code that's lower quality than human code that's either because you don't have the money to spend on frontier models or because you actively choose a lower quality standard because of either various pressures or just not caring. if it's the latter your human code under those same conditions would also be really bad.
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Astra writes a lot of security bugs too, the upside is that if you also use it to review the output it will find a lot (most?) of them.

This is one of the least talked about and most useful features in any harness I would say, the review feature. I have never not found issues (either security or implementation) running it, no matter the code base or language.

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My job is seeing a similar deluge of security work and >90% of the bugs have been in pre-2024 code, and zero of them have been bugs introduced after we were first officially allowed to use LLM codegen.
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That could also mean the LLM is blind for the bugs it procudes itself. Just like any ordinary programmer would, on the job.
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Theoretically yes, but empirically that's not the case. All models I've tried are really good at finding bugs they wrote themselves, especially in a fresh context.
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Considering the software is much older than LLMs. Not many.

Also, in the article they mention a significant number of patches for older versions as well.

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Software is incredibly complicated. When you get into areas like security and networking and the internet its even more complicated. We rely on layers upon layers of software and hoping they are all done perfectly with no issues. The amount of junior devs, or outsourced devs for $10 an hour working on security relevant code is very very high, and always has been. LLMs if used correctly should be raising the floor.
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This wave is largely bugs found in pre LLM code. I'm sure there will be a smaller wave of bugs from llm code from the sonnet 3.5 - opus 4.7 era, and peeople will always write sloppy code/use dumber models under time or cost pressure, but since Opus 4.8/GPT-5.5 xhigh LLM code stopped being intrinsically worse than human code. At this point shipping llm code that's lower
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An uncomfortable truth about the era of LLM code is that machine produced code is, on average, quite a bit more secure than human code.
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Unsurprising to those of us that has been screaming from the rooftops that human code reviews were barely catching anything in your typical corporate environment. The majority of the value was pointing out missing tests, because breaking bugs got past human eyeballs on a regular basis, even in very good companies.

It's not that the LLM code is that great, but that many overestimated the human code and the human review.

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I think it's more likely to be the same as average human-written code; which is what they were trained on, after all.
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No it's far beyond average human code, and it's trained in RL environments on synthetic data etc now. The average human can't solve a millenium problem in mathematics but the LLMs are able to now. Anything with a verifiable output can be RLed on to make llms superhuman it seems, and the average human is not as persistent and thorough as e.g. gpt 6.1 sol at checking every edge case and hunting for bugs because that behavior is something that's just trained into the model. Frontier models perform farm above the average human at the categories of tasks they are RLed on like code and math.
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> The average human can't solve a millenium problem in mathematics but the LLMs are able to now.

I think comparing the resources they spent at it would be a more apt comparison.

AKA if you tasked 150 mathematics departments to only focus about that problem, and stop doing things like teaching and researching anything else, you might get close to the amount of resources that these companies put into solving the problem to get a press release.

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yet LLMs dont do off-by-one errors, typos mixing up variable names, interesting they seem unable to learn these human patterns
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They definitely write off by one errors. One of the first serious work tasks I used AI for a few months ago immediately had an off by one error.

What they are really good at is writing boilerplate and reading API documentation / example code. Avoiding subtle errors is not what they're good at in my experience.

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I've seen both of those (and others besides). I don't know how much code you've looked at but your negative directly contradicts my experience. Review that code. All of it. Be super critical because it looks good but that doesn't mean it is good. Those are two entirely different things.

Also: most tests are positive tests. Tests rarely observe the negative space: that which is there but shouldn't be there.

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They do those things all the time. More often than humans, in my experience.
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Citation needed.

looks at particledb

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Do you mean "created" as in LLMs were used to discover existing vulnerabilities, or as in LLMs wrote unsecure code? If it's the latter, then the answer probably is "very few". Frontier models have been better than the average programmer at writing secure code that follows up-to-date best-practices for some time now.
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