I'd love to see data, but my intuition is that the average developer has access to dramatically better security reviews and far lower cost than ever.
There's more software being written than ever so maybe raw numbers of RCE's could be up, but as a percentage, I'd really expect them to be down. Especially among any fairly common software, as all it takes is anyone working on it to get the idea to test.
What I’m seeing is more developers pushing more code of dubious quality without the ability to respond to feedback on said code.
You can have the best security review in the world, but if the author of the code is not equipped to understand the feedback it ends up being a moot point.
The challenge to me seems less technical and more cultural: how do we keep ourselves intellectually honest and engaged when we now spend the majority of our time orchestrating agents and outsourcing the design and thought processes?
> I'd love to see data, but my intuition is that the average developer has access to dramatically better security reviews and far lower cost than ever.
Where? If I ask Claude to do a “security review” of my software, it gets blocked as a possible hacking attempt.
One issue for developers is that the most powerful models refuse to do comprehensive reviews. You can’t ask Fable 5.1 to find every exploit in your codebase, because that’s indistinguishable from what a bad actor would do.
Sad that this could well be that the path to OpenAI and Anthropic profitability of this arms race between defending LLM white hatting a company’s website and the black hat LLMs attacking it?
So the whole thing is forcing the good guys to outspend on tokens to preemptively defend against the risk of the bad guys outspending them on tokens, rather than buying tokens to actually add features to the product etc.
So are they creating a market for the solution by helping create the problem? A kind of rent-seeking AI security-industrial complex!!
The only thing AI has changed is that it has dropped both: the cost of attack and the cost of defense. Nothing in the game has materially changed; the game has just sped up.
for example the barrier to being a skiddie is basically gone, and low-skill would be hackers can hit very hard.
to develop a CVE into a KEV in 2017 might take 2-3 months with a skilled team of serious security engineers; now my intern can get into police radios without knowing anything about the underlaying technology, essentially on a whim.
any random tier 1 IT drone who can define a VLAN can potentially hit as hard as that team of security engineers now
That is the direct effect of reduced cost. Jevon's paradox type effect: cost goes down demand goes up. You can AI-check so many more things that would be very time consuming earlier.
Assuming an equal level of impact per token spent, the scales have tipped in favour of the attacker.
White hats are constrained by needing to pay for their own tokens, only using (expensive) vendors who meet governance and risk requirements etc. Black hats are free to take over accounts and steal services from wherever they can.
The price has gone up if you’re getting AI to do it. In terms of finding low hanging fruit, reasonably good code scanning tools have been around for a while.
The thing that’s changed for attackers is speed. The things that got you hacked yesterday are the same things getting you hacked today.
Finding and weaponising things like memory corruption bugs required an enormous amount of relatively hard to find skill, and considerable time. An idiot can now throw tokens at the problem and have something they can reliably use within minutes or hours.
The path to vast OpenAI profitability is trivial: advertising. Monetizing several hundred million users = $100+ billion ad network. 900 million active weekly users. Silicon Valley can do ad networks extraordinarily easily. Anybody doubting the ability of OpenAI to build an ad network around GPT will likely be embarassed in the near future.
The path to substantial profitability for Anthropic is questionable. The Chinese LLMs threaten them by far the most of the three major US LLMs. The money for Anthropic is certainly not in $20-$200 subscriptions. And they don't have anywhere near the consumer potential that GPT does, in terms of unleashing an ad spigot. So how far will the API money scale while being undercut by China.
OpenAI has to fight with Google for the ad business, they're specifically building Gemini to focus on consumer + search. Anthropic's business looks cute next to Google's search ad business (which is entirely at risk in this inflection). Meta looks like the biggest potential loser right now, ad dollars will be sucked out of the rotting Facebook network (not Instagram) and redirected to the rapidly expanding, hyper rich context LLM interaction. Advertising on Facebook will feel like running dumb banner ads on Excite in a few years, compared to what GPT will know about its users.
People that think Chinese LLMs are a general threat, don't understand consumer destination services, which is what GPT's future is. China currently has nothing to threaten with in that realm. There is half a trillion dollars of advertising up for grabs.
> Silicon Valley can do ad networks extraordinarily easily.
This is just not true, building an effective advertising platform costs significant amounts of money, time and people.
Remember that you need to hire a sales force for this, and sales scales linearly rather than sub-linearly like engineering.
Additionally, you need to spend a lot of money dealing with fraud, fake and malicious ads.
Furthermore, you need to figure out where to put the ads and how to rank them.
Finally, advertising is a zero sum game (given that the internet has already killed lots of print & OOH advertising), so the only way to win is to better better/cheaper (preferably both) than Google/Meta/Amazon. Best of luck with that (although to be fair to OpenAI they did hire Fidji who knows a lot of this stuff from her time at Facebook).
They don't have a Sheryl Sandberg type figure, and she was also really important in selling FB ads to large advertisers.
Just looking at their leadership team I don't see anyone with a background in (successful) ads companies, so I'm pretty sceptical that they can build this out quickly enough to matter.
Because it's far cheaper to to not spend the tokens finding the vulnerabilities, and software is now being created and released magnitudes faster than ever before. I could see the huge software companies maybe having fewer vulnerabilities, but I expect to see so much more in the smaller side of things.
The surface of potential issues is growing with complexity of all connected parts of the system. That applies to not only software. To prevent issues you either spend proportional amount (dollars, tokens, hours) on testing or reduce complexity of the system.
Because people need to spend time and money on that, which they won’t.
The implementation is cheap, the review and follow-up is not (speaking from a pure LLM only workflow).
My ratio is around 1:2 currently, so twice as much time spent fixing vs building.
> What makes you think RCEs are being found & fixed at a rate that’s faster than they’re being introduced?
It could go either way but we're already at a point where successful exploits in some software (like Chrome) require an absurd amount of exploits to be chained to lead to an actual RCE. We've seen chains requiring more than ten exploits: not kidding.
We'll learn to put more and more sandboxes / guards / checks / defensive techniques everywhere and then all that's going to be needed is for AI looking for security issues to find something ridiculous like 10% of all the actual issues to stop RCEs dead in their tracks.
Also arguably the current SNAFU was expected: we fully knew hardly anyone was taking security seriously.
Now: not so much. Many projects had tens and even hundreds of issues pointed to them.
I think we'll see several things: projects beginning to take security seriously, defense in depth getting generalized and hence RCEs requiring ever more bugs/exploits to be chained to achieve anything, low-hanging fruits getting patched at an insane pace, new code being immediately checked, by LLMs, for not just low-hanging fruits but also more advanced security weaknesses, etc.
We may also see things like the lost art of configuring firewalls making a comeback, the generalization of hardware security modules (where applicable), and even things offering physical guarantees, like time-bounded retrieval protocols, beginning to get used seriously.
If I had to bet I'd say it shall go both ways: some projects are going to extremely sloppy and full of holes but others are going to get so secure nobody shall ever break them.
People didn't store their entire life in the cloud and had every service connected with each other 20 years ago. People pay more attention today, and companies pay a lot more attention today.
Of course, depends heavily on what country you live in.
It's a bad shuffle implementation + using time of day as seed (reducing search space). Using the player's 2 cards and the 3 flop cards, it finds the RNG seed in real time, and then future hands (on the same server) are solved in "under one second!"
After my brief read, one of the main reasons they’re using PCC to produce the signed JPEGs, instead of doing everything on device, is that it maintains your privacy.
If you were signing with the iPhone, an attacker could then correlate photos taken with the same device.
Apple certainly has the data that “this sensor, in this device, took this exact photo” in PCC at the time of signing, but they discard that data.
As someone on a google workspace plan, where the monthly price went up to include AI features, I’m disappointed that google won’t let me sign up for antigravity development plans with my workspace google ID. To the best of my knowledge, as of last week, the recommendation is to sign up with a different, personal google id.
It feels like such a stereotypical big corp misstep, but based on the reputation that google’s AI products have, I guess I’m not _that_ heartbroken. It’s mostly frustration that they forced the price increase on everyone, and I’m definitely not receiving the benefit.
I have a similar machine, and briefly poked at running a local LLM, but got discouraged after a couple days. The quality, responsiveness, and impact on the rest of the system didn’t seem worth it to me.
What sorts of things are you doing with the local LLM? Anything interactive? Should I take another look?
Yes, 15-30 t/sec is pretty slow for local models so I recommend running local LLM tasks overnight where (vs paid plans) there isn't a risk of chewing through your token budget from a rogue loop or sub-agent. Even if it takes hours, you're sleeping anyway so no concern. herdr + pi works great for this but there are lots of harnesses.
I’ve casually used Draw Things a couple of times (great tool!), and I did not find it beginner friendly. I remember feeling a lot of doubt, confusion, and frustration.
Am I getting these results because I picked the wrong model? Or I need to improve my prompt? Or the tool just can’t do what I’m trying to do? How current are these model recommendations? Have they been superseded by something newer?
As a true beginner to AI at the time, even the sizes and bits were meaningless to me. And I don’t remember having any context as to what I should be attempting to run on my mac.
So I think you need to include steps 4, 5, and 6 of swapping between different models, quants, and prompts. And step 7 is probably wading through the complicated UI, full of jargon that most people don’t know.
Don’t get me wrong, I recognize that it’s a powerful tool, and the steep learning curve exists because it exposes quite a few power-user features. But for someone graduating from commercial AI image generators that take a text prompt and maybe a choice of couple models, it’s not easy.
Yes, I heard you! I think one of the issue Draw Things inherited is the baggage of supporting too much models. Once you settled on a model, then it is just "try recommended settings", and prompt.
The model part is unfortunate, but luckily converging now.
On the LLM side, hopefully it is not an issue too as long as getting aggressive at pruning models.
The sentence you’ve quoted actually links to a list of ways to corrupt SQLite, which I think is interesting in its own right: https://www.sqlite.org/howtocorrupt.html
I believe your flaky SD card is category 4, Disk Drive and Flash Memory Failures.
I was just reading about candy bars for a trivia night, and the internet tells me the UK Milky Way is the same as the US 3 Musketeers bar. As in, it omits the caramel.
Also, the 3 Musketeers candy bars used to come with 3 flavors in the package: chocolate, strawberry, and vanilla. Cost cutting led to eliminating the 2 less popular flavors.
Every year when DST transitions out, I say “next time we should move the cats’ dinner time gradually leading up to it”, but never remember. I now have reminders for the 5 days leading up to move it later by 10 minutes a day. I don’t know if it will actually make a difference
I could see it going either way.
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