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Anthropic's open-source framework for AI-powered vulnerability discovery

https://github.com/anthropics/defending-code-reference-harness
The thing about things like this is that they're shop jigs. You can buy a crosscut sled if you really want to, but most woodworkers just make their own.

It was a different situation 2 years ago, when there was significant cost to building your own harness (but then: you probably weren't doing AI vuln research 2 years ago). Today, I think your best bet is to look at something like this for ideas, and then just ask for your own, to fit your own work style, with your own interface, your own notion of target and effort specification, and your own alerting.

"Shop jigs" is a great way to put it. I think a lot of software has gone from being made for general use to extremely individualised use. Before the Age of AI, it took so much human effort to write something that solved your problem that you might often go the extra mile so that others could re-use it. Now, it takes almost no effort, so the software stays ungeneralised. Some of the incentive has changed, I think. Most of the time I no longer share the things I've been building[0] because, for one thing they simply couldn't possibly have any benefit for others, and if they need something like it, they can build exactly the thing they want instead of having to extend or modify my thing. Like a jig!

0: https://redfloatplane.lol/blog/17-why-share/ (and related posts, I guess)

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This is exactly it.

I've said many times that I believe "using the computer will transparently involve having it write and run code for you" (and if you're not technical you won't even know it!). What you're saying goes in that direction as well.

I feel that it's often better for us to create purpose-built tools for our lives, and with every model release, the complexity of those tools grows.

These are really personal tools: they solve a problem that other people might have, but are very tied to your own specific way of working, and would be hard to explain or adapt to someone else. So: shop jigs.

I have about 10 custom scripts and programs that are like this -- I haven't felt like this since college! Back then I had all the time in the world to customize my setup...now I have agents!

In a way, I want to show this to all my friends, but whenever I mentally trace how that would go, I realize they wouldn't really understand a bunch of the quirks they have, because they are _my_ quirks. They're reasonably complex pieces of tech that solve my problems very well, which are themselves particular versions of broader problems, and which I (at least for now) have no interest in supporting.

It's so clear we're heading in this direction, and yet so many people still believe code will be for the elites. Maybe production-code...As for the rest, I think soon your mom and dad are going to have their computer running code it wrote to serve them. Security-wise it's scary, but it's exciting to think about!

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I wonder how much this thing costs to run.

https://github.com/anthropics/defending-code-reference-harne... says:

> As a rough guideline, expect ~10K uncached input tokens/min and ~2K output tokens/min per agent. You can scale parallelism up to your account's ITPM limit (roughly 10 agents per 100K ITPM).

My guess would be hundreds of dollars with Opus and thousands of dollars with Mythos.

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We actually created a calculator to estimate scanning costs (including whether you do this continuously or not) https://ai-cost-calculator.arnica.io

It's an estimate, so it might be wrong, but it gives the ballpark based on our experience. Happy to hear everyone's feedback.

It's becoming apparent that it requires more tokens to secure code than it does to write it

May even be an order of magnitude more

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Claude workflows in ultra code mode works in a very similar fashion and it consumes a moderate amount of the session usage limit, depending on the complexity of the task. With the API it would probably get expensive quickly though
If you compare to their managed service, that estimate is likely 1/10th expectation, depending on codebase.

But even this larger number, in turn, can be about 1/10th the cost of a formal engagement to discover the type of findings it seems to be going for: things that do not show up from PR reviews or even /security-review without the pre-work steps in the open-source framework guided by an expert. That's not counting the time and delay to figure out how to do that engagement.

Bluntly: if it matters, while this is a month's vibing budget for a single scan, it is also "pennies on the dollar" dirt cheap.

At the same time, its findings still need an expert. Its suggestions may be helpful, they may be actively harmful, depends on the prework quality.

Recommendation to IT department heads: spend a couple grand on this, use the scare page to rustle up the budget to build a relationship with a red team that can find, triage, help remediate if needed, and train your in-house team to be "security minded".

I mean, you don't need to run it all the time, right? You do it once over your entire existing codebase to start and then once over the diff in your CI/CD pipeline when you make a new change. I'm sure it's not literally that simple but I doubt these need to churn 24/7/365 either.
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>This repo is not maintained and is not accepting contributions.

Hm :)

Why isn't Claude maintaining it?
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Our experience has been that without a good harness you don't really get much out of codex/claude. And you really need to spend time and energy figuring out why coding agents can't find bugs like you can.

Every week I see bugs (as an auditor) that our own harness (https://zkao.io/) can't find, and we have to figure out pretty interesting techniques in order to make the tool find them. Mind you I'm talking mostly about cryptographic vulnerabilities, not just webapp bugs. So IMO it's going to make a lot of sense for companies to have both their own harness (as tptacek is talking about) and pay for services that focus on making a good harness from experience (and audit firms are going to be the best at doing this, as they see a lot of bugs and can spend time "teaching" their harness about these bugs)

On the other hand, you have to find equally as good techniques to triage, because otherwise you just have some machinery that I call "vibe auditing" that just produces enough false positives to tire all the developers (who are already overwhelmed with crappy AI submissions in bugbounties and other AI tool that review all of their PRs).

At the end of the day, when your harness doesn't return any bug, you're left wondering "does it mean there's no bugs?" We're basically back in this reputation game, where you want to use the best tool, or the best team (that knows what the best tools are), and need to figure out which one is.

To be sure, security is an amazing AI/LLM use case. A huge swath of the work is pattern matching known security issues against stuff that's very precise to analyze -- programming language text.

Something that stands out is that for the strongest use cases, AI companies will prefer to sell the technique as a service rather than its raw output. For use cases where the output is less valuable, tokens are sold. If AI tokens were so magical in creating new value in developing software applications generally, they wouldn't be selling tokens directly. They'd hoard the tokens are use them to dominate SaaS software in any industry they want.

The same way as someone selling an expensive course in the stock market is signaling that they have more to gain by selling the course rather than taking their knowledge and making money in the stock market directly.

> The same way as someone selling an expensive course in the stock market is signaling that they have more to gain by selling the course rather than

Or they want to diversify

> If AI tokens were so magical in creating new value in developing software applications generally, they wouldn't be selling tokens directly.

That requires to build and sell a whole product they have little experience with, competing with their own customers. Not a great place for an AI vendor still trying to establish itself. It’s a lot of distraction, when you already have a lot to deal with the existing business. And strategically not too valuable

> They'd hoard the tokens are use them to dominate SaaS software in any industry they want.

I don't understand this argument. I've ran and sold a semi-successful SaaS. The exhausting and frustrating parts are all the things an LLM cannot help you with. Coding the product is not the bottleneck or what grants you success.

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> If AI tokens were so magical in creating new value in developing software applications generally, they wouldn't be selling tokens directly. They'd hoard the tokens are use them to dominate SaaS software in any industry they want.

This doesn't follow at all. Anthropic's revenue is growing 10x year over year selling tokens. Their tokens can be super magical, let them enter established industries and displace incumbents, and get 100% annual growth in those industries, and they would still be better off prioritizing selling tokens, because it's a great business.

What your argument shows is that there are limits. Their tokens are not quite powerful enough to make infinite money instantly in every area of software. Admittedly, that does seem true.

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Maybe, but an alternative argument that building an ecosystem is more valuable in the long run.

We started out with many companies forbidding their employees to use remote LLMs on their source code because of security concerns. Now many companies are starting to believe that they must analyze their all their source code with remote LLMs because of security concerns. When trusting Anthropic becomes normalized, that means they can sell more services that require access to the source code.

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Surprised we havent gotten an integrated "MetaSploit" AI update where it calls and messages a ton of people in a company and once it starts to find someone possibly vulnerable lets a human red teamer take over or guide it more by hand.
> If AI tokens were so magical in creating new value in developing software applications generally, they wouldn't be selling tokens directly.

If hardware were so magical in creating new value generally, TSMC would be designing the chips instead of selling fabrication as a service.

That is what US chip companies used to do, by the way (back when there was silicon in Silicon Valley, before they got their lunch eaten by Taiwan). If TSMC had to design all of the chips they fabricate now, they would be doing a lot less business. Conversely, if any other company that wanted to design a chip had to build their own cutting-edge fab first, NVIDIA would not exist.

They can only do that if they're a monopoly, which they're not
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Sligthly off topic: it seems that someone is in a dead/flag rampage killing all good links to Github in this post, why?
It will always be easier to find a single hole than it will be to seal every one. The hackers have all the same tools, so this is an arms race that cannot be won.
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Very interesting.

I have working on and using a similar tool for a while now :

https://github.com/bobinson/vulture

I have been struggling with false positives and using Claude + MCP as a poor man’s audit tool. As of last few days found better result with nvidia hosted models.

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Let's see how better it is in comparison to ZAP and Burp. I will test on https://github.com/SasanLabs/VulnerableApp which i built under SasanLabs
https://github.com/Mainframework/Anthropic-Cybersecurity-Ski...

Be aware: the .py/s will not pass the antivirus but basically they do the job.

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I wonder how this sort of product is going over at Coverity and others like it. Proper SAST vendors I mean. Is it an existential threat?
If I had to guess, they'l eventually just add it into their own product and hike the prices up to cover tokens lol.
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Is Anthropic still majority French-owned? It would explain a lot about their entire approach to the wider ecosystem.
If anyone wonders how much it can cost to run scans like this on your entire codebase with SOTA models: https://ai-cost-calculator.arnica.io

tl;dr - not that it's surprising, but it's not cheap, especially if you want to do this continuously.

Interesting it's in python!
Open source crap to connect to an LLM blob.
> Anthropic engineers on average ship 8x as much code per quarter

Are they making 8x more features or the same amount just with more code?

Going by the issues on their repos, it's 2x features and 6x regressions of bugs that were "already fixed".
I still find it so weird that they haven't bought out whoever controls the `anthropic` github username.
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Looking forward to trying this tomorrow (it's late here). Has anyone run it on a real codebase yet? Curious about setup friction, cost, and signal/noise.
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'open source' crap to connect to their LLM blob.