alt Hacker NewsAnthropic's open-source framework for AI-powered vulnerability discovery
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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.
Anthropic realized security and safety are their main value prop compared to the competition. Either mythos or anything else since seem purpose built to streamline the messaging. It’s good, am not complaining, but i wonder how much this is intended to showcase what Claude can do over using it as is
They seem to be using this to advertise their "Claude Security" product which promises to find vulnerabilities in your software.
This makes for a somewhat amusing set of product offerings given that according to Dario 90% of all software is being AI generated.
Maybe next they can sell something to find the bugs in the security scanner ?
>This repo is not maintained and is not accepting contributions.
Hm :)
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.
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.
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.
Ran this last night and it correctly identified a sql injection that could allow cross tenant data access via snowflake. It burnt A LOT of tokens to get there.
Like others I suspect this is exactly what they are going to paywall with product features going forward.
It’s clear that Anthropic is building harnesses for specific use cases now and turns them into products.
This is the equivalent of Claude Design but for security.
Different harness, different packaging and obviously different distribution because the persona is different.
It’s funny because from all the posts I’ve read from companies reporting on Mythos, everyone is building their own harness for it.
Cisco even published a specification for one.
But Anthropic is the one who has figured out how to package and distribute this. Great GTM!
This isn't as useful as it sounds, unless we know that Claude efficiently spends tokens using this harness
"This repo is not maintained and is not accepting contributions."
Nice
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.
This is a good addition tool for people are in the security Practitioners. To save time for hunting vulnerability.
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?
I don't trust it and I cannot test it (gated by what ng cartel web engines).
Anthropics vs Anthropic.
That repo is Anthropics.
This post title should clarify that it is not Anthropic (no "s").
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?
I still find it so weird that they haven't bought out whoever controls the `anthropic` github username.
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.
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.