ActivityPub API Client Reputation
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For the ActivityPub API Task Force, I started an issue to discuss OAuth client reputation systems.
A reputation system tracks which OAuth clients are known good, known bad, or unknown. Servers could use this information to limit what clients can do. For example, a server could prevent users from logging in with a known bad client.
The reputation could be based on human curation and review, or on automated collection of evidence from historical behaviour of the client.
I'm trying to find examples in the OAuth ecosystem of this kind of reputation systems -- either local or distributed.
App store approval (and user reviews) are a good example for native apps. OpenBanking keeps a client directory that needs human curation and review.
I don't have examples from OAuth -- especially with dynamic registration or CIMD.
Any ideas?
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undefined evan@cosocial.ca shared this topic on
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@evan@activitypub.space I want to take a moment to note how nice the NodeBB content looks in Mastodon.
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@evan what factors would impact the reputation and who decides what is a good or bad client?
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I'd suggest that there are two parties that should get to decide what is a good or bad client:
- The ActivityPub user who uses the client.
- The administrator of the server that the ActivityPub user uses.
I think there's a third group, which is other admins, developers, and users, who share similar values with the user and the admin. They may have information to share with the user and/or admin.
I don't think these values are universal, so I don't think we need a universal reputation. But I can give what I think are bad things for an API client to do.
- Generating activities on behalf of the user that don't match the user's express or implied intentions. For example, if the user logs into a client app, and it posts a public message, "I think this client app is the best and everyone should try it!"
- Extracting the user's data for reasons that the user wasn't informed of. For example, a client app that copies all your private messages to cloud backup controlled by the app developer.
- Abusing public or private resources, even if the user intends to abuse. For example, a client app for spamming, or a client app for brigading.
I think there are a few signals that could identify what I would call "bad" clients:
- User complaints would be the biggest
- Complaints from other users about the user's behaviour when using the app
- Security researcher reports
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@evan
Sounds good!I suppose it would be useful to be able to specify the version too so that you may ban a known buggy version of a client or any version prior to a known CVE fix.
It could also be useful to make those lists shareable so that a new Fedi instance can start with something if they wish to.
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Client reputation isn't really something you can track and share in a decentralized network without introducing some centralisation. You could try to do web of trust style things, but that would mean writing a record that publicly says "good client is good", but then a malicious app could just write that record on sign-in: how many iOS apps nag you for a positive review? Particularly with somewhat dark patterns of "are you enjoying ? Yes / no" where "no" pushes you to the app's feedback and yes pushes to write a review, trying to deliberately avoid negative reviews.
The other downside of publicly disclosing which clients you use is that it tells attackers where to look for security exploits, because now you can pick a set of targets and try to attack the software they use.
Raw usage numbers also doesn't help because a bad client can quite easily become viral, see for example Cambridge analytica, who iirc used games to gain access to sensitive data.
You'd also need moderation tools that can moderate clients in some sort of meaningful way — that's near impossible for dynamic client registration. That's why we wrote the CIMD spec. A large Mastodon server usually has 10-20x the number of registered clients as number of accounts.
Things that can add up to trust are things like:
- privacy policies & terms of service
- client_uri (website) matching the client metadata (requires some crawling)
- client authentication mechanism (public client vs private_key_jwt auth)
- scopes/authorization requested being fine grain enough, instead of asking for full unrestricted access.
But OAuth security and trust models are complex and generally proprietary
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undefined informapirata@mastodon.uno shared this topic
Gli ultimi otto messaggi ricevuti dalla Federazione
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Client reputation isn't really something you can track and share in a decentralized network without introducing some centralisation. You could try to do web of trust style things, but that would mean writing a record that publicly says "good client is good", but then a malicious app could just write that record on sign-in: how many iOS apps nag you for a positive review? Particularly with somewhat dark patterns of "are you enjoying ? Yes / no" where "no" pushes you to the app's feedback and yes pushes to write a review, trying to deliberately avoid negative reviews.
The other downside of publicly disclosing which clients you use is that it tells attackers where to look for security exploits, because now you can pick a set of targets and try to attack the software they use.
Raw usage numbers also doesn't help because a bad client can quite easily become viral, see for example Cambridge analytica, who iirc used games to gain access to sensitive data.
You'd also need moderation tools that can moderate clients in some sort of meaningful way — that's near impossible for dynamic client registration. That's why we wrote the CIMD spec. A large Mastodon server usually has 10-20x the number of registered clients as number of accounts.
Things that can add up to trust are things like:
privacy policies & terms of service client_uri (website) matching the client metadata (requires some crawling) client authentication mechanism (public client vs private_key_jwt auth) scopes/authorization requested being fine grain enough, instead of asking for full unrestricted access.But OAuth security and trust models are complex and generally proprietary
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@evan
Sounds good!I suppose it would be useful to be able to specify the version too so that you may ban a known buggy version of a client or any version prior to a known CVE fix.
It could also be useful to make those lists shareable so that a new Fedi instance can start with something if they wish to.
-
I'd suggest that there are two parties that should get to decide what is a good or bad client:
The ActivityPub user who uses the client. The administrator of the server that the ActivityPub user uses.I think there's a third group, which is other admins, developers, and users, who share similar values with the user and the admin. They may have information to share with the user and/or admin.
I don't think these values are universal, so I don't think we need a universal reputation. But I can give what I think are bad things for an API client to do.
Generating activities on behalf of the user that don't match the user's express or implied intentions. For example, if the user logs into a client app, and it posts a public message, "I think this client app is the best and everyone should try it!" Extracting the user's data for reasons that the user wasn't informed of. For example, a client app that copies all your private messages to cloud backup controlled by the app developer. Abusing public or private resources, even if the user intends to abuse. For example, a client app for spamming, or a client app for brigading.I think there are a few signals that could identify what I would call "bad" clients:
User complaints would be the biggest Complaints from other users about the user's behaviour when using the app Security researcher reports
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@evan brilliant!
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@evan what factors would impact the reputation and who decides what is a good or bad client?
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@evan@activitypub.space I want to take a moment to note how nice the NodeBB content looks in Mastodon.
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For the ActivityPub API Task Force, I started an issue to discuss OAuth client reputation systems.
A reputation system tracks which OAuth clients are known good, known bad, or unknown. Servers could use this information to limit what clients can do. For example, a server could prevent users from logging in with a known bad client.
The reputation could be based on human curation and review, or on automated collection of evidence from historical behaviour of the client.
I'm trying to find examples in the OAuth ecosystem of this kind of reputation systems -- either local or distributed.
App store approval (and user reviews) are a good example for native apps. OpenBanking keeps a client directory that needs human curation and review.
I don't have examples from OAuth -- especially with dynamic registration or CIMD.
Any ideas?
-
@evan sorry I missed the meeting! Sounds like something right up my alley on what to work on next.
Thanks for sharing the link.
