And far from a disgrace, NTP was a brilliant design for its time, and has proven to be far more future-proof than should have been expected.
77 karma · joined February 18, 2008
@TychoTithonus@infosec.exchange
[ my public key: https://keybase.io/royce; my proof: https://keybase.io/royce/sigs/P6FdliUdxVFgzdkLyNQ9znil63lGBFzZSFriZh1tDbY ]
And far from a disgrace, NTP was a brilliant design for its time, and has proven to be far more future-proof than should have been expected.
Although Diameter was intended to replace RADIUS, the
protocol itself offers no security when used over TCP. As a
result, RFC 6733 suggests that Diameter messages should
be secured using TLS or DTLS; 5G has replaced Diameter
with signaling over HTTP/2 [30].
Edit: here's ref [30]:
https://mailarchive.ietf.org/arch/msg/radext/Zcuud3GyG221DXn..."5G completely replaced DIAMETER with signaling over HTTP/2. DIAMETER is only used in legacy systems that has not yet been updated. Early 5G can be deployed as Non-standalone (NSA) or standalone (SA). NSA means a 4G core with 5G radio while SA means both 5G core and 5G radio. NSA has a lot of severe limitations. Many networks are already SA and the rest are working on rolling out SA."
Nothing on oss-security list yet, but it'll appear here: https://www.openwall.com/lists/oss-security/2024/07/09/
Essential reading is this FAQ from DeKok, FreeRADIUS maintainer who revised the RADIUS UDP approach to mitigate: https://www.inkbridgenetworks.com/blastradius/faq
Prior to this change the state machine attempted to change the remote resolve to a local resolve if the hostname was longer than 255 characters. Unfortunately that did not work as intended and caused a security issue."
Further, password hashes are not "decrypted" - they are cracked, because it's not reversing anything - https://www.techsolvency.com/passwords/dehashing-reversing-d... (disclaimer: my explainer)
While I consider my page to provide useful color, and I validate and summarize and cache info updates locally to add value ... it won't scale for long. It's really a stopgap - to buy defenders time until better efforts emerge.
A few efforts likely to become higher leverage than mine, because they can be driven by pull requests:
* https://github.com/NCSC-NL/log4shell - already quite comprehensive
* Whatever CISA may spin up - https://github.com/cisagov/log4j-affected-db
* Kevin Beaumont (@GossiTheDog) - turns out he worked with CISA on this
That being said, I'll keep working on mine as long as it still provides value; updates/corrections welcome.
https://minnie.tuhs.org/pipermail/tuhs/2019-October/019124.h...
Instead, one could present the financial institution with the hypothetical question, and then only give the specifics if the institution's policies are non-destructive.
If the finder is a decent sort, and has the time to try alternatives, I'd rather get the cards back intact than to have to do the cancellation dance.
Code signing reduces ops overhead and latency in environments that are using app whitelisting.
If the code is signed, then the signing certificate can be trusted once. All upgrades and patches that are signed with that certificate can be automatically whitelisted, with no intervention from teams managing the whitelisting.
But if the code isn't signed, then if even a single byte changes in the executable, it must be re-whitelisted - usually manually.
The more signed apps there are, the easier it is for companies to start using application whitelisting, the fewer people are needed to maintain it, and the faster patches to those applications can be deployed. Making it easier for companies to move to whitelisting increases security for the ecosystem in the aggregate.
https://productforums.google.com/d/msg/websearch/H4XbbwWmtAY...
I don't think it was ever officially announced/admitted anywhere, though. But it was exactly around the time that Google+ was rolled out.
In the initial rollout, all services were served from a single physical host with just one listening IP, which the bare 'example.net' resolved to. (Was this naive of us? You bet.) Other service hostnames (www., smtp., etc) were all just either CNAMEs to that hostname, or A records to that IP.
When our SMTP usage started to exceed the capacity of that single host, we tried to move 'smtp.example.net' to a different host. This is when we we discovered that many users were configured to use 'example.net' for SMTP instead. We had to update all of those users' configs before we could turn down SMTP on the original host. (We couldn't afford big-iron load balancers, and they were less common then - we just used DNS round-robin for load distribution).
At that point, we realized that customers were using bare "example.net" for everything - homepage, SMTP, POP3, IMAP, FTP, DNS, shell access - you name it. It was easy to remember - and it worked. So it was hard-coded everywhere - FTP scripts, non-dynamic DNS settings, etc. And this was looong before email clients had automatic configuration detection, so that was all hard-coded, too.
So we had to painfully track down all the users who were still hitting 'example.net' for SMTP, and help them update their configs before we could turn down SMTP on the original ancient host. The other services had to go through a similar painful transition.
We concluded that the only way to prevent this from happening again was to make sure that the bare hostname never offered any services at all - except for a single HTTP service whose sole purpose was to redirect 'example.net' to 'www.example.net'.
From then on, each new vISP domain had the same non-overlapping service namespace ... so that the otherwise inevitable configuration drift would be impossible.
Later, with the rise of things like email autoconfiguration, load balancers, and POP/IMAP multiplexors (like 'smunge'), we had more options. But at the time, avoiding services on 'example.net' was the only way to go (for us). Having a bare 'example.com' as the sole hostname in the browser bar was a sign of brokenness. :)
Nothing. That's why I was agreeing with you for that subset.
But N may be smaller than you might think, when frequency data is also supplied by the API.
https://gist.github.com/roycewilliams/60b77640a962125b04ae67...
Wait, what?
If they were randomly generated and of sufficient length, yes.
If they weren't randomly generated, even if not exactly reused, they are very likely to reveal the psychology of that user's password selection habits. This is of definite value to a focused attacker. Not only could it inform guessing passwords on other systems, it could also inform guessing that user's _next_ password on _this_ system.
> They don't need to crack your password to gain any more access to that same site.
Just because they have the hashes doesn't mean that they have other access. Hash lists are bought, sold, traded, and stolen all the time. Someone who possesses that particular hash may be multiple hops away from the group that originally acquired them.
Also, just because the database layer that the passwords are stored in is owned, does not mean that a particular target level of access has been acquired. Password storage can be abstracted into an entirely standalone subsystem, for which knowing, say, an admin of that system's password would be quite valuable.
At that speed, processing the entire Pwned Passwords list would almost take longer to read from disk and into memory than it would take to exhaust against a single password. Password cracking specialists would of course try raw wordlists first (And therefore "more useful", in a way) ... but we many other tools in their arsenal that generate far more than a half a billion candidate passwords. And at that rate, you can exhaust all 8-character passwords made up of printable ASCII - 95^8, ~6x10^15 - in a couple of days. Other techniques (mask, hybrid, rules) can achieve similar rates, and combinator attacks are slower but still pretty efficient.
By contrast, attacking bcrypt cost 12 on the same system can only try ~660 hashes per second - against a single hash. At that rate, if you just tell the attacker "it's somewhere in the Pwned Passwords list", it would take about 210 hours to exhaust the raw list, and 36 years to exhaust all 6-character passwords made up of printable ASCII.
In other words, if a service is storing passwords poorly, that service should be fixing that long before they should be trying to blacklist a half billion passwords. The purpose of blacklisting up front in the password-changing UI isn't to forbid a half-billion passwords. It's a way to reduce risk of online attack - and an opportunity to guide users towards better selection methods. There's a reason why Dropbox only blacklists the top 30K.
If high-value targets are selecting passwords that would be vulnerable to a targeted cracking attack, the solution isn't to blacklist a half-billion passwords (when they could just as easily come up with literally trillions of other passwords that would also be bad, yet are not included in the blacklist). The solution is to show them how to manage their specialized threat model - 2FA, creating strong passphrases, using a password manager, etc.
1) In an online attack, against a properly-configured service, even if password spraying is used, only the first few thousand passwords can be tried before rate-limiting, CAPTCHAs, etc. kick in.
Would a user with a known leaked password at a different site be vulnerable to an online correlation attack? Yes. And that's why some big services supplement their approach by proactively searching for those leaks and forcing a password reset for those specific users.
2) In an offline attack, when the passwords are properly hashed with a modern slow hash, even an expensive GPU or FPGA cluster would take weeks to exhaust a 10,000 word dictionary against a large user corpus, and a significant amount of time even when a single user is targeted.
Would users with '123456' get cracked pretty quickly? Yes. And that's why the top X are forbidden - to make offline attackers have to dig deeper into their wordlists (and thereby also their pocketbooks) to crack a password in a useful amount of time.
c4a8d09ca3762af61e59520943dc26494f8941b:123456 (20760336)
f7c3bc1d808e04732adf679965ccc34ca7ae3441:123456789 (7016669)
b1b3773a05c0ed0176787a4f1574ff0075f7521e:qwerty (3599486)
5baa61e4c9b93f3f0682250b6cf8331b7ee68fd8:password (3303003)
3d4f2bf07dc1be38b20cd6e46949a1071f9d0e3d:111111 (2900049)
7c222fb2927d828af22f592134e8932480637c0d:12345678 (2680521)
6367c48dd193d56ea7b0baad25b19455e529f5ee:abc123 (2670319)
e38ad214943daad1d64c102faec29de4afe9da3d:password1 (2310111)
20eabe5d64b0e216796e834f52d61fd0b70332fc:1234567 (2298084)
8cb2237d0679ca88db6464eac60da96345513964:12345 (2088998)
So ... what is the "right" threshold for N? $ for topx in 1 100 1000 5000 10000 20000 50000 100000 200000 500000 1000000; do \
echo -n "$topx: "; head -n ${topx} pwned-passwords-2.0.txt | tail -1;
done
1: 7C4A8D09CA3762AF61E59520943DC26494F8941B:20760336
100: 482FA19D5C487CB69ACDA19EEE861CC69D82CC94:272371
1000: 5B9FE558F673D63309BEB13BFA5DA6C30A3CA1BF:64912
5000: FE648FC459A6F6EF6CD347BEE3D494766239BBB5:19860
10000: 2682A3DBA7A1452EE7EE9980F195C6A768055DA6:11055
20000: 53490A3C8567342B57B6A4FF24908DF73182B357:6309
50000: 7517CD23A308BBCD05E5AD24AA6AD054237ED470:3153
100000: BA6D6A41B9548C523833627A8B0E5170558BE1EA:1752
200000: E50E6893264519636E90E95B6B1A85D0A691E0B1:931
500000: AF8DF653177BBB3FEE2DA68D314B94CB5281B4F3:381
1000000: BDD57A4CAA691A3441C1190C6F087B58B2EE3EF6:186
2000000: C824AF24AA8F2FD99AD6842DC0E4B49100D96161:93
10000000: 352DB7177AB7848DF1C102234401097FE40EB87D:22
The third field indicates how common the password is in the corpus (for example, the single most common password - "123456" - appears in the corpus 20,760,366 times).So ... based on this data ... what is a reasonable value for that count, such that if the value is exceeded, the user should be disallowed from using the password? How much real-world online or offline resistance is provided by disallowing, say, passwords used at least 186 times in the corpus (roughly a million passwords, though 5201 passwords are at the 186 mark)? (The answer should be self-evident; if it isn't, I can provide more background).
Put another way ... if the corpus was only 1M in size, those right-hand values would be much smaller. How could you determine the threshold then? What I'm trying to illustrate here is that it's not the absolute value of that commonality number that matters; it's the relative rank. But that relative rank can't be determined via the API; you must analyze the entire corpus directly - and then discard the vast majority of it for blacklisting purposes.
I totally get that the threshold might vary per implementation. But it varies much less once the hash is slow enough, and the authentication service is suitable rate-limited. In other words, any system that would get real benefit from a 1-million-word blacklist is one that needs to be improved elsewhere instead.
But Troy didn't provide any guidance about that, or even how to judge for yourself what the threshold might be. He just provided an API to blacklist a corpus of passwords that is three orders of magnitude larger than a properly designed system would ever need.
1. https://blogs.dropbox.com/tech/2012/04/zxcvbn-realistic-pass...
A full 80% of the v1 corpus can be avoided by simply requiring a minimum password length of 12. As Troy has pointed out elsewhere, this wouldn't be great UX, either. While it would dramatically increase the chances that they came up with a word that would be A) not in the existing blacklist, and B) harder to attack offline ... it would still be significantly bad UX compared to the best-practice alternative that I lay out in a separate thread branch.
But it would still be much better UX than use of the full blacklist.