How many CRAN package maintainers have been pwned?

The alternative title of this blog post is HIBPwned version 0.1.7 has been released! W00t!. Steph’s HIBPwned package utilises the API to check whether email addresses and/or user names have been present in any publicly disclosed data breach. In other words, this package potentially delivers bad news, but useful bad news!

This release is mainly a maintenance release, with some cool code changes invisible to you, the user, but not only that: you can now get account_breaches for several accounts in a data.frame instead of a list, and you’ll be glad to know that results are cached inside an active R session. You can read about more functionalities of the package in the function reference.

Wouldn’t it be a pity, though, to echo the release notes without a nifty use case? Another blog post will give more details about the technical aspects of the release, but here, let’s make you curious! How many CRAN package maintainers have been pwned?

Get the email addresses of CRAN package maintainers


Data was gathered thanks to an adaptation of the code published in this blog post of David Smith’s about prolific package maintainers. We are after the most endangered package maintainers on CRAN!

The helper function below extracts the email address of a string such as “Jane Doe”. On top of using the as.person conversion, this function also deals with a few particular cases.

get_maintainer_email <- function(maintainer_string){
  if(inherits(maintainer_string, "data.frame")){
    maintainer_string <- maintainer_string$Maintainer[1]
  if(maintainer_string != "ORPHANED"){
     maintainer_string <- stringr::str_replace_all(maintainer_string,
                                                '"', '')
     maintainer_string <- stringr::str_replace_all(maintainer_string,
                                                ',', '')
     # particular case!
     maintainer_string <- stringr::str_replace_all(maintainer_string,
                                                'Berlin School of Economics and Law', '')
    maintainer <- as.person(maintainer_string)

Here it is in action.

get_maintainer_email("Jane Doe <>")
#> [1] ""

The following code then gathers the email addresses of all CRAN package maintainers.

tools::CRAN_package_db() %>%
  .[, c("Package", "Maintainer")] %>%
  tidyr::nest(Maintainer, .key = "Maintainer") %>%
  # get the email out of the maintainer
  dplyr::mutate(email = purrr::map_chr(Maintainer,
                                       get_maintainer_email)) %>%
  dplyr::select(- Maintainer) %>%
  # only keep the ones with email
  dplyr::filter(email != "") %>%
  # save result
  readr::write_csv(path = "data/all_packages.csv")
emails <- readr::read_csv("data/all_packages.csv")

We obtained 12444 packages with 7173 unique email addresses. We do not have to care about their uniqueness: since HIBPwned implements caching inside an active R session via memoise duplicate emails do not mean duplicate requests! :nail_care: Another aspect we users do not need to care about is rate limiting: HIBPwned uses the nice ratelimitr package in order to automatically pause R when needed.

So, have CRAN package maintainers been pwned?

Thanks to setting the new as_list option to FALSE we get a data.frame as output. Note that choosing this means we only get back accounts with breaches. Depending on the analysis, we could supplement the original emails data.frame with the information using dplyr::left_join for instance.

pwned <- HIBPwned::account_breaches(emails$email,
                                    as_list = FALSE)
pwned <- unique(pwned)

There are 7173 unique CRAN maintainer emails, among which 3613 i.e. 50% have been pwned. Dear reader, why not compare this to the proportion of Python module maintainers who’ve been pwned? Ping us if you complement this analysis!

Looking at these pwned maintainers, here are the number of breaches they’ve been victims of:

pwned %>%
  dplyr::count(account) %>%
  dplyr::summarise(median = median(n),
                   min = min(n),
                   max = max(n)) %>%
median min max
2 1 18

There are 136 unique breaches. What were the most common ones?

pwned %>%
  dplyr::group_by(Title, BreachDate) %>%
  dplyr::tally() %>%
  dplyr::arrange(desc(n)) %>%
  head(10) %>%
Title BreachDate n
Dropbox 2012-07-01 1534
LinkedIn 2012-05-05 1140
Onliner Spambot 2017-08-28 943
GeekedIn 2016-08-15 782
Adobe 2013-10-04 694
MDPI 2016-08-30 558 2012-03-22 350
NetProspex 2016-09-01 310
B2B USA Businesses 2017-07-18 279
Disqus 2012-07-01 259

Maybe or probably some you’ve heard of, which might make you wonder about your own security, being a CRAN maintainer or not…

What about you?

You could check if you’ve been victim of any known breach right now by installing HIBPwned from CRAN!

# install.packages("HIBPwned")
#> List of 1
#>  $'data.frame':    4 obs. of  16 variables:
#>   ..$ Title       : chr [1:4] "Adobe" "Disqus" "LinkedIn" "Onliner Spambot"
#>   ..$ Name        : chr [1:4] "Adobe" "Disqus" "LinkedIn" "OnlinerSpambot"
#>   ..$ Domain      : chr [1:4] "" "" "" ""
#>   ..$ BreachDate  : chr [1:4] "2013-10-04" "2012-07-01" "2012-05-05" "2017-08-28"
#>   ..$ AddedDate   : chr [1:4] "2013-12-04T00:00:00Z" "2017-10-06T23:03:51Z" "2016-05-21T21:35:40Z" "2017-08-29T19:25:56Z"
#>   ..$ ModifiedDate: chr [1:4] "2013-12-04T00:00:00Z" "2017-10-06T23:03:51Z" "2016-05-21T21:35:40Z" "2017-08-29T19:25:56Z"
#>   ..$ PwnCount    : int [1:4] 152445165 17551044 164611595 711477622
#>   ..$ Description : chr [1:4] "In October 2013, 153 million Adobe accounts were breached with each containing an internal ID, username, email,"| __truncated__ "In October 2017, the blog commenting service <a href=\"\"| __truncated__ "In May 2016, <a href=\"\" target="| __truncated__ "In August 2017, a spambot by the name of <a href=\""| __truncated__
#>   ..$ DataClasses :List of 4
#>   .. ..$ : chr [1:4] "Email addresses" "Password hints" "Passwords" "Usernames"
#>   .. ..$ : chr [1:3] "Email addresses" "Passwords" "Usernames"
#>   .. ..$ : chr [1:2] "Email addresses" "Passwords"
#>   .. ..$ : chr [1:2] "Email addresses" "Passwords"
#>   ..$ IsVerified  : logi [1:4] TRUE TRUE TRUE TRUE
#>   ..$ IsFabricated: logi [1:4] FALSE FALSE FALSE FALSE
#>   ..$ IsSensitive : logi [1:4] FALSE FALSE FALSE FALSE
#>   ..$ IsActive    : logi [1:4] TRUE TRUE TRUE TRUE
#>   ..$ IsRetired   : logi [1:4] FALSE FALSE FALSE FALSE
#>   ..$ IsSpamList  : logi [1:4] FALSE FALSE FALSE TRUE
#>   ..$ LogoType    : chr [1:4] "svg" "svg" "svg" "png"

If one of your addresses has been pwned, Steph says you should change passwords in other locations is you re-used passwords. Even if your address hasn’t been pwned yet you should use a password manager that will allow you not to re-use passwords, and set up two factor authentication, e.g. read more about 2FA for GitHub.

And how could you now right away if a known data breach is of concern to you? Well, don’t only let your .Rprofile tell you you’re a rrrrock star, but also add some code checking whether you’ve been pwned, as explained in this blog post! Pro-tip, you can use the usethis::edit_r_profile function to easily open your .Rprofile. Steph also says you can register for notifications and ask your organisation to watch breaches at the domain level. Stay safe!