This updates changes the THP_READALL logic, and adds THP_ECHO_THIS and THP_ALLOW_LIST.
simple_listener_v0_1_3.zip (http)MD5: 6C90E789D4C10B6EF5E918306A7A58E7
SHA256: 16E55E8983E4208151CB407F72238537C7631396FFFECC431230F7879AFAC664
This updates changes the THP_READALL logic, and adds THP_ECHO_THIS and THP_ALLOW_LIST.
simple_listener_v0_1_3.zip (http)This new versions adds 2 new features:
Option -H adds a human hash for each hash:

Option -r renames a file to its hash (hash) or to its hash with extension .vir (vir).
When more that one hash algorithm is used (default: md5, sha1, sha256), the last hash algorithm is used for the rename operation.

This update of zipdump.py adds parsing for external attributes and DOSDATE and DOSTIME fields when options -f and -E are used.

This update is just a definition update to detect MSO (ActiveMime files).
file-magic_V0_0_7.zip (http)This new update can produce JSON output for each part (option–jsonoutput).
emldump_V0_0_13.zip (http)This is an update linked to option -f l to find PKZIP records.
When option -E all is used, field externalattributes is parsed now:

Here is a YARA rule I developed to detect PDF/ActiveMime maldocs I wrote about in “Quickpost: Analysis of PDF/ActiveMime Polyglot Maldocs“.
It looks for files that start with %PDF- (this header can be obfuscated) and contain string QWN0aXZlTWlt (string ActiveMim in BASE64), possibly obfuscated with whitespace characters.
rule rule_pdf_activemime {
meta:
author = "Didier Stevens"
date = "2023/08/29"
version = "0.0.1"
samples = "5b677d297fb862c2d223973697479ee53a91d03073b14556f421b3d74f136b9d,098796e1b82c199ad226bff056b6310262b132f6d06930d3c254c57bdf548187,ef59d7038cfd565fd65bae12588810d5361df938244ebad33b71882dcf683058"
description = "look for files that start with %PDF- and contain BASE64 encoded string ActiveMim (QWN0aXZlTWlt), possibly obfuscated with extra whitespace characters"
usage = "if you don't have to care about YARA performance warnings, you can uncomment string $base64_ActiveMim0 and remove all other $base64_ActiveMim## strings"
strings:
$pdf = "%PDF-"
// $base64_ActiveMim0 = /[ \t\r\n]*Q[ \t\r\n]*W[ \t\r\n]*N[ \t\r\n]*0[ \t\r\n]*a[ \t\r\n]*X[ \t\r\n]*Z[ \t\r\n]*l[ \t\r\n]*T[ \t\r\n]*W[ \t\r\n]*l[ \t\r\n]*t/
$base64_ActiveMim1 = /Q [ \t\r\n]*W[ \t\r\n]*N[ \t\r\n]*0[ \t\r\n]*a[ \t\r\n]*X[ \t\r\n]*Z[ \t\r\n]*l[ \t\r\n]*T[ \t\r\n]*W[ \t\r\n]*l[ \t\r\n]*t/
$base64_ActiveMim2 = /Q \t[ \t\r\n]*W[ \t\r\n]*N[ \t\r\n]*0[ \t\r\n]*a[ \t\r\n]*X[ \t\r\n]*Z[ \t\r\n]*l[ \t\r\n]*T[ \t\r\n]*W[ \t\r\n]*l[ \t\r\n]*t/
$base64_ActiveMim3 = /Q \r[ \t\r\n]*W[ \t\r\n]*N[ \t\r\n]*0[ \t\r\n]*a[ \t\r\n]*X[ \t\r\n]*Z[ \t\r\n]*l[ \t\r\n]*T[ \t\r\n]*W[ \t\r\n]*l[ \t\r\n]*t/
$base64_ActiveMim4 = /Q \n[ \t\r\n]*W[ \t\r\n]*N[ \t\r\n]*0[ \t\r\n]*a[ \t\r\n]*X[ \t\r\n]*Z[ \t\r\n]*l[ \t\r\n]*T[ \t\r\n]*W[ \t\r\n]*l[ \t\r\n]*t/
$base64_ActiveMim5 = /Q\t [ \t\r\n]*W[ \t\r\n]*N[ \t\r\n]*0[ \t\r\n]*a[ \t\r\n]*X[ \t\r\n]*Z[ \t\r\n]*l[ \t\r\n]*T[ \t\r\n]*W[ \t\r\n]*l[ \t\r\n]*t/
$base64_ActiveMim6 = /Q\t\t[ \t\r\n]*W[ \t\r\n]*N[ \t\r\n]*0[ \t\r\n]*a[ \t\r\n]*X[ \t\r\n]*Z[ \t\r\n]*l[ \t\r\n]*T[ \t\r\n]*W[ \t\r\n]*l[ \t\r\n]*t/
$base64_ActiveMim7 = /Q\t\r[ \t\r\n]*W[ \t\r\n]*N[ \t\r\n]*0[ \t\r\n]*a[ \t\r\n]*X[ \t\r\n]*Z[ \t\r\n]*l[ \t\r\n]*T[ \t\r\n]*W[ \t\r\n]*l[ \t\r\n]*t/
$base64_ActiveMim8 = /Q\t\n[ \t\r\n]*W[ \t\r\n]*N[ \t\r\n]*0[ \t\r\n]*a[ \t\r\n]*X[ \t\r\n]*Z[ \t\r\n]*l[ \t\r\n]*T[ \t\r\n]*W[ \t\r\n]*l[ \t\r\n]*t/
$base64_ActiveMim9 = /Q\r [ \t\r\n]*W[ \t\r\n]*N[ \t\r\n]*0[ \t\r\n]*a[ \t\r\n]*X[ \t\r\n]*Z[ \t\r\n]*l[ \t\r\n]*T[ \t\r\n]*W[ \t\r\n]*l[ \t\r\n]*t/
$base64_ActiveMim10 = /Q\r\t[ \t\r\n]*W[ \t\r\n]*N[ \t\r\n]*0[ \t\r\n]*a[ \t\r\n]*X[ \t\r\n]*Z[ \t\r\n]*l[ \t\r\n]*T[ \t\r\n]*W[ \t\r\n]*l[ \t\r\n]*t/
$base64_ActiveMim11 = /Q\r\r[ \t\r\n]*W[ \t\r\n]*N[ \t\r\n]*0[ \t\r\n]*a[ \t\r\n]*X[ \t\r\n]*Z[ \t\r\n]*l[ \t\r\n]*T[ \t\r\n]*W[ \t\r\n]*l[ \t\r\n]*t/
$base64_ActiveMim12 = /Q\r\n[ \t\r\n]*W[ \t\r\n]*N[ \t\r\n]*0[ \t\r\n]*a[ \t\r\n]*X[ \t\r\n]*Z[ \t\r\n]*l[ \t\r\n]*T[ \t\r\n]*W[ \t\r\n]*l[ \t\r\n]*t/
$base64_ActiveMim13 = /Q\n [ \t\r\n]*W[ \t\r\n]*N[ \t\r\n]*0[ \t\r\n]*a[ \t\r\n]*X[ \t\r\n]*Z[ \t\r\n]*l[ \t\r\n]*T[ \t\r\n]*W[ \t\r\n]*l[ \t\r\n]*t/
$base64_ActiveMim14 = /Q\n\t[ \t\r\n]*W[ \t\r\n]*N[ \t\r\n]*0[ \t\r\n]*a[ \t\r\n]*X[ \t\r\n]*Z[ \t\r\n]*l[ \t\r\n]*T[ \t\r\n]*W[ \t\r\n]*l[ \t\r\n]*t/
$base64_ActiveMim15 = /Q\n\r[ \t\r\n]*W[ \t\r\n]*N[ \t\r\n]*0[ \t\r\n]*a[ \t\r\n]*X[ \t\r\n]*Z[ \t\r\n]*l[ \t\r\n]*T[ \t\r\n]*W[ \t\r\n]*l[ \t\r\n]*t/
$base64_ActiveMim16 = /Q\n\n[ \t\r\n]*W[ \t\r\n]*N[ \t\r\n]*0[ \t\r\n]*a[ \t\r\n]*X[ \t\r\n]*Z[ \t\r\n]*l[ \t\r\n]*T[ \t\r\n]*W[ \t\r\n]*l[ \t\r\n]*t/
$base64_ActiveMim17 = /QW [ \t\r\n]*N[ \t\r\n]*0[ \t\r\n]*a[ \t\r\n]*X[ \t\r\n]*Z[ \t\r\n]*l[ \t\r\n]*T[ \t\r\n]*W[ \t\r\n]*l[ \t\r\n]*t/
$base64_ActiveMim18 = /QW\t[ \t\r\n]*N[ \t\r\n]*0[ \t\r\n]*a[ \t\r\n]*X[ \t\r\n]*Z[ \t\r\n]*l[ \t\r\n]*T[ \t\r\n]*W[ \t\r\n]*l[ \t\r\n]*t/
$base64_ActiveMim19 = /QW\r[ \t\r\n]*N[ \t\r\n]*0[ \t\r\n]*a[ \t\r\n]*X[ \t\r\n]*Z[ \t\r\n]*l[ \t\r\n]*T[ \t\r\n]*W[ \t\r\n]*l[ \t\r\n]*t/
$base64_ActiveMim20 = /QW\n[ \t\r\n]*N[ \t\r\n]*0[ \t\r\n]*a[ \t\r\n]*X[ \t\r\n]*Z[ \t\r\n]*l[ \t\r\n]*T[ \t\r\n]*W[ \t\r\n]*l[ \t\r\n]*t/
$base64_ActiveMim21 = /QWN[ \t\r\n]*0[ \t\r\n]*a[ \t\r\n]*X[ \t\r\n]*Z[ \t\r\n]*l[ \t\r\n]*T[ \t\r\n]*W[ \t\r\n]*l[ \t\r\n]*t/
condition:
$pdf at 0 and any of ($base64_ActiveMim*)
}
The regex used to detect characters QWN0aXZlTWlt interspersed with whitespace characters (YARA string $base64_ActiveMim0) has no atoms (for YARA’s Aho-Corasic algorithm) larger than 1 byte, and thus generates a warning, that prohibits its use for hunting with VirusTotal.
That is why I replaced that regex with 21 regexes that all start with 3 fixed bytes and thus allow YARA to select atoms that are large enough.
jpcert reported a new type of maldoc: “MalDoc in PDF – Detection bypass by embedding a malicious Word file into a PDF file –“.
These maldocs are PDF files that embed a Word document (ActiveMime) in MIME format.
ActiveMime documents can be analyzed by combining my emldump.py tool and oledump.py.
ActiveMime documents were heavily obfuscated in the past, and this is also the case here. As emldump.py version 0.0.11 was only able to handle the obfuscation of 2 of the 3 samples mentioned by jpcert, I released a new version to handle more obfuscation.
Here is an analysis example for sample 5b677d297fb862c2d223973697479ee53a91d03073b14556f421b3d74f136b9d.
Run emldump (version 0.0.12 or later) with option -F to fix the obfuscation of the mime-version header:

To find the part where the ActiveMime file was hidden, use option -E %HEADASCII% to view the first 20 characters of each part:

Here we can see that part 14 is not a JPEG file, but an ActiveMime file.
We extract it and pipe it into oledump.py:

That ActiveMime file contains VBA code:

These maldocs (at least the 3 samples shared by jpcert) can be detected by pdfid with option -e to display extra information:

There are a lot of bytes outside streams (usually for PDFs, there shouldn’t be) and the count of stream and endstream documents is different.
But like I said, these are detections for these 3 samples, it’s possible to modify those samples to remove the anomalies.