Didier Stevens

When I scan executables on a Windows machine looking for malware or suspicious files, I often use the Reference Data Set of the National Software Reference Library to filter out known benign files.

nsrl.py is the program I wrote to do this. nsrl.py can read the Reference Data Set directly from the ZIP file provided by the NSRL, no need to unzip it.

Usage: nsrl.py [options] filemd5 [NSRL-file]
NSRL tool

Options:
–version             show program’s version number and exit
-h, –help            show this help message and exit
-s SEPARATOR, –separator=SEPARATOR
separator to use (default is ; )
-H HASH, –hash=HASH  NSRL hash to use, options: SHA-1, MD5, CRC32 (default
MD5)
-f, –foundonly       only report found hashes
-n, –notfoundonly    only report missing hashes
-a, –allfinds        report all matching hashes, not just first one
-q, –quiet           do not produce console output
-o OUTPUT, –output=OUTPUT
output to file
-m, –man             Print manual

Manual:

nsrl.py looks up a list of hashes in the NSRL database and reports the
results as a CSV file.

The program takes as input a list of hashes (a text file). By default,
the hash used for lookup in the NSRL database is MD5. You can use
option -H to select hash algorithm sha-1 or crc32. The list of hashes
is read into memory, and then the NSRL database is read and compared
with the list of hashes. If there is a match, a line is added to the
CSV report for this hash. The list of hashes is deduplicated before
matching occurs. So if a hash appears more than once in the list of
hashes, it is only matched once. If a hash has more than one entry in
the NSRL database, then only the first occurrence will be reported.
Unless option -a is used to report all matching entries of the same
hash. The first part of the CSV report contains all matching hashes,
and the second part all non-matching hashes (hashes that were not
found in the NSRL database). Use option -f to report only matching
hashes, and option -n to report only non-matching hashes.

The CSV file is outputted to console and written to a CSV file with
the same name has the list of hashes, but with a timestamp appended.
To prevent output to the console, use option -q. T choose the output
filename, use option -o. The separator used in the CSV file is ;. This
can be changed with option -s.

The second argument given to nsrl.py is the NSRL database. This can be
the NSRL database text file (NSRLFile.txt), the gzip compressed NSRL
database text file or the ZIP file containing the NSRL database text
file. I use the “reduced set” or minimal hashset (each hash appears
only once) found on http://www.nsrl.nist.gov/Downloads.htm. The second
argument can be omitted if a gzip compressed NSRL database text file
NSRLFile.txt.gz is stored in the same directory as nsrl.py.

nsrl_V0_0_1.zip (https)
MD5: 5063EEEF7345C65D012F65463754A97C
SHA256: ADD3E82EDABA7F956CDEBE93135096963B0B11BB48473EEC2C45FC21CFB32BAA

Jump List files are actually OLE files. These files (introduced with Windows 7) give access to recently accessed applications and files. They have forensic value. You can find them in C:\Users\%USERNAME%\AppData\Roaming\Microsoft\Windows\Recent\AutomaticDestinations and C:\Users\%USERNAME%\AppData\Roaming\Microsoft\Windows\Recent\CustomDestinations.

The AutomaticDestinations files are the OLE files, so you can analyze them with oledump. There are a couple of tools that can extract information from these files.

Here you can see oledump analyzing an automatic Jump List file:

The stream DestList contains the Jump List data:

There are several sites on the Internet explaining the format of this data, like this one. I used this information to code a plugin for Jump List files:

The plugin takes an option (-f) to condense the information to just filenames: